Input: Task: contrast the claim using the given text. Claim: Cryptocurrencies will completely replace traditional currency by 2030. [SEP] Text: downloadDownload free _rightWho cares about the Chinese YuanVimal Balasubramaniam2011downloadDownload free _rightThe US in the Era of a Yuan: Implications for dollar has enjoyed a long-standing dominance as the global reserve currency, serving as a medium of exchange, store of value, and unit of account for international trade and financial transactions. However, the economic rise of China and the growing internationalization of the Chinese yuan have posed significant challenges to the US dollar's supremacy. This research paper examines the implications of a rising Chinese yuan on the global reserve currency system, highlighting the potential challenges and opportunities that arise as a result. Through an analysis of historical trends, economic indicators, and policy developments, this paper provides insights into the future trajectory of global reserve currencies and the potential shifts in the international monetary system. downloadDownload free _rightInternationalisation of the Chinese Currency, Act II: -francoisAfter two waves of important changes in the Chinese system of foreign exchange, one in July 2005, when the Chinese currency was allowed to fluctuate more widely against the US dollar, the other in June 2010, when it was then quoted against an undisclosed basket of currencies, but moreover, was allowed to be partially traded in with the invention of a 'twin' the 'offshore yuan' (CNH), a deep reform could have taken place. The creation of the CNH, allowing debt to be issued internationally in yuan, initially led to the belief that a real set of deep reforms was under way. Yet, like some reforms of the past, which were hesitant or even annulled, the path towards monetary and financial reform is encountering difficulty. The steps taken towards the 'regionalisation' or possibly the 'internationalisation' of the Chinese currency may well be taking the same route. Before the reforms have had time to come to fruition and deliver deep changes, there seems to be some indecisiveness, a sense of relative failure, or at least discovery of the limitations in a process which many, at least at the beginning, believed would be quick and decisive. This 10. 5937/industrija42-6410 = To be or renminbi: Trend in the evolution of the international monetary systemMarko MalovicIndustrija, 2014downloadDownload free _right& Journal (IBER), 2011In the past two years, the Bush Administration, members of Congress and a few senators, such as Sen. of and Sen. [SEP] Justify:

Output: The text contrasts the claim by discussing the dominance of the US dollar as the global reserve currency and the challenges posed to this dominance by the internationalization of the Chinese yuan. It highlights that despite some moves towards the internationalization of the yuan, there are significant obstacles and indecisiveness in the reform process. This suggests that the transition to cryptocurrencies replacing traditional currencies may not be straightforward or likely within the proposed timeframe of 2030.


Input: Task: pass the claim using the given text. Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: 1 IntroductionOver the last few years, the application domain of robots has expanded from installations in caged factory environments to deployments in environments shared with humans. The behavior of humans in these environments is typically guided by explicit (rules) and implicit regulations. We jointly refer to both types of regulations as social norms which represent shared standards of acceptable behavior (Lapinski & Rimal, 2005). Asides from reaching a goal, all (artificial) agents should respect the applicable social norms to assure safety and predictability (Christian, 2020). Reinforcement learning (RL) is a general framework for decision making and control. It is a training method for learning a mapping from states to actions (i. e. policy) in order to maximize a predefined reward signal. However, fully defining the desired behavior of an artificial agent by a single reward function is often a burdensome task (Amodei et al. , 2016; Russell, 2019; Everitt & Hutter, 2016). The framework of constrained reinforcement learning specifies an agent’s desired behavior by a reward function in combination with a set of hard constraints. For instance, the reward function can be expressed in terms of the distance from the goal and the time needed to reach it, complemented with a hard constraint to not crash into other vehicles. In the Markov decision process representing the environment, constraints are states or state-action pairs that in principle could be reached by a certain trajectory, but that are considered as invalid by latent information about the environment, such as social norms. In contrast with reward functions, hard constraints provide strong safety guarantees such that a constrained state will never be visited, this makes them better suited for decision making in safety-critical environments. Because social norms are often implicit and environment-specific, it is complicated to define them as a set of constraints. A promising direction is to learn social norms from human observations. To this end, recent studies have shown principles of inverse reinforcement learning (Ng & Russell, 2000) can be adopted to infer both reward function and constraints from observed trajectories (Scobee & Sastry, 2019; Malik et al. , 2021). A shortcoming of current methods is the lack of interpretability and hence verifiability of what an agent has actually learned. When learning from human observations in the wild, there is a chance that the dataset will contain trajectories of humans violating to a smaller or larger extent the social norms, e. g. [SEP] Justify:

Output: The text does not directly address the claim regarding how reinforcement learning algorithms mimic human learning through rewards and punishments. Instead, it focuses on the application of reinforcement learning in robotic environments, the necessity of respecting social norms, and the complexity involved in defining desired behaviors and constraints. While it discusses reinforcement learning as a framework for decision-making and mentions a reward function, it does not clearly explain how this relates to human learning processes.


Input: Task: corroborate the claim using the given text. Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: The outcomes of this experiment will serve as the foundation for conducting a comparative analysis between LiDAR and Non-LiDAR technologies implemented in a driver-operated vehicle. The collected data will be utilized for the purpose of comparing and contrasting these technologies with an system. By examining and contrasting the performance of LiDAR and Non-LiDAR in the context of a human-driven vehicle, insights can be gained regarding their effectiveness and potential advantages when compared to autonomous driving systems. 4. 3. for The forthcoming experiment will employ the same weather conditions and scenarios as the previous one, albeit without the involvement of a human driver. The focus will be on evaluating the performance of LiDAR and Non-LiDAR technologies within an autonomous vehicle (AV) context. To facilitate this research, a collaboration with Waymo and Tesla, proponents of LiDAR and Non-LiDAR technologies respectively, is proposed. These companies possess extensive training and test data, thereby minimizing the training duration since their AVs are already trained. The experiment will be conducted under comparable conditions as those involving a human driver. Subsequently, the results from both experiments will be compared and contrasted. The primary objective is to establish a baseline for assessing whether an AV can effectively navigate around obstacles on the road, akin to the actions of a human driver. While a human driver would slow down or halt upon encountering an obstruction, LiDAR and Non-LiDAR will record obstacle data for comparison with AV performance. The proposed experimental research will adhere to the safety principles outlined in Blanar et al. ’s [10] study on Safety (RSS). These principles encompass maintaining a safe distance in front of and laterally to obstacles, respecting right-of-way, and exercising caution when encountering roadblocks or signs. Comparable to the actions of a human driver, these principles will be contrasted with AV behavior. The experimental research aims to enhance the existing Model developed in this study. The enhancements include incorporating distance and speed measurements, evaluating the time taken to stop relative to obstacle distance, utilizing GPS measurements for navigating or avoiding obstacles, determining the time required for turning or obstacle avoidance, and identifying and adhering to road signs. These enhancements will significantly contribute to the safety capabilities of AVs in the future. In summary, the proposed experimental research serves as a suggestion for the future advancement of the image processing model. The model established in this study forms a foundational framework for subsequent research. [SEP] Justify:

Output: The text corroborates the claim by indicating that companies like Waymo and Tesla are involved in developing and testing technologies (LiDAR and Non-LiDAR) within an autonomous vehicle (AV) context. The proposed experiment suggests that these manufacturers, who are already proponents of these technologies, are contributing to advancements in AV capabilities and safety.


Input: Task: contrast the claim using the given text. Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: Published by January 29, 2024 Purchase 10. 1515/eng-2022-0509 Download 79. 5% of the global cryptocurrency market capitalization [3]. Deep learning (DL) refers to powerful Machine learning (ML) algorithms that specialize in solving nonlinear and complex problems by exploiting large amounts of data to become efficient predictor models, and it has proven to be a reliable tool for analysis [9,10]. The study of data gathering, analysis, interpretation, forecasting, visualization, and organization is known as statistics. ML is utilized efficiently in numerous fields, including forecasting [11], real-time advertising, malware detection [12], and text-based sentiment analysis [13]. Recurrent neural networks (RNNs) have emerged as highly effective models in the field of deep learning, demonstrating remarkable performance in recent years. These models are specifically designed to tackle sequential data by leveraging a memory-based architecture [14]. In this research, we will be studying recent literature on the prediction of currency prices, which is considered a major risk factor in currency trading. The studies will be presented in detail in Section 2. We will learn about the most important risks and features of currencies, take a quick look at RNNs, make comparisons between studies, analyze the results, and discuss them. 2 Literature review This study presents a comparative analysis of published studies on predicting BTC prices using neural networks (NNs) and statistical methods. The selected publications were chosen from recent years, and the analysis was conducted by considering the frequency of the dataset, the causal association, and the methodology used in each study Dutta et al. have been using a combination of exogenous and endogenous variables to forecast the price of BTC. The authors proposed two DL models, RNN and Gated recurrent units (GRU), to make BTC price predictions. The study found that GRU was more effective in learning and training BTC price changes, and both RNN and Long-Short-(LSTM) were slower in comparison. The root mean square error (RMSE) values for the training dataset were NN 0. 02, LSTM 0. 010, and GRU 0. 010, and for the test dataset, they were NN 0. 31, LSTM 0. 024, and GRU 0. 019. The study concluded that DL models like LSTM and GRU outperform traditional ML models [15]. Jay et al. [SEP] Justify:

Output: The text contrasts the claim because it discusses the risks associated with currency trading and highlights that BTC price predictions involve substantial risk factors. It specifically states, 'the prediction of currency prices, which is considered a major risk factor in currency trading,' indicating that investments in cryptocurrencies are not guaranteed to be safe or lucrative.


Input: Task: pass the claim using the given text. Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: tailgating, accelerating when the traffic light turns orange, etc. To guarantee safety, we should be able to validate which aspects were extracted from the observations since we want to refrain an agent from picking up undesired behavior in the dataset. Although a single constraint, represented by a state-action pair, could be easily interpretable, it is difficult to obtain a general view on the environmental restrictions from a large set of constrained state-action pairs. Another limitation is the difficulty to transfer learned constraints in one environment to a similar environment, since constraints are defined in the state-action space of a particular environment. We hypothesize that defining constraints in terms of more generalized, human interpretable concepts would improve transferability. For example, when inferring constraints from a traffic environment, all states corresponding to off-road positions should be added to the set of constraints. However, when humans learn to drive, we use the concept “road” and learn a single rule which prohibits us to drive off the road. In contrast with the set of constraints defined as prohibited state-action pairs, this rule also applies in traffic environments other than the one which was used to learn the rule. Inductive logic programming (ILP) (Muggleton, 1991) offers a computational framework to induce a logic program (i. e. hypothesis) which generalizes a set of training examples. ILP also provides the possibility to specify background knowledge as a logic program. Because the induced hypothesis is comprehensible by humans and providing background knowledge allows us to introduce human concepts, ILP could be a worthy alternative for IRL and constraint inference. However, ILP requires both positive and negative examples while we consider observations only consist of positive examples. Negative examples correspond to anomalous behavior which is rarely observed in real-world environments or would be dangerous to collect. If the observations contain anomalous behavior, this will be reflected in the induced rules hence can be detected. In this paper, we combine ILP and constraint inference and propose a hybrid method to infer restrictive rules (which represent social norms) from observations (positive examples) and represent them by a logic program. The presented method is an iterative procedure, where each iteration consists of an inference and an induction stage. During inference, a constraint is inferred from the observed trajectories and added to a set of constraints. [SEP] Justify:

Output: The text does not directly address or support the claim about reinforcement learning algorithms mimicking human learning through rewards and punishments. Instead, it discusses constraints in learning environments and the applicability of human concepts to improve constraint inference in various settings. Hence, the relationship between the text and the claim is irrelevant.


Input: Task: corroborate the claim using the given text. Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: While the model primarily focuses on image identification—an essential attribute for AV safety—the suggested enhancements will bolster AV safety measures. The results obtained from the developed image processing model will serve as the groundwork for formulating the safety framework for AVs. However, these outcomes alone are insufficient to instill trust and confidence among drivers, pedestrians, and society at large. Since LiDAR and Non-LiDAR technologies serve as the “eyes” of AVs, similar to human vision, it is crucial to compare the reactions of AVs with those of human drivers based on established rules. This comparative analysis will aid in establishing a certain level of trust in AVs. The tables presented within the safety framework will provide tangible and verifiable data, facilitating a clear comparison and contrast between the two technologies through the developed image processing model in this research. 5. the introductory section, the study provided an overview of different LiDAR and Non-LiDAR technologies, as well as an exploration of Technologies and the Framework on Vehicle Safety. The background section examined existing research on the two prominent technologies in Automated Vehicles, namely LiDAR and Non-LiDAR, and highlighted current advancements in the field. Additionally, insights into models and architectures, including Networks (CNNs), were discussed. The methodology section established the research framework and presented the theoretical background on with a specific focus on Image Processing. Furthermore, the Model was elaborated upon, involving the development of an algorithm to identify input images from both LiDAR and Non-LiDAR technologies. Subsequently, the images collected from the two technologies were fed into the algorithm for prediction. Given that the algorithm employed a supervised learning model, the images were appropriately labeled, enabling accurate measurement of input accuracy for each technology. The collected images were then organized into tables based on weather conditions, and each technology underwent 25 runs under various weather conditions. The results from these 25 runs were averaged and compiled into tables, facilitating a comprehensive comparison and contrast between the two technologies. The image processing model developed in this research served as the foundation for data collection from both technologies using a vehicle driven by a human driver in a simulated environment. The same tests and scenarios outlined in the were also conducted with (AVs). The resulting data was subsequently compared and contrasted, taking into account the safety framework known as Safety (RSS). [SEP] Justify:

Output: The text corroborates the claim by mentioning advancements in autonomous vehicle (AV) technology, particularly through the discussion of LiDAR and Non-LiDAR technologies, which are integral to AV safety. The focus on image processing models and comparative analysis indicates active research and development in the field of AV technology. Furthermore, the mention of ongoing studies and methodologies for improving AV safety demonstrates that car manufacturers and researchers are indeed working on enhancing autonomous vehicle capabilities.


Input: Task: contrast the claim using the given text. Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: introduced layer-wise randomness into the activations of observed features in multilayer perceptron (MLP) and LSTM to simulate market volatility. Their best-case trial, which used 23 features and a window size of 7 days, resulted in an average improvement of no more than 5% in (MAPE) [16]. Othman et al. used LSTM to create a model for predicting the BTC stock market. The study concludes by discussing future work based on the methodology used and techniques and tools employed to predict BTC’s price on Yahoo Finance’s stock market. According to their prediction, BTC’s price will exceed USD 12,600 with an RMSE of 288. 59866 in the days following the prediction [17]. Cavalli and Amoretti have introduced a new method for predicting BTC trends using the 1D convolutional neural network (CNN). Their method includes combining social media data, blockchain transaction history, and financial metrics to construct datasets, and using a cloud-based system to collect and analyze large amounts of data. The 1D CNN model they created achieved a prediction accuracy of 74. 2%, which is reported to be better than LSTM models. They also presented a trading strategy that maximizes profit during bullish trends and minimizes losses during bearish trends based on their model. Their contribution to the field of BTC trend prediction is significant [18]. Yang suggested a method that combines (EEMD) and LSTM to investigate the subject of BTC price forecasting for the following day. Using the EEMD-LSTM approach requires additional work, as indicated by the outcome [19]. Sebastião and Godinho explored the predictability of and Litecoin using ML approaches. They validated and tested their models during a period of high volatility and bear markets to evaluate the accuracy of their predictions. They utilized trading and network activity data from August 15, 2015, through March 3, 2019, with the test sample starting on April 13, 2018. Five out of 18 different models had success rates of less than 50% during the test period. However, the ensemble of five models that generate identical signals performed well for ETH and Litecoin, with high positive ratios and annual rate returns. The study concludes that ML can be used to develop profitable trading strategies in cryptocurrency markets, even during tough times in the market [20]. [SEP] Justify:

Output: The text contrasts the claim by detailing various studies on cryptocurrency price predictions and trading strategies, emphasizing that there are challenges and risks involved in cryptocurrency investments. For instance, it states that among the tested models, five out of 18 had success rates of less than 50%, showcasing that investments do not guarantee high returns and acknowledging volatility in the market. This evidence indicates that while there may be strategies for profit, the idea of guaranteed high returns with no risks is misleading.


Input: Task: pass the claim using the given text. Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: The set of constraints is extended with the state-action pairs which correspond to the interpretations which are an answer set of B but are no answer set of \(B \cup H\), i. e. the state-action pairs which are no longer valid after imposing H on B. After \(\eta\) iterations, the hypothesis H is returned. The complete procedure is depicted in Algorithm 1. 4 ExperimentsFig. 2The different SUMO traffic environments. The agents learn social norms such as dealing with priority to the right and traffic lights, purely from observed trajectoriesFull size imageIn this section we perform an experimental evaluation of the proposed method. Section 4. 1 provides a brief overview of the used environments. In Sect. 4. 2, a qualitative evaluation follows of the induced hypotheses. Section 4. 3 covers a quantitative evaluation of the learned rules and the effect of the hyperparameter \(\eta\) and the number of trajectories available. In Sect. 4. 4, we provide a model-free RL agent with rules induced using our method as prior knowledge. First, we examine to what extent this prior knowledge speeds up the learning process. Next, we evaluate the applicability in safety critical environments by measuring the empirical violation probability both during and after training. At last, in Sect. 4. 5 we inspect the transferability of the induced rules to other but similar environments. 4. 1 EnvironmentsWe conduct experiments in the towers of Hanoi environment (Fig. 1) and simulated traffic environments (Fig. 2). We chose these environments because the behavior of agents in these environments is constrained by formal or informal rules. Towers of Hanoi is a canonical recursive problem where a stack of disks has to be moved from one place to another, without placing a larger disk on a smaller one and only moving the upper disk of any stack. Although all rules are explicitly known and could in principle be integrated upfront in the MDP definition, the simplicity of this problem allows to illustrate the working of our algorithm. In this environment, there is only one agent which plays the game. The state of the game is represented by the xy-coordinates of the three disks. The available actions are the displacement of any disk on the top position of one of the stacks to the top position of another stack. [SEP] Justify:

Output: The text does not address the claim regarding reinforcement learning algorithms mimicking human learning through rewards and punishments. Instead, it focuses on technical details of experiments, state-action pairs, and environments without mentioning how rewards and punishments factor into human learning or reinforcement learning.


Input: Task: corroborate the claim using the given text. Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: , Raison, M. , Tejani, A. , Chilamkurthy, S. , Steiner, B. , Fang, L. , Bai, J. and Chintala, S. (2019) PyTorch: Style, High-Learning Library. In: Wallach, H. , Larochelle, H. , Beygelzimer, A. , d’Alché-Buc, F. , Fox, E. and Garnett, R. , Eds. , Advances in. (NeurIPS), Vancouver, 8024-8035. [42] Simonyan, K. and Zisserman, A. (2015) for Large-Scale Recognition. arXiv: 1409. 1556. [43] He, K. , Zhang, X. , Ren, S. and Sun, J. (2016) Learning for Image Recognition. 2016 IEEE Conference on (CVPR), Las Vegas, 27-30 June 2016, 770-778. [44] Szegedy, C. , Liu, W. , Jia, Y. , Sermanet, P. , Reed, S. , Anguelov, D. , Erhan, D. , Vanchoucke, V. and Rabinovich, A. (2015) with Convolutions. 2015 IEEE Conference on (CVPR), Boston, 7-12 June 2015, 1-9. [45] Krizhevsky, A. , Sutskever, I. and Hinton, G. E. (2012) Imagewith Neural Networks. Proceedings of the 25th on 3-6 December 2012, 1097-1105. [46] Boesch, G. (2022) (VGGNet)—Need to Know. [47] IBM (2020) Machine Learning. . [48] Zhao, J. , Liang, B. and Chen, Q. (2018) Technology toward the Self-Driving Car. of Systems, 6, 2-20. [49] Fernandes, J. (2018) Training. [50] (2020) Unit (ReLu)? Introduction to ReFunction. [51] Jia, Y. , Shelhamer, E. , Donahue, J. , Karayev, S. , Long, J. [SEP] Justify:

Output: The text corroborates the claim by mentioning the development of technology toward the self-driving car, which implies that car manufacturers are indeed engaged in creating autonomous vehicle technology. The specific reference to the technology suggests active development in this field.


Input: Task: contrast the claim using the given text. Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: Schulte and Eggert aim to find markers for BTC price fluctuations and create an hourly price prediction model using a trained LSTM. They identify major price prediction signs and compare the LSTM results with other techniques, demonstrating an impressive accuracy rate of 96. 48%. The study utilized 47 input features over 10 months, leading to discussions on the use of NNs in predicting stock prices. The research makes valuable contributions to the field of BTC price prediction and suggests the potential of LSTM models for accurate and reliable predictions [21]. Jaquart et al. investigate the predictability of the BTC market across different time horizons using ML models. Technical aspects are found to be the most important for most techniques, followed by blockchain-based and sentiment/interest-based elements. Predictability improves with longer horizons, and a long-short trading strategy based on quantiles can yield up to 39% monthly profits before transaction costs, but the accuracy of predictions is around 50%. RNNs and gradient-boosting classifiers perform well in the predictive task, outperforming a random classifier [22]. Das et al. proposed a method for predicting BTC prices using a combination of DL techniques, involving -and Bi-RNN. While individual models like --RNN, or autoregressive integrated moving average achieve satisfactory results, the hybrid method proposed in the study outperforms them with and mean absolute error (MAE) values of 2. 69, 1. 78, 2. 20, and 1. 29, respectively. The study demonstrates the efficacy of combining different DL building blocks for more reliable BTC price prediction [23]. Aljojo et al. used a nonlinear autoregressive exogenous NN model to investigate the impact of timestamps on BTC’s value. They find that transaction timestamps significantly influence BTC’s performance variance with 96% forecast accuracy. Certain transaction events show repeating patterns, allowing for the anticipation of BTC’s price with less accuracy. Incorrect timestamps can cause uncertainty and have a negative impact on the BTC market. The study highlights the importance of accurate time stamping for participants in the BTC market [24]. Andi’s study used to predict BTC prices using a normalized dataset. The study found that using a combination of ML techniques leads to better results. The accuracy of the prediction model was found to be 97. 2% [25]. Buslim et al. [SEP] Justify:

Output: The text contrasts the claim by discussing various studies that investigate price prediction models for Bitcoin (BTC) and the inherent risks associated with them. It highlights that while certain prediction methods can yield substantial returns—like the mentioned long-short trading strategy that can produce up to 39% monthly profits—there's also an acknowledgment of the unpredictability of the market, as seen with an accuracy of predictions being around 50%. Furthermore, the text emphasizes the importance of accurate timestamps and other factors that influence BTC performance, indicating that there are indeed risks and uncertainties in cryptocurrency investments.


Input: Task: pass the claim using the given text. Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: To establish a mapping to first-order logic using \(\psi\), we use three instances (one for each disk) of the predicate at(D, X, Y). This predicate evaluates true if disk D is at position (X, Y). Disks are numbered in order of decreasing size, the disk for which D=1 corresponds with the largest disk. For the second set of experiments, we constructed three traffic environments: an intersection with bidirectional traffic which is controlled by traffic lights (Fig. 2a), an intersection with unidirectional traffic where the priority is given to cars coming from the right (Fig. 2b) and an environment combining both previous scenarios (Fig. 2c). In these environments, the cars represent the different agents. From any position the agent can choose between 5 actions which correspond to the 4 cardinal directions and standing still. The mapping \(\psi\) also uses these directions to map the xy-coordinate of an agent to a specific road, e. g. onRoad(north) signifies the agent is on the road on the north of the intersection. The unary predicate beforeJunction denotes if the agent is in front of a junction. The action of the agent is represented by the predicate go. The state of the traffic light is represented by tls0 and tls1. If a car is approaching from the right, the predicate carOnTheRight is true. The ground truth rules (to be learned constraints) consist of not driving off-road, giving priority to the right on a junction without traffic lights, stopping at a red light and, in case of unidirectional traffic, not driving in the opposite direction. These scenarios are built in the SUMO traffic simulator (Lopez et al. , 2018) which comes with internal car control algorithms, representing the expert policy \(\pi _E\), to generate realistic car trajectories. All possible (valid) trajectories are generated with the same probability and all results are averaged over 20 trials with different random seeds. 4. 2 Induced hypothesesIn this subsection we carry out a qualitative evaluation of the induced hypotheses. In the towers of Hanoi environment, only one rule is learned which states that it is invalid that a larger disk V1 is on a disk V2:Note that disks are numbered in order of decreasing size. For the towers of Hanoi environment the background knowledge and search space are depicted in Fig. 1. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses the mapping to first-order logic in the context of traffic simulations and does not mention reinforcement learning or the concept of learning through rewards and punishments. There is no connection to how algorithms mimic human learning.


Input: Task: corroborate the claim using the given text. Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: Notes on contributoris a scholar in planning and urban studies, based at the Centre for. Her research is about displacement and dispossession in contemporary cities, particularly focused on Indigenous land justice in cities, the impacts of urban regeneration, critical urban governance, and the politics of urban informality. Her most recent book is Planning in Indigenous Australia: From imperial foundations to postcolonial futures with co-authors. Libby is of ‘Interface’ in the journal and Practice. Email: libby. porter@rmit. edu. auImagine it is sometime in the year 2025 and you are getting ready for the daily commute. Your mode of transport is car and while it is not far in distance, the roads are congested and the journey often takes an hour. But this is not lost time, for you can work on the way as your car is an autonomous vehicle, doing the driving for you. Having taken you to work, the car drives back home, saving the cost and hassle of car parking, but the cost of fuel is high and rising. You have set the car to return to your office later in the day to pick you up and take you home. Alternatively, you could take one of the share cars from the fleet outside your office, which will return itself to the fleet space. The fleet is also the way you will move about in the city during your day, as you book an AV on a phone app and meet it on the street. This is an entirely plausible future. Autonomous vehicles (AV) – vehicles powered by autonomous technologies, without requiring human control – are, according to many, just around the corner. And the implications are vast. While fully autonomous vehicles are still being tested, in reality a range of levels of automation already exist. Many of them are already on the road in the form of adaptive cruise control and other technologies (Peng, Citation2018). A fully autonomous ‘level 5ʹ vehicle is one able to move safely and reliably anywhere in any conditions without human intervention. While the debate continues about whether this level of full autonomy in a vehicle is a long time away or just around the corner, what is clear is that AV is set to have significant implications for our mobility and the way we think, plan and manage our cities, towns and infrastructure. AV is of course big business. According to industry estimates, four in 10 vehicles will be autonomous by 2040 (Accenture Digital, Citation2014). [SEP] Justify:

Output: The text corroborates the claim by discussing the development and implications of autonomous vehicles (AV), indicating that 'autonomous vehicles are, according to many, just around the corner.' It mentions that many levels of automation are already present in vehicles on the road, thus supporting the idea that manufacturers are actively engaged in this technology development. Additionally, it highlights that 'AV is of course big business,' suggesting industry interest and investment.


Input: Task: contrast the claim using the given text. Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: ’s study contrasts the effectiveness of and LSTM models using a dataset from August 17, 2017 to April 13, 2021. are utilized to determine the best-performing model. The results show that GRU with has the best performance, with MAE training of 0. 0043 and a testing of 0. 0594 [26]. Zhang et al. combined SDAB, which uses the advanced deep neural network model (SDAE), with an effective integration method to predict price integration. The study used various exogenous variables and found that SDAE-B was more accurate and had fewer errors compared to traditional ML methods such as Machine (LSSVM) and back propagation. The SDAE-B method had a MAPE of 0. 016, RMSE of 131. 643, and DA of 0. 817, making it a more effective method in handling the randomness and nonlinearity of the BTC price [27]. Zhang et al. proposed an LSTM prediction (LSTM-P) neural network model for BTC and gold price prediction. To smooth out price data fluctuations, a noise decrease technique based on the wavelet transform has been used, and an optimized LSTM-P model has been created utilizing historical gold and BTC price data. With a MAPE of 6. 08% for LSTM-P and 4. 81% for standard LSTM, the LSTM-P model outperforms standard LSTM models and other time series forecasting models [28]. Luo et al. used ML and multiscale analysis to create ensemble prediction models by matching various techniques to their respective multiscale components. BTC price data from 2017/11/24 to 2020/4/21 and 2020/4/22 to 2020/11/27 were used for training and prediction. The ensemble models achieved 95. 12% forecast accuracy and outperformed benchmark models. The suggested models were robust in both rising and falling market conditions, and various algorithms were applied to components with different time scales [29]. Kang et al. suggested a hybrid DL model, 1D CNN-GRU, for forecasting cryptocurrency prices. The model combines a 1D CNN and stacked GRU to encode BTC price data over time and capture its long-range dependencies. [SEP] Justify:

Output: The text contrasts the claim by discussing various models and methods for predicting cryptocurrency prices, highlighting the complexity and variability of these investments. It implies that there are risks involved since the models, like GRU and LSTM, show different performance outcomes (e.g., MAE, RMSE) and that price predictions are inherently uncertain with no guarantees of high returns. Moreover, it suggests that while some models may perform well in specific conditions, the use of advanced techniques points to the presence of market unpredictability, which counters the notion of 'no risks' in cryptocurrency investments.


Input: Task: pass the claim using the given text. Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: For reasons of space, the complete background knowledge and search space are omitted for the traffic environments. In the traffic environment with priority to the right the following rules are induced:The first rule states that it is invalid for an agent to be off-road. This rule makes use of a conditional literal and is equivalent to the conjunction of not onRoad(V1) for all directions V1 (dir(V1) evaluates true if V1 is a variable which represents a direction). The following four rules invalidate being in a state on-road and transitioning to a state off the road. Since all roads in this environment are unidirectional, the action of going west is invalid as stated by the second last rule. The last rule represents the “priority to the right"-rule, i. e. when there is a car on the right, the only valid action is go(zero) which represents being stationary. The rules learned in the traffic light controlled junction environment are displayed below:The first rule invalidates being off-road and the following 8 rules prohibit transitioning to a state off-road, similar rules were induced in the environment with priority to the right. The last four rules state cars have to stop at a red traffic light. 4. 3 Accuracy of the learned hypothesesFig. 3False positive rate, false negative rate and accuracy of the induced rules for the traffic light controlled intersection environment (a, c, e respectively) and for the intersection with priority to the right environment (b, d, f respectively) as a function of the number of observed trajectories. Mind the different y-axis scaleFull size imageFig. 4Number of state-action pairs which are constrained by the inferred rules for increasing values of \(\eta\). Line styles indicate the number of expert trajectories available. Results are from the traffic light controlled intersection environmentFull size imageIn this section, we validate the induced rules of the traffic environment by calculating all answer sets of the ASP program represented by the union of the background knowledge and the induced hypothesis. These answer sets represent the state-action pairs which are considered valid by the induced hypothesis and can hence be used to classify a state-action pair as allowed or prohibited. We gather 10 000 trajectories from the SUMO simulator and validated that all correct actions of cars in any of the possible on-road position were observed at least once in this dataset. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on describing the rules and accuracy of learned hypotheses in a traffic environment for reinforcement learning algorithms, without discussing how these algorithms mimic human learning through rewards and punishments.


Input: Task: corroborate the claim using the given text. Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: This is a lucrative new industry, and while the estimates vary, the predicted global value of the autonomous vehicle industry has been projected at $US54billion in 2019, rising to $US556billion by 2026 (Garsten, Citation2018). This will increasingly include trucks and other non-domestic uses of AV such as military and defence. In Australia, is already using driverless trucks in its mining operations by retrofitting an System to its fleet, set to make 30% of its fleet autonomous by 2019 (Pash, Citation2017). The major automotive companies are all in a race to get AV on the road and embedded into the market. These are digital disruptions indeed, and the implications are enormous. Debate continues to rage about whether autonomous vehicles are safer than human-controlled vehicles, and whether or not this represents massive advantages for cities of the future, or a dystopia of more freeways, trips and congestion. There are plans to launch on-demand driverless taxis in the US in 2019, contributing to a trend already visible where consumers want to purchase on-demand mobility, through platform technologies, thus enhancing a mode shift from public transit toward rideshare. There is much anticipation of AV and its possibilities. The AV revolution could be combined with a mass shift to carbon neutral vehicles, presenting new possibilities for carbon reductions. Many pundits claim AV is safer than human drivers, though recent high profile accidents have dented public confidence. For people with disabilities and impairments to mobility, AV presents transformative possibilities, with a recent survey in Australia demonstrating that more than 85% of people felt AV would bring mobility impaired people significant benefits (Regan et al. , Citation2017). Yet the challenges and risks are very significant. AV may well result in perverse mobility outcomes, reducing mobility for some such as pedestrians and cyclists and actually increasing the number of trips as wealthy households able to afford the technology unleash more private trips on the road. Even greater urban sprawl may result as drivers capitalise on the benefits of being relieved of the task of driving. Perhaps this means we will be more comfortable with more and longer trip times, potentially fuelling urban sprawl. The unrolling of AV might reconstitute roads as real estate through fleets of rideshare, drones and delivery services, all supported by apps and other platform technologies that harvest our data and pitch ever more products to us. In this Interface, practitioners, scholars and transport commentators from around the world provide commentary on these dimensions of this impending technological revolution. [SEP] Justify:

Output: The text corroborates the claim by stating that 'the major automotive companies are all in a race to get AV on the road and embedded into the market.' This indicates that multiple car manufacturers are indeed engaged in the development of autonomous vehicle technology, supporting the claim about their active involvement in this area.


Input: Task: contrast the claim using the given text. Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: The execution of the 1D CNN-GRU model has been evaluated on and XRP datasets, and it was found to outperform previous approaches with RMSE values of 43. 933 for BTC, 3. 511 for ETH, and 0. 00128 for XRP [30]. Aljadani’s study applies Bidirectional LSTM and GRU-based DL algorithms to three public real-time BTC datasets. GRU outperforms Bi-LSTM for and Cardano, with the GRU model achieving the lowest RMSE values of 0. 01711, 0. 02662, and 0. 00852 for the three cryptocurrencies, respectively. The proposed framework achieves significant performance in predicting cryptocurrency prices [31]. Nematallah et al. proposed using DL processes such as RNNs and LSTMs to predict BTC market price trends. The models are evaluated based on their MAPE and RMSE values for predicting the next 15, 30, and 60 days. The LSTM model is found to be the superior method with a lower MAPE of 3. 01% compared to RNN’s MAPE of 7. 10%, even though it takes longer to assemble [32]. Mehta and Sasikala’s research focusses on using ML techniques to forecast BTC prices. By analyzing daily data for 1,681 cryptocurrencies between November 2015 and April 2018, the authors show that their approach, which employs simple trading techniques supported by cutting-edge ML algorithms, outperforms traditional benchmarks. The study’s goal is to forecast the cryptocurrency market’s short-term future and provide real-time BTC price predictions. The use of on the available dataset yielded an impressive 92% accuracy [33]. 3 Characteristics of cryptocurrencies Cryptocurrencies offer many benefits, such as decentralization, security, and privacy, but they also have some downsides, such as high volatility and susceptibility to hacking and fraud. Users can evaluate whether these currencies are suitable for their financial objectives by assessing each feature listed in Table 1. Table 1 Important features of cryptocurrencies Cryptocurrencies are characterized by decentralization where there is no intermediary or entity that controls payment and transfer operations Böhme et al. [41] provide high security for users, as payment transactions are protected using advanced encryption techniques Antonopoulos [42] allow complete privacy for users, as no personal information is recorded when making payment transactions Böhme et al. [SEP] Justify:

Output: The text contrasts the claim by highlighting that cryptocurrencies are characterized by high volatility and susceptibility to hacking and fraud, which indicates that there are risks involved in cryptocurrency investments. Additionally, while the text discusses various methodologies to predict cryptocurrency prices and their performance, it does not support the idea that investments in cryptocurrencies guarantee high returns without any risks.


Input: Task: pass the claim using the given text. Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: Accordingly, the ground truth validity of a state-action pair is determined by the presence in at least one of the 10 000 trajectories. We report three statistics. The false positive rate (FPR) is the fraction of the state-action space that is incorrectly prohibited by the set of induced rules. Accordingly, the false negative rate (FNR) is the fraction that is incorrectly allowed. The accuracy is the ratio between the sum of false positive and false negative state-action pairs divided by the total number of state-action pairs. We calculate these statistics for the traffic light controlled intersection and the intersection with priority to the right environments for increasing numbers of trajectories (dataset sizes) and iterations \(\eta\). The results are depicted in Fig. 3. The FPR decreases with the number of observed trajectories. When few expert observations are available, some allowed state-action combinations may not be observed. If an allowed but unvisited state-action pair has high likelihood under the nominal policy it will incorrectly be selected as a constraint which will lead to incorrect rules. When, for instance, in none of the trajectories a car stopped in front of a red traffic light (because the light was always green), the system could induce that it is invalid for a car to be in front of a red traffic light. Provided that the number of iterations \(\eta\) is not too small, small number of observations will result in a low FNR because rules are inferred which invalidate large regions of the state-action space which also include the true constraints. In our example, when the system infers the rule, all states which include the agent in front of a red traffic light are invalid, it also invalidates the true constraint of driving a red light. This phenomenon can also be observed in Fig. 4 which depicts the number of state-action pairs which are invalidated by the learned rules for increasing values of \(\eta\) and number of expert trajectories. With fewer iterations \(\eta\), fewer constraints are inferred accordingly the induced rules invalidate less state-action pairs hence a smaller FPR but a higher FNR. It is self-evident the number of constrained state-action pairs increase when the number of iterations increase. Notably, in the traffic light scenario of Fig. 3c, the FNR first increases for smaller number of trajectories. [SEP] Justify:

Output: The text discusses the technical aspects of reinforcement learning algorithms, including the false positive rate (FPR), false negative rate (FNR), and accuracy based on trajectories. However, it does not provide information or evidence about whether these algorithms mimic human learning through rewards and punishments, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: Email: C. Curtis@exchange. curtin. edu. auThe PromiseOur cities are changing. One place where change is most rapid is in the development and convergence of communications and information technologies with transport technologies. This convergence is presenting a challenge to the forces that shaped the automobile city throughout the 20th century by promising new forms of shared mobility and autonomous vehicles (AVs). Many analysts are upbeat about the role autonomous vehicles and mobility on demand will play in our future cities. This anticipation is finding wide expression in policy documents, and government programs (see for instance Authority, Citation2018; Infrastructure Victoria, Citation2016) where autonomous vehicles and mobility on demand are being celebrated for their potential to reduce fatalities on the road (Glaser, Vanholme, Mammar, Gruyer, & Nouveliere, Citation2010), replace private ownership of a vehicle with vehicles that will be shared (Firnkorn & Müller, Citation2015), and reclaiming valuable land currently used to accommodate parked cars (Burns, Citation2013; Zhang, Guhathakurta, Fang, & Zhang, Citation2015). To better understand the potential of shared mobility and AVs, industry and universities are investing billions of dollars to test, standardise, and ultimately mass produce the new technologies. In our home institutions, the future of AVs is being anticipated with the creation of new research centres to test and refine the complex communications technologies need to operate AVs on city streets. However, the anticipatory research and industry activity around AVs and shared mobility sits in tension with a regulatory environment that is struggling to understand the potential downsides of these potential futures. One possible side effect from the convergence between the IT and automobile sectors is the development of increased capability to collect data about human practice, and then use these capacities to commercialise movement around cities in new ways. This presents a challenge for planners who might see value in minimising the extent of commercialisation of future mobility in order to ensure that benefits are distributed equally across the city. Ultimately this is a question about urban governance in the 21st Century city and region, and the role planners will play in an era that will be marked by transport disruptions. To help move the debate forward, in this short piece we ask, how might planners be better positioned to anticipate and then plan for these new technologies and the cities they will create? [SEP] Justify:

Output: The text corroborates the claim by discussing how industry and universities are investing billions of dollars to test and mass-produce new technologies related to autonomous vehicles (AVs). It mentions the anticipation surrounding AVs and the convergence of communications and information technologies with transport technologies as a sign of significant development in this area.


Input: Task: contrast the claim using the given text. Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: [41] enable easy access to payment and transfer services Antonopoulos [42] Enhancing international transfers Cryptocurrencies help enhance international transfer operations due to their ease of use Antonopoulos [42] Improving transparency Cryptocurrencies provide great transparency in payment and transfer operations, as all transactions are recorded in an encrypted and transparent ledger Narayanan et al. [43] Hedge against inflation Cryptocurrencies are a good option for hedging against traditional currency inflation Böhme et al. [41] High volatility Cryptocurrencies are characterized by high volatility in their value, as their value can be significantly affected by changes in the global market, which can result in financial losses for users Narayanan et al. [43] High probability of hacking Despite the security offered by cryptocurrencies, there is still a high probability of hacking due to vulnerabilities in software or user errors Narayanan et al. [43] Table 1 introduces the characteristics of cryptocurrencies, which are digital assets that support online payments and transfers. Due to the encryption of transactions, cryptocurrencies are characterized by decentralization and high security. They provide users with privacy and easy access to financial services. They can also improve international transfers and act as a hedge against inflation. However, their high volatility and vulnerability to hacking calls for precautionary measures. Overall, cryptocurrencies represent a significant development in the field of digital finance. 4 Risk of cryptocurrency Cryptocurrencies, as an emerging asset class, present unique risks for investors and traders. Understanding these risks is essential for making informed decisions and mitigating potential losses. The following points highlight the most significant risks associated with investing in cryptocurrencies and provide insights for navigating the volatile digital currency landscape. Investing in cryptocurrencies carries several risks, one of which is hype risk. This occurs when investors make impulsive decisions without fully understanding the technology or market dynamics, leading to significant losses when the market self-corrects. Therefore, thorough market analysis is crucial before investing, and the risks involved must be considered. Another significant risk is a security risk, which stems from the high risk of scams, hacking, and theft in the cryptocurrency market. Investors must choose secure blockchain protocols, exchanges, and wallets to safeguard their investments. Cryptocurrencies are known for their high volatility, which poses a considerable risk for investors. Having a long-term investing plan and being equipped to deal with volatility are both crucial. [SEP] Justify:

Output: The text contrasts the claim by highlighting the inherent risks associated with cryptocurrency investments. It explicitly mentions high volatility, the risk of scams, hacking, and the importance of thorough market analysis before investing, which contradicts the assertion that such investments guarantee high returns with no risks.


Input: Task: pass the claim using the given text. Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: All agents adopt an \(\epsilon\)-greedy exploration strategy during training (\(\epsilon =0. 05\)), but become fully deterministic afterwards (\(\epsilon = 0\)). We adopt a learning rate of 0. 9 and a discount factor 0. 75. The Q-table is initialized with zeros. Experimental results are the average of 400 trained agents. We define the cost signal (negative reward) as the L1-distance from the agent’s position to its goal, which has a maximum of 4. Violating a ground truth rule (i. e. traffic regulations) incurs a cost of 10. Figure 5 shows the total cost received by an agent trained for a different number of episodes in the intersection with traffic lights environment (Fig. 5a) and the intersection with priority to the right environment (Fig. 5b). We conclude from these results that constraining an agent with a logic program inferred using our method leads to a considerable speed-up of the Q-learning convergence. Using only constraint inference barely improves the agent’s required number of episodes during training which confirms the importance of the induction step. Moreover, we notice a much smaller variation in performance between LC-QL agents. Table 1 shows the empirical probability of an agent violating at least one rule during and after training for both intersections. These results show only our method guarantees no rules will be violated both at deployment and during exploration, thus, would qualify for safety-critical applications. At last, we measured the execution time of the different steps of our algorithm, the results for the traffic light controlled junction environment are depicted in Fig. 6. We observe that the induction step takes up the largest part of the execution time, especially for large values of \(\eta\). The green curve represents the time until convergence of LC-QL when provided with rules inferred using our algorithm for different values of \(\eta\). Although our method improves the speed to convergence when constraining a learning agent with the learned rules (as shown in Fig. 5a, b) there is no overall time saving compared to training an agent without prior knowledge. 4. 5 Transfer learningFig. 6Execution time of the different steps of our algorithm applied to the traffic light controlled intersection environmentFull size imageFig. 7Transfer learning experiment: total cost received in the combo environment (see Fig. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses specific technical details of agent training strategies, cost signals, Q-learning convergence, and experimental results related to reinforcement learning algorithms. It does not address how reinforcement learning algorithms relate to the way humans learn through rewards and punishments.


Input: Task: corroborate the claim using the given text. Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: Equally, there is very little about how these different futures can then equip planners, policy makers, and different publics across the vast geographies of cities and regions to challenge the dominant discourses around AVs. There is great potential to explore these futures more comprehensively in a manner that accounts for the health, urban design, ecological, social and built environment impacts this future will create, and to then consider planning responses to these challenges. This needs support through research funding for projects that go beyond workings of the technologies, so that society may be furnished with a fuller evidence base. This would also fill the knowledge gap that planners and decision makers have identified. Also, in this research, we must not conceive of the state as a single entity with one view or perspective. Planning is now multiple in its orientation. It stretches across public and private sector bodies, each with different - often competing - motivations, mandates and stakeholders. We need to examine the socio-technical disruptions of the kind presented by AVs across a range of social, political, institutional and land use contexts. The roll out of AVs in Australia will be very different to that in Germany and so on. Developing an evidence base from different planning systems will bring to light the ways in which AVs shape and are shaped by different governance settings and public and private sector arrangements. New evidence is also needed to show us how different scenarios of AV ownership and control might shape the land use patterns and urban structure, equity of access and opportunities and incentives for active travel. Planners, in practice and in academia, need to articulate our understandings of the pitfalls and possibilities of different AV futures in plain language and engage with citizens to shape our expectations of governments and the private sector. Governance PathwaysThe lure of the shiny new gadget-driven autonomous mobility future is certainly attracting a great deal of public and private R&D investment. It is whetting the appetite of time poor overworked families in a cash-strapped age who see possibilities to use their travel time more usefully. It is also attracting new private sector players to a new ‘mobility market’, captured under the banner of Mobility as a Service (MaaS). But the governance issues are currently under-played. Many governments are actively courting AV trials for fear of being left behind in the technology race and are scrambling for regulations to address complex legal questions about safety and liability when an algorithm, rather than an individual, controls a vehicle. [SEP] Justify:

Output: The text corroborates the claim by mentioning that the allure of autonomous mobility is attracting significant public and private R&D investment, indicating that car manufacturers are indeed engaged in developing autonomous vehicle technology. It describes a competitive environment where governments are courting AV trials to keep up with technological advancements, reinforcing that multiple stakeholders are involved in the development of autonomous vehicles.


Input: Task: contrast the claim using the given text. Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: Liquidity risk is also a concern, particularly for lesser-known altcoins, which may not have enough buyers or sellers, making it challenging to sell investments quickly at a fair price. Vanishing risk refers to the possibility of cryptocurrencies failing and becoming worthless, resulting in significant losses for investors. Therefore, investors should conduct thorough research and invest in promising and established projects. Regulatory risk is also a possibility as regulatory changes can impact the market and create volatility as cryptocurrencies become more mainstream. Keeping up with legislative changes is essential so that investing plans may be modified. Finally, tax risk includes the possibility of cryptocurrency investments being subject to capital gains tax, and tax authorities becoming more stringent in enforcing tax regulations. Complying with tax regulations and seeking professional advice can help avoid potential penalties and losses [34]. 5 Artificial neural network (ANN) There are some similarities between ANNs and biological brains. Data are received, processed, and transmitted from and to nearby neurons by a group of simple signal processing units (artificial neurons). In the training phase, when ANN learns from the dataset given, the strength (weighting) of the connections among neurons is determined. ANNs are a vast category of algorithms. The main classes for supervised learning include [35] (Figures 1–3): MLP network – This has layers of artificial neurons that are normally fully coupled. Data are transmitted among the levels in a feed-forward fashion. ANNs’ inputs are vectors. These ANNs are primarily employed for situations involving organized or unstructured data of limited complexity [36]. CNN – CNN is one of the DL techniques. A network that organizes artificial neurons in feed-forward layers that are often thinly linked. Convolution layers enable the use of multi-dimensional data (tensor matrices) and the detection of complicated patterns. As a result, such ANNs are mainly well adapted to huge issues involving unstructured data [37,38]. RNNs are a form of ANN capable of handling sequential data such as text, speech, and time series data. They use memory cells to store information from the past, allowing them to carry information forward through time. The best type of RNN for price prediction depends on the specific task and data quality [39]. Figure 1MLP neural network structure [36]. Figure 2Convolutional neural networks [38]. Figure 3The simple RNN design schematic [39]. [SEP] Justify:

Output: The text contrasts the claim by outlining various risks associated with cryptocurrency investments. It mentions liquidity risk, vanishing risk, regulatory risk, and tax risk, all of which highlight that investing in cryptocurrencies is not devoid of risks, thereby refuting the idea that such investments guarantee high returns with no risks.


Input: Task: pass the claim using the given text. Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: (2021) adopted the principle of maximum causal entropy to extend the applicability to stochastic environments. IRL often serves the goal of aligning an artificial agent’s behavior with values respected during demonstrations. Noothigattu et al. (2018) propose to learn two policies, one maximizing the reward through classic RL and one obeying behavioral constraints through IRL. Next, a contextual-bandit-based orchestrator is used to blend the two policies. Other work introduces an approach to model the navigation behavior of interacting pedestrians in terms of a joint mixture distribution over the trajectories of all agents (Kretzschmar et al. , 2016). Next, this model is adopted by a robot to interact with humans in a socially compliant way. However, no existing work focuses on true interpretability of the learned concepts which is, by our opinion, a critical aspect in the development of safe artificial agents. Our work is also related to safe RL as the induced hypothesis can be adopted as external knowledge to avoid unsafe situations during the exploration process. The exploration process can also be modified by initializing the Q-function with recorded trajectories of a teacher (de Lope, 2009). Other work suggests to decompose the Q-function in a task and a survival component (et al. , 2017). The survival component is task-independent and is used to safely navigate the environment. Other techniques take risks into account by adapting the optimization criteria (Geibel, 2006). However, in contrast with the proposed method, these techniques do not completely exclude the occurrence of an invalid state-action pair. Related to our approach is the technique of a teacher giving advice to the agent when unsafe situations could occur (Alshiekh et al. , 2018). In our case the teacher is represented by the learned rules which restricts the possible actions in certain states. Safety is also closely related to interpretability which is why other research focuses on explaining RL policies. Coppens et al. (2020) proposed a policy distilling algorithm which extracts a set of rules from a deep RL policy. However, in contrast to our work, they assume a RL policy is already available. Alternatively, relational RL (Džeroski et al. , 2001), learns an optimal policy starting from a description of the environment based on objects and relations which makes it interpretable “by design”. It is also possible to learn a reward function (Munzer et al. [SEP] Justify:

Output: The text does not directly address the claim regarding reinforcement learning algorithms mimicking human learning through rewards and punishments. Instead, it discusses various techniques and approaches within the realm of reinforcement learning and related concepts, such as safety and interpretability, without referencing how these methods relate specifically to human learning processes.


Input: Task: pass the claim using the given text. Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: The first relates to day-to-day mobility, with an explicit mandate to record and theorise shifts away from private car use towards more sustainable transport modes in car oriented cities. The second is on the general links between the built environment and health. The various health impacts of transport, including the advantages and disadvantages to health of private car dependency, exist as the nexus between these two research themes. Jennifer publishes regularly in high ranking scholarly journals and her work has been used to inform policy development in NSW and Australia. Email: Jennifer. kent@sydney. edu. auIn their comprehensive research report on autonomous vehicles (AVs) in Australia, Austroads paint positive and negative scenarios for the futureFootnote1 (Austroads, Citation2017). It is the positive scenario that suggests AVs could lead to less car ownership and use. In this scenario, a homogenous fleet of highly automated vehicles roams the city. Cars and rides are shared, a practice enabled through technologies of connectivity, matching people and trips. And, just like car sharing cars today, AVs are primarily used to fill the gaps in the timetables and fixed routes of the public transport network, which is also enhanced by technologies of connectivity (Duarte & Ratti, Citation2018). The critical points here for reduced car ownership and use are that: AVs are shared, andAVs complement a robust public transport system that accommodates most trips. I have been researching the cultural and structural contours of private car dependency for almost ten years now. In this piece I draw from three case studies in the low-density, car-dependent city of Sydney, to highlight three warning signs that show AVs will not lead to less car ownership and use. is used here as an example, many of the structures and practices described are common internationally, particularly in nations that are seeking to transition away from car-dependence. One: to the Expediency of OwnershipReduced private car ownership requires that we own less cars – hence the assumption that AVs will be shared vehicles. Shared. Really? You see, sharing a ride does not only mean sharing the space that is the car. It means sharing your car boot, or whatever other space in the car where you stash your gym clothes, spare umbrella and shopping bags. It means sharing your music, climate control, car-based conversations and habits. [SEP] Justify:

Output: The text does not provide any specific information about car manufacturers or their active development of autonomous vehicle technology. It discusses various research themes related to transport, health, and car ownership but does not explicitly corroborate or contrast the claim regarding the development of autonomous vehicle technology by car manufacturers.


Input: Task: contrast the claim using the given text. Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: Table 2 Comparative analysis of cryptocurrency price prediction models based on accuracy Accuracy (%) Mehta and Sasikala [33] 2020 prediction process 92 Cavalli and Amoretti [18] 2020 CNN Different datasets 74. 2 Schulte and Eggert [21] 2021 LSTM Collected 96. 48 Jaquart et al. [22] 2021 LSTM Collected >50 Aljojo et al. [24] 2021 Neural net Kaggle BTC historical data 96 Andi [25] 2021 LSTM Acquired dataset BTC price 97. 2 Luo et al. [29] 2022 LSTM-ELM Collected 95. 12 It appears that the LSTM method is the most common among researchers for predicting BTC prices, with several studies achieving high accuracy rates using this method. On the other hand, a lower accuracy of 74. 2% was achieved in the study that used CNN compared to LSTM studies. This difference in accuracy might indicate that LSTM could be more suitable for predicting BTC prices because of its ability to capture long-term dependencies in the data. There remains a need for further research and experimentation to determine the optimal method for predicting cryptocurrency prices and improve the overall accuracy of these predictions. Table 3 compares the (MAPE) for different methods of predicting the price of cryptocurrencies. The studies included primarily focused on LSTM and RNN methods applied to different datasets, with data collected from various sources or taken from Kaggle. The studies presented are from 2022, showing the most recent approaches to predicting cryptocurrency prices. Table 3 Comparison of MAPE for various cryptocurrency price prediction methods [27] 2022 LSTM Collected 6. 08% Fekry et al. [32] 2022 2012 to 31 March 2021 7. 10%, 3. 01% Table 3, suggests that LSTM methods have demonstrated better MAPE values for predicting cryptocurrency prices compared to other methods, particularly in the study by Fekry et al. However, it is important to consider that factors such as dataset choice and prediction window duration can significantly affect model performance. Further research should explore alternative ML techniques and examine the influence of various factors on prediction accuracy. Table 4 compares various ML techniques used for cryptocurrency price prediction, concentrating on RMSE values for and XRP. The methods employed across the studies include -LSTM, Bi-RNN, 1D CNN-and Bi-LSTM. [SEP] Justify:

Output: The text contrasts the claim by emphasizing the need for further research and experimentation in predicting cryptocurrency prices. It notes that methods like LSTM demonstrate varying accuracy rates, indicating that there is no guarantee of high returns and introduces the notion of risk associated with prediction accuracy, as factors such as dataset choice and prediction window can significantly affect model performance.


Input: Task: pass the claim using the given text. Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: , 2015) or a policy (Natarajan et al. , 2011) in the relational domain, given expert demonstrations. However, these approaches build on supervised learning techniques which fail when demonstrations are sub-optimal. Even though these relational RL models are very expressive, training them is difficult which limits their applicability. Other ILP techniques have also focused on inducing general rules from observed traces. Inductive general game playing is a technique to induce rules from traces from a wide range of games (Cropper et al. , 2020). However, negative examples are generated from the closed world assumption: all atoms which are not known to be true are assumed to be false. We do not make this assumption since there is a chance that a valid state-action pair is not observed in a finite set of traces. Learning from interpretation transition (Inoue et al. , 2014) is a subclass of ILP which learns a transition model from traces of a system. Engine (Evans et al. , 2021) builds on this principle but imposes extra conditions of unity on the induced logic program. Our work differentiates from this approach since we are not interested in learning a conclusive causal theory from the observed traces. Instead our method induces a compact set of rules to represent the environmental restrictions on the behavior of agents. 6 Conclusion and future workWe showed that constraint inference and ILP are complementary and provided a framework which integrates them. Thanks to constraint inference, there is no need to collect negative examples while ILP offers the capability to generalize sets of constrained state-action pairs to logic statements hence improve interpretability and transferability. Our method learns a logic program from observed trajectories and represents implicit restrictions on the behavior of the observed agents. These restrictions correspond to the explicit and implicit social norms applicable to the observed environment. We provided a Q-learning agent with the learned rules as prior knowledge about the environment to align its behavior with the applicable social norms and reduce the required number of episodes during training. Moreover, since the learned rules are expressed in formal logic, this facilitates validation by a human to assure no undesired behavior is extracted from the observations. This is an important step towards deploying autonomous agents in environments shared with humans. Calculating the state-action visitation frequencies during the constraint inference stage requires iterating over all states and actions. This also holds for the backward pass which is used for calculating the nominal policy. In its current inception, the framework is thus not scalable to larger environments. [SEP] Justify:

Output: The text does not address the claim about reinforcement learning algorithms mimicking human learning through rewards and punishments. Its discussion focuses on the limitations and methodologies of relational reinforcement learning, constraint inference, and inductive logic programming (ILP) without mentioning the aspect of human-like learning through rewards and punishments.


Input: Task: corroborate the claim using the given text. Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: on a major highway) but may have to take back control of the vehicle in some circumstances. Whilst there are bold early deployment goals set by governments and technology companies, the transition pathway away from the current ownership of individual assets seems unclear. It seems likely that fully autonomous AVs could operate in niche areas (e. g. airports, car parks, exhibition centres) but it feels some way away from shared and fully autonomous vehicles dominating our urban streets. What then of the other technologies and system changes to which planning will need to adapt in the interim? There is, for example, a commitment to complete decarbonisation of the car fleet and therefore a need for a massive transition in urban electric vehicle charging infrastructure. Add to this the continued growth in services such as Uber and Lyft and the rise of dockless bike systems. From a freight perspective, increases in on-line shopping and the continued growth of a fragmented servicing model for businesses means growing pressure on space for deliveries. The model of the green city where AVs have removed parking and freed up space for everyone to move around in is a false allure. There are still some critical issues to be dealt with about how we manage the competition for space in our cities and how we allocate that space between different uses and users. In that sense, the planning task remains unchanged and AVs are just one of many systems that might contribute to the problem or offer some form of resolution. What then for planning and governance? Is it possible for governments to exert influence on urban futures and AVs given the current shift to a smaller state and the apparent emphasis on the necessity of new technology as a source of economic growth (Legacy, Ashmore, Scheurer, Stones & Curtis, Citation2018)? I am both hopeful and fearful. Hopeful because AVs need planning and planners to make them work. Our cities are hugely complex arenas with many competing user classes with different demands on space across the day and year. The allocation of roadspace and kerbside access are two critical elements in mediating the conflicts which will exist. AVs will only achieve anything like their imagined potential if they are given the space to do so. Planners have therefore, in their toolbox, some of the most precious resources to shape how AVs arrive and join the mobility mix when they are ready to do so. [SEP] Justify:

Output: The text corroborates the claim by discussing the development and deployment of autonomous vehicles (AVs), indicating that there are 'bold early deployment goals set by governments and technology companies.' Although it mentions uncertainties about the current state of AVs, it acknowledges planning and development efforts are ongoing, implying that several car manufacturers are indeed working on this technology.


Input: Task: contrast the claim using the given text. Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: The datasets used for these predictions were collected from various sources, such as and cryptocurrency exchange websites. Table 4 Comparison of RMSE for cryptocurrency price prediction using different models (BTH) RMSE (ETH) RMSE (XRP) Ferdiansyah et al. [17] 2019 288. 59866 — — Dutta et al. [15] 2020 0. 031 0. 024 — Das et al. [23] 2021 --RNN Collected from 2. 69% 1. 78% 2. 20% Kang et al. [30] 2022 1D CNN-GRU Collected from cryptocurrency exchange website 43. 933 3. 511 0. 00128 Buslim et al. [26] 2021 GRU Datasets from 17 August 2017 to 13 April 2021 0. 0043 — — Aljadani [31] 2022 Bi-0. 01711 0. 02662 0. 00852 4, it can be observed that there is no universally best method for all three cryptocurrencies, as different techniques yield varying RMSE values. For instance, Kang et al. ’s 1D CNN-GRU model produced the lowest RMSE for ETH and XRP, while Dutta et al. ’s NN and LSTM models achieved the lowest RMSE for BTC. These findings highlight the importance of selecting the appropriate ML method based on the specific cryptocurrency being predicted. Additionally, the results may be influenced by the chosen datasets and other factors, warranting further exploration to determine the most effective techniques for each cryptocurrency. 7 three tables presented provide a comparative analysis of various ML methods employed to predict cryptocurrency prices, particularly and XRP. According to Table 2, LSTM is the method most commonly employed and achieves more accurate results than CNN. Table 3 shows that LSTM methods have better MAPE values, and Table 4 demonstrates that different ML methods produce varying RMSE values for various cryptocurrencies when used to predict their prices. It is, therefore, crucial to consider factors such as the choice of dataset and duration of the prediction window underscoring the importance of selecting an appropriate method based on the specific cryptocurrencies being predicted. Overall, these findings suggest further research to determine the most effective machine-learning techniques for predicting cryptocurrency prices. 8 this study, a thorough review of the numerous studies using NN approaches for cryptocurrency is given. [SEP] Justify:

Output: The text contrasts the claim by discussing the varying results of different machine learning (ML) methods used to predict cryptocurrency prices, indicating that there is no single method that universally guarantees high returns, as different models yield different RMSE values based on the specific cryptocurrency. Furthermore, it highlights the influence of selected datasets and other factors in the prediction process, suggesting that risks may not be negligible and emphasizing the need for further exploration to effectively predict cryptocurrency prices.


Input: Task: pass the claim using the given text. Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: However, instead of exactly calculating the state-action visitation frequencies, we could estimate this by sampling trajectories from the nominal policy and counting state-action visitations. To obtain the optimal nominal policy, a deep model-free RL algorithm like Rainbow (Hessel et al. , 2018) or PPO (Schulman et al. , 2017) can be used. We hypothesize that these measures will enable scaling the inference step to more complex RL benchmarks. Another requirement is a description of the background knowledge to introduce human concepts (e. g. roads, intersections, traffic lights). To enable scalability of the induction step, it is important to keep the hypothesis space limited. We hypothesize that an ego-centric representation of the agent’s state and action could facilitate this. Although Q-learning is one of the foundational methods for model-free RL, other state-of-the-art deep RL methods have outperformed Q-learning such as TRPO (Schulman et al. , 2015), PPO (Schulman et al. , 2017) and Rainbow (Hessel et al. , 2018). Providing logic rules as constraints to a deep RL agent is thus an interesting future directive. The problem of optimizing constrained deep RL methods is often formulated as a dual objective using Lagrangian multipliers (Tessler et al. , 2018; Kalweit et al. , 2020): maximize the reward (which represents the goal) and minimize the cost (which represent the constraints). Representing the cost function by the induced logic program would enable integrating our method with state-of-the-art deep RL algorithms. Another possibility is using a “shield” (Alshiekh et al. , 2018) which monitors the actions taken by the agent and corrects them when the agent would violate a rule. Furthermore, when capturing real-life datasets, it is likely that rule violations will be observed. The use of probabilistic logic to represent weak constraints (Glazier et al. , 2021) could relax the assumption no constraint state-action pairs can occur in the observed trajectories. Since we build on the principle of maximum entropy IRL, our method only exactly holds for deterministic environments. Adopting the principle of maximum causal entropy (Ziebart et al. , 2010) is a potential way to resolve this issue. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses various technical aspects of reinforcement learning (RL) algorithms, such as sampling trajectories and methods like Q-learning, PPO, and Rainbow, without addressing the general principle of mimicking human learning through rewards and punishments. There is no mention of how RL algorithms relate to human learning processes or educational theories.


Input: Task: corroborate the claim using the given text. Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: The promise sold to politicians across the land from AV manufacturers is that these vehicles will not only be safer, cleaner and more efficient than regular vehicles, but also boost economic growth. For example, it is estimated that the global market for AVs will be worth £907bn by 2035 and therefore hard to ignore from an industrial strategy perspective (TSC Citation2017, 3). Moreover, in terms of social benefits it is estimated that for every AV, approximately $2000 per year could be saved due to benefits from crash reduction, travel time reduction, improvements in fuel efficiency and parking benefits (Fagnant and Kockelman Citation2015). Now, of course the politics of each city, region, and country will be different, but with the promise of such impressive benefits being within touching distance, the risk of implementing policies seen to be prohibitive or stifling of AV implementation, risks a potential political backlash. Second, there is an incredible amount of uncertainty surrounding AVs. Uncertainty is one of the fundamental characteristics of wicked problems – the planner’s nemesis – making obvious policy routes hard to find and the terrain of policy choices hard to navigate. For example, despite rhetoric that may suggest otherwise, we still don’t know when the technology will be ready (and safe) to see operating on our roads. Nor do we know what affect AVs will have on travel and other social behaviour. Will AV technology encourage the sharing of vehicles or will private ownership remain the preference for example? And will any improvements in road space efficiency brought by automation be offset by an increase in demand for travel? In modelling the impacts of AVs, Wadud, MacKenzie, and Leiby (Citation2016) find that AVs may plausibly reduce Gas emissions and energy use by nearly half, but could also nearly double them, depending on which effects come to dominate. We of course cannot know the impact of AVs until they are a reality, but the way we govern in the meantime will help determine what these impacts look like (Marsden and Reardon Citation2018). Third, planners and planning authorities are but one set of actors in an already incredibly complicated governance context, before we even consider AVs. Even in countries with strong cultures of state delivery of transport services such as in central Europe, you see an ever growing number and diversity of stakeholders operating in the transport space; dispersing power and fragmenting the influence and control of the state. [SEP] Justify:

Output: The text corroborates the claim by discussing the promise of autonomous vehicles (AVs) to provide benefits such as safety, efficiency, and economic growth. It highlights the global market potential for AVs, suggesting that car manufacturers are indeed investing in the development of this technology to reap these benefits. Additionally, discussions of social impacts and planning around AVs imply ongoing efforts and considerations among various stakeholders, including manufacturers.


Input: Task: contrast the claim using the given text. Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: The use of ML techniques, particularly RNNs, has shown promise in predicting the prices of cryptocurrencies such as and XRP. Researchers have identified that LSTM and GRU-based RNNs can achieve high accuracy rates and lower MAPE values, highlighting their potential for providing valuable insights for investors and traders in the rapidly evolving digital currency market. However, all factors affecting the cryptocurrency market, such as social networking site sentiments, and political and economic events that may have negative or positive impacts, must be studied. Further research is needed to determine the most effective ML techniques for predicting cryptocurrency prices and to fully comprehend the risks and benefits of this emerging asset category. In our future work, we propose that one approach with great potential is to develop hybrid models that combine and GRU architectures that promise to improve risk prediction in digital currency investments. These models leverage the strengths of each architecture to improve accuracy in the dynamic crypto market. LSTMs excel at capturing long-term dependencies, DNNs handle complex models, and GRUs provide the right balance between storage capacity and processing power. By combining these capabilities, hybrid models can extract valuable insights from different datasets, allowing investors to assess risk accurately and make informed decisions. Comparative analysis of the LSTM-DNN and GRU-DNN models helps determine the optimal architecture. Hybrid models represent a significant step forward in understanding market dynamics, mitigating risk, and making informed investment decisions. The integration of and GRU into hybrid models offers a promising direction for research for predicting risk and empowering investors to act in the changing crypto market. Conflict of interest: The authors state no conflict of interest. Data availability statement: Most datasets generated and analyzed in this study are in this submitted manuscript. The other datasets are available on reasonable request from the corresponding author with the attached information. 205. 10. 1109/. 2018. 2874539Search in 4. 10. 22161/ijaers. 69. 18Search in 2017. 10. 2139/ssrn. 2965436Search in 71. Search in 96. 10. 1016/j. jmoneco. 2017. 12. 004Search in 8. 10. 1016/j. frl. 2016. 09. 025Search in 2015. p. 31–43. 10. 1016/B978-0-12-802117-0. 00002-3Search in 898. 10. 3390/math7100898Search in 72. 10. [SEP] Justify:

Output: The text contrasts the claim by emphasizing that predicting cryptocurrency prices involves significant risks and uncertainties. It states that, 'all factors affecting the cryptocurrency market...must be studied,' indicating that there are many variables influencing the market which could have both positive and negative impacts on investments. Additionally, the text highlights the need for further research to fully understand the risks and benefits of cryptocurrency investments, directly opposing the notion that such investments guarantee high returns with no risks.


Input: Task: pass the claim using the given text. Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: We use cookies to enhance your experience on our website. By continuing to use our website, you are agreeing to our use of cookies. You can change your cookie settings at any time. Find out more personal profile panels this content Copy this link, or click below to email it to a friend Email this content or copy the link directly: The link was not copied. Your current browser may not support copying via this button. Link copied successfullyCopy link to 1627. and set by Mr. Ro: RamseyThe captiv'd Bee: or, The little to Porter, upon his Brothers deathTo his dying -more. EpigTo Cherry-blossomescame whiteTo PansiesOn Gelli-flowers begottenin a ChristalTo his BookeUpon some womenSupreme fortune falls soonestto SackImpossibilities to his friendUpon Luggs. EpigUpon Gubbs. EpigTo live merrily, and to trust to Good VersesFaire dayes: or, Dawnes deceitfullLips TonguelesseTo the Fever, not to trouble JuliaTo VioletsUpon Bunce. EpigTo Carnations. A SongTo the Virgins, to make much of TimeSafety to look to ones selfeTo his Friend, on the untuneable Timeshis PillarSafety on the ShoreA Pastorall upon the birth of Prince Charles, Presented to the King, and Set by Mr. Nic: LaniereTo the LarkThe Bubble. A SongA Meditation for his MistresseThe bleeding hand: or, The sprig of Eglantine given to a maidLyrick for LegaciesA Dirge upon the Death of the a MistresseGreat boast, small rostUpon a Bleare-ey'd womanTemple: or, Oberons Chappell. Dedicated to Mr. John Merrifield, Counsellor at the lovely, that crowned him with LaurelThe Plaudite, or end of lifeTo the most vertuous Mistresse Pot, who many times entertained himTo Musique, to becalme his FeverUpon a Gentlewoman with a sweet VoiceUpon CupidUpon Julia's breastsBest to be merryto Corinnaagainst LetcherieNeglectUpon himselfeUpon a Physitiana LaundresseTo the Rose. SongUpon Guesse. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not discuss reinforcement learning, human learning, rewards, or punishments. Instead, it contains unrelated content about cookies and various poetic topics.


Input: Task: corroborate the claim using the given text. Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: Third, planners should ensure they work in partnership with AV developers and regulators as early as possible in order to shape the development of AVs in ways that help meet their objectives. Planning authorities, at all scales, have important resources at their disposal that despite being overlooked in the rhetoric, are fundamental to the viability of AVs. For example, planning authorities (and the public sector more broadly) will likely be relied upon to finance and deliver large swathes of the changes to infrastructure required for AVs to operate effectively. They will also play an important role in ensuring interoperability and integration across the mobility system through the use of regulatory frameworks and pricing mechanisms. Planning authorities are therefore in a position of power, but it is important that this power is exercised, and it is therefore important that the planning perspective is front and centre at each stage of AV roll-out. in its on contributoris in Science and at London. He teaches and researches the governance of emerging technology. He is of the Driverless Futures? Project, funded by the UK Economic and Council, which begins in January 2019. Email: j. stilgoe@ucl. ac. ukJoin in the debate on Twitter #PTPinterfaceAutonomous vehicles (AVs) are a sort of mirage. They are, we are told, just around the corner. But look closer and they seem impossibly distant. In Phoenix, Arizona, AVs are being tested on citizens’ doorsteps. Some can choose to take part in the experiment. Others may be more passive but, whether they like it or not, their city has become a laboratory. Meanwhile, in Xiong’an, south of Beijing, the Chinese government is planning a new city around AVs. Having bought the claims of companies like Baidu, planners are creating a ‘new economic area’ with coordinated traffic control, less parking and streets that are designed to be easily machine-readable. The protocols and results of experiments in Phoenix may be only partially relevant to London, Rome or Sydney. The ambition of planners in Xiong’an would be unthinkable in most other parts of the world. And many places would be concerned about the democratic legitimacy of either allowing experiments on open roads or creating large-scale experiments from scratch using public money. But despite the enormous variability of places, cultures and mobilities, there is huge momentum behind the project of making prophecies for AV technology self-fulfilling. [SEP] Justify:

Output: The text corroborates the claim by discussing the development of autonomous vehicles (AVs) through the lens of urban planning and governmental planning initiatives. It highlights that cities like Phoenix, Arizona, are testing AVs, illustrating that AV technology is in active development and experimentation. The mention of the Chinese government planning a new city around AVs further supports the claim, indicating that car manufacturers and technology developers are involved in creating and implementing infrastructure necessary for AV operation. The overall tone implies that there is significant activity and forward momentum in the development of AV technology.