are fresh or frozen vegetables healthier

Frozen meal

Following this, the dinners are either covered with aluminum foil or paper, and the product is tightly packed with a partial vacuum created to ensure no evaporation takes place that can cause the food to dry out. Then the packaged dinners are placed in a refrigerated storage facility, transported by refrigerated truck, and stored in the grocer's freezer. TV dinners prepared with the aforementioned steps—that is, frozen and packaged properly—can remain in near-perfect condition for a long time, so long as they are stored at  during shipping and storage.
Health concerns
Frozen meals are often heavily processed with extra salt and fat to make foods last longer. In addition, stabilizing the product for a long period typically means that companies will use partially hydrogenated vegetable oils for some items (typically dessert). Partially hydrogenated vegetable oils are high in trans fats and are shown to adversely affect  cardiovascular health. The dinners are almost always significantly less nutritious than fresh food and are formulated to remain edible after long periods of storage, thus often requiring preservatives such as butylated hydroxytoluene. There is, however, some variability between brands.
In recent years there has been a push by a number of independent manufacturers and retailers to make meals that are low in salt and fat and free of artificial additives. In the UK, most British supermarkets also produce their own "healthy eating" brands. Nearly all chilled or frozen ready meals sold in the UK are now clearly labeled with the salt, sugar and fat content and the recommended daily intake. Concern about obesity and government publicity initiatives such as those by the Food Standards Agency  and the National Health Service  have encouraged manufacturers to reduce the levels of salt and fat in ready prepared food.

Fresh Fruit for Rotting Vegetables

Fresh Fruit for Rotting Vegetables is the debut studio album by the American punk rock band Dead Kennedys. It was first released on September 2, 1980, through Cherry Red Records in the United Kingdom, and I.R.S. Records in the United States. It was later issued by Jello Biafra's own Alternative Tentacles label in the United States. It is the only Dead Kennedys studio album to feature drummer Bruce Slesinger and (on one track) guitarist Carlo Cadona.
Recording and release
The photo on the front cover, showing several police cars on fire, was taken during the White Night riots of May 21, 1979, that resulted from the light sentence given to former San Francisco City Supervisor Dan White for the murder of Mayor George Moscone and Supervisor Harvey Milk. When Biafra ran for mayor, one of his policies had been for a statue to be erected to Dan White, and for eggs, tomatoes and stones to be available nearby for pelting it. In addition, the band's version of "I Fought the Law" has rewritten lyrics making reference to the incident.


how does qualtrics fit in with the big data trend?

Qualtrics

Qualtrics is an American experience management company, with co-headquarters in Seattle, Washington, and Provo, Utah, in the United States. The company was founded in 2002 by Scott M. Smith, Ryan Smith, Jared Smith, and Stuart Orgill.
Qualtrics offers a cloud-based subscription software platform for experience management, which it launched in March 2017.

Qualtrics

Awards and ranking In 2020, Qualtrics earned a ‘Leader’ designation in Gartner's Magic Quadrant for Voice of Customer, a ‘leader’ designation in Forrester's Employee Experience for Enterprise wave, and the top ranking in G2's Experience Management category. In 2016, Qualtrics was ranked #12 on the Forbes Cloud 100 list, moving to #6 in 2017. In March 2020, Qualtrics's CoreXM platform was named a 2020 gold winner by the Edison Awards in the Applied Technology category.
Acquisitions
In May 2016, Qualtrics acquired statistical analysis startup Statwing for an undisclosed sum. Statwing was a San Francisco-based company that created point-and-click software for advanced statistical analysis.
In April 2018 the firm acquired Delighted for an undisclosed sum. Delighted had more than 1,500 customers at the time of acquisition.
In October 2021 the firm acquired Clarabridge in an all-stock deal for $1.125 billion. Clarabridge was a Virginia-based company that created software for omnichannel conversational analytics.
Acquisition by SAP SE
In November 2018, SAP announced its intent to acquire Qualtrics. SAP acquired all outstanding shares of Qualtrics for US$8 billion in an all cash deal. SAP secured €7 billion in financing. At the time it was announced, the Qualtrics acquisition was SAP's second-biggest purchase ever, behind the $8.3 billion acquisition of travel and expense management firm Concur in 2014. The acquisition was formally closed January 23, 2019.


Orders of magnitude (numbers)

Computing – UTF-16/Unicode: There are 1,114,112 (220 + 216) distinct values encodable in UTF-16, and, thus (as Unicode is currently limited to the UTF-16 code space), 1,114,112 valid code points in Unicode (1,112,064 scalar values and 2,048 surrogates).
Ludology – Number of games: Approximately 1,181,019 video games have been created as of 2019.
Biology – Species: The World Resources Institute claims that approximately 1.4 million species have been named, out of an unknown number of total species (estimates range between 2 and 100 million species). Some scientists give 8.8 million species as an exact figure.
Genocide: Approximately 800,000–1,500,000 (1.5 million) Armenians were killed in the Armenian genocide.
Linguistics: The number of possible conjugations for each verb in the Archi language is 1,502,839.
Info: The freedb database of CD track listings has around 1,750,000 entries .
Computing – UTF-8: 2,164,864 (221 + 216 + 211 + 27) possible one- to four-byte UTF-8 sequences, if the restrictions on overlong sequences, surrogate code points, and code points beyond U+10FFFF are not adhered to.  (Note that not all of these correspond to unique code points.)
Mathematics – Playing cards: There are 2,598,960 different 5-card poker hands that can be dealt from a standard 52-card deck.
Mathematics: There are 3,149,280 possible positions for the Skewb.

Computer number format

The relation between numbers and bit patterns is chosen for convenience in computer manipulation; eight bytes stored in computer memory may represent a 64-bit real, two 32-bit reals, or four signed or unsigned integers, or some other kind of data that fits into eight bytes. The only difference is how the computer interprets them. If the computer stored four unsigned integers and then read them back from memory as a 64-bit real, it almost always would be a perfectly valid real number, though it would be junk data.
Only a finite range of real numbers can be represented with a given number of bits. Arithmetic operations can overflow or underflow, producing a value too large or too small to be represented.
The representation has a limited precision. For example, only 15 decimal digits can be represented with a 64-bit real. If a very small floating-point number is added to a large one, the result is just the large one. The small number was too small to even show up in 15 or 16 digits of resolution, and the computer effectively discards it. Analyzing the effect of limited precision is a well-studied problem. Estimates of the magnitude of round-off errors and methods to limit their effect on large calculations are part of any large computation project. The precision limit is different from the range limit, as it affects the significand, not the exponent.


Training an adapter for a retrieval model

Title: Parameter-Efficient Sparse Retrievers and Rerankers using Adapters

Abstract: Parameter-Efficient transfer learning with Adapters have been studied in Natural Language Processing (NLP) as an alternative to full fine-tuning. Adapters are memory-efficient and scale well with downstream tasks by training small bottle-neck layers added between transformer layers while keeping the large pretrained language model (PLMs) frozen. In spite of showing promising results in NLP, these methods are under-explored in Information Retrieval. While previous studies have only experimented with dense retriever or in a cross lingual retrieval scenario, in this paper we aim to complete the picture on the use of adapters in IR. First, we study adapters for SPLADE, a sparse retriever, for which adapters not only retain the efficiency and effectiveness otherwise achieved by finetuning, but are memory-efficient and orders of magnitude lighter to train. We observe that Adapters-SPLADE not only optimizes just 2\% of training parameters, but outperforms fully fine-tuned counterpart and existing parameter-efficient dense IR models on IR benchmark datasets. Secondly, we address domain adaptation of neural retrieval thanks to adapters on cross-domain BEIR datasets and TripClick. Finally, we also consider knowledge sharing between rerankers and first stage rankers. Overall, our study complete the examination of adapters for neural IR

Title: On Discrete Painleve Equations Associated with the Lattice KdV Systems and the Painleve VI Equation

Abstract: A new integrable nonautonomous nonlinear ordinary difference equation is presented which can be considered to be a discrete analogue of the Painleve V equation. Its derivation is based on the similarity reduction on the two-dimensional lattice of integrable partial difference equations of KdV type. The new equation which is referred to as GDP (generalised discrete Painleve equation) contains various ``discrete Painleve equations'' as subcases for special values/limits of the parameters, some of which were already given before in the literature. The general solution of the GDP can be expressed in terms of Painleve VI (PVI) transcendents. In fact, continuous PVI emerges as the equation obeyed by the solutions of the discrete equation in terms of the lattice parameters rather than the lattice variables that label the lattice sites. We show that the bilinear form of PVI is embedded naturally in the lattice systems leading to the GDP. Further results include the establishment of Baecklund and Schlesinger transformations for the GDP, the corresponding isomonodromic deformation problem, and the self-duality of its bilinear scheme.


prompt optimization using a reward model

Title: Invariants of moduli spaces of abelian surfaces

Abstract: Compactifications of moduli spaces of (1,p)-polarized abelian surfaces with level structures of canonical type have been described in great detail by Hulek, Kahn and Weintraub. The aim of this paper is to determine some invariants of smooth models of these moduli spaces. In particular, a geometric description of their canonical divisors is given and their Chern numbers are computed.

Title: Reward Collapse in Aligning Large Language Models

Abstract: The extraordinary capabilities of large language models (LLMs) such as ChatGPT and GPT-4 are in part unleashed by aligning them with reward models that are trained on human preferences, which are often represented as rankings of responses to prompts. In this paper, we document the phenomenon of \textit{reward collapse}, an empirical observation where the prevailing ranking-based approach results in an \textit{identical} reward distribution \textit{regardless} of the prompts during the terminal phase of training. This outcome is undesirable as open-ended prompts like ``write a short story about your best friend'' should yield a continuous range of rewards for their completions, while specific prompts like ``what is the capital of New Zealand'' should generate either high or low rewards. Our theoretical investigation reveals that reward collapse is primarily due to the insufficiency of the ranking-based objective function to incorporate prompt-related information during optimization. This insight allows us to derive closed-form expressions for the reward distribution associated with a set of utility functions in an asymptotic regime. To overcome reward collapse, we introduce a prompt-aware optimization scheme that provably admits a prompt-dependent reward distribution within the interpolating regime. Our experimental results suggest that our proposed prompt-aware utility functions significantly alleviate reward collapse during the training of reward models.


distilling the reranking ability of large language models to cross encoders

Title: ReFIT: Relevance Feedback from a Reranker during Inference

Abstract: Retrieve-and-rerank is a prevalent framework in neural information retrieval, wherein a bi-encoder network initially retrieves a pre-defined number of candidates (e.g., K=100), which are then reranked by a more powerful cross-encoder model. While the reranker often yields improved candidate scores compared to the retriever, its scope is confined to only the top K retrieved candidates. As a result, the reranker cannot improve retrieval performance in terms of Recall@K. In this work, we propose to leverage the reranker to improve recall by making it provide relevance feedback to the retriever at inference time. Specifically, given a test instance during inference, we distill the reranker's predictions for that instance into the retriever's query representation using a lightweight update mechanism. The aim of the distillation loss is to align the retriever's candidate scores more closely with those produced by the reranker. The algorithm then proceeds by executing a second retrieval step using the updated query vector. We empirically demonstrate that this method, applicable to various retrieve-and-rerank frameworks, substantially enhances retrieval recall across multiple domains, languages, and modalities.

Title: A Systematic Investigation of Distilling Large Language Models into Cross-Encoders for Passage Re-ranking

Abstract: Cross-encoders distilled from large language models (LLMs) are often more effective re-rankers than cross-encoders fine-tuned on manually labeled data. However, the distilled models usually do not reach their teacher LLM's effectiveness. To investigate whether best practices for fine-tuning cross-encoders on manually labeled data (e.g., hard-negative sampling, deep sampling, and listwise loss functions) can help to improve LLM ranker distillation, we construct and release a new distillation dataset: Rank-DistiLLM. In our experiments, cross-encoders trained on Rank-DistiLLM reach the effectiveness of LLMs while being orders of magnitude more efficient. Our code and data is available at https://github.com/webis-de/msmarco-llm-distillation.


How can I use an LLM to train an embedding model?

Title: Language Models are Universal Embedders

Abstract: In the large language model (LLM) revolution, embedding is a key component of various systems. For example, it is used to retrieve knowledge or memories for LLMs, to build content moderation filters, etc. As such cases span from English to other natural or programming languages, from retrieval to classification and beyond, it is desirable to build a unified embedding model rather than dedicated ones for each scenario. In this work, we make an initial step towards this goal, demonstrating that multiple languages (both natural and programming) pre-trained transformer decoders can embed universally when finetuned on limited English data. We provide a comprehensive practice with thorough evaluations. On English MTEB, our models achieve competitive performance on different embedding tasks by minimal training data. On other benchmarks, such as multilingual classification and code search, our models (without any supervision) perform comparably to, or even surpass heavily supervised baselines and/or APIs. These results provide evidence of a promising path towards building powerful unified embedders that can be applied across tasks and languages.

Title: Embedding-Aligned Language Models

Abstract: We propose a novel approach for training large language models (LLMs) to adhere to objectives defined within a latent embedding space. Our method leverages reinforcement learning (RL), treating a pre-trained LLM as an environment. Our embedding-aligned guided language (EAGLE) agent is trained to iteratively steer the LLM's generation towards optimal regions of the latent embedding space, w.r.t. some predefined criterion. We demonstrate the effectiveness of the EAGLE agent using the MovieLens 25M dataset to surface content gaps that satisfy latent user demand. We also demonstrate the benefit of using an optimal design of a state-dependent action set to improve EAGLE's efficiency. Our work paves the way for controlled and grounded text generation using LLMs, ensuring consistency with domain-specific knowledge and data representations.


I am looking for a paper to help me deal with data constraints as I scale up my language model.

Title: Like Sparrows on a Clothes Line: The Self-Organization of Random Number Sequences

Abstract: We study sequences of random numbers {Z[1],Z[2],Z[3],...,Z[n]} -- which can be considered random walks with reflecting barriers -- and define their "types" according to whether Z[i] > Z[i+1], (a down-movement), or Z[i] < Z[i+1] (up-movement). This paper examines the means, xi, to which the Zi converge, when a large number of sequences of the same type is considered. It is shown that these means organize themselves in such a way that, between two turning points of the sequence, they are equidistant from one another. We also show that m steps in one direction tend to offset one step in the other direction, as m -> infinity. Key words:random number sequence, self-organization, random walk, reflecting barriers.

Title: Data Mixing Made Efficient: A Bivariate Scaling Law for Language Model Pretraining

Abstract: Large language models exhibit exceptional generalization capabilities, primarily attributed to the utilization of diversely sourced data. However, conventional practices in integrating this diverse data heavily rely on heuristic schemes, lacking theoretical guidance. This research tackles these limitations by investigating strategies based on low-cost proxies for data mixtures, with the aim of streamlining data curation to enhance training efficiency. Specifically, we propose a unified scaling law, termed BiMix, which accurately models the bivariate scaling behaviors of both data quantity and mixing proportions. We conduct systematic experiments and provide empirical evidence for the predictive power and fundamental principles of BiMix. Notably, our findings reveal that entropy-driven training-free data mixtures can achieve comparable or even better performance than more resource-intensive methods. We hope that our quantitative insights can shed light on further judicious research and development in cost-effective language modeling.


Can we submit to other venues after review at ARR without withdrawing the paper?

Tex Stackexchange

Q: Online Overleaf Editor: Clicked the Submit Button by mistake By mistake I clicked on the "Submit" button provided in the overleaf editor. Now my document shows:
"Manuscript Submitted to ACM"
I have following questions:

*

*What I have to do with my document, it was an incomplete document?

*Will my document be leaked out now?

*How can I remove the line "Manuscript Submitted to ACM" from my document?

Some body please guide me.
Zulfi.

A: (Tom from Overleaf Support here.)
If you accidentally submitted a paper via the Overleaf's Submit modal, there are three options:

*

*Accidental submissions to the Overleaf Gallery: Simply contact us via support@overleaf.com and as for the submission to be rejected. We're happy to assist with this.

*Accidental submissions to journals with direct submission: In these cases, the files are not with us anymore. You'll need to get in touch with the journal's Editorial Office and ask for the submission to be disregarded. It shouldn't be an issue.

*Accidental submissions when a ZIP file was provided to you: Nothing is needed. You were given the ZIP file yourself, it hasn't been sent to any 3rd party.


do you have to have a gun permit to shoot at a range

Shooting range

A shooting range, firing range, gun range or shooting ground is a specialized facility, venue, or field designed specifically for firearm usage qualifications, training, practice, or competitions. Some shooting ranges are operated by military or law enforcement agencies, though the majority of ranges are privately owned by civilians and sporting clubs and cater mostly to recreational shooters. Each facility is typically overseen by one or more supervisory personnel, known as a Range Officer (RO), or sometimes a range master in the United States. Supervisory personnel are responsible for ensuring that all safety rules and relevant laws are followed at all times.

Overview of gun laws by nation

Poland Gun ownership in Poland is regulated by the Weapons and Munitions Act of 1999 as further amended. A permit is required to own or possess firearms. Permits are granted for specific purposes, such as self-defense, sport, hunting, collecting etc. and are  except the ones for self-defense, which are . Institutional permits allow for firearm ownership by private security companies, shooting ranges, sport clubs etc.
Permits are issued to permanent residents over 21, not addicted to alcohol or other substances, having no criminal record, being in good physical and mental health, who passed an exam before sporting association, hunting association or the Police. Permits specify the purposes, categories and quantities of allowed guns; categories depend on permit purpose while quantities are assigned at the discretion of the Police, based on documentation provided. Quantities can be increased when one uses up all available slots.
Carrying loaded guns is allowed on all types of permits except permits for collecting and memorial purposes. Hunters can carry loaded only on hunting grounds. Guns shall be carried concealed in a holster close to the body. Carrying in public transport is limited only to self-defense permit holders. It is prohibited to carry while intoxicated, and when attending public gatherings or mass events.
Everyone, including foreigners, is allowed to use legal guns at registered shooting ranges, regardless of actual gun's ownership, e.g. rented from the range or shared by a legal owner present. Discharging a firearm for training or leisure out of a registered shooting range (even on a large private property) is prohibited.


University of Waterloo

Coat of arms The university's coat of arms was first used by the university in October 1961, but was only officially granted by the Lord Lyon King of Arms in August 1987. It was not registered with the Canadian Heraldic Authority until 15 February 2001. Four variations of the coat of arms existed. The first was used from 1961 to 1996, when the second bright-yellow shield using slightly different shaped lions was introduced. The yellow background was dulled in 2000, and finally, the original lions were reintroduced in 2010 in conjunction with the attempt to replace the use of the coat of arms with a futuristic W logo. The new logo was eventually rejected after student opposition.
The red-on-gold lions on the university's arms were adopted from those of Arthur Wellesley, 1st Duke of Wellington, who defeated Napoleon at the Battle of Waterloo. The City of Waterloo was named after an area just south of Brussels, Belgium, where the battle occurred. The chevron on the arms was taken from the arms of Herbert Kitchener, 1st Earl Kitchener, Field Marshal with the British Army during World War I. The black and white pattern used on the chevron was based on the colours of Prussia, as homage to the German heritage of the area. The City of Kitchener was originally known as Berlin, but was renamed after Earl Kitchener in 1916 during World War I.

University of Waterloo

The University of Waterloo (UWaterloo, UW, or Waterloo) is a public research university with a main campus in Waterloo, Ontario, Canada. The main campus is on  of land adjacent to uptown Waterloo and Waterloo Park. The university also operates three satellite campuses and four affiliated university colleges. The university offers academic programs administered by six faculties and thirteen faculty-based schools. Waterloo operates the largest post-secondary co-operative education program in the world, with over 20,000 undergraduate students enrolled in the university's co-op program. Waterloo is a member of the U15, a group of research-intensive universities in Canada.


what is the difference between service and services?

Stackoverflow Stackexchange

Q: Spring RestTemplate POST Request with URL encoded data I'm new to Spring and trying to do a rest request with RestTemplate. The Java code should do the same as below curl command:
curl --data "name=feature&color=#5843AD" --header "PRIVATE-TOKEN: xyz" "https://someserver.com/api/v3/projects/1/labels"

But the server rejects the RestTemplate with a 400 Bad Request
RestTemplate restTemplate = new RestTemplate();
HttpHeaders headers = new HttpHeaders();
headers.add("PRIVATE-TOKEN", "xyz");
HttpEntity<String> entity = new HttpEntity<String>("name=feature&color=#5843AD", headers);
ResponseEntity<LabelCreationResponse> response = restTemplate.exchange("https://someserver.com/api/v3/projects/1/labels", HttpMethod.POST, entity, LabelCreationResponse.class);

Can somebody tell me what I'm doing wrong?

A: I think the problem is that when you try to send data to server didn't set the content type header which should be one of the two: "application/json" or "application/x-www-form-urlencoded" . In your case is: "application/x-www-form-urlencoded" based on your sample params (name and color). This header means "what type of data my client sends to server".
RestTemplate restTemplate = new RestTemplate();

HttpHeaders headers = new HttpHeaders();
headers.setContentType(MediaType.APPLICATION_FORM_URLENCODED);
headers.add("PRIVATE-TOKEN", "xyz");

MultiValueMap<String, String> map = new LinkedMultiValueMap<>();
map.add("name","feature");
map.add("color","#5843AD");

HttpEntity<MultiValueMap<String, String>> entity = new HttpEntity<>(map, headers);

ResponseEntity<LabelCreationResponse> response =
    restTemplate.exchange("https://foo/api/v3/projects/1/labels",
                          HttpMethod.POST,
                          entity,
                          LabelCreationResponse.class);

Stackoverflow Stackexchange

Q: What is a service? I keep hearing the term "service" a lot, but I have a vague understanding of it. Could someone explain what a service is citing some practical examples?

A: It depends on the context. Very abstractly speaking, a service is some sort of program that offers some function, but the internals of which are completely opaque. I.e., a web service is something running somewhere on the web, having some sort of API which you can contact to get or submit some data. What exactly it does is none of your business, it just offers its service to you. On a system, for example a Windows Service may be a program running persistently in the background, doing something. Inside an application, you may have a service layer, which offers some functionality which you can use in the rest of the application through an API, but how exactly it works is irrelevant.
That's in contrast to, e.g., a function or library or class, which you usually import, manipulate, use more directly. A service is more self-contained, offering only its functionality with nothing much in the way of introspecting it.


What's the purpose of `if __name__ == '__main__'`?

Stackoverflow Stackexchange

Q: html dangerous tags to avoid while developing a chat application I am developing a chat application using PHP and jQuery... all messages sent by the chat pair are appended to a <div>. While doing this, I found that tags such as <script> can be entered by users. What tags should I avoid while appending the user's message?

A: Avoid tags that will inject content into the page:

*

*applet

*script

*style

*link

*iframe

Other tags may be necessary to remove, as well. Maybe you don't want to embed images. These are things that you will need to determine. At the bare minimum, allow <p> and <br />.

Stackoverflow Stackexchange

Q: Why does it do this ? if - __name__ == '__main__'
Duplicate of:
What does if __name__== "__main__" do?

Consider this code:
if __name__ == '__main__':
    import pdb
    pdb.run("interact()\n")

What does the following line mean?
if(__name__=='__main__')

I fainted.

A: That is a check to see if you are directly running the script or if it is included in a library.
When you run a python script like this:
python myScript.py

It sends a parameter, telling you to run the programs first method, which is widely called "main", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.


Latent semantic analysis

Semantic hashing In semantic hashing  documents are mapped to memory addresses by means of a neural network in such a way that semantically similar documents are located at nearby addresses. Deep neural network essentially builds a graphical model of the word-count vectors obtained from a large set of documents. Documents similar to a query document can then be found by simply accessing all the addresses that differ by only a few bits from the address of the query document. This way of extending the efficiency of hash-coding to approximate matching is much faster than locality sensitive hashing, which is the fastest current method.
Latent semantic indexing
Latent semantic indexing (LSI) is an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text.  LSI is based on the principle that words that are used in the same contexts tend to have similar meanings.  A key feature of LSI is its ability to extract the conceptual content of a body of text by establishing associations between those terms that occur in similar contexts.
LSI is also an application of correspondence analysis, a multivariate statistical technique developed by Jean-Paul Benzécri in the early 1970s, to a contingency table built from word counts in documents.

Information retrieval

Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need.  The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.
Automated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; it also stores and manages those documents. Web search engines are the most visible IR applications.


Milk coffee

Milk coffee is a category of coffee-based drinks made with milk.
Johan Nieuhof, the Dutch ambassador to China, is credited as the first person to drink coffee with milk when he experimented with it around 1660.
Varieties
Breve
A breve is an espresso made with a steamed mixture of half milk and half cream (i.e., half and half). Its size can vary. The name relates to the use of cream and milk.

Cha chaan teng

Black coffee: Hongkongers usually called this jaai fea (lit. "vegetarian coffee" / "just coffee", emphasising its plain texture) or "fei sha jaau naai" (lit. "get rid of sand and milk", i.e. coffee without sugar (the "sand") and milk)
Hong Kong-style milk tea: A highly popular drink in Hong Kong. Its standard is judged by its aroma, smoothness, and concentration. The tea is soaked in an iron container for several hours to let the flavour come out. If the customer wants condensed milk instead of normal milk, people will often say "caa zau" (lit. "tea go")
Sweet soy milk (豆漿): Soy milk sweetened with cane sugar and served either hot or cold.
Yuenyeung: A mixture of coffee and tea, originated in Hong Kong. According to traditional Chinese medicine, coffee and tea are "hot" and "cold" in nature, respectively. A mixing of both thus then yields the best combination for the beverage.
Black and White Yuenyeung: A mixture of Ovaltine and Horlicks, originated in Hong Kong.
Horlicks
Ovaltine
Lemon Tea
Lemonade: Commonly served without sugar or syrup.
Lemon with Ribena
Lemon with Honey: Often, to reduce cost, the honey is replaced with "Watercress Honey"
Salted lemon with 7-Up (鹹檸七): 7-Up, adding lemon marinated in salt, which is good for sore throat
Black cow (黑牛): Coca-Cola with vanilla ice cream.
Snow White (白雪公主): Sprite or 7-Up with vanilla ice cream.


Wabush

Wabush is a small town in the western tip of Labrador, bordering Quebec, known for transportation and iron ore operations.

George W. Bush

George Walker Bush (born July 6, 1946) is an American politician and businessman who served as the 43rd president of the United States from 2001 to 2009. A member of the Republican Party, he previously served as the 46th governor of Texas from 1995 to 2000.


What is the name of the third president of france

President of France

Eighteen years later, during the opening phases of the Second Republic, the title was created for a popularly elected head of state, the first of whom was Louis-Napoléon Bonaparte, nephew of Emperor Napoleon. Bonaparte served as president until he staged an auto coup against the republic, proclaiming himself Napoleon III, Emperor of the French.
Under the Third Republic the president was at first quite powerful, mainly because the royalist party was strong when the constitutional laws of 1875 were established, and it was hoped that a member of one of the two branches of the royal family would be able to serve as president and turn France into a constitutional monarchy. However, the next legislature was dominated by Republicans, and after President Patrice de MacMahon had unsuccessfully tried to obtain a new royalist majority by dissolving the Chambre des Députés, his successor Jules Grévy promised in 1879 that he would not use his presidential power of dissolution, and therefore lost his control over the legislature, effectively creating a parliamentary system that would be maintained for 80 years until the accession of Charles de Gaulle as president in 1959.
Indeed, when the Fourth Republic was created, after the Second World War, it was a parliamentary system, in which the office of President of the Republic was a largely ceremonial one.

Napoleon

On 1 August 1798, the British fleet under Sir Horatio Nelson captured or destroyed all but two vessels of the French fleet in the Battle of the Nile, preventing Bonaparte from strengthening the French position in the Mediterranean. His army had succeeded in a temporary increase of French power in Egypt, though it faced repeated uprisings. In early 1799, he moved an army into the Ottoman province of Damascus (Syria and Galilee). Bonaparte led these 13,000 French soldiers in the conquest of the coastal towns of Arish, Gaza, Jaffa, and Haifa. The attack on Jaffa was particularly brutal. Bonaparte discovered that many of the defenders were former prisoners of war, ostensibly on parole, so he ordered the garrison and some 1,500–5,000 prisoners to be executed by bayonet or drowning. Men, women, and children were robbed and murdered for three days.
Bonaparte began with an army of 13,000 men. 1,500 were reported missing, 1,200 died in combat, and thousands perished from disease—mostly bubonic plague. He failed to reduce the fortress of Acre, so he marched his army back to Egypt in May. Bonaparte was alleged to have ordered plague-stricken men to be poisoned with opium to speed the retreat. Back in Egypt on 25 July, Bonaparte defeated an Ottoman amphibious invasion at Abukir.
Bonaparte stayed informed of European affairs. He learned that France had suffered a series of defeats in the War of the Second Coalition. On 24 August 1799, fearing that the Republic's future was in doubt, he took advantage of the temporary departure of British ships from French coastal ports and set sail for France, despite the fact that he had received no explicit orders from Paris. The army was left in the charge of Jean-Baptiste Kléber.
Ruler of France


Stackoverflow Stackexchange

Q: How to use CSS to control the style of an HTML unordered list? Basically I want to control the margin on the left of the list. Here's how I have it structured:

Main

        Sub1

        Sub2

The sub-sections are too far to the right with the current style I'm using. How do I change it?

A: My ul "reset" looks something like this:
ul { list-style-type: none; margin: 0; padding: 0 }

Tex Stackexchange

Q: Improving chemfig drawing As a learning exercise I have been trying to replicate the structure of LSD using chemfig. I've taken this figure for reference.
My best effort so far has been this...
\documentclass[border=10pt]{standalone}
\usepackage[version=3]{mhchem} % Package for chemical equation typesetting
\usepackage{chemfig}
\begin{document}
\chemfig{*6(=(*5(-N-=(*6(--([:-30]<H)(*6(-N(-)--([:120]<:H)(-([:30]=O)([::60]-N([:180]-[::45]-)([:60]-[:10]-)))-=-))---))--))-=-=-)}
\end{document}

which produces ...

I have not been able to figure out how to create the proper triple ring nor have I gotten a proper understanding of the bond angles on the two chains off the N.
I would certainly appreciate any and all advice.

A: Here's how I would do it, although I admit I'm not very expert with chemfig.
Output

Code
\documentclass[border=10pt]{standalone}
\usepackage[version=3]{mhchem} % Package for chemical equation typesetting
\usepackage{chemfig}
\begin{document}
\chemfig{*6(=(-[:-70]N-[:0]=[:70])-(*6(---(*6(([:90]<H)-
N(-[:-30]CH_3)--(-[:90](=[:30]O)-[:145]N(-[:210]-[:150])
(--[:150]))([:145]<:H)-=))---))=-=-)}
\end{document}


Stackoverflow Stackexchange

Q: How to calculate 32 bit CRC in Ruby on rails? i want to calculate 32 bit CRC value for 'input field value"  in Ruby on rails.
need the sample code , please help me anyone.

A: You could use Ruby's Zlib module.
require 'zlib'
crc32 = Zlib::crc32('input field value')

English Stackexchange

Q: letter has been sent to office by yesterday I just want to say, the letter already send to our head office yesterday. Please advise the correct usage
letter has been sent to office by yesterday - this usage is correct or not? please advise

A: Stylistically, "was sent" would be better ("has been" would indicate more recently than yesterday.  As soon as you give a definite time [yesterday, on the 10th of January, last week, last month, etc.], "has been" is not the most felicitous choice).


Stackoverflow Stackexchange

Q: Why did Rust go with "match" instead of "switch" or "case"? I'm curious about the history of this. I would assume switch would appeal to C/C++/Java/etc programmers and thus be a natural choice. If one wanted to avoid confusion in semantics (since match is more powerful than switch) I'd assume one would borrow case from Haskell instead.

A: The Rust Reference cites Standard ML (SML) and OCaml as an influence for pattern matching. In OCaml, pattern matching uses the match keyword.
The original Rust compiler was written in OCaml, so it makes sense that OCaml would have had a stronger influence on Rust than, say, Haskell.

Stackoverflow Stackexchange

Q: erlang vs golang I intend to learn a new language for better concurrency.
Erlang is a old but powerful language with a runtime to support its whole concurrency system as well as a well-known library, OTP. This is more like JVM.
Go is another language good at concurrency, while it's more like C.
My major field is about C/S and B/S. What should I prefer?


Title: Goal-Conditioned Supervised Learning with Sub-Goal Prediction

Abstract: Recently, a simple yet effective algorithm -- goal-conditioned supervised-learning (GCSL) -- was proposed to tackle goal-conditioned reinforcement-learning. GCSL is based on the principle of hindsight learning: by observing states visited in previously executed trajectories and treating them as attained goals, GCSL learns the corresponding actions via supervised learning. However, GCSL only learns a goal-conditioned policy, discarding other information in the process. Our insight is that the same hindsight principle can be used to learn to predict goal-conditioned sub-goals from the same trajectory. Based on this idea, we propose Trajectory Iterative Learner (TraIL), an extension of GCSL that further exploits the information in a trajectory, and uses it for learning to predict both actions and sub-goals. We investigate the settings in which TraIL can make better use of the data, and discover that for several popular problem settings, replacing real goals in GCSL with predicted TraIL sub-goals allows the agent to reach a greater set of goal states using the exact same data as GCSL, thereby improving its overall performance.

Title: Supporting the GLAST User Community

Abstract: The Gamma-ray Large Area Space Telescope (GLAST) Science Support Center (GSSC) is the scientific community's interface with GLAST. The GSSC will provide data, analysis software and documentation. In addition, the GSSC will administer the guest investigator program for NASA HQ. Consequently, the GSSC will provide proposal preparation tools to assist proposers in assessing the feasibility of observing sources of interest.


Stackoverflow Stackexchange

Q: Why does it do this ? if - __name__ == '__main__'
Duplicate of:
What does if __name__== "__main__" do?

Consider this code:
if __name__ == '__main__':
    import pdb
    pdb.run("interact()\n")

What does the following line mean?
if(__name__=='__main__')

I fainted.

A: That is a check to see if you are directly running the script or if it is included in a library.
When you run a python script like this:
python myScript.py

It sends a parameter, telling you to run the programs first method, which is widely called "main", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.

Stackoverflow Stackexchange

Q: Reading h264 stream in Python I am trying to read a video stream from a Parrot Bebop 2 drone.
The video stream is written as "socket" to a file as H264 stream.
$ ffmpeg -i [STREAM]
Input #0, h264, from 'stream_h264':
  Duration: N/A, bitrate: N/A
  Stream #0:0: Video: h264 (Constrained Baseline), 1 reference frame, yuv420p(progressive, left), 1280x720, 23.98 fps, 23.98 tbr, 1200k tbn, 47.95 tbc

Reading the video stream in MPlayer is not a problem using the parameters below. Playing it using VLC or ffmpeg should also not be too hard. For MPlayer the following works:
mplayer -fs -demuxer h264es -benchmark stream_h264

This plays the stream in high-res. However my goal is to perform image processing on the frames using Python (mostly OpenCV). Therefore, I would like to read the frames into NumPy arrays. I have already considered using cv2.VideoCapture but this does not seem to work for my stream. Other (somewhat easy) to use options I am not aware of, therefore my question is whether someone recommend me how to read the video frames in Python?
All recommendations are more than welcome!


Input/output

In computing, input/output (I/O, i/o, or informally io or IO) is the communication between an information processing system, such as a computer, and the outside world, such as another computer system, peripherals, or a human operator. Inputs are the signals or data received by the system and outputs are the signals or data sent from it. The term can also be used as part of an action; to "perform I/O" is to perform an input or output operation.
are the pieces of hardware used by a human (or other system) to communicate with a computer. For instance, a keyboard or computer mouse is an input device for a computer, while monitors and printers are output devices. Devices for communication between computers, such as modems and network cards, typically perform both input and output operations. Any interaction with the system by an interactor is an input and the reaction the system responds is called the output.


I am looking for recent work on generating text with personalities and style

Title: Cloning Ideology and Style using Deep Learning

Abstract: Text generation tasks have gotten the attention of researchers in the last few years because of their applications on a large scale.In the past, many researchers focused on task-based text generations.Our research focuses on text generation based on the ideology and style of a specific author, and text generation on a topic that was not written by the same author in the past.Our trained model requires an input prompt containing initial few words of text to produce a few paragraphs of text based on the ideology and style of the author on which the model is trained.Our methodology to accomplish this task is based on Bi-LSTM.The Bi-LSTM model is used to make predictions at the character level, during the training corpus of a specific author is used along with the ground truth corpus.A pre-trained model is used to identify the sentences of ground truth having contradiction with the author's corpus to make our language model inclined.During training, we have achieved a perplexity score of 2.23 at the character level. The experiments show a perplexity score of around 3 over the test dataset.

Title: Text-to-Image Synthesis for Any Artistic Styles: Advancements in Personalized Artistic Image Generation via Subdivision and Dual Binding

Abstract: Recent advancements in text-to-image models, such as Stable Diffusion, have demonstrated their ability to synthesize visual images through natural language prompts. One approach of personalizing text-to-image models, exemplified by DreamBooth, fine-tunes the pre-trained model by binding unique text identifiers with a few images of a specific subject. Although existing fine-tuning methods have demonstrated competence in rendering images according to the styles of famous painters, it is still challenging to learn to produce images encapsulating distinct art styles due to abstract and broad visual perceptions of stylistic attributes such as lines, shapes, textures, and colors. In this paper, we introduce a new method, Single-StyleForge, for personalization. It fine-tunes pre-trained text-to-image diffusion models to generate diverse images in specified styles from text prompts. By using around 15-20 images of the target style, the approach establishes a foundational binding of a unique token identifier with a broad range of the target style. It also utilizes auxiliary images to strengthen this binding, resulting in offering specific guidance on representing elements such as persons in a target style-consistent manner. In addition, we present ways to improve the quality of style and text-image alignment through a method called Multi-StyleForge, which inherits the strategy used in StyleForge and learns tokens in multiple. Experimental evaluation conducted on six distinct artistic styles demonstrates substantial improvements in both the quality of generated images and the perceptual fidelity metrics, such as FID, KID, and CLIP scores.


Find a paper on a large multimodal model achieving SoTA in VQA while being used for robotics tasks

Title: ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models

Abstract: The integration of Multimodal Large Language Models (MLLMs) with robotic systems has significantly enhanced the ability of robots to interpret and act upon natural language instructions. Despite these advancements, conventional MLLMs are typically trained on generic image-text pairs, lacking essential robotics knowledge such as affordances and physical knowledge, which hampers their efficacy in manipulation tasks. To bridge this gap, we introduce ManipVQA, a novel framework designed to endow MLLMs with Manipulation-centric knowledge through a Visual Question-Answering format. This approach not only encompasses tool detection and affordance recognition but also extends to a comprehensive understanding of physical concepts. Our approach starts with collecting a varied set of images displaying interactive objects, which presents a broad range of challenges in tool object detection, affordance, and physical concept predictions. To seamlessly integrate this robotic-specific knowledge with the inherent vision-reasoning capabilities of MLLMs, we adopt a unified VQA format and devise a fine-tuning strategy that preserves the original vision-reasoning abilities while incorporating the new robotic insights. Empirical evaluations conducted in robotic simulators and across various vision task benchmarks demonstrate the robust performance of ManipVQA. Code and dataset will be made publicly available at https://github.com/SiyuanHuang95/ManipVQA.


I am looking for a paper to help me deal with data constraints as I scale up my language model.

Title: Data Mixing Made Efficient: A Bivariate Scaling Law for Language Model Pretraining

Abstract: Large language models exhibit exceptional generalization capabilities, primarily attributed to the utilization of diversely sourced data. However, conventional practices in integrating this diverse data heavily rely on heuristic schemes, lacking theoretical guidance. This research tackles these limitations by investigating strategies based on low-cost proxies for data mixtures, with the aim of streamlining data curation to enhance training efficiency. Specifically, we propose a unified scaling law, termed BiMix, which accurately models the bivariate scaling behaviors of both data quantity and mixing proportions. We conduct systematic experiments and provide empirical evidence for the predictive power and fundamental principles of BiMix. Notably, our findings reveal that entropy-driven training-free data mixtures can achieve comparable or even better performance than more resource-intensive methods. We hope that our quantitative insights can shed light on further judicious research and development in cost-effective language modeling.

Title: Like Sparrows on a Clothes Line: The Self-Organization of Random Number Sequences

Abstract: We study sequences of random numbers {Z[1],Z[2],Z[3],...,Z[n]} -- which can be considered random walks with reflecting barriers -- and define their "types" according to whether Z[i] > Z[i+1], (a down-movement), or Z[i] < Z[i+1] (up-movement). This paper examines the means, xi, to which the Zi converge, when a large number of sequences of the same type is considered. It is shown that these means organize themselves in such a way that, between two turning points of the sequence, they are equidistant from one another. We also show that m steps in one direction tend to offset one step in the other direction, as m -> infinity. Key words:random number sequence, self-organization, random walk, reflecting barriers.


List of capitals of France

This is a chronological list of capitals of France. The capital of France has been Paris since its liberation in 1944.


TensorFlow

TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.
It was developed by the Google Brain team for Google's internal use in research and production. The initial version was released under the Apache License 2.0 in 2015. Google released an updated version, TensorFlow 2.0, in September 2019.
TensorFlow can be used in a wide variety of programming languages, including Python, JavaScript, C++, and Java, facilitating its use in a range of applications in many sectors.
History


Title: The State of the Art Forensic Techniques in Mobile Cloud Environment: A Survey, Challenges and Current Trends

Abstract: Smartphones have become popular in recent days due to the accessibility of a wide range of applications. These sophisticated applications demand more computing resources in a resource constraint smartphone. Cloud computing is the motivating factor for the progress of these applications. The emerging mobile cloud computing introduces a new architecture to offload smartphone and utilize cloud computing technology to solve resource requirements. The popularity of mobile cloud computing is an opportunity for misuse and unlawful activities. Therefore, it is a challenging platform for digital forensic investigations due to the non-availability of methodologies, tools and techniques. The aim of this work is to analyze the forensic tools and methodologies for crime investigation in a mobile cloud platform as it poses challenges in proving the evidence. The advancement of forensic tools and methodologies are much slower than the current technology development in mobile cloud computing. Thus, forces the available tools, and techniques become increasingly obsolete. Therefore, it opens up the door for the new forensic tools and techniques to cope up with recent developments. Hence, this work presents a detailed survey of forensic methodology and corresponding issues in a mobile device, cloud environment, and mobile cloud applications. It mainly focuses on digital forensic issues related to mobile cloud applications and also analyze the scope, challenges and opportunities. Finally, this work reviewed the forensic procedures of two cloud storage services used for mobile cloud applications such as Dropbox and SkyDrive.

Title: Bipartita: Physics, Geometry & Number Theory

Abstract: Bipartite graphs, especially drawn on Riemann surfaces, have of late assumed an active role in theoretical physics, ranging from MHV scattering amplitudes to brane tilings, from dimer models and topological strings to toric AdS/CFT, from matrix models to dessins d'enfants in gauge theory. Here, we take a brief and casual promenade in the realm of brane tilings, quiver SUSY gauge theories and dessins, serving as a rapid introduction to the reader.


Neural network (machine learning)

the reverse mode of automatic differentiation or reverse accumulation, due to Seppo Linnainmaa (1970). The term "back-propagating errors" was introduced in 1962 by Frank Rosenblatt, but he did not have an implementation of this procedure, although Henry J. Kelley and Bryson had dynamic programming based continuous precursors of backpropagation already in 1960–61 in the context of control theory.
In 1973, Dreyfus used backpropagation to adapt parameters of controllers in proportion to error gradients.
In 1982, Paul Werbos applied backpropagation to MLPs in the way that has become standard. In 1986 Rumelhart, Hinton and Williams showed that backpropagation learned interesting internal representations of words as feature vectors when trained to predict the next word in a sequence.
In the late 1970s to early 1980s, interest briefly emerged in theoretically investigating the Ising model created by Wilhelm Lenz (1920) and Ernst Ising (1925)
in relation to .
The Ising model is essentially a non-learning artificial recurrent neural network (RNN) consisting of neuron-like threshold elements.
In 1972, Shun'ichi Amari described an adaptive version of this architecture,
In 1981, the Ising model was solved exactly by Peter Barth for the general case of closed Cayley trees (with loops) with an arbitrary branching ratio
and found to exhibit unusual phase transition behavior in its local-apex and long-range site-site correlations.
John Hopfield popularised this architecture in 1982,
and it is now known as a Hopfield network.


TensorFlow

In May 2019, Google announced TensorFlow Graphics for deep learning in computer graphics.
Tensor processing unit (TPU)
In May 2016, Google announced its Tensor processing unit (TPU), an application-specific integrated circuit (ASIC, a hardware chip) built specifically for machine learning and tailored for TensorFlow. A TPU is a programmable AI accelerator designed to provide high throughput of low-precision arithmetic (e.g., 8-bit), and oriented toward using or running models rather than training them. Google announced they had been running TPUs inside their data centers for more than a year, and had found them to deliver an order of magnitude better-optimized performance per watt for machine learning.
In May 2017, Google announced the second-generation, as well as the availability of the TPUs in Google Compute Engine. The second-generation TPUs deliver up to 180 teraflops of performance, and when organized into clusters of 64 TPUs, provide up to 11.5 petaflops.
In May 2018, Google announced the third-generation TPUs delivering up to 420 teraflops of performance and 128 GB high bandwidth memory (HBM). Cloud TPU v3 Pods offer 100+ petaflops of performance and 32 TB HBM.
In February 2018, Google announced that they were making TPUs available in beta on the Google Cloud Platform.


Haute-Savoie

History The historical region of Savoy was governed by the House of Savoy, the ruling dynasty of Savoy from 1032 to 1860. The Duchy of Savoy were rulers of the Savoy region from 1416 to 1720.
The territory occupied by modern Haute-Savoie and the adjoining department of Savoie became part of the Kingdom of Sardinia after the Treaty of Utrecht in 1713. Annexation of the region by France was formalised in the Treaty of Turin on March 24, 1860.
From November 1942 to September 1943, Haute-Savoie was subjected to military occupation by Fascist Italy. The Maquis des Glières (a band of Free French Resistance fighters who opposed the Nazi, Vichy and Milice regimes during World War II) operated from Haute-Savoie. In the winter of 1943–1944, German troops burned down around 500 farms in response to French Resistance activities.
Politics
Departmental Council of Haute-Savoie
The Departmental Council of Haute-Savoie has 34 seats. As of 2020, fifteen councillors are part of the Haute-Savoie Union group (miscellaneous right), fourteen are part of the Avenir Haute-Savoie group (The Republicans) and five are part of the Union du Centre group (The Centrists). Martial Saddier had been President of the Departmental Council since 2021.
Representation in Paris

Paris

Education Paris is the département with the highest proportion of highly educated people. In 2009, around 40 percent of Parisians held a licence-level diploma or higher, the highest proportion in France, while 13 percent have no diploma, the third-lowest percentage in France. Education in Paris and the Île-de-France region employs approximately 330,000 people, 170,000 of whom are teachers and professors teaching approximately 2.9 million students in around 9,000 primary, secondary, and higher education schools and institutions.
The University of Paris, founded in the 12th century, is often called the Sorbonne after one of its original medieval colleges. It was broken up into thirteen autonomous universities in 1970, following the student demonstrations in 1968. Most of the campuses today are in the Latin Quarter where the old university was located, while others are scattered around the city and the suburbs.
The Paris region hosts France's highest concentration of the grandes écoles – 55 specialised centres of higher-education outside or inside the public university structure. The prestigious public universities are usually considered grands établissements. Most of the grandes écoles were relocated to the suburbs of Paris in the 1960s and 1970s, in new campuses much larger than the old campuses within the crowded City of Paris, though the École Normale Supérieure, PSL University has remained on rue d'Ulm in the 5th arrondissement.


Paris

Education Paris is the département with the highest proportion of highly educated people. In 2009, around 40 percent of Parisians held a licence-level diploma or higher, the highest proportion in France, while 13 percent have no diploma, the third-lowest percentage in France. Education in Paris and the Île-de-France region employs approximately 330,000 people, 170,000 of whom are teachers and professors teaching approximately 2.9 million students in around 9,000 primary, secondary, and higher education schools and institutions.
The University of Paris, founded in the 12th century, is often called the Sorbonne after one of its original medieval colleges. It was broken up into thirteen autonomous universities in 1970, following the student demonstrations in 1968. Most of the campuses today are in the Latin Quarter where the old university was located, while others are scattered around the city and the suburbs.
The Paris region hosts France's highest concentration of the grandes écoles – 55 specialised centres of higher-education outside or inside the public university structure. The prestigious public universities are usually considered grands établissements. Most of the grandes écoles were relocated to the suburbs of Paris in the 1960s and 1970s, in new campuses much larger than the old campuses within the crowded City of Paris, though the École Normale Supérieure, PSL University has remained on rue d'Ulm in the 5th arrondissement.


how many students study in london, ontario

London, Ontario

The city is home to two post-secondary institutions: the University of Western Ontario (UWO) and Fanshawe College, a college of applied arts and technology. UWO, founded in 1878, has about 3,500 full-time faculty and staff members and almost 30,000 undergraduate and graduate students. The Richard Ivey School of Business, part of UWO, was formed in 1922. UWO has three affiliated colleges: Brescia University College, founded in 1919 (Canada's only university-level women's college); Huron University College, founded in 1863 (also the founding college of UWO) and King's University College, founded in 1954. All three are liberal arts colleges with religious affiliations: Huron with the Anglican Church of Canada, King's and Brescia with the Roman Catholic Church. London is also home to Lester B. Pearson School for the Arts one of few of its kind.
Fanshawe College has an enrollment of approximately 15,000 students, including 3,500 apprentices and over 500 international students from more than 30 countries. It also has almost 40,000 students in part-time continuing education courses.
The Ontario Institute of Audio Recording Technology (OIART), founded in 1983, offers recording studio experience for audio engineering students.
Westervelt College is also in London. This private career college was founded in 1885 and offers several diploma programs.

London, Ontario

Law enforcement and crime the London Police Service (LPS) is headed by Chief of Police Thai Truong. He is supported by two deputy chiefs: Paul Bastien, in charge of operations, and Trish McIntyre, in charge of administration. The service is governed by a seven-member civilian police board, of which the current board chair is Ali Chabar, General Legal Counsel and Executive Officer with the Thames Valley District School Board c. As of December 2020, the LPS had the fewest police officers per capita in Southwestern Ontario. Its vehicles include light armoured vehicles donated by General Dynamics Land Systems, which the CBC observed in 2019 were rarely used.
Statistics from police indicate that total overall crimes in London held steady between 2010 and 2016, at roughly 24,000 to 27,000 incidents per year. The majority of incidents are property crimes, with violent crimes dropping markedly (up to about 20%) between 2012 and 2014 but rising again in 2015–2016. In July 2018, Police Deputy Chief Steve Williams was quoted as saying many crimes go unreported to police. However, in 2021, the city surpassed its 2005 homicide record, with the city reporting 16 murders with a rate of 3.8 per 100,000 people.
The city has been home to several high-profile incidents over the years such as the Ontario Biker War and the London Conflict, it was also the location where most of the trial for the Shedden Massacre took place.
Research by Michael Andrew Arntfield, a police officer turned criminology professor, has determined that on a per-capita basis, London had more active serial killers than any locale in the world from 1959 to 1984. Arntfield determined there were at least six serial killers active in London during this era. Some went unidentified, but known killers in London included Russell Maurice Johnson, Gerald Thomas Archer, and Christian Magee.


London, Ontario

London and the Thames were named after the English city and river in 1793 by John Graves Simcoe, who proposed the site for the capital city of Upper Canada. The first European settlement was between 1801 and 1804 by Peter Hagerman. The village was founded in 1826 and incorporated in 1855. Since then, London has grown to be the largest southwestern Ontario municipality and Canada's 11th largest metropolitan area, having annexed many of the smaller communities that surround it.
London is a regional centre of healthcare and education, being home to the University of Western Ontario (which brands itself "Western University"), Fanshawe College, and three major hospitals: Victoria Hospital, University Hospital and St. Joseph's Hospital. The city hosts a number of musical and artistic exhibits and festivals, which contribute to its tourism industry, but its economic activity is centered on education, medical research, manufacturing, financial services, and information technology. London's university and hospitals are among its top ten employers. London lies at the junction of Highways 401 and 402, connecting it to Toronto, Windsor, and Sarnia. These highways also make the Detroit-Windsor, Port Huron-Sarnia, and Niagara Falls border crossings with the United States easily accessible. The city also has railway stations and bus stations and is home to the London International Airport.

London, Ontario

The city is home to two post-secondary institutions: the University of Western Ontario (UWO) and Fanshawe College, a college of applied arts and technology. UWO, founded in 1878, has about 3,500 full-time faculty and staff members and almost 30,000 undergraduate and graduate students. The Richard Ivey School of Business, part of UWO, was formed in 1922. UWO has three affiliated colleges: Brescia University College, founded in 1919 (Canada's only university-level women's college); Huron University College, founded in 1863 (also the founding college of UWO) and King's University College, founded in 1954. All three are liberal arts colleges with religious affiliations: Huron with the Anglican Church of Canada, King's and Brescia with the Roman Catholic Church. London is also home to Lester B. Pearson School for the Arts one of few of its kind.
Fanshawe College has an enrollment of approximately 15,000 students, including 3,500 apprentices and over 500 international students from more than 30 countries. It also has almost 40,000 students in part-time continuing education courses.
The Ontario Institute of Audio Recording Technology (OIART), founded in 1983, offers recording studio experience for audio engineering students.
Westervelt College is also in London. This private career college was founded in 1885 and offers several diploma programs.


Rimegepant

Rimegepant, sold under the brand name Nurtec ODT among others, is a medication used for the acute treatment of migraine with or without aura in adults and the prophylactic/ preventive treatment of episodic migraine in adults. It is taken by mouth to dissolve on or under the tongue. It works by blocking CGRP receptors.

Oirats

Oirats (, Oirad, ) or Oirds (, Oird; ; , Wǎlà/Wǎlā), also formerly Eluts and Eleuths (, Èlǔtè), are the westernmost group of the Mongols whose ancestral home is in the Altai region of Siberia, Xinjiang and western Mongolia.
The first documented reference to Elut and Yelut  was in the Onginsk "rune" inscriptions dated in the sixth century. Historically, the Oirats were composed of four major tribes: Dzungar (Choros or Olots/Elut/Yelut/Èlǔtè), Torghut, Dörbet and Khoshut.
The political elite of the Rouran and Tobgach empires were YELÜ-T Mongolic speakers. Although these two empires encompassed multilingual populations, the language of diplomacy, trade, and culture was an ÖLÜ (YELÜ) dialect of ancient Mongolic descent. When the Tobgach destroyed the Rouran Empire, the Mongolic-speaking Avar people escaped into the Caspian steppes.
This displacement triggered a series of events. Settling in the Caucasus around 558, the Mongolic Avars intervened in Germanic tribal conflicts, forming alliances such as with the Lombards to overthrow the Gepidae, who were Byzantine allies. Between 550 and 575, they solidified their presence by establishing the Khanate of the Mongolic Avars (6th to 8th century) in the Caspian and Hungarian steppes.
The modern Kalmyks of Kalmykia on the Caspian Sea in southeastern Europe are Oirats.


What kind of ivy plant grows wild in golden gate park?

Hedera

Stinging insects The flowers of ivy are pollinated by Hymenoptera and are particularly attractive to the common wasp.
Etymology and other names
The name ivy derives from Old English ifig, cognate with German Efeu, of unknown original meaning. The scientific name Hedera is the classical Latin name for the plant. Old regional common names in Britain, no longer used, include "Bindwood" and "Lovestone", for the way it clings and grows over stones and bricks. US Pacific Coast regional common names for H. canariensis include "California ivy" and "Algerian ivy".  For H. helix, regional common names include "common ivy" (Britain and Ireland) and "English ivy" (North America).
The name ivy has also been used as a common name for a number of other unrelated plants, including Boston ivy (Japanese Creeper Parthenocissus tricuspidata, in the family Vitaceae), Cape-ivy (used interchangeably for Senecio angulatus and Delairea odorata, Asteraceae), poison-ivy (Toxicodendron radicans, Anacardiaceae), Swedish ivy (Whorled Plectranthus Plectranthus verticillatus, Lamiaceae) and ground ivy (Glechoma hederacea, also Lamiaceae), and Kenilworth ivy (Cymbalaria muralis, Plantaginaceae).
Cultural symbolism
Like many other evergreen plants, which impressed European cultures by persisting through the winter, ivy has traditionally been imbued with a spiritual significance. It was brought into homes to drive out evil spirits.

Golden Gate Park

The California live oak is the only tree native to the park. Some of the oldest plants in the park are the coast live oaks in the Oak Woodlands in the northeastern portion of the park which are hundreds of years old. Oaks also grow on Strawberry Hill and in the AIDS Memorial Grove. Acorns from the oak trees were an important food source to Native American groups in San Francisco.
Other than the oak trees, the plants that are currently in the park are non-native, some of which are considered invasive species. Many have disrupted the ecosystem and harm birds, mammals, reptiles, and insects in the park. Volunteers with the Strawberry Hill Butterfly Habitat Restoration Project are removing and replacing invasive plant species to help restore the butterfly population on Strawberry Hill. Under the Significant Natural Resource Areas Management Plan, the city will remove many invasive species and replace them with native plants.
Blue gum eucalyptus, Monterey pine, and Monterey cypress were the most commonly planted trees in the park during the late 1800s. Blue gum continued to grow and spread and is now one of the most important trees found in the park. They can be found near McLaren Lodge, on Hippie Hill, and in a eucalyptus forest near Middle Lake. Monterey pines are also prevalent today and can found in the Strybing Arboretum, the Japanese Tea Garden, and in the western portions of the park around the Buffalo Paddock.


A spoken language understanding framework achieving state-of-the-art performance by integrating intent information into slot filling.

Title: A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding

Abstract: Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system. The two tasks are closely tied and the slots often highly depend on the intent. In this paper, we propose a novel framework for SLU to better incorporate the intent information, which further guides the slot filling. In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge. In addition, to further alleviate the error propagation, we perform the token-level intent detection for the Stack-Propagation framework. Experiments on two publicly datasets show that our model achieves the state-of-the-art performance and outperforms other previous methods by a large margin. Finally, we use the Bidirectional Encoder Representation from Transformer (BERT) model in our framework, which further boost our performance in SLU task.

Title: The Influence of Predator-Prey Population Dynamics on the Long-term Evolution of Food Web Structure

Abstract: We develop a set of equations to describe the population dynamics of many interacting species in food webs. Predator-prey interactions are non-linear, and are based on ratio-dependent functional responses. The equations account for competition for resources between members of the same species, and between members of different species. Predators divide their total hunting/foraging effort between the available prey species according to an evolutionarily stable strategy (ESS). The ESS foraging behaviour does not correspond to the predictions of optimal foraging theory. We use the population dynamics equations in simulations of the Webworld model of evolving ecosystems. New species are added to an existing food web due to speciation events, whilst species become extinct due to coevolution and competition. We study the dynamics of species-diversity in Webworld on a macro-evolutionary timescale. Coevolutionary interactions are strong enough to cause continuous overturn of species, in contrast to our previous Webworld simulations with simpler population dynamics. Although there are significant fluctuations in species diversity because of speciation and extinction, very large scale extinction avalanches appear to be absent from the dynamics, and we find no evidence for self-organised criticality.


List of French people

Jacques Delors Félix Faure, President of France who died of a heart attack while making love to his mistress
Charles de Gaulle, heroic World War II general, commander of the Free French Forces, French president
Valéry Giscard d'Estaing
François Guizot, Prime Minister
Gisèle Halimi, lawyer and feminist activist
François Hollande, former PS (Socialist Party) leader, former French president (15 May 2012 – 14 May 2017)
Jean Jaurès, politician, pacifist
Lionel Jospin, socialist, former prime minister
Bernard Kouchner, founder of Médecins du Monde
Jean-Marie Le Pen, leader of the extreme right party in France, Front National, presidential candidate
Louis Lépine, Paris police chief, governor of Algiers, founder of the Concours Lépine
Émile Loubet, President of France who was elected in 1899, after the death of Félix Faure
Henri-Auguste Lozé, Paris police chief, senator of the Third Republic
Emmanuel Macron, founder and current President of Renaissance, current President of France (from 14 May 2017)
Jean-Paul Marat, politician during the Revolution, journalist, physician, scientist
Jean-Claude Martinez, lawyer and European deputy
Pierre Mendès France, lawyer and statesman, prime minister
Honoré Mirabeau
François Mitterrand, lawyer and statesman, president
Jean Monnet
Philippe Pétain, head of Vichy France
Alexandre de Prouville, Viceroy of New France
Marthe Richard
Maximilien Robespierre, statesman and major figure in the French Revolution
Gilberte Roca (1911–2004), Communist

President of France

Eighteen years later, during the opening phases of the Second Republic, the title was created for a popularly elected head of state, the first of whom was Louis-Napoléon Bonaparte, nephew of Emperor Napoleon. Bonaparte served as president until he staged an auto coup against the republic, proclaiming himself Napoleon III, Emperor of the French.
Under the Third Republic the president was at first quite powerful, mainly because the royalist party was strong when the constitutional laws of 1875 were established, and it was hoped that a member of one of the two branches of the royal family would be able to serve as president and turn France into a constitutional monarchy. However, the next legislature was dominated by Republicans, and after President Patrice de MacMahon had unsuccessfully tried to obtain a new royalist majority by dissolving the Chambre des Députés, his successor Jules Grévy promised in 1879 that he would not use his presidential power of dissolution, and therefore lost his control over the legislature, effectively creating a parliamentary system that would be maintained for 80 years until the accession of Charles de Gaulle as president in 1959.
Indeed, when the Fourth Republic was created, after the Second World War, it was a parliamentary system, in which the office of President of the Republic was a largely ceremonial one.


Nuraghe

The nuraghe, or nurhag, is the main type of ancient megalithic edifice found in Sardinia, Italy, developed during the Nuragic Age between 1900 and 730 BC. Today it has come to be the symbol of Sardinia and its distinctive culture known as the Nuragic civilization. More than 7,000 nuraghes have been found, though archeologists believe that originally there were more than 10,000.

Rimegepant

Rimegepant, sold under the brand name Nurtec ODT among others, is a medication used for the acute treatment of migraine with or without aura in adults and the prophylactic/ preventive treatment of episodic migraine in adults. It is taken by mouth to dissolve on or under the tongue. It works by blocking CGRP receptors.


Ormat Technologies

After the decommissioning of the solar pond project, the firm was not active in the solar energy market until its entry into the Solar PV market.
Oil sands
Ormat's former parent company, Ormat Industries Ltd.,  developed an energy-efficient technology, OrCrude, for extracting crude oil from oil sands. The process is claimed to be more efficient than other technologies as it includes gasification, which substantially reduces the requirement for natural gas, typically the largest input cost in an in-situ oil sands project. The technology is used in the Long Lake project, a former joint venture between Nexen and OPTI Canada. In 2010, Ormat Industries sold all its holdings (5.1%) in OPTI Canada. In 2011, OPTI Canada was acquired by CNOOC Luxembourg S.à r.l, an indirect wholly owned subsidiary of CNOOC Limited.
Spin offs
Ormat has been involved in the establishment and development of several companies:
Orbotech develops and manufactures automated optical inspection (AOI) systems for bare and assembled printed circuit boards and flat panel displays. The company's systems, imaging and computer-aided manufacturing technologies enable electronic manufacturers to achieve the increased yields and throughput essential for electronics production.
Orad Hi-Tec Systems Ltd. develops video and real-time image processing technologies for TV broadcasting, Internet, production studio and sports events.


OD

OD or Od may refer to:
Education
Old Diocesan, a former pupil of Diocesan College
Old Dunelmian, a former pupil of Durham School
Medicine
OD or o.d., an abbreviation used in medical prescriptions for  or "once daily" both meaning "take once every day"
Doctor of Optometry (O.D.)
, , or , meaning "right eye" in general ophthalmologic or optometric usage, particularly in eyeglass prescriptions
Osteochondritis dissecans, a medical disease affecting the joints of both humans and animals
Overdose, the inadvertent drug effects of the user rendered unresponsive or fatally succumbed due to unsophistication of self-medicating being unaccustomed to quantities greater than recommended
Music
Oblivion Dust, a Japanese rock band
Old Dominion (band), an American country music band
One Direction, a British/Irish pop music band
Orphei Drängar, a Swedish male choir
The OD EP, a 2012 extended play by Danny Brown
Overly Dedicated, a 2010 mixtape by Kendrick Lamar
"O.D", a song by Sunna from the album One Minute Science (2000)
"OD", an alternative name for the song "DTM" by SZA from Lana (2024)
Organizations
Batik Air Malaysia (IATA airline code OD)

Open Document Architecture

The Open Document Architecture (ODA) and interchange format (informally referred to as just ODA) is a free and open international standard document file format maintained by the ITU-T to replace all proprietary document file formats. ODA is detailed in the standards documents CCITT T.411-T.424, which is equivalent to ISO/IEC 8613.


Open Document Architecture

The Open Document Architecture (ODA) and interchange format (informally referred to as just ODA) is a free and open international standard document file format maintained by the ITU-T to replace all proprietary document file formats. ODA is detailed in the standards documents CCITT T.411-T.424, which is equivalent to ISO/IEC 8613.

OD

OD or Od may refer to:
Education
Old Diocesan, a former pupil of Diocesan College
Old Dunelmian, a former pupil of Durham School
Medicine
OD or o.d., an abbreviation used in medical prescriptions for  or "once daily" both meaning "take once every day"
Doctor of Optometry (O.D.)
, , or , meaning "right eye" in general ophthalmologic or optometric usage, particularly in eyeglass prescriptions
Osteochondritis dissecans, a medical disease affecting the joints of both humans and animals
Overdose, the inadvertent drug effects of the user rendered unresponsive or fatally succumbed due to unsophistication of self-medicating being unaccustomed to quantities greater than recommended
Music
Oblivion Dust, a Japanese rock band
Old Dominion (band), an American country music band
One Direction, a British/Irish pop music band
Orphei Drängar, a Swedish male choir
The OD EP, a 2012 extended play by Danny Brown
Overly Dedicated, a 2010 mixtape by Kendrick Lamar
"O.D", a song by Sunna from the album One Minute Science (2000)
"OD", an alternative name for the song "DTM" by SZA from Lana (2024)
Organizations
Batik Air Malaysia (IATA airline code OD)


OpenDocument

The Open Document Format for Office Applications (ODF), also known as OpenDocument, standardized as ISO 26300, is an open file format for word processing documents, spreadsheets, presentations and graphics and using ZIP-compressed XML files. It was developed with the aim of providing an open, XML-based file format specification for office applications.
The standard is developed and maintained by a technical committee in the Organization for the Advancement of Structured Information Standards (OASIS) consortium. It was based on the Sun Microsystems specification for OpenOffice.org XML, the default format for OpenOffice.org and LibreOffice. It was originally developed for StarOffice "to provide an open standard for office documents."
In addition to being an OASIS standard, it is published as an ISO/IEC international standard ISO/IEC 26300 Open Document Format for Office Applications (OpenDocument).
From 2021, the current version is 1.3.
Specifications
The most common filename extensions used for OpenDocument documents are:
.odt and .fodt for word processing (text) documents
.ods and .fods for spreadsheets
.odp and .fodp for presentations
.odg and .fodg for graphics
.odf for formula, mathematical equations
The original OpenDocument format consists of an XML document that has  as its root element. OpenDocument files can also take the format of a ZIP compressed archive containing a number of files and directories; these can contain binary content and benefit from ZIP's lossless compression to reduce file size. OpenDocument benefits from separation of concerns by separating the content, styles, metadata, and application settings into four separate XML files.


OD

Ordnungsdienst, Jewish order service in ghettos during the war
Organization development
People and characters
Super OD, a Ghanaian actor
O.D (Gatchaman Crowds), a character in the anime series Gatchaman Crowds
Ód or Óðr, a Nordic god
Places
Odisha, India
Stormarn (district), Germany (vehicle plate code OD)
Science and technology
Optical density
Outside diameter (also: O.D.), cylinder or pipe external diameter
Ordnance datum, in Britain, an Ordnance Survey data point
od (Unix), a Unix program
Object Desktop, a suite of skinning and customization programs by Stardock, or ObjectDock, another program by the same company
Optical disc
Organization development, the study of organizational change and performance
Ordinal definable set, a set requiring only finitely-many ordinals to define under first-order logic.
Other uses
OD (video game), an upcoming video game developed by Kojima Productions and published by Xbox Game Studios
Od language, an Indo-Aryan language of Pakistan
Odic force or Od, a life force
Occupation Double, a Quebec reality show that started in 2003
Official Declaration, part of Latter-day Saint scripture
Olive drab, a color, particularly of some United States Army uniforms
Online dating
Original dance, the second part of an ice dancing competition
Order of Distinction, a civil honour of Jamaica
OD - original designation in Taxonomy


Orally disintegrating tablet

ODTs are available in HPDE bottles (Parcopa) or individually sealed in blister packs to protect the tablets from damage, moisture, and oxidation.  Because ODTs are soft in nature, the ability to successfully package an ODT in a bottle is difficult. However, CIMA Labs markets their Durasolv ODT as being able to be placed into bottle for commercial sale, while CIMA's Orasolv is marketed for blisters only.  Zydis ODT tablets manufactured by Catalent Pharma Solutions and Lyophilized Freeze drying tablets manufactured by Galien-LPS are delivered in a blister pack. The differences between the two CIMA products are proprietary, however, the primary difference is expected to be the use of microcrystalline cellulose (MCC), such as Avicel PH101, in the Durasolv product.  MCC serves multiple purposes in an ODT but in the case of CIMA's products, it acts as a binder, increasing the internal strength of the tablet and making it more robust for packaging in bottles.
ODTs currently or previously available
Advantages of ODTs
Ved Parkash et al. note the following advantages of ODTs:
they are easy to consume and as such are convenient for such patients as "the elderly, stroke victims, bedridden patients, patients affected by kidney failure, and people who refuse to swallow, such as pediatric, geriatric, and psychiatric patients";
increased bioavailability (rapid absorption) due to pregastric absorption;
don't require water to consume and thus suitable for "patient compliant for disabled, bedridden patients, and for travelers and busy people who do not always have access to water";
good mouth feel;
improved safety due to low risk of choking or suffocation during oral administration.
Disadvantages of ODTs
Ved Parkash et al. lists the following disadvantages of ODTs:
unpleasant taste;
cost-intensive production process;
lack of physical resistance in standard blister packs;
limited ability to incorporate higher concentrations of active drug.