1752947
Learning decision rules for pattern classification under a family of probability measures
In this paper, uniformly consistent estimation (learnability) of decision rules for pattern classification under a family of probability measures is investigated. In particular, it is shown that uniform boundedness of the metric entropy of the class of decision rules is both necessary and sufficient for learnability under each of two conditions: (i) the family of probability measures is totally bounded, with respect to the total variation metric, and (ii) the family of probability measures contains an interior point, when equipped with the same metric. In particular, this shows that insofar as uniform consistency is concerned, when the family of distributions contains a total variation neighborhood, nothing is gained by this knowledge about the distribution. Then two sufficient conditions for learnability are presented. Specifically, it is shown that learnability with respect to each of a finite collection of families of probability measures implies learnability with respect to their union; also, learnability with respect to each of a finite number of measures implies learnability with respect to the convex hull of the corresponding families of uniformly absolutely continuous probability measures.
53223319
Learning Changing Concepts by Exploiting the Structure of Change
This paper examines learning problems in which the target function is allowed to change. The learner sees a sequence of random examples, labelled according to a sequence of functions, and must provide an accurate estimate of the target function sequence. We consider a variety of restrictions on how the target function is allowed to change, including infrequent but arbitrary changes, sequences that correspond to slow walks on a graph whose nodes are functions, and changes that are small on average, as measured by the probability of disagreements between consecutive functions. We first study estimation, in which the learner sees a batch of examples and is then required to give an accurate estimate of the function sequence. Our results provide bounds on the sample complexity and allowable drift rate for these problems. We also study prediction, in which the learner must produce online a hypothesis after each labelled example and the average misclassification probability over this hypothesis sequence should be small. Using a deterministic analysis in a general metric space setting, we provide a technique for constructing a successful prediction algorithm, given a successful estimation algorithm. This leads to sample complexity and drift rate bounds for the prediction of changing concepts.
6063474
PAC learning with generalized samples and an application to stochastic geometry
In this paper, we introduce an extension of the standard PAC learning model which allows the use of generalized samples. We view a generalized sample as a pair consisting of a functional on the concept class together with the value obtained by the functional operating on the unknown concept. It appears that this model can be applied to a number of problems in signal processing and geometric reconstruction to provide sample size bounds under a PAC criterion. We consider a specific application of the model to a problem of curve reconstruction, and discuss some connections with a result from stochastic geometry.
2125855
A deterministic approach to throughput scaling in wireless networks
We address the problem of how throughput in a wireless network scales as the number of users grows. Following the model of Gupta and Kumar, we consider n identical nodes placed in a fixed area. Pairs of transmitters and receivers wish to communicate but are subject to interference from other nodes. Throughput is measured in bit-meters per second. We provide a very elementary deterministic approach that gives achievability results in terms of three key properties of the node locations. As a special case, we obtain /spl Omega/(/spl radic/n) throughput for a general class of network configurations in a fixed area. Results for random node locations in a fixed area can also be derived as special cases of the general result by verifying the growth rate of three parameters. For example, as a simple corollary of our result we obtain a stronger (almost sure) version of the /spl radic/n//spl radic/(logn) throughput for random node locations in a fixed area obtained by Gupta and Kumar. Results for some other interesting non-independent and identically distributed (i.i.d.) node distributions are also provided.
15102047
Separation of random number generation and resolvability
We consider the problem of determining when a given source can be used to approximate the output due to any input to a given channel. We provide achievability and converse results for a general source and channel. For the special case of a full-rank discrete memoryless channel we give a stronger converse result than we can give for a general channel.
877181
Density Estimation from an Individual Numerical Sequence
The article considers univariate density estimation from an individual numerical sequence. It is assumed that (i) the limiting relative frequencies of the numerical sequence are governed by an unknown density, and (ii) there is a known upper bound for the variation of the density an an increasing sequence of intervals. A simple estimation scheme is proposed, and its L/sub 1/ consistency is established when (i) and (ii) apply. In addition it is shown that there is no consistent estimation scheme for the set of individual sequences satisfying only condition (i).
15941649
Multitolerance in Distributed Reset
A reset of a distributed system is safe if it does not complete ``prematurely,'''' i.e., without having reset some process in the system. Safe resets are possible in the presence of certain faults, such as process fail-stops and repairs, but are not always possible in the presence of more general faults, such as arbitrary transients. In this paper, we design a bounded-memory distributed-reset program that possesses two tolerances: (1) in the presence of fail-stops and repairs, it always executes resets safely, and (2) in the presence of a finite number of transient faults, it eventually executes resets safely. Designing this multitolerance in the reset program introduces the novel concern of designing a safety detector that is itself multitolerant. A broad application of our multitolerant safety detector is to make any total program likewise multitolerant.
15091862
A physical design tool for built-in self-repairable static RAMs
A novel physical design tool, BISRAMGEN, that generates layout geometries of parametrized built-in self-repairable SRAM modules, producing significant improvement in testability, reliability, production yield and manufacturing cost of ASICs and microprocessors with embedded RAMs, is presented.
11497337
Secret instantiation in ad-hoc networks
Abstract In this paper, we focus our attention on the problem of assigning initial secrets to users in ad-hoc network (respectively, sensors in a sensor network) so that they can use those secrets to ensure authentication and privacy during their communication. The goal of this assignment is to ensure that any two users can communicate securely with each other even though each user maintains only a small number of secrets. With this motivation, we present a protocol that maintains O ( n ) secrets per user where n is the number of users in the system. We show that our secret distribution protocol suffices for privacy and authentication as well as secure multihop communication between two users. Furthermore, we show that the number of secrets maintained in this protocol is within a constant factor of the optimal. For the case where user capability prevents them from maintaining the necessary secrets, we propose two probabilistic protocols that maintain O(log n) secrets and where the probability of security compromise between two users is inversely proportional to the number of secrets they maintain. Thus, our protocols provide a continuum where the level of privacy and authentication depends upon user requirements and capabilities.
38081004
A high-speed 32-bit parallel correlator for spread spectrum communication
This paper describes a high speed 32-bit pipelined digital parallel correlator implemented in a Lattice field programmable gate array (FPGA). The parallel correlator is of use in CDMA and spread spectrum transceivers for the continuous calculation of correlation between an incoming data stream with a PN sequence. The maximum frequency of operation of the FPGA based correlator is 87 MHz providing a throughput of one 32-bit correlation every 11.5 ns resulting in considerable improvement over commercially available correlators in terms of speed as well as number of bits. The high speed of operation of the correlator coupled with its ability to handle up to 32 chips per data bit alleviates the problem of low information bit rates due to signal encoding in spread spectrum communication systems. A CMOS integrated circuit implementation of the parallel correlator is presented.
35260529
Transformations for Write-All-with-Collision Model
In this paper, we consider a new atomicity model, write all with collision (WAC), and compare it with existing models considered in the literature. This model captures the computations in sensor networks. We show that it is possible to transform a program from WAC model into a program in read/write model, and vice versa. Further, we show that the transformation from WAC model to read/write model is stabilization preserving, and the transformation from read/write model to WAC model is stabilization preserving for timed systems. In the transformation from read/write model to WAC model, if the system is untimed (asynchronous) and processes are deterministic then under reasonable assumptions, we show that (1) the resulting program in WAC model can allow at most one process to execute, and (2) the resulting program in WAC model cannot be stabilizing. In other words, if a deterministic program cannot read then it is important that it can tell time.