Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Temporal Difference Learning with Game-Tree Search

In this paper we present TDLeaf( ), a variation on the TD( ) algorithm that enables it to be used in conjunction with minimax search. We present some experiments in both chess and backgammon which demonstrate its utility and provide comparisons with TD( ) and another less radical variant, TDdirected( ). In particular, our chess program, “KnightCap,” used TDLeaf( ) to learn its evaluation function while playing on the Free Internet Chess Server (FICS, fics.onenet.net). It improved from a 1650 rating to a 2100 rating in just 308 games. We discuss some of the reasons for this success and the relationship between our results and Tesauro’s results in backgammon.
Some Intriguing Aspects about Lipschitz Continuity of Neural Networks

Lipschitz continuity is a crucial functional property of any predictive model, that naturally governs its robustness, generalisation, as well as adversarial vulnerability. Contrary to other works that focus on obtaining tighter bounds and developing different practical strategies to enforce certain Lipschitz properties, we aim to thoroughly examine and characterise the Lipschitz behaviour of Neural Networks. Thus, we carry out an empirical investigation in a range of different settings (namely, architectures, datasets, label noise, and more) by exhausting the limits of the simplest and the most general lower and upper bounds. As a highlight of this investigation, we showcase a remarkable fidelity of the lower Lipschitz bound, identify a striking Double Descent trend in both upper and lower bounds to the Lipschitz and explain the intriguing effects of label noise on function smoothness and generalisation.
Spectral Elements for Very Large Offshore Acoustic-Elastic Wave Simulations

The Spectral Element Method (SEM), widely used in engineering sciences but almost unknown in Oil&Gas exploration, is a numerical technique for large scale and multi-physics simulations. SEM is an h-p approximation of the problem solution that allows a quasi-optimal distribution of computational points on unstructured grids so as to reduce both memory usage and processing time. Thus, when accuracy is mandatory in complex 3D geometries, SEM becomes an excellent alternative to Finite Elements (FE) and Finite Differences (FD). Its implementation on the elemental domain discretization, taking advantage of data locality, presents perspectives of significant speedups on acceleration architectures. Our SEM application is designed for the simulation of acoustic/elastic waves traveling across 3D subsurface models with liquid-solid interfaces and is capable of dealing with anisotropic and non-linear solid materials. In this work, some details of our SEM implementation and its HPC infrastructure are illustrated together with some simulation results.
Simulating trait evolution for cross-cultural comparison

Cross-cultural anthropologists have increasingly used phylogenetic methods to study cultural variation. Because cultural behaviours can be transmitted horizontally among socially defined groups, however, it is important to assess whether phylogeny-based methods—which were developed to study vertically transmitted traits among biological taxa—are appropriate for studying group-level cultural variation. Here, we describe a spatially explicit simulation model that can be used to generate data with known degrees of horizontal donation. We review previous results from this model showing that horizontal transmission increases the type I error rate of phylogenetically independent contrasts in studies of correlated evolution. These conclusions apply to cases in which two traits are transmitted as a pair, but horizontal transmission may be less problematic when traits are unlinked. We also use the simulation model to investigate whether measures of homology (the consistency index and the retention index) can detect horizontal transmission of cultural traits. Higher rates of evolutionary change have a stronger depressive impact on measures of homology than higher rates of horizontal transmission; thus, low consistency or retention indices are not necessarily indicative of ‘ethnogenesis’. Collectively, these studies demonstrate the importance of using simulations to assess the validity of methods in cross-cultural research.
Big Data Mining and Analytics Big Data Mining and Analytics

: In recent years, the recommendation systems have become increasingly popular and have been used in a broad variety of applications. Here, we investigate the matrix completion techniques for the recommendation systems that are based on collaborative ﬁltering. The collaborative ﬁltering problem can be viewed as predicting the favorability of a user with respect to new items of commodities. When a rating matrix is constructed with users as rows, items as columns, and entries as ratings, the collaborative ﬁltering problem can then be modeled as a matrix completion problem by ﬁlling out the unknown elements in the rating matrix. This article presents a comprehensive survey of the matrix completion methods used in recommendation systems. We focus on the mathematical models for matrix completion and the corresponding computational algorithms as well as their characteristics and potential issues. Several applications other than the traditional user-item association prediction are also discussed.
Learning Supervised PageRank with Gradient-Free Optimization Methods

In this paper, we consider a problem of learning supervised PageRank models, which can account for some properties not considered by classical approaches such as the classical PageRank algorithm. Due to huge hidden dimension of the optimization problem we use random gradient-free methods to solve it. We prove a convergence theorem and estimate the number of arithmetic operations needed to solve it with a given accuracy. We find the best settings of the gradient-free optimization method in terms of the number of arithmetic operations needed to achieve given accuracy of the objective. In the paper, we apply our algorithm to the web page ranking problem. We consider a parametric graph model of users' behavior and evaluate web pages' relevance to queries by our algorithm. The experiments show that our optimization method outperforms the untuned gradient-free method in the ranking quality.
Semantics of programming languages - structures and techniques

Semantics of Programming Languages exposes the basic motivations and philosophy underlying the applications of semantic techniques in computer science. It introduces the mathematical theory of programming languages with an emphasis on higher-order functions and type systems. Designed as a text for upper-level and graduate-level students, the mathematically sophisticated approach will also prove useful to professionals who want an easily referenced description of fundamental results and calculi. Basic connections between computational behavior, denotational semantics, and the equational logic of functional programs are thoroughly and rigorously developed. Topics covered include models of types, operational semantics, category theory, domain theory, fixed point (denotational). semantics, full abstraction and other semantic correspondence criteria, types and evaluation, type checking and inference, parametric polymorphism, and subtyping. All topics are treated clearly and in depth, with complete proofs for the major results and numerous exercises.
On Coding Over Sliced Information

The interest in channel models in which the data is sent as an unordered set of binary strings has increased lately, due to emerging applications in DNA storage, among others. In this paper we analyze the minimal redundancy of binary codes for this channel under substitution errors, and provide a code construction for a single substitution that is shown to be asymptotically optimal up to constants. The surprising result in this paper is that while the information vector is sliced into a set of unordered strings, the amount of redundant bits that are required to correct errors is orderwise equivalent to the amount required in the classical error correcting paradigm.
An Extraction Method of Video Text in Complex Background

This paper presents a novel method to extract video caption text from background in the candidate text region. Firstly, the image of text region is binarized. Then a polarity estimation algorithm is proposed to detect the right color polarity of text. Especially, as to the hollow character, a complicated instance where text shares the same color with its background and is separated from it by stroke contours, we bring forward resolution accordingly. Finally, a connected component filtering method is employed to filter out disturbances and generate a clear binary result for recognition. Experimental results confirm that the proposed method is effective and robust.
On the local correctness of ℓ1-minimization for dictionary learning

The idea that many important classes of signals can be well-represented by linear combinations of a small set of atoms selected from a given dictionary has had dramatic impact on the theory and practice of signal processing. For practical problems in which an appropriate sparsifying dictionary is not known ahead of time, a very popular and successful heuristic is to search for a dictionary that minimizes an appropriate sparsity surrogate over a given set of sample data. While there is a body of empirical evidence suggesting this approach does learn very effective representations, there is little theoretical guarantee. In this paper, we show that under mild hypotheses, the dictionary learning problem is locally well-posed: the desired solution is indeed a local minimum of the ℓ1 norm. Namely, if A ∈ ℝm×n is an incoherent (and possibly overcomplete) dictionary, and the coefficients X ∈ ℝn×p follow a random sparse model, then with high probability (A, X) is a local minimum of the ℓ1 norm over the manifold of factorizations (A', X') satisfying A'X' = Y, provided the number of samples p = Ω(n3k). For overcomplete A, this is the first result showing that the dictionary learning problem is even locally solvable using ℓ1-minimization.
Multi-Modal Data Fusion in Enhancing Human-Machine Interaction for Robotic Applications: A Survey

—Human-machine interaction has been around for several decades now, with new applications emerging every day. One of the major goals that remain to be achieved is designing an interaction similar to how a human interacts with another human. Therefore, there is a need to develop interactive systems that could replicate a more realistic and easier human-machine interaction. On the other hand, developers and researchers need to be aware of state-of-the-art methodologies being used to achieve this goal. We present this survey to provide researchers with state-of-the-art data fusion technologies implemented using multiple inputs to accomplish a task in the robotic application domain. Moreover, the input data modalities are broadly classiﬁed into uni-modal and multi-modal systems and their application in myriad industries, including the health care industry, which contributes to the medical industry’s future development. It will help the professionals to examine patients using different modalities. The multi-modal systems are differentiated by a combination of inputs used as a single input, e.g., gestures, voice, sensor, and haptic feedback. All these inputs may or may not be fused, which provides another classiﬁcation of multi-modal systems. The survey concludes with a summary of technologies in use for multi-modal systems.
Robustly representing inferential uncertainty in deep neural networks through sampling

As deep neural networks (DNNs) are applied to increasingly challenging problems, they will need to be able to represent their own uncertainty. Modeling uncertainty is one of the key features of Bayesian methods. Using Bernoulli dropout with sampling at prediction time has recently been proposed as an efficient and well performing variational inference method for DNNs. However, sampling from other multiplicative noise based variational distributions has not been investigated in depth. We evaluated Bayesian DNNs trained with Bernoulli or Gaussian multiplicative masking of either the units (dropout) or the weights (dropconnect). We tested the calibration of the probabilistic predictions of Bayesian convolutional neural networks (CNNs) on MNIST and CIFAR-10. Sampling at prediction time increased the calibration of the DNNs' probabalistic predictions. Sampling weights, whether Gaussian or Bernoulli, led to more robust representation of uncertainty compared to sampling of units. However, using either Gaussian or Bernoulli dropout led to increased test set classification accuracy. Based on these findings we used both Bernoulli dropout and Gaussian dropconnect concurrently, which we show approximates the use of a spike-and-slab variational distribution without increasing the number of learned parameters. We found that spike-and-slab sampling had higher test set performance than Gaussian dropconnect and more robustly represented its uncertainty compared to Bernoulli dropout.
Discrete fluctuations in memory erasure without energy cost: further details

Thermodynamics with multiple-conserved quantities offers a promising direction for designing novel devices. For example, Vaccaro and Barnett's proposed information erasure scheme, where the cost of erasure is solely in terms of a conserved quantity other than energy, allows for new kinds of heat engines. In recent work, we studied the discrete fluctuations and average bounds of the erasure cost in spin angular momentum. Here we clarify the costs in terms of the spin equivalent of work, called spinlabor, and the spin equivalent of heat, called spintherm. We show that the previously-found bound on the erasure cost of $\gamma^{-1}\ln{2}$ can be violated by the spinlabor cost, and only applies to the spintherm cost. We obtain three bounds for spinlabor for different erasure protocols and determine the one that provides the tightest bound. For completeness, we derive a generalized Jarzynski equality and probability of violation which shows that for particular protocols the probability of violation can be surprisingly large. We also derive an integral fluctuation theorem and use it to analyze the cost of information erasure using a spin reservoir.
Collaboration in computer science: a network science approach. Part II

We represent collaboration of authors in computer science papers in terms of both affiliation and collaboration networks and observe how these networks evolved over time since 1960. We investigate the temporal evolution of bibliometric properties, like size of the discipline, productivity of scholars, and collaboration level in papers, as well as of large-scale network properties, like reachability and average separation distance among scientists, distribution of the number of scholar collaborators, network clustering and network assortativity by number of collaborators.
Rate-distortion function for a Heegard-Berger problem with two sources and degraded reconstruction sets

In this work, we investigate an instance of the Heegard-Berger problem with two sources and arbitrarily correlated side information at two decoders, in which the reconstruction sets at the decoders are degraded. Specifically, two sources are to be encoded in a manner that one of the two is reproduced losslessly by both decoders, and the other is reproduced to within some prescribed distortion level at one of the two decoders. We establish the optimal rate-distortion function for this model. The investigation of the result in some special cases also sheds light on the utility of joint compression of the two sources.
Controllability metrics, limitations and algorithms for complex networks

This paper studies the problem of controlling stable and symmetric complex networks, that is, the joint problem of selecting a set of control nodes and of designing a control input to drive a network to a target state. We adopt the smallest eigenvalue of the controllability Gramian as metric for the controllability degree of a network, as it identifies the energy needed to accomplish the control task. In the first part of the paper we characterize tradeoffs between the control energy and the number of control nodes, based on the network topology and weights. Our bounds show for instance that, if the number of control nodes is constant, then the control energy increases exponentially with the number of network nodes. Consequently, despite the classic controllability notion, few nodes cannot in practice arbitrarily symmetric control complex networks. In the second part of the paper we propose a distributed open-loop strategy with performance guarantees for the control of complex networks. In our strategy we select control nodes based on network partitioning, and we design the control input based on optimal and distributed control techniques. For our control strategy we show that the control energy depends on the controllability properties of the clusters and on their coupling strength, and it is independent of the network dimension.
Towards Automated Integration of Guess and Check Programs in Answer Set Programming: a Meta-interpreter and Applications towards Automated Integration of Guess and Check Programs in Answer Set Programming: a Meta-interpreter and Applications

Answer set programming (ASP) with disjunction offers a powe rful tool for declaratively representing and solving hard problems. Many NP-complete problems can be encoded in the answer set semantics of logic programs in a very concise a nd intuitive way, where the encoding reflects the typical “guess and check” nature of NP problems: The property is encoded in a way such that polynomial size certificates for it correspond to s table models of a program. However, the problem-solving capacity of full disjunctive logic progra ms (DLPs) is beyondNP, and captures a class of problems at the second level of the polynomial hiera rchy. While these problems also have a clear “guess and check” structure, an encoding in a DLP refle cting it intuitively may sometimes be a non-obvious task, in particular if the “check” itself is a co-NP-complete problem; usually, such problems are solved by interleaving separate guess and check programs, where the check is expressed by inconsistency of the check program. In this pap er, we present general transformations of head-cycle free (extended) logic programs into str atified and positive (extended) disjunctive logic programs based on meta-interpretation techniques. T he answer sets of the original and the transformed program are in simple correspondence, and, mor eove , inconsistency of the original program is indicated by a designated answer set of the transf ormed program. This enables one to integrate separate “guess” and “check” programs, which are often easy to obtain, automatically into a single disjunctive logic program. Our results complement r cent results on meta-interpretation in ASP, and extend methods and techniques for a declarative “ gu ss and check” problem solving paradigm through ASP.
Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization by

In partially observed environments, it can be useful for a human to provide the robot with declarative information that augments its direct sensory observations. For instance, given a robot on a search-and-rescue mission, a human operator might suggest locations of interest. We provide a representation for the robot’s internal knowledge that supports efficient combination of raw sensory information with high-level declarative information presented in a formal language. Computational efficiency is achieved by dynamically selecting an appropriate factoring of the belief state, combining aspects of the belief when they are correlated through information and separating them when they are not. This strategy works in open domains, in which the set of possible objects is not known in advance, and provides significant improvements in inference time, leading to more efficient planning for complex partially observable tasks. We validate our approach experimentally in two open-domain planning problems: a 2D discrete gridworld task and a 3D continuous cooking task. Thesis Supervisor: Leslie P. Kaelbling Title: Panasonic Professor of Computer Science and Engineering Thesis Supervisor: Tomás Lozano-Pérez Title: Professor of Computer Science and Engineering
Ddocent: a Radseq, Variant-calling Pipeline Designed for Population Genomics of Non- 1 Model Organisms 2

View the peer-reviewed version (peerj.com/articles/431), which is the preferred citable publication unless you specifically need to cite this preprint. pipeline designed for population genomics of non-model organisms. PeerJ ABSTRACT 14 Restriction-site associated DNA sequencing (RADseq) has become a powerful and useful 15 approach for population genomics. Currently, no software exists that utilizes both paired-end 16 reads from RADseq data to efficiently produce population-informative variant calls, 17 especially for organisms with large effective population sizes and high levels of genetic 18 polymorphism but for which no genomic resources exist. dDocent is an analysis pipeline with 19 a user-friendly, command-line interface designed to process individually barcoded RADseq 20 data (with double cut sites) into informative SNPs/INDELs for population-level analyses. The 21 pipeline, written in BASH, uses data reduction techniques and other stand-alone software 22 packages to perform quality trimming and adapter removal, de novo assembly of RAD loci, 23 read mapping, SNP and INDEL calling, and baseline data filtering. Double-digest RAD data 24 from population pairings of three different marine fishes were used to compare dDocent with 25 Stacks, the first generally available, widely used pipeline for analysis of RADseq data. 26 dDocent consistently identified more SNPs shared across greater numbers of individuals and 27 with higher levels of coverage. This is most likely due to the fact that dDocent quality trims 28 instead of filtering and incorporates both forward and reverse reads in assembly, mapping, 29 and SNP calling, thus enabling use of reads with INDEL polymorphisms. The pipeline and a 30 comprehensive user guide can be found at
An entropy admissible time splitting scheme for a conservation law model of manufacturing system

This paper deals with a splitting method applied to a conservation law model of manufacturing system incorporating yield loss. A splitting scheme has been proposed. The yield loss term is treated by solving implicitly an ordinary differential equation and the hyperbolic part is approximated by a finite volume scheme. Bounded variation stability has been studied. Due to yield loss, proposed scheme is total variation bounded. The convergence of the numerical solution towards entropy solution (in the Kruzkov sense) is proved. Numerical experiments are presented to demonstrate the performance of the scheme.
A ranking approach to global optimization

We start to show that there exists a strictly increasing function ψ : R → R such that f = ψ ◦M . To properly define this function, we first show (by contradiction) that for any pair of points (x1, x2) ∈ X 2 satisfyingM(x1) = M(x2) necessarily f(x1) = f(x2). Let (x1, x2) ∈ X 2 be a pair of points satisfying M(x1) = M(x2) and assume that f(x1) < f(x2). The equality of the rankings implies that (i) h(x1) < h(x2), (ii) M(x1) = μ({x : h(x) < h(x1)}) and (iii) M(x2) = μ({x : h(x) < h(x2)}). Putting (i), (ii) and (iii) altogether and using the continuity of the function h leads to the next contradiction: M(x1) = μ({x ∈ X : h(x) < h(x1)}) < μ({x ∈ X : h(x) < h(x2)}) = M(x2). Assuming that f(x2) < f(x1) leads to a similar contradiction. We deduce that for any (x1, x2) ∈ X 2 satisfying M(x1) = M(x2), necessarily f(x1) = f(x2). As a direct consequence, for any y ∈ Im(M), the function f is constant over the iso level setM−1(y) = {x ∈ X : M(x) = y} of the functionM . We are now ready to introduce the function ψ̄ : Im(M)→ R defined by, ψ̄ : y 7→ f(x) where x ∈M−1(y).