Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental evaluation and cross-benchmarking of univariate real solvers

: Real solving of univariate polynomials is a fundamental problem with several important applications. This paper focuses on the eﬃcient and generic black-box implementations of state-of-the-art algorithms for isolating all real roots of polynomials with integer coeﬃcients, motivated by geometric applications and the recent need to develop software that handles exactly complex geometric objects, particularly in the case of the CGAL library. We summarize a large set of experimental results from cross-benchmarking three univariate algebraic kernels developed at the GALAAD group at INRIA, MPI-Saarbrücken, and the VEGAS group at LORIA. We have tested 6 solvers from the INRIA kernel, which are based on Sturm sequences, symbolic-numeric methods, and Continued Fractions (CF); two solvers from the MPI kernel, namely Descartes and Bitstream Descartes ; and one solver from the LORIA kernel, relying on the Descartes-based RS solver developed at the SALSA group of INRIA. We used a total of 5000 polynomials of 5 types and various degrees and bitsizes, distributed in 150 datasets. The CF family of solvers, Descartes , Bitstream Descartes , and RS are numerically and combinatorially robust, i.e. they always provide the correct results. With respect to speed, the results are not decisive in most cases; overall, two CF methods seem faster, even though currently they do not use symbolic-numeric techniques, while for very large bitsizes Bitstream Descartes and RS seem more eﬃcient, provided the degree is
Shape Constrained Kernel PDF and PMF Estimation

We consider shape constrained kernel-based probability density function (PDF) and probability mass function (PMF) estimation. Our approach is of widespread potential applicability and includes, separately or simultaneously, constraints on the PDF (PMF) function itself, its integral (sum), and derivatives (finite-differences) of any order. We also allow for pointwise upper and lower bounds (i.e., inequality constraints) on the PDF and PMF in addition to more popular equality constraints, and the approach handles a range of transformations of the PDF and PMF including, for example, logarithmic transformations (which allows for the imposition of log-concave or log-convex constraints that are popular with practitioners). Theoretical underpinnings for the procedures are provided. A simulation-based comparison of our proposed approach with those obtained using Grenander-type methods is favourable to our approach when the DGP is itself smooth. As far as we know, ours is also the only smooth framework that handles PDFs and PMFs in the presence of inequality bounds, equality constraints, and other popular constraints such as those mentioned above. An implementation in R exists that incorporates constraints such as monotonicity (both increasing and decreasing), convexity and concavity, and log-convexity and log-concavity, among others, while respecting finite-support boundaries via explicit use of boundary kernel functions.
Market-based grid resource co-allocation and reservation for applications with hard deadlines

Grid computing technology enables the creation of large-scale IT infrastructures that are shared across organizational boundaries. In such shared infrastructures, conflicts between user requirements are common and originate from the selfish actions that users perform when formulating their service requests. The introduction of economic principles in grid resource management offers a promising way of dealing with these conflicts. We develop and analyze both a centralized and a decentralized algorithm for economic grid resource management in the context of compute bound applications with deadline-based quality of service requirements and non-migratable workloads. Through the use of reservations, we co-allocate resources across multiple providers in order to ensure that applications finish within their deadline. An evaluation of both algorithms is presented and their performance in terms of realized user value is compared with an existing market-based resource management algorithm. We establish that our algorithms, which operate under a more realistic workload model, can closely approximate the performance of this algorithm. We also quantify the effect of allowing local workload preemption and different scheduling heuristics on the realized user value. Copyright © 2009 John Wiley & Sons, Ltd.
Altered Oligodendroglial and Neuroglial Gene Expression in Adult Rat Cerebral White Matter Following Short- and Long-Term Ethanol Exposures and Abbreviated Abstinence

Ethanol-mediated white matter degeneration is associated with impaired oligodendrocyte functions linked to myelin maintenance. This study examines ethanol-induced time course-dependent shifts in myelin gene expression in a chronic+binge exposure model and assesses therapeutic responses to short-term abstinence. Six-week-old male Long Evans rats were fed isocaloric liquid diets with 0% or 26% ethanol (caloric content) for 3 or 8 weeks. During the last 2 weeks of ethanol feeding, the rats were binged with 2 g/kg ethanol by intraperitoneal injection, 3x/week. In a subgroup pair-fed for 6 weeks, ethanol was tapered over 2 days and then withdrawn for 12 days. Gene expression in temporal lobe white matter was measured by qRT-PCR analysis. Chronic+binge ethanol exposures caused short-term, long-term, or step-wise changes in neuroglial gene expression such that: 1) NCAM1 mRNA levels were significantly elevated after 3 weeks (P=0.04); 2) PLP was reduced after 8 weeks (P=0.003); and 3) MAG (P=0.008) and RTN4 (P=0.02) progressively declined from 3 to 8 weeks of exposure. Abstinence did not reverse long-term ethanol effects on PLP, MAG or RTN4. Instead, it significantly reduced RPAIN (P=0.002) and Olig2 (P=0.02) relative to responses after 8 weeks of continuous ethanol exposure. In conclusion, chronic+binge ethanol exposures mainly inhibited mature myelin-associated gene expression. Short-term abstinence failed to abrogate those adverse effects of ethanol. Instead, the corresponding reductions in RPAIN and Olig2 expression suggest that during the acute phase of withdrawal or abstinence, molecular indices of white matter degeneration may worsen and that additional therapeutic measures may be required for optimum recovery.
Translating and the Computer 39

Statistical and neural approaches have permitted fast improvement in the quality of machine translation, but we are yet to discover how those technologies can best “serve translators and end users of translations” (Kenny, 2017). To address human issues in machine translation, we propose an interdisciplinary approach linking Translation Studies, Natural Language Processing and Philosophy of Cognition. Our collaborative project is a first step in connecting sound knowledge of Machine Translation (MT) systems to a reflection on their implications for the translator. It focuses on the most recent Statistical MT (SMT) and Neural MT (NMT) systems, and their impact on the translator's activity. BTEC-corpus machine translations, from in-house SMT and NMT systems, are subjected to a comparative quantitative analysis, based on BLEU, TER (Translation Edit Rate) and Meteor. Then, we qualitatively analyse translation errors from linguistic criteria (Vilar, 2006) using LIG tools, to determine for each MT system, which syntactic patterns imply translation errors and which error type is mainly made. We then assess translators’ interactions with the main error types in a short evaluati on task
Dynamics of Task Allocation Based on Game Theory in Multi-Agent Systems

Division of labor is a widely studied subject of collective behavior in natural systems. It is concerned with the question of how the regulation of the division of labor may contribute to the performance of the multi-agent system. This brief investigates the evolution of the division of labor with three strategies by employing the evolutionary game theory. Thus, these available strategies are provided for the unstructured multi-agent system: strategy A (performing task A), strategy B (performing task B), and strategy C (not performing any task but only interfere other’s actions). Depending on the parameter settings in the payoff matrix, four specific scenarios occur and the corresponding stability of the equilibrium points there are provided by calculation. Numerical calculation results are also provided for an intuitive description of the strategy evolution state. Results show that larger synergistic benefit contributes to the effective division of labor in the system, providing high benefits for the multi-agent system. These results help design a effective mechanism where self-organized division of labor occurs in the form of maximizing the benefit of the multi-agent system.
Con uence of Computer Vision and Interactive Graphics for Augmented Reality

Augmented reality (AR) is a technology in which a user's view of the real world is enhanced or augmented with additional information generated from a computer model. Using AR technology, users can interact with a combination of real and virtual objects in a natural way. This paradigm constitutes the core of a very promising new technology for many applications. However, before it can be applied successfully, AR has to ful ll very strong requirements including precise calibration, registration and tracking of sensors and objects in the scene, as well as a detailed overall understanding of the scene. At ECRC we see computer vision and image processing technology play an increasing role in acquiring appropriate sensor and scene models. To balance robustness with automation, we integrate automatic image analysis with both interactive user assistance and input from magnetic trackers and CAD-models. Also, in order to meet the requirements of the emerging global information society, future human-computer interaction will be highly collaborative and distributed. We thus conduct research pertaining to distributed and collaborative use of AR technology. We have demonstrated our work in several prototype applications, such as collaborative interior design, and collaborative mechanical repair. This paper describes our approach to AR with examples from applications, as well as the underlying technology.
On estimating the phase of a periodic waveform in additive Gaussian noise, part 1

Motivated by advances in signal processing technology that support more complex algorithms, a new look is taken at the problem of estimating the phase and other parameters of a periodic waveform in additive Gaussian noise. The general problem was introduced and the maximum a posteriori probability criterion with signal space interpretation was used to obtain the structures of optimum and some suboptimum phase estimators for known constant frequency and unknown constant phase with an a priori distribution. Optimal algorithms are obtained for some cases where the frequency is a parameterized function of time with the unknown parameters and phase having a joint a priori distribution. In the last section, the intrinsic and extrinsic geometry of hypersurfaces is introduced to provide insight to the estimation problem for the small noise and large noise cases.
NETWORK FEATURE DOMAIN FROM HIGHLY CLUTTERED NATURAL SCENES,

1. Marine megafauna are difficult to observe and count because many species travel widely 34 and spend large amounts of time submerged. As such, management programs seeking to 35 conserve these species are often hampered by limited information about population 36 levels. 37 2. Unoccupied aircraft systems (UAS, aka drones) provide a potentially useful technique for 38 assessing marine animal populations, but a central challenge lies in analyzing the vast 39 amounts of data generated in the images or video acquired during each flight. Neural 40 networks are emerging as a powerful tool for automating object detection across data 41 domains and can be applied to UAS imagery to generate new population-level insights. 42 To explore the utility of these emerging technologies in a challenging field setting, we 43 used neural networks to enumerate olive ridley turtles ( Lepidochelys olivacea ) in drone 44 images acquired during a mass-nesting event on the coast of Ostional, Costa Rica. 45 3. Results revealed substantial promise for this approach; specifically, our model detected 46 8% more turtles than manual counts while effectively reducing the manual validation 47 burden from 2,971,554 to 44,822 image windows. Our detection pipeline was trained on 48 a relatively small set of turtle examples (N=944), implying that this method can be easily 49 bootstrapped for other applications, and is practical with real-world UAS datasets. 50 4. Our findings highlight the feasibility of combining UAS and neural networks to estimate 51 population levels of diverse marine animals and suggest that the automation inherent in 52 these techniques will soon permit monitoring over spatial and temporal scales that would 53 previously have been impractical.
Adaptive Power Decoupling Strategy for Single-Phase Grid-Connected Converter

This study analyzes the power coupling in the single-phase grid-connected system and designs a novel power decoupling method for restraining the ripple voltage on the dc bus. First, the phenomena of power coupling under a non-ideal power grid, and the circuits with an active power decoupling method are derived. By introducing a ratio factor between dc bus capacitors, the decoupling ability in the proposed method can be improved. In order to achieve accurate power decoupling, a neural filter is adopted for clearly identifying the dc bus voltage level and extracting the ripple voltage on the dc bus. In addition, a total sliding-mode controller is further designed in the control strategy to enhance the system robustness with respect to varied power coupling conditions, e.g., under ideal or distorted power grids. The effectiveness of the proposed method is verified by numerical simulations and experimental results in comparison with a traditional capacitor decoupling method and an active power decoupling method with multi-proportional-resonant controllers in previous researches.
Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning

An interesting way to verify how the models are pairing complementary patterns is by fixing 32×32 samples (query samples) from one of the boundaries and recovering samples of the complementary side. As illustrated in Figure 1, one can select xr as a query sample and try to recover the top-1 xl’s, i.e., that the sample of the left boundary which minimizes the distance to the anchor in the embedding space. Figure 9 in our manuscript has shown some queries for both xr and xl restricted to one shredded document of the test collection. Here, we mixed samples from 3 documents and, similarly, show 28 query samples and their respective top-3 complementary samples (distance increasing from top to bottom).
Improved Impossible Differential Cryptanalysis on SMS4

This paper presents an improved impossible differential attack on the block cipher SMS4 which is used in WAPI (the Chinese WLAN national standard). Combining with some new observations, this paper can filter out the wrong keys more efficiently, and present an impossible differential attack on 17-round SMS4, which updates the best known impossible differential attacks on reduced SMS4.
Proceedings of the Society for Computation in Linguistics Proceedings of the Society for Computation in Linguistics

In this paper, we conduct a magnetoencephalography (MEG) lexical decision exper-iment and computationally model morphological processing in the human brain, especially the Visual Word Form Area (VWFA) in the visual ventral stream. Five neuro-computational models of morphological processing are constructed and evaluated against human neural activities: Character Markov Model and Syllable Markov Model as “amor-phous” models without morpheme units, and Morpheme Markov Model, Hidden Markov Model (HMM), and Probabilistic Context-Free Grammar (PCFG) as “morphous” models with morpheme units structured linearly or hierarchically. Our MEG experiment and computational modeling demonstrate that “mor-phous” models outperformed “amorphous” models, PCFG was most neurologically accurate among “morphous” models, and PCFG better explained nested words with non-local dependencies between preﬁxes and sufﬁxes. These results strongly suggest that morphemes are represented in the human brain and parsed into hierarchical morphological structures.
Audio analysis for surveillance applications

We proposed a time series analysis based approach for systematic choice of audio classes for detection of crimes in elevators in R. Radhakrishnan et al. (2005). Since all the different sounds in a surveillance environment cannot be anticipated, a surveillance system for event detection cannot completely rely on a supervised audio classification framework. In this paper, we propose a hybrid solution that consists two parts; one that performs unsupervised audio analysis and another that performs analysis using an audio classification framework obtained from off-line analysis and training. The proposed system is capable of detecting new kinds of suspicious audio events that occur as outliers against a background of usual activity. It adaptively learns a Gaussian mixture model (GMM) to model the background sounds and updates the model incrementally as new audio data arrives. New types of suspicious events can be detected as deviants from this usual background model. The results on elevator audio data are promising.
A Massively Scalable Architecture For Instant Messaging & Presence

In this thesis we design a scalable architecture for the instant messaging & presence service offered by Hyves. The current architecture consists of a variable number of application nodes and database slave nodes, which are located pair-wise on machines. Persistent presence information is distributed using two database master nodes. Network and database bottlenecks exist in the slave and master nodes, preventing the architecture to scale to future workloads. We have developed a modelling and analysis approach to measure scalability properties of three new architectures. The first architecture was the result of applying database partitioning to the current architecture. The other two architectures are inspired by the architectures of Facebook chat and of Windows Live Messenger, which have been published by their respective creators. The Facebook-inspired architecture aggregates presence updates to reduce the amount of internal messages, whereas the Windows Live Messenger-inspired architecture uses subscriptions to coordinate presence propagation. We use HIT to model the architectures and analyse the relation between workload and usage of databases and network links. It shows that the first architecture does not scale linearly: doubling the workload requires more than twice the number of machines. The second architecture scales sublinearly for increased workloads, but is influenced the most by changes in the total number of users or the portion of users online at peak times. The third architecture shows linear scalability, as it has a constant relation between workload and utilisation of resources. We recommend the third, subscription-based alternative as a scalable replacement for the current architecture.
An Improved Scalar Multiplication Over GF(2) for ECC

Since the introduction of public-key cryptography by Diffe and Hellman in 1976, the potential for the use of the discrete logarithm problem in public-key cryptosystems has been recognized. Although the discrete logarithm problem as first employed by Diffe and Hellman was defined explicitly as the problem of finding logarithms with respect to a generator in the multiplicative group of the integers module a prime, this idea can be extended to arbitrary groups and in particular, to elliptic curve groups. The resulting public – key systems provide relatively small block size, high speed, and high security. This paper identified an efficient performance of concurrent algorithm using complementary recoding over GF(2 ) for scalar multiplication in the polynomial basis (PB) to use in an elliptic curve cryptosystems, which enhances security. So this scheme is of less computation cost which is valuable in applications with limited memory, communications bandwidth or computing power.
Support Recovery With Sparsely Sampled Free Random Matrices

Consider a Bernoulli-Gaussian complex <i>n</i>-vector whose components are <i>V</i><sub>i</sub> = <i>X</i><sub>i</sub><i>B</i><sub>i</sub>, with <i>X</i><sub>i</sub> ~ <i>C N</i>(0, <i>P</i><sub>x</sub>) and binary <i>B</i><sub>i</sub> mutually independent and iid across <i>i</i>. This random <i>q</i>-sparse vector is multiplied by a square random matrix <b>U</b>, and a randomly chosen subset, of average size <i>n p</i>, <i>p</i> ∈ [0,1], of the resulting vector components is then observed in additive Gaussian noise. We extend the scope of conventional noisy compressive sampling models where <b>U</b> is typically a matrix with iid components, to allow <b>U</b> satisfying a certain freeness condition. This class of matrices encompasses Haar matrices and other unitarily invariant matrices. We use the replica method and the decoupling principle of Guo and Verdú, as well as a number of information-theoretic bounds, to study the input-output mutual information and the support recovery error rate in the limit of <i>n</i> → ∞. We also extend the scope of the large deviation approach of Rangan and characterize the performance of a class of estimators encompassing thresholded linear MMSE and <i>l</i><sub>1</sub> relaxation.
Building Bayesian Networks: Elicitation, Evaluation, and Learning

As a compact graphical framework for representation of multivariate probabilitydistributions, Bayesian networks are widely used for efficient reasoning underuncertainty in a variety of applications, from medical diagnosis to computertroubleshooting and airplane fault isolation. However, construction of Bayesiannetworks is often considered the main difficulty when applying this frameworkto real-world problems. In real world domains, Bayesian networks are often built by knowledge engineering approach. Unfortunately, eliciting knowledge from domain experts isa very time-consuming process, and could result in poor-quality graphicalmodels when not performed carefully. Over the last decade, the research focusis shifting more towards learning Bayesian networks from data, especially withincreasing volumes of data available in various applications, such asbiomedical, internet, and e-business, among others.Aiming at solving the bottle-neck problem of building Bayesian network models, thisresearch work focuses on elicitation, evaluation and learning Bayesiannetworks. Specifically, the contribution of this dissertation involves the research in the following five areas:a) graphical user interface tools forefficient elicitation and navigation of probability distributions, b) systematic and objective evaluation of elicitation schemes for probabilistic models, c)valid evaluation of performance robustness, i.e., sensitivity, of Bayesian networks,d) the sensitivity inequivalent characteristic of Markov equivalent networks, and the appropriateness of using sensitivity for model selection in learning Bayesian networks,e) selective refinement for learning probability parameters of Bayesian networks from limited data with availability of expert knowledge. In addition, an efficient algorithm for fast sensitivity analysis is developed based on relevance reasoning technique. The implemented algorithm runs very fast and makes d) and e) more affordable for real domain practice.