Miracle-dz/UniFAR-FTUs huggingface dataset:
Inducing a Semantically Annotated Lexicon via EM-Based Clustering

We present a technique for automatic induction of slot annotations for subcategorization frames, based on induction of hidden classes in the EM framework of statistical estimation. The models are empirically evaluated by a general decision test. Induction of slot labeling for subcategorization frames is accomplished by a further application of EM, and applied experimentally on frame observations derived from parsing large corpora. We outline an interpretation of the learned representations as theoretical-linguistic decompositional lexical entries.
Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models

This paper deals with a simplified version of the evolving Takagi-Sugeno (eTS) learning algorithm - a computationally efficient procedure for on-line learning TS type fuzzy models. It combines the concept of the scatter as a measure of data density and summarization ability of the TS rules, the use of Cauchy type antecedent membership functions, an aging indicator characterizing the stationarity of the rules, and a recursive least square algorithm to dynamically learn the structure and parameters of the eTS model
Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales

We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to five "stars"). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, "three stars" is intuitively closer to "four stars" than to "one star". We first evaluate human performance at the task. Then, we apply a meta-algorithm, based on a metric labeling formulation of the problem, that alters a given n-ary classifier's output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem.
Neuropsychology of facial expressions. The role of consciousness in processing emotional faces

Neuropsychological studies have underlined the significant presence of distinct brain correlates deputed to analyze facial expression of emotion. It was observed that some cerebral circuits were considered as specific for emotional face comprehension as a function of conscious vs. unconscious processing of emotional information. Moreover, the emotional content of faces (i.e. positive vs. negative; more or less arousing) may have an effect in activating specific cortical networks. Between the others, recent studies have explained the contribution of hemispheres in comprehending face, as a function of type of emotions (mainly related to the distinction positive vs. negative) and of specific tasks (comprehending vs. producing facial expressions). Specifically, ERPs (event-related potentials) analysis overview is proposed in order to comprehend how face may be processed by an observer and how he can make face a meaningful construct even in absence of awareness. Finally, brain oscillations is considered in order to explain the synchronization of neural populations in response to emotional faces when a conscious vs. unconscious processing is activated.
Is Quantum Bit Commitment Really Possible?

Work on quantum cryptography was started by S. J. Wiesner in a paper written in about 1970, but remained unpublished until 1983 [1]. Recently, there have been lots of renewed activities in the subject. The most wellknown application of quantum cryptography is the socalled quantum key distribution (QKD) [2–4], which is useful for making communications between two users totally unintelligible to an eavesdropper. QKD takes advantage of the uncertainty principle of quantum mechanics: Measuring a quantum system in general disturbs it. Therefore, eavesdropping on a quantum communication channel will generally leave unavoidable disturbance in the transmitted signal which can be detected by the legitimate users. Besides QKD, other quantum cryptographic protocols [5] have also been proposed. In particular, it is generally believed [4] that quantum mechanics can protect private information while it is being used for public decision. Suppose Alice has a secret x and Bob a secret y. In a “two-party secure computation” (TPSC), Alice and Bob compute a prescribed function f(x,y) in such a way that nothing about each party’s input is disclosed to the other, except for what follows logically from one’s private input and the function’s output. An example of the TPSC is the millionaires’ problem: Two persons would like to know who is richer, but neither wishes the other to know the exact amount of money he/she has. In classical cryptography, TPSC can be achieved either through trusted intermediaries or by invoking some unproven computational assumptions such as the hardness of factoring large integers. The great expectation is that quantum cryptography can get rid of those requirements and achieve the same goal using the laws of physics alone. At the heart of such optimism has been the widespread belief that unconditionally secure quantum bit commitment (QBC) schemes exist [6]. Here we put such optimism into very serious doubt by showing
Nonmonotonic probabilistic logics between model-theoretic probabilistic logic and probabilistic logic under coherence

Recently, it has been shown that probabilistic entailment under coherence is weaker than model-theoretic probabilistic entailment. Moreover, probabilistic entailment under coherence is a generalization of default entailment in System . In this paper, we continue this line of re- search by presenting probabilistic generalizations of mor e sophisticated notions of classical default entailment that lie between model-theoretic probabilisti c entailment and probabilistic entailment under coherence. That is, the new formalisms properly generalize their counterparts in classical de- fault reasoning, they are weaker than model-theoretic prob abilistic entailment, and they are stronger than probabilistic entailment under coherence. The new formalisms are useful especially for han- dling probabilistic inconsistencies related to condition ing on zero events. They can also be applied for probabilistic belief revision. More generally, in the s ame spirit as a similar previous paper, this paper sheds light on exciting new formalisms for probabilistic reasoning beyond the well-known standard ones.
A Near-Optimal Algorithm for L1-Difference

We give the first L_1-sketching algorithm for integer vectors which produces nearly optimal sized sketches in nearly linear time. This answers the first open problem in the list of open problems from the 2006 IITK Workshop on Algorithms for Data Streams. Specifically, suppose Alice receives a vector x in {-M,. ,M}^n and Bob receives y in {-M,. ,M}^n, and the two parties share randomness. Each party must output a short sketch of their vector such that a third party can later quickly recover a (1 +/- eps)-approximation to ||x-y||_1 with 2/3 probability given only the sketches. We give a sketching algorithm which produces O(eps^{-2}log(1/eps)log(nM))-bit sketches in O(n*log^2(nM)) time, independent of eps. The previous best known sketching algorithm for L_1 is due to [Feigenbaum et al., SICOMP 2002], which achieved the optimal sketch length of O(eps^{-2}log(nM)) bits but had a running time of O(n*log(nM)/eps^2). Notice that our running time is near-linear for every eps, whereas for sufficiently small values of eps, the running time of the previous algorithm can be as large as quadratic. Like their algorithm, our sketching procedure also yields a small-space, one-pass streaming algorithm which works even if the entries of x,y are given in arbitrary order.
Implementation of repeat HIV testing during pregnancy in southwestern Kenya: progress and missed opportunities

Repeat HIV testing during the late antenatal period is crucial to identify and initiate treatment for pregnant women with incident HIV infection to prevent perinatal HIV transmission and keep mothers alive. In 2012, the Kenya Ministry of Health adopted international guidelines suggesting that pregnant women be offered retesting three months after an initial negative HIV test. Our objectives were to determine the current rate of antenatal repeat HIV testing; identify successes, missed opportunities and factors associated with retesting; and estimate the incidence of HIV during pregnancy.
A Computational Study of Rotating Spiral Waves and Spatio-Temporal Transient Chaos in a Deterministic Three-Level Active System

Spatio-temporal dynamics of a deterministic three-level cellular automaton (TLCA) of Zykov-Mikhailov type (Sov. Phys. - Dokl., 1986, Vol.31, No.1, P.51) is studied numerically. Evolution of spatial structures is investigated both for the original Zykov-Mikhailov model (which is applicable to, for example, Belousov-Zhabotinskii chemical reactions) and for proposed by us TLCA, which is a generalization of Zykov-Mikhailov model for the case of two-channel diffusion. Such the TLCA is a minimal model for an excitable medium of microwave phonon laser, called phaser (D. N. Makovetskii, Tech. Phys., 2004, Vol.49, No.2, P.224; cond-mat/0402640). The most interesting observed forms of TLCA dynamics are as follows: (a) spatio-temporal transient chaos in form of highly bottlenecked collective evolution of excitations by rotating spiral waves (RSW) with variable topological charges; (b) competition of left-handed and right-handed RSW with unexpected features, including self-induced alteration of integral effective topological charge; (c) transient chimera states, i.e. coexistence of regular and chaotic domains in TLCA patterns; (d) branching of TLCA states with different symmetry which may lead to full restoring of symmetry of imperfect starting pattern. Phenomena (a) and (c) are directly related to phaser dynamics features observed earlier in real experiments at liquid helium temperatures on corundum crystals doped by iron-group ions. ACM: F.1. 1, I.6, J.2; PACS:05.65. +b, 07.05. Tp, 82.20. Wt
Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications

The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
BOSAM: A Tool for Visualizing Topological Structures Based on the Bitmap of Sorted Adjacent Matrix

Though the adjacent matrix is a representation of a topology, it does not tell the topological structures explicitly. A visualization tool which can represent topological structures explicitly is desired for model validation or measurements comparison. However, displaying nodes and links directly in a space as a routine is ineffective for massive topologies, like that of Internet. Instead, researchers have recourse to various statistical metrics extracted from the adjacent matrix. Such a statistical metric often reflects only a single aspect of the topology but at the cost of burdensome computation. In this paper a novel simple visualization tool based on displaying the Bitmap of Sorted Adjacent Matrix, BOSAM, is proposed to fill the gap. Validations of AS-level topology models and comparisons of topology measurements are provided to show that BOSAM works efficiently. The bitmap displaying can avoid messy cross lines and make the correlation between nodes stand out. The rules of sorting node indexes can be defined flexibly to expose diverse topological structures. Moreover, some fractal features in the AS graphs or generated graphs, which are not easily to be covered by statistical analysis, can be exposed explicitly as well. Parting from the huge burden of statistical computation, BOSAM can be realized easily.
Defensive forecasting for optimal prediction with expert advice

The method of defensive forecasting is applied to the problem of prediction with expert advice for binary outcomes. It turns out that defensive forecasting is not only competitive with the Aggregating Algorithm but also handles the case of “second-guessing” experts, whose advice depends on the learner’s prediction; this paper assumes that the dependence on the learner’s prediction is continuous.
Robust Subspace Clustering

Subspace clustering refers to the task of nding a multi-subspace representation that best ts a collection of points taken from a high-dimensional space. This paper introduces an algorithm inspired by sparse subspace clustering (SSC) (25) to cluster noisy data, and develops some novel theory demonstrating its correctness. In particular, the theory uses ideas from geometric functional analysis to show that the algorithm can accurately recover the underlying subspaces under minimal requirements on their orientation, and on the number of samples per subspace. Synthetic as well as real data experiments complement our theoretical study, illustrating our approach and demonstrating its eectiveness.
Impact of seafood regulations for Vibrio parahaemolyticus infection and verification by analyses of seafood contamination and infection

SUMMARY Consumption of seafood contaminated with Vibrio parahaemolyticus causes foodborne infections, which are on the rise owing to increased consumption of raw seafood in Asia, Europe, North America, and other regions. V. parahaemolyticus infections have been common in Japan since the 1960s. Following an epidemic in 1997, the Japanese Ministry of Health, Labour, and Welfare instituted regulations for seafood in 1999, which appear to be reducing V. parahaemolyticus infections. In this review, we describe the scientific findings for these regulations. Analyses of the V. parahaemolyticus serotypes and isolate characteristics in samples from infected patients and contaminated seafood are discussed. In addition, based on the results of a survey, we show that new food safety regulations have led to improvements in food hygiene at many seafood retail shops, food service facilities, and restaurants. This example from Japan could be of immense help to control foodborne infections in other countries.
Semicontinuity properties of Kazhdan-Lusztig cells

Computations in small Coxeter groups or dihedral groups suggest that the partition into Kazhdan-Lusztig cells with unequal parameters should obey to some semicontinuity phenomenon (as the parameters vary). The aim of this paper is to provide a rigorous theoretical background for supporting this intuition that will allow to state several precise conjectures.
Mining frequent itemsets from secondary memory

Mining frequent itemsets is at the core of mining association rules, and is by now quite well understood algorithmically for main memory databases. In this paper, we investigate approaches to mining frequent itemsets when the database or the data structures used in the mining are too large to fit in main memory. Experimental results show that our techniques reduce the required disk accesses by orders of magnitude, and enable truly scalable data mining.
Designing and mining multi-terabyte astronomy archives: the Sloan Digital Sky Survey

The next-generation astronomy digital archives will cover most of the sky at fine resolution in many wavelengths, from X-rays, through ultraviolet, optical, and infrared. The archives will be stored at diverse geographical locations. One of the first of these projects, the Sloan Digital Sky Survey (SDSS) is creating a 5-wavelength catalog over 10,000 square degrees of the sky (see http://www.sdss.org/). The 200 million objects in the multi-terabyte database will have mostly numerical attributes in a 100+ dimensional space. Points in this space have highly correlated distributions. The archive will enable astronomers to explore the data interactively. Data access will be aided by multidimensional spatial and attribute indices. The data will be partitioned in many ways. Small tag objects consisting of the most popular attributes will accelerate frequent searches. Splitting the data among multiple servers will allow parallel, scalable I/O and parallel data analysis. Hashing techniques will allow efficient clustering, and pair-wise comparison algorithms that should parallelize nicely. Randomly sampled subsets will allow de-bugging otherwise large queries at the desktop. Central servers will operate a data pump to support sweep searches touching most of the data. The anticipated queries will require special operators related to angular distances and complex similarity tests of object properties, like shapes, colors, velocity vectors, or temporal behaviors. These issues pose interesting data management challenges.
Prevalence and dynamics of the K65R drug resistance mutation in HIV-1-infected infants exposed to maternal therapy with lamivudine, zidovudine and either nevirapine or nelfinavir in breast milk.

BACKGROUND K65R is a relatively rare drug resistance mutation (DRM) selected by the NRTIs tenofovir, didanosine, abacavir and stavudine and confers cross-resistance to all NRTIs except zidovudine. Selection by other NRTIs is uncommon. OBJECTIVES In this study we investigated the frequency of emergence of the K65R mutation and factors associated with it in HIV-1-infected infants exposed to low doses of maternal lamivudine, zidovudine and either nevirapine or nelfinavir ingested through breast milk, using specimens collected from the Kisumu Breastfeeding Study. METHODS Plasma specimens with viral load ≥1000 copies/mL collected from HIV-infected infants at 0-1, 2, 6, 14, 24 and 36 weeks of age and maternal samples at delivery were tested for HIV drug resistance using Sanger sequencing of the polymerase gene. Factors associated with K65R emergence were assessed using Fisher's exact test and the Wilcoxon rank-sum test. RESULTS K65R was detected in samples from 6 of the 24 infants (25%) who acquired HIV-1 infection by the age of 6 months. K65R emerged in half of the infants by 6 weeks and in the rest by 14 weeks of age. None of the mothers at delivery or the infants with a positive genotype at first time of positivity had the K65R mutation. Infants with K65R had low baseline CD4 cell counts (P = 0.014), were more likely to have DRMs earlier (≤6 weeks versus ≥14 weeks, P = 0.007) and were more likely to have multiclass drug resistance (P = 0.035). M184V was the most common mutation associated with K65R emergence. K65R had reverted by 3 months after cessation of breastfeeding. CONCLUSIONS A high rate of K65R emergence may suggest that ingesting low doses of lamivudine via breast milk could select for this mutation. The presence of this mutation may have a negative impact on future responses to NRTI-based ART. More in vitro studies are, however, needed to establish the molecular mechanism for this selection.
Multi-Channel Speech Recognition : LSTMs All the Way Through

Long Short-Term Memory recurrent neural networks (LSTMs) have demonstrable advantages on a variety of sequential learning tasks. In this paper we demonstrate an LSTM “triple threat” system for speech recognition, where LSTMs drive the three main subsystems: microphone array processing, acoustic modeling, and language modeling. This LSTM trifecta is applied to the CHiME-4 distant recognition challenge. Our previous state-of-the-art ASR systems for the previous CHiME challenge employed LSTM mask estimation based beamforming, noise robust features, in addition to DNN/RNNLM based back end. The proposed system refines each module of the previous system including bidirectional LSTM (BLSTM) mask estimation based beamforming, BLSTM-DNN hybrid acoustic model, and language model rescoring based on LSTM. We perform constrained re-estimation based speaker adaptation, and also prepare several complementary systems by changing the beamforming strategy and the acoustic model configurations, and combine these systems based on word-posterior based system combination. The final system achieved 2.98% WER for the real test set in the 6-channel track, which reduces the WER from the baseline by 8.5% absolute, and also outperforms our previous CHiME-3 system by 6.1% absolutely.
Mapping of Rat Brain Using the Synuclein‐1 Monoclonal Antibody Reveals Somatodendritic Expression of α‐Synuclein in Populations of Neurons Homologous to those Vulnerable to Lewy Body Formation in Human Synucleopathies

The neuronal protein α-synuclein has been implicated in the pathogenesis of Parkinson disease and other neurodegenerative diseases. Although many studies report that α-synuclein expression is restricted to neuronal presynaptic terminals, this protein aggregates in Lewy bodies in somata that are typically distant from their axon terminals. Few studies have addressed this paradox and there has been no compelling explanation proposed for the apparent discrepancy between the locus of neuronal α-synuclein expression and the loci of Lewy bodies in the majority of Parkinson disease cases. We explored this issue by extensively characterizing the monoclonal antibody Synuclein-1 (Syn-1) and using this highly selective antibody to map the distribution of α-synuclein throughout rat brain and in human substantia nigra (SN). Additionally, α-synuclein expression in rat SN detected by 2 polyclonal antibodies against α-synuclein was compared with that detected by the Syn-1 antibody. In contrast with many previous reports, α-synuclein was detected by Syn-1 in neuronal somata and dendrites in restricted brain regions, as well as more ubiquitously in axons and terminals. The strongest α-synuclein neuronal expression in rat was found in brainstem and cortical regions that are homologous to regions prone to Lewy body formation in humans. The Syn-1 antibody labeled abundant somatodendritic α-synuclein in both rat and human SN, a major locus of Lewy body formation and neurodegeneration in Parkinson disease. By contrast, very few immunoreactive somata in the rat SN were labeled by the 2 polyclonal antibodies. We explore possible explanations for the differences in conflicting reports of patterns of α-synuclein expression in brain, including differences among antibodies.