Miracle-dz/UniFAR-FTUs huggingface dataset:
Projective dictionary pair learning for pattern classification

Discriminative dictionary learning (DL) has been widely studied in various pattern classification problems. Most of the existing DL methods aim to learn a synthesis dictionary to represent the input signal while enforcing the representation coefficients and/or representation residual to be discriminative. However, the l0 or l1-norm sparsity constraint on the representation coefficients adopted in most DL methods makes the training and testing phases time consuming. We propose anew discriminative DL framework, namely projective dictionary pair learning (DPL), which learns a synthesis dictionary and an analysis dictionary jointly to achieve the goal of signal representation and discrimination. Compared with conventional DL methods, the proposed DPL method can not only greatly reduce the time complexity in the training and testing phases, but also lead to very competitive accuracies in a variety of visual classification tasks.
On the performance evaluation of Bayesian network classifiers in modulation identification for cooperative MIMO systems

Modulation recognition plays an important role in monitoring the intercepted signals. In this paper, we present an algorithm for modulation classification designed for cooperative MIMO system based on pattern recognition approach. This is done using higher order statistics (HOS) features and a Bayesian network classifiers. In order to evaluate the effectiveness of Bayesian network methods, a comparative study is performed between the naive Bayes using discretization (NBD), the tree augmented naive Bayes (TAN) and the decision tree (J48) classifiers. Through the receiver operating characteristics (ROC) curves, the probability of identification and the training time, we show that the NBD and the TAN classifiers achieve nearly similar performance compared to the J48 classifier. Hence, these classifiers can be used to distinguish between different M-ary shift keying linear modulation types and thus lead to better monitoring of the intercepted signals in broadband technologies with low complexity.
Obstacle Avoidance System for Ass isting Visually Impaired People

The aim of the paper is focused on the design of an obstacle avoidance system for assisting visually impaired people. A disparity map will be generated thanks to the use of a stereo camera carried by the user. Working on this map will allow to develop an algorithm for the obstacle detection in any kind of scenario. The design will be completed with the introduction of audio signal to assist the blind person to avoid obstacles. To do that, we will use the frequency of the signal to codify the obstacle’s angle and its intensity to codify proximity. Some experimental results are presented as well as the conclusions and the future works.
Towards Wide-Coverage Semantic Interpretation

Wide-coverage and robust NLP techniques always seemed to go hand in hand with shallow analyses. This was certainly true a couple of years ago, but the state-of-the-art in stochastic approaches has advanced considerably and nowadays there are sophisticated parsers available achieving high coverage and producing accurate syntactic analyses. It seems we have finally reached a stage in NLP where we can apply well known techniques of formal and computational semantics to a larger scale, and get a detailed semantic analysis from a wide-coverage parser. A proof of concept of this idea was demonstrated in [BCS04], with a coverage of over 95% on newspaper texts. In this paper we discuss the further developments in this work, generating semantic representations for sentences or small texts, showing how we can calculate background knowledge required for reasoning, and performing inferences using state-of-the-art theorem provers and model builders. The semantic representation language that we will use is a first-order language, arguing that given the current state of automated deduction, any language with more expressive power (such as second or higher-order logic) cannot be used efficiently to perform inference tasks. There are however highly sophisticated inference tools for first-order logic available which we will use in our work. Despite the tradition in formal semantics to use higher-order logics, firstorder logic is able to cover a (perhaps surprisingly) large variety of interesting natural language phenomena. The language we are going to adopt is developed in Discourse Representation Theory (DRT), closed under a translation to first-order logic, and is described in Section 2. The choice for DRT is motivated by its impressive theoretical coverage of linguistic phenomena [KR93, VdS92]. Next, of course, we need a grammar formalism suitable for computational semantics (i.e. one that is able to produce fine-grained syntactic analyses).
Joint energy and route optimization in MIMO Wireless Sensor Networks

In this paper, we study the reliable packet forwarding in Wireless Sensor Networks (WSNs) with the multiple-input multiple-output (MIMO) and orthogonal space time block codes (OSTBC) techniques. The objective is to propose a cross-layer optimized forwarding scheme to maximize the Successful Transmission Rate (STR) while satisfying the given end-to-end energy consumption constraint. The channel coding, energy allocation, and route planning are jointly considered to improve the STR. After formulating the problem as a local stochastic optimization, we propose a low-complexity distributed scheme to reduce the implementation complexity. We show by simulations that the proposed scheme can achieve much higher STR in comparison with existed energy-efficient routing protocols, in which no joint design is considered.
Building phylogenetic lexical ontologies ∗

This paper describes a new application of the theory of cladistics to automatic acquisition of lexical ontologies. In the case of life forms, those which are phylogenetically related are expected to share common properties and hence to appear in similar contexts in texts. The procedure has been tested with other words that do not refer to life beings, and a standard phylogenetic algorithm is used to learn automatically lexical ontologies from free text. The resulting ontologies are semantically coherent, as shown by comparing them with WordNet.
Private Regulation on the Environment: Bilateral Voluntary Agreements in U.S. Toxic Chemical Policy

Firms play a key role in pollution abatement and control by engaging in beyondcompliance actions without the force of law in voluntary environmental programs. This study examines the effectiveness of a bilateral voluntary agreement, one type of voluntary environmental programs, negotiated between the U.S. Environmental Protection Agency (EPA) and the pressure-treated wood industry to phase-out the use of chromated copper arsenate (CCA), a poisonous arsenic compound in residential uses. Arsenic is ranked number one on the EPA’s priority list of hazardous substances. Unlike a majority of earlier studies on voluntary programs, results show that the CCA voluntary agreement has lowered arsenic use in the U.S. to levels not seen since the 1920s. Moreover, a government-driven policy, namely the EPA’s Toxic Release Inventory, was effective in decreasing arsenic use, albeit to a lesser magnitude than the industry voluntary ban. Prioritizing environmental protection through financial resources, as measured by Congress-allocated dollars to the EPA, has also reaped environmental benefits in the form of reduced arsenic use. Finally, systematic surveys of key stakeholders provide institutional, political, and economic insights into the impact estimates of the CCA voluntary agreement on arsenic use.
Cold Side-Effect Effect: Affect Does Not Mediate the Influence of Moral Considerations in Intentionality Judgments

Research has consistently shown that people consider harmful side effects of an action more intentional than helpful side effects. This phenomenon is known as the side-effect effect (SEE), which refers to the influence of moral considerations in judgments of intentionality and other non-moral concepts. There is an ongoing debate about how to explain this asymmetric pattern of judgment and the psychological factors involved in it. It has been posited that affective reactions to agents that bring about harmful side-effects could bias intentionality attributions in these cases, explaining the asymmetric pattern of intentionality judgments that we observe in the SEE. We call this the affective bias hypothesis (ABH). Evidence for the ABH is mixed, with some findings suggesting a role for affective processes, while others suggesting that affective processes play no role in the SEE. A possible explanation for these apparently contradictory results points to affective processes involved in the SEE being confined to anger. In a series of empirical studies, we systematically measured and manipulated participants’ anger in order to test this possibility. Our findings suggest that anger play no role in intentionality judgments in SEE cases, while providing support for a non-emotional motivation to blame as a factor underlying the SEE.
Speech enhancement via sparse coding with ideal binary mask

An improved algorithm is presented for speech enhancement via sparse representation and ideal binary mask (IBM) methods. In the traditional IBM, the basic idea is to identify voiced components as target signal and label unvoiced ones as interference noise vice versa. However, such voiced and unvoiced components still cannot be well separated in target signal and interference noise. To fully exploit the merits of sparse representation theory, we extract the exact voiced component from both the above twofold to obtain the final enhanced speech. Experimental results demonstrate the proposed method can achieve higher PESQ scores than the traditional IBM to efficiently improve speech intelligibility.
Enhancing diffusion magnetic resonance images by aligning and averaging in tensor domain

One of the most effective strategies to improve image quality is the averaging of several pieces of information. The method is particularly useful if there are correlated patterns along the samples. The averaging diminish the effect of non-correlated information such as Gaussian noise for the rightness, or not perfectly overlapped data with devastating consequences on the images. As simple as it may appear initially, applying this strategy in medical images is not always straightforward. The diffusion Magnetic Resonance Imaging (dMRI) is one of those challenging cases. The dMRI presents to the operator a myriad of possibilities with countless applications. It is the preferred technique for discovering and analyzing neuronal-related pathologies. However, its potential for human assessments in the clinics has not been fully exploited due to the excessive required scanning time. This technique is also valued in the small animal modeling of neurodegenerative pathologies where several imaging sessions are performed with the aim of monitoring the progression of the maladies. But again, the produced images lack a good quality due to the high spatial resolution imposed by the dimensions of the subjects. Here, we present the theoretical framework to demonstrate that dMRI images can be quality enhanced in a post-acquisition stage. Moreover, we postulate that quality-improving efforts are better employed in the tensor field than in the commonly used raw data domain. To reinforce the claims mentioned above, two cases with real data are presented in the document.
Edge: A model of the euro area with applications to monetary policy

Mallin tarkeimpiin ominaisuuksiin kuuluvat Blanc-paaomavarallisuuden arvostaminen paaomatulojen nykyarvona, tyomarkkinoiden limittaiset Calvo-sopimukset, ja neoklassinen tarjonta Cobb-Douglas hyotyfunktiolla.Mallin ensimmainen ja toinen momentti kalibroidaan vastaamaan julkisesti saatavilla olevaa euroalueen dataa.Stokastisilla simuloinneilla tuotettu mallin ristikorrelaatiorakenne muistuttaa euroalueen datan ristikorrelaatiorakennetta.Mallin diagnostiset simulointitulokset ovat talousteoreettisesti luontevia.Heterogeenisten odotusten merkitysta rahapolitiikassa tarkastellaan erottamalla rahapolitiikkaa koskevat yksityisen sektorin ja keskuspankin odotukset toisistaan.Simulointitulokset osoittavat, etta kun yksityisen sektorin odotukset poikkeavat keskuspankin odotuksista, talouteen aiheutuu reaalisia kustannuksia.Lisaksi osoitetaan, etta nama kustannukset ovat pienemmat, jos taloudessa on jokin oppimekanismi, joka poistaa erot eri agenttien odotuksissa. Havaitaan myos, etta rahapolitiikkaan kohdistuvissa yleison odotuksissa ilmenevat satunnaiset poikkeamat, joiden tulkitaan kuvaavan hairioita rahapolitiikan lapinakyvyydessa, aiheuttavat kustannuksia vaihtelevuuden voimistumisen vuoksi.Tulokset osoittavat, etta heterogeeniset rahapolitiikkaodotukset voivat aiheuttaa merkittavia taloudellisia kustannuksia. Asiasanat: EDGE, rationaaliset odotukset, heterogeeniset odotukset, oppiminen, dynaaminen yleisen tasapainon malli
Epigenetic and Genetic Factors Predict Women's Salivary Cortisol following a Threat to the Social Self

Evidence suggests that the reactivity of the Hypothalamus-Pituitary-Adrenal axis (HPAA) is modulated by both genetic and environmental variables. Of special interest are the underlying molecular mechanisms driving gender differences to psychosocial stressors. Epigenetic mechanisms that sculpt the genome are ideal candidates for mediating the effects of signals on the HPAA. In the current study, we analyzed by pyrosequencing, bisulfite-treated buccal DNA from male and female university students who participated in the Trier Social Stress Test (TSST). A linear regression model was used to ascertain the effects of sex, CpG methylation and genes on stress response. Total cortisol output (area under the curve, AUC) was significantly predicted by glucocorticoid receptor (NR3C1) exon 1F methylation (averaged across 39 CpG sites) solely in female subjects. A single CpG site located in the exon 1F noncanonical nerve growth factor-inducible protein A (NGFI-A) transcription factor was a highly significant predictor of AUC in female subjects. Additionally, variations in the estrogen receptor alpha (ESR1) and the serotonin transporter promoter (5-HTTLPR) genes were independent additive predictors of AUC. The full model accounted for half of the variance (50.06%) in total cortisol output. Notably, this is the first demonstration that epigenetic changes at the GR exon 1F correlate with HPAA reactivity. These findings have important implications for understanding the molecular mechanisms underlying gender differences in stress-related disorders and underscore the unique value of modeling both epigenetic and genetic information in conferring vulnerability to stress.
The effects of external controls on teachers' development of technology competencies in an economically disadvantaged district

Oscar Rodriguez THE EFFECTS OF EXTERNAL CONTROLS ON TEACHERS’ DEVELOPMENT OF TECHNOLOGY COMPETENCIES IN AN ECONOMICALLY DISADVANTAGED DISTRICT 2015 Hector Rios, Ph.D. Doctor of Education Exponential growth in technological innovations has changed the dynamics of global economic competition. These changes have redefined the relationships between economy and education, which has redirected national and state interest toward the development of human capital within public schools to meet the demands of a new knowledge-based economy (Martens, Rusconi, & Leuze, 2007; Sahlberg, 2006). This study will explore the effects of external controls on education as they affect reform policies, the technical core of “teaching and learning,” and teachers’ development of technology competencies at an elementary school level within a socioeconomically disadvantaged setting. The study will attempt to determine whether externalized mechanisms of control created by federal and state policies are unintentionally hindering teachers’ technological competency development as a result of competing demands to determine whether reform policies can or are unintentionally operating as technology immobilizing agents capable of creating digital inequality (Keller & Bichelmeyer, 2004; Lawton, McKevitt, & Millar, 2000).
State equality detection for implementation-level model-checking of distributed applications

The work presented in this paper occurs in the context of validating implementations of distributed algorithms through model- checking. Among the properties that can be checked, we focus on the verification of liveness properties and the detection of acceptance cycle. We propose a new heap comparison algorithm based on a re-implementation of malloc, which reduces the amount of false negatives in the system state equality detection, thanks to system-level information analysis.
Spatial distribution of state densities dominating strain sensitivity of carbon nanotubes

In any electronic devices and sensors, internal strain is induced because of the thermal change or the lattice mismatch between different materials. It is, therefore, expected that when carbon nanotubes (CNTs) are used for electronic devices, their electronic properties are changed caused by the deformation. In this study, we study the mechanism of the change in the band gap of CNTs under the radial strain in terms of state density distribution. We found that the spatial distribution of the state density dominates its strain sensitivity, and thus, the strain sensitivity of electronic properties of CNTs. We also calculated the change in the current through the deformed CNTs. The founding indicates that the state density analysis should be useful for the development of novel electronic devices and nano electro mechanical systems and for assuring their reliable performance.
Why are U.S. Stocks More Volatile?

U.S. stocks are more volatile than stocks of similar foreign firms. A firm's stock return volatility can be higher for reasons that contribute positively (good volatility) or negatively (bad volatility) to shareholder wealth and economic growth. We find that the volatility of U.S. firms is higher mostly because of good volatility. Specifically, stock volatility is higher in the United States because it increases with investor protection, stock market development, new patents, and firm-level investment in R&D. Each of these factors is related to better growth opportunities for firms and better ability to take advantage of these opportunities.
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals

This paper formulates the search for a set of bounding boxes (as needed in object proposal generation) as a monotone submodular maximization problem over the space of all possible bounding boxes in an image. Since the number of possible bounding boxes in an image is very large O(#pixels2), even a single linear scan to perform the greedy augmentation for submodular maximization is intractable. Thus, we formulate the greedy augmentation step as a Branch-and-Bound scheme. In order to speed up repeated application of B&B, we propose a novel generalization of Minoux's 'lazy greedy' algorithm to the B&B tree. Theoretically, our proposed formulation provides a new understanding to the problem, and contains classic heuristic approaches such as Sliding Window+Non-Maximal Suppression (NMS) and and Efficient Subwindow Search (ESS) as special cases. Empirically, we show that our approach leads to a state-of-art performance on object proposal generation via a novel diversity measure.
Detecting Information Need In Large Scale Social Media Content Zhe Zhao and Caoxie

In this paper, we present the idea of detecting Information Need in large social media network. Information need is the need from people to locate and obtain specific information. Social medias can benefit the users by successfully detecting such needs. We formulate this problem as a classification problem, adopting multiple sources of feature and perform linear learning. We propose a distributed algorithm for large-scale linear classification using the alternating direction method of multipliers coupled with dual coordinate descent. Our experiments show that (1) the proposed distributed algorithm outperforms state-of-art solvers on large data sets in a distributed system (2) the proposed solution is effective in our Twitter data set.
Human Resource Information Systems: An Integrated Research Agenda

This chapter aims at setting an agenda for HRIS research from an integrative perspective. This perspective assumes that organization and information systems cannot be separated. By first elaborating on this integrated perspective in terms of a web of causes and consequences of the implementation of IT in organizations, a list of new organizational phenomena is presented. Subsequently, research on HRISs to date is summarized, resulting in the observation that HRIS research needs to be broadened and deepened. In the third section we combine the list of emerging phenomena with how HRISs are being implemented and used in mainly large global companies. We raise a number of critical questions for HRIS research per each emerging phenomena and suggest a number of appropriate research topics
Prevalence of Vitamin D Insufficiency in Qatar: a Systematic Review

Qatar has a high burden of chronic diseases including obesity, cardiovascular disease and type 2 diabetes mellitus. Low serum vitamin D levels have been implicated in the development and progression of a range of these chronic conditions. The prevalence of vitamin D insufficiency or deficiency in the general population of Qatar has still not been investigated. The aim of this study was to carry out a systematic review of published studies documenting the prevalence of vitamin D insufficiency or deficiency in the Qatari population. A search strategy was developed for online databases (PubMed, Ovid MEDLINE, Embase and Embase Classic) between 1980 to the last week of August 2012, and bibliographies of the included studies were further searched for additional reports. Search terms used were QATAR and VITAMIN D. Studies reporting the serum levels of vitamin D in several Qatari sub-populations were identified. Weighted-average vitamin D serum levels and prevalence of low vitamin D status (<75 nmol/L) were calculated. Subgroup analysis was carried out by age. The quality of each study was evaluated according to four criteria: national representativeness, representation of males and females, the sample size, and the sampling protocol. A total of 16 relevant publications were identified, and 8 of these (reporting from 7 unique studies) met our inclusion and exclusion criteria with a total number of 1,699 Qatari subjects. The pooled sample size weighted-average vitamin D concentration (±SD) was 45.3±14.3 nmol/L (95% CI: 44.6-46.0; range 29.2-66.9 nmol/L). The weighted-average prevalence of low vitamin D status was 90.4% (95% CI: 90.1-91.0; range 83%-91%). Age was inversely correlated with vitamin D levels and directly with its insufficiency/deficiency prevalence. There have only been a few studies on the prevalence of low vitamin D in Qatar a very high prevalence of vitamin D insufficiency/deficiency in Qatar that increases with age has been suggested. The present report underlines the need to develop a nationally representative study to further evaluate vitamin D status in Qatar. Given the growing evidence of the role of vitamin D in chronic disease, this study could help develop public health strategies for disease prevention in Qatar.