Miracle-dz/UniFAR-FTUs huggingface dataset:
Two Dimensional Generalization in Information Extraction

In a user-trained information extraction system, the cost of creating the rules for information extraction can be greatly reduced by maximizing the effectiveness of user inputs. If the user specifies one example of a desired extraction, our system automatically tries a variety of generalizations of this rule including generalizations of the terms and permutations of the ordering of significant words. Where modifications of the rules are successful, those rules are incorporated into the extraction set. The theory of such generalizations and a measure of their usefulness is described.
A Quadrupedal Locomotion Central Pattern Generator Based on Oscillatory Building Block Networks

This work presents the mimicking of the main rhythmic gait patterns of a quadrupedal animal as an illustration of a novel approach to the prediction and generation of coupled neural oscillation. Based on Scheduling by Multiple Edge Reversal (SMER), a simple though powerful distributed algorithm, it is shown how oscillatory building blocks (OBBs) can be created for the generation of complex rhythmic patterns and implemented as asymmetric Hopfield neural networks. The network is able to reproduce the different firing rates observed among pairs of biological motor neurons working during different gait patterns.
Hierarchical Accountability in Government

This paper studies a setting where a relatively uninformed voter holds a policymaker accountable through an informed intermediary. In equilibrium the voter uses the intermediary to insulate the policymaker from pandering incentives when the voter's policy expertise is low or the policymaker's congruence is high. The voter can thus enjoy the benefits of bureaucratic expertise without forfeiting electoral responsiveness. We examine the model's predictions using U.S. city-level data, and find that hierarchically-accountable managers reduce popular city employment, and adjust it more flexibly, than electorally-accountable mayors. The estimated incentive effects are smaller in cities with high voter expertise and larger during election years, and are robust to instrumentation by precipitation shocks that influenced early 20th century manager government adoptions for reasons obsolete today.
Network Analysis of Inflammatory Genes and Their Transcriptional Regulators in Coronary Artery Disease

Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD). Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting Genes) and visualized using Cytoscape v 2.8. 3. Based on betweenness centrality (BC) and node degree as key topological parameters, we identified interleukin-6 (IL-6), vascular endothelial growth factor A (VEGFA), interleukin-1 beta (IL-1B), tumor necrosis factor (TNF) and prostaglandin-endoperoxide synthase 2 (PTGS2) as hub nodes. The backbone network constructed with these five hub genes showed 111 nodes connected via 348 edges, with IL-6 having the largest degree and highest BC. Nuclear factor kappa B1 (NFKB1), signal transducer and activator of transcription 3 (STAT3) and JUN were identified as the three core transcription factors from the regulatory network derived using MatInspector. For the purpose of validation of the hub genes, 97 test networks were constructed, which revealed the accuracy of the backbone network to be 0.7763 while the frequency of the hub nodes remained largely unaltered. Pathway enrichment analysis with ClueGO, KEGG and REACTOME showed significant enrichment of six validated CAD pathways - smooth muscle cell proliferation, acute-phase response, calcidiol 1-monooxygenase activity, toll-like receptor signaling, NOD-like receptor signaling and adipocytokine signaling pathways. Experimental verification of the above findings in 64 cases and 64 controls showed increased expression of the five candidate genes and the three transcription factors in the cases relative to the controls (p<0.05). Thus, analysis of complex networks aid in the prioritization of genes and their transcriptional regulators in complex diseases.
Introducing alarms in adversarial patrolling games: extended abstract

Adversarial patrolling games (APGs) can be modeled as Stackelberg games where a patroller and an intruder compete. The former moves with the aim of detecting an intrusion, while the latter tries to intrude without being detected. In this paper, we introduce alarms in APGs, namely devices that can remotely inform the patroller about the presence of the intruder at some location. We introduce a basic model, provide an extended formulation of the problem and show how it can be cast as partially observable stochastic game. We then introduce the general resolution approach.
General Examination on Technical Area : Recognizing human activity in audio / visual scenes

Note: I provide a broad survey of Bayesian Networks, a framework which helps characterize what are generally considered HMMs, Kalman filters, and static and dynamic DBNs. There is much confusion in terminology in the literature; often these models are only slight structural variations of another, but their particular representational expressiveness and computational methods make them better suited for certain kinds of problems. I emphasize the distinctions and limitations as I understand them, and discuss known applications.
A Diet-Induced Hypercholesterolemic Murine Model to Study Atherogenesis Without Obesity and Metabolic Syndrome

Objective—Western-type high-fat/high-cholesterol diets used to induce atherogenesis in low-density lipoprotein (LDL) receptor-deficient mice also lead to obesity with concomitant metabolic complications, eg, hypertriglyceridemia, hyperinsulinemia, and insulin resistance. Our aim was to design a diet inducing atherosclerosis through moderate hypercholesterolemia without associated parameters of the metabolic syndrome. Methods and Results—Male LDL receptor-deficient mice were fed regular chow (RC; 0.01% cholesterol/4.4% fat), cholesterol-enriched regular chow (HC; 1% cholesterol/4.4% fat), or Western diet (WD 0.06% cholesterol/21% milk fat) for 28 weeks. HC-feeding led to elevated plasma (≈20.7 mmol/L [800 mg/dL]) and LDL cholesterol and accelerated atherosclerosis. Plasma triglycerides were unaffected. Compared with RC-fed controls, HC-fed mice had normal body weight gain and normal fasting levels of glucose, free fatty acids, and insulin. In contrast, WD-fed mice were extremely hypercholesterolemic (>41.4 mmol/L), obese, hypertriglyceridemic, hyperinsulinemic, insulin resistant, and showed adverse health such as skin/fur abnormalities and hepatic steatosis. Although atherosclerotic surface areas in the entire aorta were similar in HC-fed and WD-fed mice, lesions in aortic origin cross sections were significantly larger in WD-fed mice. However, morphology was similar in lesions of equal size. Conclusions—The HC diet induced moderate hypercholesterolemia and extensive atherosclerosis and should be useful to study specific aspects of atherogenesis in the absence of confounding effects of the metabolic syndrome.
The boundary conditions of the hypersensitive agency detection device: an empirical investigation of agency detection in threatening situations

ABSTRACT It has been hypothesized that humans have evolved a hypersensitivity to detect intentional agents at a perceptual level, as failing to detect these agents may potentially be more harmful than incorrectly assuming that agents are absent. Following this logic, ambiguous threatening situations should lead people to falsely detect the presence of agents. In six threat-inducing experiments (N = 233) we have investigated whether threat induction increases agent detection. We operationalized human agent detection by means of a Biological Motion Detection Task (Experiments 1 and 2) and an Auditory Agent Detection Task (Experiment 4). Intentionality detection was operationalized by means of a Geometrical Figures Task (Experiment 3). Threat manipulations that were either weak (threatening pictures, classical horror music) or moderate (virtual reality) did not increase false human agent or intentionality detection. Moreover, participants generally had a response bias towards assuming that agents were absent (Experiments 1a, 1b, 2a, 2b, and 4). Further, agent and intentionality detection measures were unrelated to individual differences in supernatural beliefs, although they were related to the negativity bias. This study reveals the boundary conditions under which the agent and intentionality detection is not intensified and provides recommendations for future research.
Modulation Spectrum Equalization for Improved Robust Speech Recognition

We propose novel approaches for equalizing the modulation spectrum for robust feature extraction in speech recognition. Common to all approaches in that the temporal trajectories of the feature parameters are first transformed into the magnitude modulation spectrum. In spectral histogram equalization (SHE) and two-band spectral histogram equalization (2B-SHE), we equalize the histogram of the modulation spectrum for each utterance to a reference histogram obtained from clean training data, or perform the equalization with two sub-bands on the modulation spectrum. In magnitude ratio equalization (MRE), we define the magnitude ratio of lower to higher modulation frequency components for each utterance, and equalize this to a reference value obtained from clean training data. These approaches can be viewed as temporal filters that are adapted to each testing utterance. Experiments performed on the Aurora 2 and 4 corpora for small and large vocabulary tasks indicate that significant performance improvements are achievable for all noise conditions. We also show that additional improvements can be obtained when these approaches are integrated with cepstral mean and variance normalization (CMVN), histogram equalization (HEQ), higher order cepstral moment normalization (HOCMN), or the advanced front-end (AFE). We analyze and discuss the reasons for these improvements from different viewpoints with different sets of data, including adaptive temporal filtering, noise behavior on the modulation spectrum, phoneme types, and modulation spectrum distance measures.
Yoga exercise increases chest wall expansion and lung volumes in young healthy Thais

Yoga has been reported to improve respiratory function in healthy and respiratory diseases. This study compared the effect of Yoga on chest wall expansion and lung volumes in young healthy Thais. Five positions of Hatha Yoga were used in the present study because of their dominant effects on chest wall function. Fifty‐eight healthy young volunteers (20.1±0.6 years) were randomly allocated into Yoga (n=29) and control (n=29). The Yoga practice was 20 min/session and 3 sessions/week for 6 weeks. The matching control subjects were designed to come to the Yoga practice unit and stayed free without Yoga exercise in a similar period. Before and after training lung expansion was measured by the tape at three levels: upper (sternal angle), middle (rib 5), and lower (rib 8). Lung volumes (tidal volume, FEV1, FEV25–75%, and FVC) were measured by spirometer. Compared to pre‐training, Yoga exercise significantly increased (p<0.05) chest wall expansion in all levels (upper 3.2±0.1 versus 4.4±0.1 cm, middle 5.0±0.1 versus 5.9±0.1 cm, lower 5.9±0.2 versus 6.8±0.1 cm), FEV1 (2.5±0.1 versus 2.8±0.1 L), FEV25–75% (4.1±0.2 versus 4.8±0.2 L/sec), and FVC (2.5±0.1 versus 2.8±0.1 L). However, tidal volume was not altered by Yoga (0.53±0.03 versus 0.55±0.03 L). The control subjects did not show any change in every parameter. The present data suggest that short‐term Yoga exercise improve respiratory breathing capacity by increasing chest wall expansion and forced expiratory lung volumes.
Characterizing and Understanding Development Sessions

The understanding of development sessions, the phases during which a developer actively modifies a software system, is a valuable asset for program comprehension, since the sessions directly impact the current state and future evolution of a software system. Such information is usually lost by state-of-the-art versioning systems, because of the checkin/checkout model they rely on: a developer must explicitly commit his changes to the repository. Since this happens in arbitrary and sometimes long intervals, recovering the changes between two commits is difficult and inaccurate, and recovering the order of the changes is impossible. We have implemented an evolution monitoring prototype which records every semantic change performed on a system, and is able to completely reconstruct development sessions. In this paper we use this fine-grained information to understand and characterize the development sessions as they were carried out on two object-oriented systems.
Threats of Botnet to Internet Security and Respective Defense Strategies

Botnets are one of the most dangerous threats to internet security. The problem of Botnet is global in nature. A botnet is a network of computers infected with a malicious program that allows cybercriminals to control the infected machines remotely without the user's knowledge. Their target is to steal valuable information from organizations as well as individuals. Botnets have become a source of income for entire groups of cybercriminals. It is necessary for the IT community to develop effective techniques for detecting and mitigating the malicious behavior of botnets. The paper presents a detailed study of technology involved in the command and control of botnets and threats posed by them. The aim of this paper is to study core details of security threats that users of Internet are facing from attackers by the use of malicious botnets. This paper also offers several strategies for defense against Botnets along with measures and activities which should be carried out in order establish successful defense.
Finding Multi-criteria Optimal Paths in Multi-modal Public Transportation Networks using the Transit Algorithm

This paper presents an algorithm to find optimal routes in a multi-modal public transportation network. The model takes into account many realistic features such as walking between multi-modal stations, transfer times, traffic days, multiple objectives and finding connections between geographical locations rather than just source and destination stations. In order to provide useful routing directions, the authors consider the robustness of the provided solutions. In addition the authors present numerous speed up techniques that reduce both the preprocessing time and storage. The preliminary experiments on the Sydney transit network are promising.
A Toolbox for Counter-Example Analysis and Optimization

Counter-examples are produced by formal verification engines to witness failures of safety properties. A counterexample is a sequence of input assignments bringing the design from the initial state into a state where some property fails. In practice, these input assignments contain redundancies. This paper focuses on methods for analyzing counter-examples to detect don’t-care, optional, and essential input assignments. The proposed analysis of counter-examples helps design debugging. Additionally, it is useful to reduce the length of counter-examples derived by random simulation and for efficient refinement in localization abstraction.
EEG coherence, joint attention and language development in the second year

Recent research has raised the hypothesis that brain maturation processes may play an important role in the linkage between infant joint attention and language development. This hypothesis was examined using EEG, joint attention and parent report language measures in a longitudinal study of 29 infants assessed at 14, 18 and 24 months of age. The results indicated that both measures of joint attention and EEG coherence at 14 months were related to language development at 24 months. Furthermore, both EEG coherence measures and joint attention measures made contributions to multiple regression equations predicting individual differences in language development. Finally, coherence data from this study were consistent with Thatcher’s (1994) theory of different patterns of neural integration and differentiation in the early maturation of the left and right hemispheres. The implications of these results for understanding the nature of the relations between joint attention and language development, as well as the utility of EEG coherence measures in developmental neuroscience are discussed.
Association between Paraoxonase 1 (PON1) Polymorphisms and the Risk of Acute Coronary Syndrome in a North African Population

The purpose of the present study was to investigate the distribution of PON1 Q192R and L55M polymorphisms and activities in a North African population and to determine their association with cardiovascular complications. The prevalence of the QQ, QR, RR, LL, LM, and MM genotypes in the study population was 55.4%, 34.09%, 9.83%, 41.97%, 48.20%, and 9.83% respectively. The Q, R, L, and M alleles had a gene frequency of 0.755, 0.245, 0.67, and 0.33, respectively. The PON1 192 RR genotype was significantly more prevalent among ACS patients than among healthy subjects. There was a 4.33-fold increase in the risk of ACS in subjects presenting the PON1 192 RR genotype compared to those with the QQ genotype (OR=4.33; 95% CI=1.27–17.7). There was a significantly different distribution of PON1 L55M in the ACS patient groups (UA, STEMI, NSTEMI). Moreover, individuals presenting the PON1 55MM genotype present a higher risk for ACS than those with LL genotype (OR=3.69; 95% CI=1.61–11.80). Paraoxonase activities were significantly lower in coronary patients than in healthy subjects. The decrease in PON1 activity was inversely correlated with the number of concomitant risk factors for CVD (r=0.57, p<0.0001). The results of the present study suggested that the PON1 R and M alleles may play a role in the pathogenesis of cardiac ischemia in our North African population and that a decrease in PON1 activity may be a valuable marker for monitoring the development of the atherosclerosis process and the associated cardiovascular complications.
Hydroclimatic modulation of diatom/Phaeocystis blooms in nutrient‐enriched Belgian coastal waters (North Sea)

Statistical analysis of 14 yr (1988–2001) of intensive phytoplankton monitoring at Station 330 in the central Belgian Coastal Zone (BCZ, Southern Bight of the North Sea) indicates that the long‐term diatom biomass trend and the spring dominance of Phaeocystis colonies over diatoms are determined by the combined effect of the North Atlantic Oscillation (NAO) and freshwater and continental nitrate carried by the Scheldt. The strong correlation between diatoms and the NAO index is largely explained by the modulating effect of the latter on the water budget at the monitoring station. The relationship between Phaeocystis spring blooms and winter NAO (NAOw) is indirect, better expressed by springtime Phaeocystis dominance over diatoms because of the higher response of the latter to the NAO. The spring Phaeocystis : diatom bloom ratio is negatively (or positively) linked to positive (or negative) NAOw values. A complex cascade of events links large‐scale NAO index variations with those local meteorological conditions (wind strength and direction, rainfall) that drive the hydrography and water budget of the BCZ. Local meteorological conditions in turn modulate the geographical spread of Scheldt nutrient loads in the coastal zone and ultimately regulate the magnitude of Phaeocystis spring blooms by determining winter nitrate enrichment. Hence, the absence of a linear relationship between Phaeocystis spring blooms and NAOw is explained by the nonlinear response of river‐based nitrate pulses to NAO due to local wind‐driven hydrodynamical forcing.
Design of vision/INS integrated navigation system in poor vision navigation environments

This paper proposes a design method for an inertial and landmark-based vision integrated navigation system in poor vision navigation environments, which have fewer visible landmarks in an area. An indirect Kalman filter with feedback structure is used in the presented navigation system. The system is designed to use focal plane measurements of landmarks instead of a vision navigation solution for effective measurement usage in poor vision environments. The proposed design is confirmed through the computer simulations and its performance is evaluated comparing the integration system which uses a vision navigation solution.
On the Evaluation of Personal Knowledge Management Solutions: Evaluating Tools of the X-COSIM Semantic Desktop

In this paper, we present preliminary work on the evaluation of tool extensions we built based on our semantic desktop framework X-COSIM. We discuss different experiments for evaluating tools of a semantic desktop and present our tool extensions COSIMail, COSIFile, and SAM. We explain in detail the setup of a laboratory study designed to gain summative results that enable a comparison of our tools to conventional desktop counterparts with respect to user effectiveness, efficiency, and satisfaction.
Latent Domain Phrase-based Models for Adaptation

Phrase-based models directly trained on mix-of-domain corpora can be sub-optimal. In this paper we equip phrase-based models with a latent domain variable and present a novel method for adapting them to an in-domain task represented by a seed corpus. We derive an EM algorithm which alternates between inducing domain-focused phrase pair estimates, and weights for mix-domain sentence pairs reflecting their relevance for the in-domain task. By embedding our latent domain phrase model in a sentence-level model and training the two in tandem, we are able to adapt all core translation components together ‐ phrase, lexical and reordering. We show experiments on weighing sentence pairs for relevance as well as adapting phrase-based models, showing significant performance improvement in both tasks.
Selection of Classifier and Feature Selection Method for Microarray Data

Micro array data have a low instance-count and high dimensionality problem which prevent classifiers from building accurate models. This may result in significantly different classification accuracies across classifiers and features chosen. Therefore it is important to select the classifier and feature selection method that perform well on a specific data set. This paper proposes a novel criterion to select the best classifier and feature selection method for a specific micro array data set. Also a novel voting strategy to use the proposed selection criterion is presented. The experimental results show that the proposed criterion and voting method substantially increase classification accuracies for micro array data sets in the experiments.