Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Pattern based Dependency Matching for Exact Answer Retrieval

In this paper, we retrieve exact answer from a question/answer pair based on semantic pattern and dependency matching. Question target and the weight information of main phrase are gained from semantic pattern. Candidate exact answers to a question are retrieved from its corresponding sentence-form answer by dependency relation matching. A probabilistic model, in which weight information is included, is then adopted to select the most appropriate one. The semantic correctness of selected exact answer can be validated by question target. Our experiments achieve almost an accuracy of 70% in the open domain and 80% in the medicine domain.
Solving Nonlinear SVM in Linear Time? A Nyström Approximated SVM with Applications to Image Classification

In this paper, we improve the efficiency of kernelized support vector machine (SVM) for image classification using linearized kernel data representation. Inspired by Nystrom approximation, we propose a decomposi- tion technique for converting the kernel data matrix into an approximated primal form. This allows us to apply the approximated kernelized data in the primal form of linear SVMs, and achieve comparable recogni- tion performance as nonlinear SVMs do. Several ben- efits can be observed for our proposed method. First, we advance basis matrix selection for decomposing our proposed approximation, which can be viewed as fea- ture/instance selection with performance guarantees. More importantly, the proposed selection technique sig- nificantly reduces the computation complexity for both training and testing. Therefore, the resulting computa- tion time is comparable to that of linear SVMs. Exper- iments on two benchmark image datasets will support the use of our approach for solving the tasks of image classification.
The Toxoplasma Adhesive Protein MIC2 Is Proteolytically Processed at Multiple Sites by Two Parasite-derived Proteases*

MIC2 is an adhesive protein that participates in host cell invasion by the obligate intracellular parasiteToxoplasma gondii. Earlier studies established that MIC2 is secreted into the culture medium by extracellular parasites and that release is coincident with proteolytic modification. Since little is known about proteolytic processing of proteins secreted by T. gondii, we undertook this study to investigate the proteolytic events that accompany secretion of MIC2. We demonstrate that the C-terminal domain of MIC2 is removed by a protease, termed MPP1, when MIC2 is released into the culture supernatant. Additionally, prior to release, a second protease, termed MPP2, trims the N terminus of MIC2, resulting in the release of heterogeneously sized species of MIC2. Although MPP1 activity was unaffected by any of the protease inhibitors tested, MPP2 activity was blocked by a subset of serine and cysteine protease inhibitors. These results establish that MIC2 is proteolytically modified at multiple sites by two distinct enzymes that probably operate on the parasite surface.
Finger Vein Detection using Gabor Filter, Segmentation and Matched Filter

This paper propose a method of personal identification based on finger-vein patterns. An image of a finger captured by the web camera under the IR light transmission contains not only the vein pattern itself; but also shade produced by various thickness of the finger muscles; bones; and tissue networks surrounding the vein. In this paper; we intro-duce preliminary process to enhance the image quality worsened by light effect and noise produced by the web camera; then segment the vein pattern by using adaptive threshold method and matched them using improved template matching. The main purposes of this paper are to investigate finger-vein technology; the applicable fields and whether finger-vein detection can solve the problems fingerprint detection imposes in certain industries.
Comorbidity of select anxiety and affective disorders with alcohol dependence in southwest California Indians.

BACKGROUND Native Americans, overall, have the highest prevalence of alcohol dependence of any US ethnic group. In several large national surveys, alcohol dependence has been significantly associated with higher rates of anxiety and affective disorders (comorbidity). However, the frequencies of these disorders and their comorbidity with alcohol dependence in Native American populations are relatively unknown. METHODS Demographic information and DSM-III-R diagnoses were obtained by using the Semi-Structured Assessment for the Genetics of Alcoholism developed for the Collaborative Study on the Genetics of Alcoholism from 483 Southwest California Indian adults residing on contiguous reservations. The Semi-Structured Assessment for the Genetics of Alcoholism allowed differentiation of each anxiety and affective disorder into one of three types: independent of substance use, concurrent with alcohol use, and concurrent with drug use. RESULTS Sixty-six percent of the men and 53% of the women sampled had a lifetime diagnosis of alcohol dependence. Fourteen percent of the sample had a lifetime independent anxiety disorder, and 14% of the sample had a lifetime independent affective disorder. Alcohol- and/or drug-concurrent major depression occurred in 8%, and other alcohol- and/or drug-concurrent anxiety and affective disorders each occurred in less than 1.1% of the sample. No significant comorbidity was found between alcohol dependence and independent agoraphobia, social phobia, or major depressive disorder. CONCLUSIONS In this Southwest California Indian sample, rates of anxiety and affective disorders were substantially similar to those reported in the National Comorbidity Survey; however, comorbidity of independent disorders with alcohol dependence was not as pervasive as in the National Comorbidity Survey. Rates of concurrent anxiety and affective disorders were low. These data support the hypothesis that despite high rates of alcohol dependence, Southwest California Indians do not have higher rates of anxiety and affective disorders or comorbidity of these disorders with alcohol dependence than those reported in large surveys of non-American Indian populations.
Scaling up WSD with Automatically Generated Examples

The most accurate approaches to Word Sense Disambiguation (WSD) for biomedical documents are based on supervised learning. However, these require manually labeled training examples which are expensive to create and consequently supervised WSD systems are normally limited to disambiguating a small set of ambiguous terms. An alternative approach is to create labeled training examples automatically and use them as a substitute for manually labeled ones. This paper describes a large scale WSD system based on automatically labeled examples generated using information from the UMLS Metathesaurus. The labeled examples are generated without any use of labeled training data whatsoever and is therefore completely unsupervised (unlike some previous approaches). The system is evaluated on two widely used data sets and found to outperform a state-of-the-art unsupervised approach which also uses information from the UMLS Metathesaurus.
Faults Forecasting System

This paper presents Faults Forecasting System (FFS) that utilizes statistical forecasting techniques in analyzing process variables data in order to forecast faults occurrences. FFS is proposing new idea in detecting faults. Current techniques used in faults detection are based on analyzing the current status of the system variables in order to check if the current status is fault or not. FFS is using forecasting techniques to predict future timing for faults before it happens. Proposed model is applying subset modeling strategy and Bayesian approach in order to decrease dimensionality of the process variables and improve faults forecasting accuracy. A practical experiment, designed and implemented in Okayama University, Japan, is implemented, and the comparison shows that our proposed model is showing high forecasting accuracy and BEFORE-TIME. Keywords—Bayesian Techniques, Faults Detection, Forecasting techniques, Multivariate Analysis.
Wireless optogenetic neural dust for deep brain stimulation

In recent years, numerous research efforts have been dedicated towards developing efficient implantable devices for Deep Brain Stimulation (DBS). However, there are limitations and challenges with the current technologies. Firstly, the stimulation of neurons currently is only possible through implantable electrodes which target a population of neurons. This results in challenges in the event that stimulation at the single neuron level is required. Secondly, a major hurdle still lies in developing miniature devices that can last for a lifetime in the patient's brain. Recently, the concept of neural dust has been introduced as a way to achieve single neuron monitoring and potentially actuation. In parallel to this, the field of optogenetics has emerged where the aim is to stimulate neurons using light, usually by means of optical fibers inserted through the skull. Obviously, this introduces many challenges in terms of user friendliness and biocompatibility. We address this shortcoming by proposing the wireless optogenetic neural dust (wi-opt neural dust). The wiopt neural dust is equipped with a miniature LED that is able to stimulate the genetically engineered neurons, and at the same time harvest energy from ultrasonic vibrations. The simulation results presented in the paper investigates the behaviour of the light propagation in the brain tissue, as well as the performance of designed circuitry for the energy harvesting process. The results demonstrates the feasibility of utilizing wi-opt neural dust for long term implantation in the brain, and a new direction towards precise stimulation of neurons in the cortex.
Predictors of intention to stay for employees of casual dining restaurant in Klang Valley area.

Employees are an asset to an organisation where they could be the determinant behind organisational's success or failure in an industry. In this study, the relationship between perceived organisational support (POS), perceived supervisor support (PSS), and organisational commitment (OC) with employee's intention to stay with their current jobs were studied. For that purpose, 717 questionnaires were collected among casual dining restaurants employees in Klang Valley area and analyses Pearson correlation and multiple linear regression were run by using SPSS version 21. The results suggest that POS, PSS, and OC were positively correlated with employee's intention to stay with their current job. Furthermore, OC was also found to be the most influential factor in affecting employees' staying intention. The finding is hoped to have important implications where the management can formulate strategies to retain employees in restaurant industry in Malaysia.
Learning to Rank Figures within a Biomedical Article

Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. This ever-increasing sheer volume has made it difficult for scientists to effectively and accurately access figures of their interest, the process of which is crucial for validating research facts and for formulating or testing novel research hypotheses. Current figure search applications can't fully meet this challenge as the “bag of figures” assumption doesn't take into account the relationship among figures. In our previous study, hundreds of biomedical researchers have annotated articles in which they serve as corresponding authors. They ranked each figure in their paper based on a figure's importance at their discretion, referred to as “figure ranking”. Using this collection of annotated data, we investigated computational approaches to automatically rank figures. We exploited and extended the state-of-the-art listwise learning-to-rank algorithms and developed a new supervised-learning model BioFigRank. The cross-validation results show that BioFigRank yielded the best performance compared with other state-of-the-art computational models, and the greedy feature selection can further boost the ranking performance significantly. Furthermore, we carry out the evaluation by comparing BioFigRank with three-level competitive domain-specific human experts: (1) First Author, (2) Non-Author-In-Domain-Expert who is not the author nor co-author of an article but who works in the same field of the corresponding author of the article, and (3) Non-Author-Out-Domain-Expert who is not the author nor co-author of an article and who may or may not work in the same field of the corresponding author of an article. Our results show that BioFigRank outperforms Non-Author-Out-Domain-Expert and performs as well as Non-Author-In-Domain-Expert. Although BioFigRank underperforms First Author, since most biomedical researchers are either in- or out-domain-experts for an article, we conclude that BioFigRank represents an artificial intelligence system that offers expert-level intelligence to help biomedical researchers to navigate increasingly proliferated big data efficiently.
Platonic Graph Inference

Networks are extremely useful structures that represent relationships among objects. In general, these objects share many types of relationships. People may be friends or coworkers, companies may share joint ventures or investors, and countries participate in various agreements. Constructing networks with edges represented by any one of these relationships can reveal interesting insight, but potentially ignores useful information from various other relationship networks. Can information about one formulation of a corporate network help with predictions in another? Is there a true, platonic network seed that actually captures the essence of the underlying relationships? In this paper we seek to identify procedures that identify optimal, platonic networks that merge structure from many observed instances of networks. Specifically, our goal is to identify a network of companies from the CrunchBase data that best exemplifies multiple relationships between companies. We suggest an inference procedure that can be widely used to infer platonic seeds and test Kronecker, Bernoulli and low-rank models. We evaluate their performance on synthetic data with known platonic seeds and observe that low-rank models appear to have the lowest out-of-sample reconstruction error. Applying this procedure to the CrunchBase data, we unfortunately cannot conclude that we have identified the platonic seed and hypothesize points of failure and possible improvement.
Benchmark of Nanoparticle Tracking Analysis on Measuring Nanoparticle Sizing and Concentration

One of the greatest challenges in the manufacturing and development of nanotechnologies is the requirement for robust, reliable, and accurate characterization data. Presented here are the results of an interlaboratory comparison (ILC) brought about through multiple rounds of engagement with NanoSight Malvern and ten pan-European research facilities. Following refinement of the nanoparticle tracking analysis (NTA) technique, the size and concentration characterization of nanoparticles in liquid suspension was proven to be robust and reproducible for multiple sample types in monomodal, binary, or multimodal mixtures. The limits of measurement were shown to exceed the 30–600 nm range (with all system models), with percentage coefficients of variation (% CV) being calculated as sub 5% for monodisperse samples. Particle size distributions were also improved through the incorporation of the finite track length adjustment (FTLA) algorithm, which most noticeably acts to improve the resolution of multimodal sample mixtures. The addition of a software correction to account for variations between instruments also dramatically increased the accuracy and reproducibility of concentration measurements. When combined, the advances brought about during the interlaboratory comparisons allow for the simultaneous determination of accurate and precise nanoparticle sizing and concentration data in one measurement.
Fcs-arspa'07 Joint Workshop on Foundations of Computer Security and Automated Reasoning for Security Protocol Analysis Contents Preface 5 from Securing Navigation Systems to Securing Wireless Communication through Location- Awareness Srdjan Capkun 7 Automated Security Analysis of Ad Hoc Routing Prot

Mainstream evaluation approaches in the mobile ad hoc network routing community do not provide an automated or exhaustive security analysis capability. In this paper we offer an automated process to evaluate security properties in the route discovery phase for on-demand source routing protocols. We use the SPIN model checker to exhaustively evaluate protocol abstractions against an attacker attempting to corrupt the route discovery process.
Heterogeneous Automatic MT Evaluation Through Non-Parametric Metric Combinations

Combining different metrics into a single measure of quality seems the most direct and natural way to improve over the quality of individual metrics. Recently, several approaches have been suggested (Kulesza and Shieber, 2004; Liu and Gildea, 2007; Albrecht and Hwa, 2007a). Although based on different assumptions, these approaches share the common characteristic of being parametric. Their models involve a number of parameters whose weight must be adjusted. As an alternative, in this work, we study the behaviour of non-parametric schemes, in which metrics are combined without having to adjust their relative importance. Besides, rather than limiting to the lexical dimension, we work on a wide set of metrics operating at different linguistic levels (e.g., lexical, syntactic and semantic). Experimental results show that non-parametric methods are a valid means of putting different quality dimensions together, thus tracing a possible path towards heterogeneous automatic MT evaluation.
Direct Instrumental Conditioning of Neural Activity Using Functional Magnetic Resonance Imaging-Derived Reward Feedback

Successful learning is often contingent on feedback. In instrumental conditioning, an animal or human learns to perform specific responses to obtain reward. Instrumental conditioning is often used by behavioral psychologists to train an animal (or human) to produce a desired behavior. Shaping involves reinforcing those behaviors, which in a stepwise manner are successively closer to the desired behavior until the desired behavior is reached. Here, we aimed to extend this traditional approach to directly shape neural activity instead of overt behavior. To achieve this, we scanned 22 human subjects with functional magnetic resonance imaging and performed image processing in parallel with acquisition. We delineated regions of interest (ROIs) in finger and toe motor/somatosensory regions and used an instrumental shaping procedure to induce a regionally specific increase in activity by providing an explicit monetary reward to reinforce neural activity in the target areas. After training, we found a significant and regionally specific increase in activity in the ROI being rewarded (finger or toe) and a decrease in activity in the nonrewarded region. This demonstrates that instrumental conditioning procedures can be used to directly shape neural activity, even without the production of an overt behavioral response. This procedure offers an important alternative to traditional biofeedback-based approaches and may be useful in the development of future therapies for stroke and other brain disorders.
FastYield: Variation-aware, layout-driven simultaneous binding and module selection for performance yield optimization

While technology scaling has presented many new and exciting opportunities, new design challenges have arisen due to increased density, and delay and power variations. High-level synthesis has been touted as a solution to these problems, as it can significantly reduce the number of man hours required for a design by raising the level of abstraction. In this paper, we propose a new variation-aware high-level synthesis binding/module selection algorithm, named FastYield, which takes into consideration multiplexers, functional units, registers, and interconnects. Additionally, FastYield connects with the lower levels of the design hierarchy through its inclusion of a timing driven floorplanner guided by a statistical static timing analysis (SSTA) engine which is used to modify/enhance the synthesis solution. FastYield is able to incorporate spatial correlations of process variations in its optimization, which are shown to affect performance yield. On average, FastYield achieves a clock period that is 14.5% smaller, and a performance yield gain of 78.9%, when compared to a variation-unaware algorithm. By making use of accurate timing information, FastYield's rebinding improves performance yield by an average of 9.8% over the initial binding, for the same clock period. To the best of our knowledge, this is the first high-level synthesis binding/module selection algorithm that is layout-driven and variation aware.
Branch Flow Model: Relaxations and Convexification—Part I

We propose a branch flow model for the analysis and optimization of mesh as well as radial networks. The model leads to a new approach to solving optimal power flow (OPF) that consists of two relaxation steps. The first step eliminates the voltage and current angles and the second step approximates the resulting problem by a conic program that can be solved efficiently. For radial networks, we prove that both relaxation steps are always exact, provided there are no upper bounds on loads. For mesh networks, the conic relaxation is always exact but the angle relaxation may not be exact, and we provide a simple way to determine if a relaxed solution is globally optimal. We propose convexification of mesh networks using phase shifters so that OPF for the convexified network can always be solved efficiently for an optimal solution. We prove that convexification requires phase shifters only outside a spanning tree of the network and their placement depends only on network topology, not on power flows, generation, loads, or operating constraints. Part I introduces our branch flow model, explains the two relaxation steps, and proves the conditions for exact relaxation. Part II describes convexification of mesh networks, and presents simulation results.
Query Optimization as a Datalog Program

In recent years, there has been a resurgence of interest in using Datalog and declarative programming techniques to capture a wide variety of problems: particularly those with natural recursion in their computation, and those for which it might be desirable to allow for custom modification by a sophisticated end user. One such problem is that of specifying the transformation rules and search process of a query optimizer [1]. In certain emerging domains it may be desirable to be able to customize query transformation rules, and Datalog is much more universally understood than the languages used in transformational optimizers like those generated by Volcano [2]. As part of the ASPEN project we envision future query processing architectures targeted at widely distributed (cloud or cluster) deployments, where statistics and state monitoring information are acquired from across the network, via declarative views that may integrate multiple kinds of data. When query optimization is performed over this set of views, it would be desirable to “push” part of the optimization process into the computation of the view, filtering as needed. Clearly, this is most feasible if the optimizer and views are specified in the same language, e.g., Datalog. The Evita Raced project [1], which to our knowledge is the only other attempt to build a declarative query optimizer, provided a proof-of-concept that it was feasible to build an optimizer purely in Datalog, but it left a number of open questions that must be answered in ASPEN: