Miracle-dz/UniFAR-FTUs huggingface dataset:
Defining Affinity with the GABAA Receptor

At nicotinic and glutamatergic synapses, the duration of the postsynaptic response depends on the affinity of the receptor for transmitter (Colquhoun et al., 1977; Pan et al., 1993). Affinity is often thought to be determined by the ligand unbinding rate, whereas the binding rate is assumed to be diffusion-limited. In this view, the receptor selects for those ligands that form a stable complex on binding, but binding is uniformly fast and does not itself affect selectivity. We tested these assumptions for the GABAAreceptor by dissecting the contributions of microscopic binding and unbinding kinetics for agonists of equal efficacy but of widely differing affinities. Agonist pulses applied to outside-out patches of cultured rat hippocampal neurons revealed that agonist unbinding rates could not account for affinity if diffusion-limited binding was assumed. However, direct measurement of the instantaneous competition between agonists and a competitive antagonist revealed that binding rates were orders of magnitude slower than expected for free diffusion, being more steeply correlated with affinity than were the unbinding rates. The deviation from diffusion-limited binding indicates that a ligand-specific energy barrier between the unbound and bound states determines GABAA receptor selectivity. This barrier and our kinetic observations can be quantitatively modeled by requiring the participation of movable elements within a flexible GABA binding site.
DIALOGUE ACT RECOGNITION IN ESTONIAN DIALOGUES USING ARTIFICIAL NEURAL NETWORKS

This paper describes two experiments of applying dialogue act recognition to estonian dialogues. Two class systems were used in both of them—a general one (with 19 classes) and a detailed one (with 107 classes). In the first experiment the task was performed using learning vector quantization (LVQ). The preprocessing was done in WEBSOM (Self-Organizing Maps for Internet Exploration) style; the used method was originally designed for processing documents with self-organizing maps, with which LVQ shares its principle. The weighing method was tf ×idf , which is the most popular method for term weight assignment. The first experiment wasn’t a success. The proposed explanation is that due to the difference between utterances and text documents the combination of LVQ and the used preprocessing method were too straightforward for this task, and a more sofisticated classifier was to be tested. In the second experiment multilayer perceptron (MLP) techniques were applied to data, preprocessed in the same way as in the first experiment. The tested networks had one and two hidden layers. The experiment ended successfully; networks with two hidden layers had shown the highest accuracies with both class systems.
Temporal Discourse Models for Narrative Structure

Getting a machine to understand human narratives has been a classic challenge for NLP and AI. This paper proposes a new representation for the temporal structure of narratives. The representation is parsimonious, using temporal relations as surrogates for discourse relations. The narrative models, called Temporal Discourse Models, are tree-structured, where nodes include abstract events interpreted as pairs of time points and where the dominance relation is expressed by temporal inclusion. Annotation examples and challenges are discussed, along with a report on progress to date in creating annotated corpora.
SDDC: A Software Defined Datacenter Experimental Framework

The rapid growth and the distributed sites of the datacenters increase the complexity of control and management processes. A new paradigm which is called Software Defined Systems (SDSys) comes as a solution to reduce the overhead of datacenters management by abstracting all the control functionalities from the hardware devices and setting it inside a software layer. These functionalities are responsible for organizing and controlling the main blocks of the datacenter; network, storage, compute and security. The Software Defined Datacenter (SDD) integrates the software defined concepts into all of these main blocks. Transferring these concepts into workable systems requires checking several performance aspects and testing its correctness before building real systems. In this paper we introduce a novel experimental framework (SDDC) to provide a novel virtualized testbed environment for SDD systems. This work builds on the Mininet simulator, where its core components, the host, the switch and the controller, are customized to build the proposed experimental simulation framework for SDD. This simulator lets the users develop and test their own SDD solutions, and at the same time gives the researchers an experimentation tool for benchmarking purposes. The developed simulator could also be used as an educational tool to train students and novice researchers.
Progress in the BBN keyword search system for the DARPA RATS program

This paper presents a set of techniques that we used to improve our keyword search system for the third phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded radio communication channels. The results for both Levantine and Farsi, which are the two target languages for the keyword search (KWS) task, are reported. About 13% absolute reduction in word error rate (from 70.2% to 57.6%) is achieved by using acoustic features derived from stacked Multi-Layer Perceptrons (MLP) and Deep Neural Network (DNN) acoustic models. In addition to score normalization and score/system combination for keyword search, we showed that the false alarm rate at the target false reject rate (15%) was reduced by about 1% (from 5.39% to 4.45%) by reducing the deletion errors of the speech-to-text system. Index Terms: speech recognition, KWS, MLP, DNN
Social Functioning in Nonverbal Learning Disorder and High Functioning Autism: A Pilot Study

The variant of autism spectrum disorder (ASD) in which there is no evidence of a comorbid intellectual disability (often informally referred to as high functioning autism [HFA]) and nonverbal learning disorder (NLD) are two clinical disorders associated with social difficulties. Whereas social functions have been studied extensively in ASD, relatively little research has investigated social functioning in NLD. As such, it is unclear whether the pattern and severity of social functioning difficulties is different between these disorders. Furthermore, it is often challenging to establish a differential diagnosis between these disorders, which is critical given the need for different treatment approaches. The present study aimed to investigate parents‘ perceptions of social adjustment in these groups, as well as to identify reliable differences in pragmatic communication, social motivation, and other aspects of social functioning based on behaviour inventories and standardized direct observation of social behaviour. Twentytwo participants (10 in the NLD group and 12 in the HFA group) between the ages of 9 and 17 years were recruited from Southwestern Ontario. The results indicated that overall both groups are characterized by social difficulties, with those of children in the HFA group tending to be more severe. The mean scores of each group were elevated in the AtRisk range on the Behavioral Symptoms Index composite of the BASC-2, indicating some social adjustment difficulties in both groups. The Social Interaction Difference Index (SIDI) from the Children‘s Communication Checklist-2 (CCC-2) and the Overall Total score from Module 3 of the Autism Diagnostic Observation Schedule-2 (ADOS-2) each significantly discriminated between the groups. The Reduced Contact and Social Interest subscale of the Children‘s Social Behaviour Questionnaire Revised (CSBQR) did SOCIAL FUNCTIONING IN NLD AND HFA v not significantly predict group membership, and both groups were reported to have low frequency of interacting with peers. The present findings provide preliminary support that children in both groups experience social adjustment difficulties, pragmatic language difficulties, as well as reduced social interest in others, and preliminary evidence regarding the clinical usefulness of the CCC-2 and the ADOS-2 in the context of establishing a differential diagnosis between HFA and NLD. SOCIAL FUNCTIONING IN NLD AND HFA vi
HeisenBase : Understanding Database Performance in Haskell Dept . of CIS-Senior Design 2012-2013

This work aims to develop a performant relational database management system, HeisenBase, in the functional programming language Haskell. Much work has been published with the intent to create sensible interaction layers between the Haskell programming language and existing database software. We build the DBMS itself in Haskell in order to understand the benefits and drawbacks of a Haskell-based implementation. Our research has led to a proof-of-concept DBMS implemented in Haskell. This DBMS supports basic relational queries and joins over data tables. We present metrics investigating the unique performance characteristics of the system, and analysis of the technology we developed.
Statistical significance of Az scores: classification of masses in screening mammograms as benign or malignant based on high dimensional texture feature space

In order to develop a method for classifying masses in digitised screening mammograms as benign or malignant, 260 image texture features were measured on 43 images of known malignant masses and 28 images of known benign masses. A genetic algorithm was used to select the optimal subset of features based on scores where is a natural number. The leave-one-out score for the optimal features ranges from 0.80 to 0.95 for . Since feature space reduction can result in optimistic estimates of classifier performance, the statistical significance of these scores were estimated by computing the empirical distribution of scores in the context of the experimental parameters. For , the scores were found to be significant at the " " % level.
Exploring Community College Students' Transfer Experiences and Perceptions and What They Believe Administration Can Do to Improve Their Experiences

Abstract This study explored the interconnectedness of the transfer student experience and used it as a foundational framework to inform the administrative leadership on a North Carolina, midsized, historically Black college of the existing transfer students' experiences. This study focused on (a) how transfer students at a midsized historically black college and university (HBCU) describe their experiences, (b) how transfer students described the factors that affected their retention, and (c) what transfer students' believed that university administrative leadership could do to ensure their retention. Twelve transferred students participated in the study. The findings from the qualitative analysis show that (a) students are aware of the differences that exist between themselves and native students and desire for administration to recognize those differences, (b) they desire more resources tailored to their needs as transfer students, and (c) they have substantial academic and non-academic barriers that need to be recognized by those who regularly come into contact with the transfer population.
Inductive Logic Boosting

Recent years have seen a surge of interest in Probabilistic Logic Programming (PLP) and Statistical Relational Learning (SRL) models that combine logic with probabilities. Structure learning of these systems is an intersection area of Inductive Logic Programming (ILP) and statistical learning (SL). However, ILP cannot deal with probabilities, SL cannot model relational hypothesis. The biggest challenge of integrating these two machine learning frameworks is how to estimate the probability of a logic clause only from the observation of grounded logic atoms. Many current methods models a joint probability by representing clause as graphical model and literals as vertices in it. This model is still too complicate and only can be approximate by pseudo-likelihood. We propose Inductive Logic Boosting framework to transform the relational dataset into a feature-based dataset, induces logic rules by boosting Problog Rule Trees and relaxes the independence constraint of pseudo-likelihood. Experimental evaluation on benchmark datasets demonstrates that the AUC-PR and AUC-ROC value of ILP learned rules are higher than current state-of-the-art SRL methods.
Reference Speaker Weighting Adaptation for Sub-Phonetic Polynomial Segment Models

Speaker adaptation has been widely used in speech recognition. With small amount of adaptation data, reference speaker weighting (RSW) adaptation was previously proposed for fast HMM adaptation, and has been shown to outperform the more commonly used maximum likelihood linear regression (MLLR) adaptation. Extending our previous work of applying the polynomial segment models (PSMs) in large vocabulary continuous speech recognition (LVCSR) on the WSJ Nov 92 evaluation, we derive the PSM-based RSW fast adaptation technique in this paper. Different from the HMMs, in which the model means are constants within a state, the PSM means are curves represented by polynomials. Experimental results showed that the PSM-based RSW gave approximately the same relative improvement over the unadapted model as in the HMM case. Comparing the PSM-based RSW and MLLR, the PSM-based RSW is more powerful when the amount of adaptation data available is limited. However, it could quickly saturate with increase in adaptation data
A Hybrid Approach to Word

This article presents a combination of unsupervised and supervised learning techniques for generation of word segmentation rules from a list of words. First, a bias for word segmentation is introduced and a simple genetic algorithm is used for the search of segmentation that corresponds to the best bias value. In the second phase, the seg-mentation obtained from the genetic algorithm is used as an input for two inductive logic programming algorithms, namely Foidl and Clog. The result is a logic program that can be used for segmentation of unseen words. The learnt program contains aaxes which are characteristic for the given language and can be used in other morphology tasks.
Surprise: user-controlled granular privacy and security for personal data in SmarterContext

The Smart Internet relies on the exploitation of information obtained from interactions of users with web applications. A critical aspect for its success is the adoption of mechanisms that guarantee the protection of information sensitive to users. This paper presents Surprise, our solution to empower users with privacy and data security control for the access to their information, stored in Personal Context Sphere repositories. These repositories are defined and maintained by SmarterContext, our Smart Internet infrastructure that improves the quality of user experience in their interactions with web applications. Surprise (i) allows users to configure access permissions to their sensitive personal information to third parties, selectively and with different levels of granularity; (ii) supports changes in these configurations at runtime to add or remove third parties or permissions, and (iii) realizes partial encryption to share non-sensitive data with not explicitly authorized third parties, while protecting user identity. We analyze details of our proof-of-concept implementation with respect to the SmarterContext's privacy and security requirements.
A Modular Non-Rigid Calabi-Yau Threefold

We construct an algebraic variety by resolving singularities of a quintic Calabi-Yau threefold. The middle cohomology of the threefold is shown to contain a piece coming from a pair of elliptic surfaces. The resulting quotient is a two-dimensional Galois representation. By using the Lefschetz fixed-point theorem in etale cohomology and counting points on the variety over finite fields, this Galois representation is shown to be modular.
Gender Differences in Emergency Department Visits and Detox Referrals for Illicit and Nonmedical Use of Opioids

Introduction Visits to the emergency department (ED) for use of illicit drugs and opioids have increased in the past decade. In the ED, little is known about how gender may play a role in drug-related visits and referrals to treatment. This study performs gender-based comparison analyses of drug-related ED visits nationwide. Methods We performed a cross-sectional analysis with data collected from 2004 to 2011 by the Drug Abuse Warning Network (DAWN). All data were coded to capture major drug categories and opioids. We used logistic regression models to find associations between gender and odds of referral to treatment programs. A second set of models were controlled for patient “seeking detox,” or patient explicitly requesting for detox referral. Results Of the 27.9 million ED visits related to drug use in the DAWN database, visits by men were 2.69 times more likely to involve illicit drugs than visits by women (95% CI [2.56, 2.80]). Men were more likely than women to be referred to detox programs for any illicit drugs (OR 1.12, 95% CI [1.02–1.22]), for each of the major illicit drugs (e.g., cocaine: OR 1.27, 95% CI [1.15–1.40]), and for prescription opioids (OR 1.30, 95% CI [1.17–1.43]). This significant association prevailed after controlling for “seeking detox. ” Conclusion Women are less likely to receive referrals to detox programs than men when presenting to the ED regardless of whether they are “seeking detox. ” Future research may help determine the cause for this gender-based difference and its significance for healthcare costs and health outcomes.
Brain BLAQ: Post-hoc thick-section histochemistry for localizing optogenetic constructs in neurons and their distal terminals

Optogenetic constructs have revolutionized modern neuroscience, but the ability to accurately and efficiently assess their expression in the brain and associate it with prior functional measures remains a challenge. High-resolution imaging of thick, fixed brain sections would make such post-hoc assessment and association possible; however, thick sections often display autofluorescence that limits their compatibility with fluorescence microscopy. We describe and evaluate a method we call “Brain BLAQ” (Block Lipids and Aldehyde Quench) to rapidly reduce autofluorescence in thick brain sections, enabling efficient axon-level imaging of neurons and their processes in conventional tissue preparations using standard epifluorescence microscopy. Following viral-mediated transduction of optogenetic constructs and fluorescent proteins in mouse cortical pyramidal and dopaminergic neurons, we used BLAQ to assess innervation patterns in the striatum, a region in which autofluorescence often obscures the imaging of fine neural processes. After BLAQ treatment of 250–350 μm-thick brain sections, axons and puncta of labeled afferents were visible throughout the striatum using a standard epifluorescence stereomicroscope. BLAQ histochemistry confirmed that motor cortex (M1) projections preferentially innervated the matrix component of lateral striatum, whereas medial prefrontal cortex projections terminated largely in dorsal striosomes and distinct nucleus accumbens subregions. Ventral tegmental area dopaminergic projections terminated in a similarly heterogeneous pattern within nucleus accumbens and ventral striatum. Using a minimal number of easily manipulated and visualized sections, and microscopes available in most neuroscience laboratories, BLAQ enables simple, high-resolution assessment of virally transduced optogenetic construct expression, and post-hoc association of this expression with molecular markers, physiology and behavior.
Análise fatorial confirmatória da versão Brasileira da Escala de Resiliência (ER - Brasil)

The development of psychological instruments with favorable validity indicators is of utmost importance for the study and development of a theoretical construct, in particular when it is a relatively new phenomenon, such as resilience, which is understood generally as the process that allows individuals to overlap their risks and obtain positive gains from this experience. The objective of this research was to evaluate the factorial structure for Brazilian context relating to the resilience scale by Wagnild and Young (1993). To conduct the study 359 students of both sexes aged 12-20 years participated. From procedures of confirmatory factor analysis it was found that the dimensional structure that has better statistical indices for the Brazilian context was that of five factors: self-sufficiency, meaning of life, fairness, perseverance and existential uniqueness.
Quality and Reporting of Diagnostic Accuracy Studies in TB, HIV and Malaria: Evaluation Using QUADAS and STARD Standards

Background Poor methodological quality and reporting are known concerns with diagnostic accuracy studies. In 2003, the QUADAS tool and the STARD standards were published for evaluating the quality and improving the reporting of diagnostic studies, respectively. However, it is unclear whether these tools have been applied to diagnostic studies of infectious diseases. We performed a systematic review on the methodological and reporting quality of diagnostic studies in TB, malaria and HIV. Methods We identified diagnostic accuracy studies of commercial tests for TB, malaria and HIV through a systematic search of the literature using PubMed and EMBASE (2004–2006). Original studies that reported sensitivity and specificity data were included. Two reviewers independently extracted data on study characteristics and diagnostic accuracy, and used QUADAS and STARD to evaluate the quality of methods and reporting, respectively. Findings Ninety (38%) of 238 articles met inclusion criteria. All studies had design deficiencies. Study quality indicators that were met in less than 25% of the studies included adequate description of withdrawals (6%) and reference test execution (10%), absence of index test review bias (19%) and reference test review bias (24%), and report of uninterpretable results (22%). In terms of quality of reporting, 9 STARD indicators were reported in less than 25% of the studies: methods for calculation and estimates of reproducibility (0%), adverse effects of the diagnostic tests (1%), estimates of diagnostic accuracy between subgroups (10%), distribution of severity of disease/other diagnoses (11%), number of eligible patients who did not participate in the study (14%), blinding of the test readers (16%), and description of the team executing the test and management of indeterminate/outlier results (both 17%). The use of STARD was not explicitly mentioned in any study. Only 22% of 46 journals that published the studies included in this review required authors to use STARD. Conclusion Recently published diagnostic accuracy studies on commercial tests for TB, malaria and HIV have moderate to low quality and are poorly reported. The more frequent use of tools such as QUADAS and STARD may be necessary to improve the methodological and reporting quality of future diagnostic accuracy studies in infectious diseases.