Miracle-dz/UniFAR-FTUs huggingface dataset:
Aligning Noisy Parallel Corpora Across Language Groups: Word Pair Feature Matching by Dynamic Time Warping

We propose a new algorithm called DK-vec for aligning pairs of Asian/Indo-European noisy parallel texts without sentence boundaries. DK-vec improves on previous alignment algorithms in that it handles better the non-linear nature of noisy corpora. The algorithm uses frequency, position and recency information as features for pattern matching. Dynamic Time Warping is used as the matching technique between word pairs. This algorithm produces a small bilingual lexicon which provides anchor points for alignment.
3rd party observer gaze as a continuous measure of dialogue flow

We present an attempt at using 3rd party observer gaze to get a measure of how appropriate each segment in a dialogue is for a speaker change. The method is a step away from the current dependency of speaker turns or talkspurts towards a more general view of speaker changes. We show that 3rd party observers do indeed largely look at the same thing (the speaker), and how this can be captured and utilized to provide insights into human communication. In addition, the results also suggest that there might be differences in the distribution of 3rd party observer gaze depending on how information-rich an utterance is.
Privacy-preserving Two-party Rational Set Intersection Protocol

Privacy-preserving data mining has been an active research area in recent years due to privacy concerns in many distributed data mining settings. Protocols for privacy-preserving data mining have considered semi-honest, malicious, and covert adversarial models in cryptographic settings, whereby an adversary is assumed to follow, arbitrarily deviate from the protocol, or behaving somewhere in between these two, respectively. Semi-honest model provides weak security requiring small amount of computation, on the other hand, malicious and covert models provide strong security requiring expensive computations like homomorphic encryptions. However, game theory allows us to design protocols where parties are neither honest nor malicious but are instead viewed as rational and are assumed (only) to act in their self-interest. In this paper, we build efficient and secure two-party set-intersection protocol in game-theoretic setting using cryptographic primitives. Our construction allow to avoid the use of expensive tools like homomorphic encryption and zero knowledge proof. We also show that our protocol satisfies computational versions of strict Nash equilibrium and stability with respect to trembles. Povzetek: Predstavljen je protokol med dvema stranema na osnovi Nashevega ravnoteja.
HCN2 Overexpression in Newborn and Adult Ventricular Myocytes: Distinct Effects on Gating and Excitability

Abstract— Ventricular pacemaker current (If) shows distinct voltage dependence as a function of age, activating outside the physiological range in normal adult ventricle, but less negatively in neonatal ventricle. However, heterologously expressed HCN2 and HCN4, the putative molecular correlates of ventricular If, exhibit only a modest difference in activation voltage. We therefore prepared an adenoviral construct (AdHCN2) of HCN2, the dominant ventricular isoform at either age, and used it to infect neonatal and adult rat ventricular myocytes to investigate the role of maturation on current gating. The expressed current exhibited an 18-mV difference in activation (V1/2 −95.9±1.9 in adult; −77.6±1.6 mV in neonate), comparable to the 22-mV difference between native If in adult and neonatal cultures (V1/2 −98.7 versus −77.0 mV). This did not result from developmental differences in basal cAMP, because saturating cAMP in the pipette caused an equivalent positive shift in both preparations. In the neonate, AdHCN2 caused a significant increase in spontaneous rate compared with control (88±5 versus 48±4 bpm). In adult, where HCN2 activates more negatively, the effect was evident only during anodal excitation, requiring significantly less stimulus energy than control (2149±266 versus 3140±279 mV · ms). Thus, ventricular maturational state influences the voltage dependence of expressed HCN2, resulting in distinct physiological impact of expressed channels in neonate and adult myocytes. The full text of this article is available at http://www.circresaha. org.
Brain Perfusion Impairment in Neurologically Asymptomatic Adult Patients with Sickle-Cell Disease Shown by Voxel-Based Analysis of SPECT Images

Cerebrovascular lesions are frequently observed in patients with sickle-cell disease (SCD) and these structural lesions are preceded by insidious perfusion deficits. Our aim was to investigate the presence of brain perfusion deficits in neurologically asymptomatic SCD patients, especially affecting microvessels. For this study, 42 SCD patients [33 sickle-cell anemia (HbSS), 6 sickle hemoglobin C disease (HbSC), and 3 sickle β-thalassemia disease (HbSβ)] with mean hematocrit of 25.1 (±4.85; 15.6–38.5) underwent brain perfusion single photon emission computerized tomography (SPECT) using the tracer 99mTc-ECD. Images from SCD patients were compared to images of a healthy control group (29 females and 20 males, mean age 31 ± 8; range 25–49 years). Images underwent voxel-wise comparison of regional tracer uptake using paired t-test to estimate the probability of each voxel to have an increased or decreased tracer uptake. When compared to controls, SCD patients exhibited significantly reduced tracer uptake in basal ganglia and thalami, the anterior frontal region and the watershed region of the temporo-parietal-occipital transition (p < 0.05). Our study showed that neurologically asymptomatic adult SCD patients exhibit a pattern of reduced 99mTc-ECD tracer uptake demonstrated by SPECT. Early diagnosis of this cerebral vasculopathy has prognostic implications and can be determinant in considering therapeutic alternatives to avoid increasing brain lesion load and progressive disability.
Efficient Power Network Analysis Considering Multidomain Clock Gating

In this paper, an efficient framework is proposed to analyze the worst case of voltage variation of power network considering multidomain clock gating. First, a frequency-domain-based simulation method is proposed to obtain the time-domain voltage response. With the vector fitting technique, the frequency-domain responses are approximated by a partial fraction expression, which can be easily converted to a time-domain waveform. Then, an algorithm is proposed to find the worst-case voltage variation and corresponding clock gating patterns, through superimposing the voltage responses caused by all domains working separately. The major computation of the whole framework is solving the frequency-domain equation system, whose complexity is about O(NalphaD log f max), where alpha is between one and two if using an iterative solver from the PETSc library. N is the node number, f max is the upper bound of frequency, and D is the number of clock domains. Numerical results show that the proposed simulation method is up to several hundred times faster than commercial fast simulators, like HSPICE and MSPICE. In addition, the proposed method is able to analyze large-scale power networks that the commercial tools are not able to afford.
GUPT: privacy preserving data analysis made easy

It is often highly valuable for organizations to have their data analyzed by external agents. However, any program that computes on potentially sensitive data risks leaking information through its output. Differential privacy provides a theoretical framework for processing data while protecting the privacy of individual records in a dataset. Unfortunately, it has seen limited adoption because of the loss in output accuracy, the difficulty in making programs differentially private, lack of mechanisms to describe the privacy budget in a programmer's utilitarian terms, and the challenging requirement that data owners and data analysts manually distribute the limited privacy budget between queries. This paper presents the design and evaluation of a new system, GUPT, that overcomes these challenges. Unlike existing differentially private systems such as PINQ and Airavat, it guarantees differential privacy to programs not developed with privacy in mind, makes no trust assumptions about the analysis program, and is secure to all known classes of side-channel attacks. GUPT uses a new model of data sensitivity that degrades privacy of data over time. This enables efficient allocation of different levels of privacy for different user applications while guaranteeing an overall constant level of privacy and maximizing the utility of each application. GUPT also introduces techniques that improve the accuracy of output while achieving the same level of privacy. These approaches enable GUPT to easily execute a wide variety of data analysis programs while providing both utility and privacy.
Data-Driven Testbed for Evaluating GPS Carrier Tracking Loops in Ionospheric Scintillation

A large set of equatorial ionospheric scintillation data has been compiled, used to characterize features of severe scintillation that impact Global Positioning System phase tracking, and used to develop a scintillation testbed for evaluating tracking loops. The data-driven testbed provides researchers a tool for studying, and the receiver developers a tool for testing, the behavior of carrier tracking loops under realistic scintillation conditions. It is known that severe equatorial scintillation causes cycle slipping and, in the worst cases, complete loss of carrier lock. Testbed results indicate that cycle slips are primarily caused by the abrupt, near half-cycle phase changes that occur during the deep power fades of severe equatorial scintillation. For a class of standard tracking loops, parameter values that minimize scintillation-induced cycle slipping are identified.
Image restoration by a neural network with hierarchical clustered architecture

This paper proposes a novel image restoration approach by a neural network with hierarchical clustered architecture (NNHCA). The method is motivated by a universally accepted concept in digital image processing that natural image formation is a local process, and treats image restoration also as a globally coordinated local process. The image restoration based on the local model is realized by NNHCA, one of the recently emerged neural networks with sophisticated architectures. In the application of restoration, NNHCA consists of four processing levels. The zeroth level represents individual processing units. The first level simulates optimization process which governs local restoration. The second level acts as a link for information exchange between local clusters in the first level. And the third level coordinates the completes process.
Increased anti-tumour efficacy of doxorubicin when combined with sulindac in a xenograft model of an MRP-1-positive human lung cancer.

BACKGROUND A number of cellular proteins, including P-glycoprotein (P-gp) and Multiple drug Resistance Protein (MRP-1), act as drug efflux pumps and are important in the resistance of many cancers to chemotherapy. We previously reported that a small number of NSAIDs could inhibit the activity of MRP-1. MATERIALS AND METHODS We chose sulindac as a candidate agent for further investigation as it has the most favourable efficacy and toxicity profile of the agents available for a potential specific MRP-1 inhibitor. NCI H460 cells expressed MRP-1 protein (by Western blot) and also the toxicity of doxorubicin (a substrate of MRP-1) could be potentiated in this line using non-toxic concentrations of the MRP-1 substrate/inhibitor sulindac. These cells were implanted in nude mice and the animals divided into various groups which were administered doxorubicin and/or sulindac. RESULTS Sulindac was shown to significantly potentiate the tumour growth inhibitor activity of doxorubicin in this MRP-1-overexpressing human tumour xenograft model. CONCLUSION Sulindac may be clinically useful as an inhibitor of the MRP-1 cancer resistance mechanism.
Automatically Mining Facets for Queries from Their Search Results

We address the problem of finding query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query. We assume that the important aspects of a query are usually presented and repeated in the query’s top retrieved documents in the style of lists, and query facets can be mined out by aggregating these significant lists. We propose a systematic solution, which we refer to as QDMiner, to automatically mine query facets by extracting and grouping frequent lists from free text, HTML tags, and repeat regions within top search results. Experimental results show that a large number of lists do exist and useful query facets can be mined by QDMiner. We further analyze the problem of list duplication, and find better query facets can be mined by modeling fine-grained similarities between lists and penalizing the duplicated lists.
A spherical cross-channel algorithm for binaural sound localization

This paper proposes a sound localization algorithm inspired by a cross-channel algorithm first studied by MacDonald et. al in 2008. The original algorithm assumes that the Head Related Transfer Functions (HRTFs) of the robotic head under study are precisely known, which is rarely the case in practice. Following the idea that any head is more or less spherical, the above assumption is relaxed by using HRTFs computed using a simple spherical head model with the same head radius as the robot head. In order to evaluate the proposed approach in realistic noisy conditions, an isotropic noise field is also computed and a precise definition of the Signal to Noise Ratio (SNR) in a binaural context is outlined. All these theoretical developments are finally assessed with simulated and experimental signals. Despite its simplicity, the proposed approach appears to be robust to noise and to provide reliable sound localization estimations in the frontal azimuthal plane.
Variations in achievement of evidence-based, high-impact quality indicators in general practice: An observational study

Background There are widely recognised variations in the delivery and outcomes of healthcare but an incomplete understanding of their causes. There is a growing interest in using routinely collected ‘big data’ in the evaluation of healthcare. We developed a set of evidence-based ‘high impact’ quality indicators (QIs) for primary care and examined variations in achievement of these indicators using routinely collected data in the United Kingdom (UK). Methods Cross-sectional analysis of routinely collected, electronic primary care data from a sample of general practices in West Yorkshire, UK (n = 89). The QIs covered aspects of care (including processes and intermediate clinical outcomes) in relation to diabetes, hypertension, atrial fibrillation, myocardial infarction, chronic kidney disease (CKD) and ‘risky’ prescribing combinations. Regression models explored the impact of practice and patient characteristics. Clustering within practice was accounted for by including a random intercept for practice. Results Median practice achievement of the QIs ranged from 43.2% (diabetes control) to 72.2% (blood pressure control in CKD). Considerable between-practice variation existed for all indicators: the difference between the highest and lowest performing practices was 26.3 percentage points for risky prescribing and 100 percentage points for anticoagulation in atrial fibrillation. Odds ratios associated with the random effects for practices emphasised this; there was a greater than ten-fold difference in the likelihood of achieving the hypertension indicator between the lowest and highest performing practices. Patient characteristics, in particular age, gender and comorbidity, were consistently but modestly associated with indicator achievement. Statistically significant practice characteristics were identified less frequently in adjusted models. Conclusions Despite various policy and improvement initiatives, there are enduring inappropriate variations in the delivery of evidence-based care. Much of this variation is not explained by routinely collected patient or practice variables, and is likely to be attributable to differences in clinical and organisational behaviour.
10-2017 MC-ADAPT : Adaptive Task Dropping in Mixed-Criticality Scheduling

Recent embedded systems are becoming integrated systems with components of different criticality. To tackle this, mixed-criticality systems aim to provide different levels of timing assurance to components of different criticality levels while achieving efficient resource utilization. Many approaches have been proposed to execute more lower-criticality tasks without affecting the timeliness of higher-criticality tasks. Those previous approaches however have at least one of the two limitations; i) they penalize all lower-criticality tasks at once upon a certain situation, or ii) they make the decision how to penalize lowercriticality tasks at design time. As a consequence, they underutilize resources by imposing an excessive penalty on lowcriticality tasks. Unlike those existing studies, we present a novel framework, called MC-ADAPT, that aims to minimally penalize lower-criticality tasks by fully reflecting the dynamically changing system behavior into adaptive decision making. Towards this, we propose a new scheduling algorithm and develop its runtime schedulability analysis capable of capturing the dynamic system state. Our proposed algorithm adaptively determines which task to drop based on the runtime analysis. To determine the quality of task dropping solution, we propose the speedup factor for task dropping while the conventional use of the speedup factor only evaluates MC scheduling algorithms in terms of the worst-case schedulability. We apply the speedup factor for a newlydefined task dropping problem that evaluates task dropping solution under different runtime scheduling scenarios. We derive that MC-ADAPT has a speedup factor of 1.619 for task drop. This implies that MCADAPT can behave the same as the optimal scheduling algorithm with optimal task dropping strategy does under any runtime scenario if the system is sped up by a factor of 1.619. Disciplines Computer Engineering | Computer Sciences Comments International Conference on Embedded Software (EMSOFT 2017), Seoul, South Korea, October 15 20, 2017 This conference paper is available at ScholarlyCommons: http://repository.upenn. edu/cis_papers/833
Tissue-specific alternative splicing of TCF 7 L 2

Genome Technology Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA, Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, 8717 Grovemont Circle, Bethesda, MD 20892, USA, Lillehei Heart Institute, Department of Medicine and Developmental Biology Center, University of Minnesota, Minneapolis, MN 55455, USA, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, 20502 Malmö, Sweden, Center for Statistical Genetics, Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109, USA, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA and Children’s National Medical Center, Center for Genetic Medicine Research (CGMR), Washington, DC 20010, USA
A WordNet-Based Semantic Model for Enhancing Text Clustering

Most of text mining techniques are based on word and/or phrase analysis of the text. The statistical analysis of a term (word or phrase) frequency captures the importance of the term within a document. However, to achieve a more accurate analysis, the underlying mining technique should indicate terms that capture the semantics of the text from which the importance of a term in a sentence and in the document can be derived. Incorporating semantic features from the WordNet lexical database is one of many approaches that have been tried to improve the accuracy of text clustering techniques. A new semantic-based model that analyzes documents based on their meaning is introduced. The proposed model analyzes terms and their corresponding synonyms and/or hypernyms on the sentence and document levels. In this model, if two documents contain different words and these words are semantically related, the proposed model can measure the semantic-based similarity between the two documents. The similarity between documents relies on a new semantic-based similarity measure which is applied to the matching concepts between documents. Experiments using the proposed semantic-based model in text clustering are conducted. Experimental results demonstrate that the newly developed semantic-based model enhances the clustering quality of sets of documents substantially.
Grounding Symbolic Operations in the Brain's Modal Systems

A central theme of modern cognitive science is that symbolic interpretation underlies human intelligence. The human brain does not simply register images, as do cameras or other recording devices. A collection of images or recordings does not make a system intelligent. Instead, symbolic interpretation of image content is essential for intelligent activity. What cognitive operations underlie symbolic interpretation? Across decades of analysis, a consistent set of symbolic operations has arisen repeatedly in logic and knowledge engineering: binding types to tokens; binding arguments to values; drawing inductive inferences from category knowledge; predicating properties and relations of individuals; combining symbols to form complex symbolic expressions; representing abstract concepts that interpret metacognitive states. It is difficult to imagine performing intelligent computation without these operations. For this reason, many theorists have argued that symbolic operations are central, not only to artificial intelligence but to human intelligence (e.g., Fodor, 1975; Pylyshyn, 1973). Symbolic operations provide an intelligent system with considerable power for interpreting its experience. Using type-token binding, an intelligent system can place individual components of an image into familiar categories (e.g., categorizing components of an image as people and cars). Operations on these categories then provide rich inferential knowledge that allows the perceiver to predict how categorized individuals will behave, and to select effective actions that can be taken (e.g., a perceived person may talk, cars can be driven).
Crowd Management using Viola Jones Algorithm and Heuristic Data Mining

processing techniques like face detection play a significant role in counting the number of people present at any given location which is under surveillance of a camera or any face detection system. This data collected can be processed and analyzed to deduce important findings. Data Mining plays a major role in analysis of this data and production of important statistics. Based on these statistics important decisions are made. This paper proposes one such system that uses Viola Jones face detection algorithm to keep a count of number of visitors at railway station throughout the day and heuristic data mining technique to analyze this stored data and suggest the required action.