Miracle-dz/UniFAR-FTUs huggingface dataset:
Therapeutic Testosterone Administration Preserves Excitatory Synaptic Transmission in the Hippocampus during Autoimmune Demyelinating Disease

Over 50% of multiple sclerosis (MS) patients experience cognitive deficits, and hippocampal-dependent memory impairment has been reported in >30% of these patients. While postmortem pathology studies and in vivo magnetic resonance imaging demonstrate that the hippocampus is targeted in MS, the neuropathology underlying hippocampal dysfunction remains unknown. Furthermore, there are no treatments available to date to effectively prevent neurodegeneration and associated cognitive dysfunction in MS. We have recently demonstrated that the hippocampus is also targeted in experimental autoimmune encephalomyelitis (EAE), the most widely used animal model of MS. The objective of this study was to assess whether a candidate treatment (testosterone) could prevent hippocampal synaptic dysfunction and underlying pathology when administered in either a preventative or a therapeutic (postdisease induction) manner. Electrophysiological studies revealed impairments in basal excitatory synaptic transmission that involved both AMPA receptor-mediated changes in synaptic currents, and faster decay rates of NMDA receptor-mediated currents in mice with EAE. Neuropathology revealed atrophy of the pyramidal and dendritic layers of hippocampal CA1, decreased presynaptic (Synapsin-1) and postsynaptic (postsynaptic density 95; PSD-95) staining, diffuse demyelination, and microglial activation. Testosterone treatment administered either before or after disease induction restores excitatory synaptic transmission as well as presynaptic and postsynaptic protein levels within the hippocampus. Furthermore, cross-modality correlations demonstrate that fluctuations in EPSPs are significantly correlated to changes in postsynaptic protein levels and suggest that PSD-95 is a neuropathological substrate to impaired synaptic transmission in the hippocampus during EAE. This is the first report demonstrating that testosterone is a viable therapeutic treatment option that can restore both hippocampal function and disease-associated pathology that occur during autoimmune disease.
Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines

ABSTRACT The anomaly detection methods are receiving growing attention in the intrusion detection community. The two main reasons for this are their ability to handle large volumes of unlabeled data and to detect previously unknown attacks. In this contribution we investigate the application of a modern machine learning technique – one-class Support Vector Machines (SVM) – for anomaly detection in unlabeled data. We propose a novel formulation of this technique which is particularly suited for the data typical for intrusion detection systems. Our evaluation on the well-known KDDCup dataset demonstrates a significant improvement over previous formulations of the one-class SVM.
A Pharmacist-Staffed, Virtual Gout Management Clinic for Achieving Target Serum Uric Acid Levels: A Randomized Clinical Trial.

CONTEXT Relatively few patients with gout receive appropriate treatment. OBJECTIVE To determine whether a pharmacist-staffed gout management program is more effective than usual care in achieving target serum uric acid (sUA) levels in gout patients. DESIGN A parallel-group, randomized controlled trial of a pharmacist-staffed, telephone-based program for managing hyperuricemia vs usual care. Trial duration was 26 weeks. MAIN OUTCOME MEASURES Primary outcome measure was achieving sUA levels at or below 6 mg/dL at the 26-week visit. Secondary outcome was mean change in sUA levels in the control and intervention groups. Participants were adults with recurrent gout and sUA levels above 6.0 mg/dL. Participants were randomly assigned to management by a clinical pharmacist following protocol or to monitoring of sUA levels but management of their gout by their usual treating physician. RESULTS Of 102 patients who met eligibility criteria, 77 subjects obtained a baseline sUA measurement and were entered into the trial. Among 37 participants in the intervention group, 13 (35%) had sUA levels at or below 6.0 mg/dL at 26 weeks, compared with 5 (13%) of 40 participants in the control group (risk ratio = 2.8, 95% confidence interval [CI] = 1.1 to 7.1, p = 0.03). The mean change in sUA levels among controls was +0.1 mg/dL compared with -1.5 mg/dL in the intervention group (sUA difference = -1.6, 95% CI = -0.9 to -2.4, p < 0.001). CONCLUSIONS A structured pharmacist-staffed program was more effective than usual care for achieving target sUA levels. These results suggest a structured program could greatly improve gout management.
Concurrency-aware compiler optimizations for hardware description languages

In this article, we discuss the application of compiler technology for eliminating redundant computation in hardware simulation. We discuss how concurrency in hardware description languages (HDLs) presents opportunities for expression reuse across different threads. While accounting for discrete event simulation semantics, we extend the data flow analysis framework to concurrent threads. In this process, we introduce a rewriting scheme named ∂VF and a graph representation to model sensitivity relationships among threads. An algorithm for identifying common subexpressions as applied to HDLs is presented. Related issues, such as scheduling correctness, are also considered.
Final report: Retirement processes and outcomes of individuals who retire to give care

Caregiving responsibilities can lead to early, often involuntary, retirement, but the connection between the two events is seldom studied. This mixed method study, using both quantitative and qualitative methods, used the concept of “retirement congruency”, which takes into account greater variation in retirement decisions (low, moderate, or high retirement congruency), to explore retired caregivers’ experiences. In the first stage of the study, descriptive analyses and multinomial logistic regression were carried out on a sample of retired caregivers (n = 569) from the 2002 General Social Survey. Women were more likely to retire for caregiving reasons than men and that male caregivers were more likely than female caregivers to report low retirement congruency. Moderate retirement congruency was the most common type of retirement congruency for caregivers who retired because of their caregiving role. Five variables predicted low retirement congruency, compared to moderate retirement congruency, and seven variables predicted low retirement congruency, compared to high retirement congruency. Three variables distinguished between moderate and high retirement congruency. Health and job problems were significant in all comparisons. Consistent with the descriptive results, retiring to give care predicted moderate retirement congruency compared to high retirement congruency, indicating that many employed caregivers say they chose to retire early because of their caregiving role yet still indicated a desire/need to have remained employed. In stage two, 44 Nova Scotian caregivers (30 women and 14 men who retired to give care to an adult in the previous six years) were interviewed. Caregiving and retirement intersected in different ways. There were four pathways to becoming a retired caregiver: sudden, coincidental, gradual, and breaking. Different types of “tipping points” were more present in certain groups versus others. Moderate retirement congruency, again, was common, yet retirement congruency did not seem related to any of the four pathways to retirement. Individuals were negotiating their identities, both as caregivers and as retirees, regardless of their pathways or length of time retired. Very few individuals indicated being satisfied with their retirement experiences. Analyses from the qualitative interviews demonstrate the complexity of caregivers’ retirement decisions and also point to limitations of survey research. Implications for caregiver policy and retirement policy are discussed, distinguishing between policies for employed caregivers and policies for caregivers who have to leave the paid labour force.
The State of the Art in Visualizing Dynamic Graphs

Dynamic graph visualization focuses on the challenge of representing the evolution of relationships between en- tities in readable, scalable, and effective diagrams. This work surveys the growing number of approaches in this discipline. We derive a hierarchical taxonomy of techniques by systematically categorizing and tagging publica- tions. While static graph visualizations are often divided into node-link and matrix representations, we identify the representation of time as the major distinguishing feature for dynamic graph visualizations: either graphs are represented as animated diagrams or as static charts based on a timeline. Evaluations of animated approaches focus on dynamic stability for preserving the viewer's mental map or, in general, compare animated diagrams to timeline-based ones. Finally, we identify and discuss challenges for future research.
Design and implementation of a high performance pedestrian detection

Research on pedestrian detection system still presents a lot of space for improvements, both on speed and detection accuracy. This paper presents a full implementation of a pedestrian detection system, using a part-based classification for the candidates identification and a feature based tracking for increasing the result robustness. The novelty of this approach relies on the use of part-based approach with a combination of Haar-cascade and HOG-SVM. Tests have been conducted using standard datasets showing results aligned with those of the other state-of-the-art systems available in literature. Real world tests also show high speed performance.
Improving part-of-speech tagging using lexicalized HMMs

We introduce a simple method to build Lexicalized Hidden Markov Models (L-HMMs) for improving the precision of part-of-speech tagging. This technique enriches the contextual Language Model taking into account a set of selected words empirically obtained. The evaluation was conducted with different lexicalization criteria on the Penn Treebank corpus using the TnT tagger. This lexicalization obtained about a 6% reduction of the tagging error, on an unseen data test, without reducing the efficiency of the system. We have also studied how the use of linguistic resources, such as dictionaries and morphological analyzers, improves the tagging performance. Furthermore, we have conducted an exhaustive experimental comparison that shows that Lexicalized HMMs yield results which are better than or similar to other state-of-the-art part-of-speech tagging approaches. Finally, we have applied Lexicalized HMMs to the Spanish corpus LexEsp.
High-speed energy-resolved STJ photometry of the eclipsing dwarf nova IY UMa

We present high-time-resolution photometry of the dwarf nova IY UMa using the S-Cam2 superconducting tunnel junction device attached to the 4.2-m William Herschel Telescope on La Palma. Exploiting the well-defined white dwarf and hotspot eclipse features, we derive an updated orbital ephemeris for IY UMa and an orbital period of 0.073 908 97(5) d. A white dwarf ingress/egress duration of 31 ± 2 s along with the contact phases of the bright-spot
Structured sparse approximation via generalized regularizers: With application to V2V channel estimation

In this paper, we consider the estimation of a signal that has both group- and element-wise sparsity (joint sparsity); motivated by channel estimation in vehicle-to-vehicle channels. A general approach for the design of separable regularizing functions is proposed to adaptively induce sparsity in the estimation. A joint sparse signal estimation problem is formulated via these regularizers and its optimal solution is computed based on proximity operations. Our optimization results are quite general and they can be applied in the context of hierarchical sparsity models as well. The proposed recovery algorithm is a nested iterative method based on the alternating direction method of multipliers (ADMM). Due to regularizer separability, key operations can be performed in parallel. V2V channels are estimated by exploiting the joint sparsity (group/element-wise) exhibited in the delay-Doppler domain. Simulation results reveal that the proposed method can achieve as much as a 10 dB gain over previously examined methods.
Improved Spatiotemporal Local Monogenic Binary Pattern for Emotion Recognition in The Wild

Local binary pattern from three orthogonal planes (LBP-TOP) has been widely used in emotion recognition in the wild. However, it suffers from illumination and pose changes. This paper mainly focuses on the robustness of LBP-TOP to unconstrained environment. Recent proposed method, spatiotemporal local monogenic binary pattern (STLMBP), was verified to work promisingly in different illumination conditions. Thus this paper proposes an improved spatiotemporal feature descriptor based on STLMBP. The improved descriptor uses not only magnitude and orientation, but also the phase information, which provide complementary information. In detail, the magnitude, orientation and phase images are obtained by using an effective monogenic filter, and multiple feature vectors are finally fused by multiple kernel learning. STLMBP and the proposed method are evaluated in the Acted Facial Expression in the Wild as part of the 2014 Emotion Recognition in the Wild Challenge. They achieve competitive results, with an accuracy gain of 6.35% and 7.65% above the challenge baseline (LBP-TOP) over video.
Inflectional paradigms have bases too : Arguments from Yiddish ∗

It is widely recognized that within derivational paradigms, forms may be constrained to resembled their bases, even when the result is otherwise illegal. For example, in English, derived forms may disobey the general process of vowel reduction in order to match their bases (cond[]nse⇒cònd[]nsátion), but not vice versa (tang[]ntial⇒*táng[]nt). In contrast, members of inflectional paradigms are not obviously derived from one another, and their influence is commonly thought to be more egalitarian. McCarthy's recent OPTIMAL PARADIGMS (OP) proposal codifies this distinction by hypothesizing that derivational paradigms always refer to privileged bases, but inflectional paradigms never do. He observes that this hypothesis leads to an interesting prediction: since inflectional paradigms have no base, faithfulness to a marked allomorph is never enforced, and paradigms should strive to be globally unmarked (“attraction to the unmarked”). In this paper, I provide several counterexamples from Yiddish, in which inflectional paradigms have leveled to become more marked. This supports a model in which bases play a role not only in derivational paradigms, but in inflectional ones as well (Kraska-Szlenk 1995, Albright 2002). Middle High German had final devoicing, creating alternations in noun paradigms, as in (1a). Early in the transition to Yiddish, however, final devoicing was rendered opaque by apocope ((1b)). Finally, devoicing was “lost”, along with most inflections ((1c)) (Sapir 1915; Kiparsky 1968; Sadock 1973; King 1980).
A framework for scheduling and managing big data applications in a distributed infrastructure

Nowadays, big data has received attention from researchers, business industries, education, and scientific communities. Big data analytics has to deal with large scale data that consist of both structured and unstructured data. These data are to be handled properly, that is extracting, processing, and analyzing those data to obtain meaningful information from them in a limited time. To yield insightful information, the processing of big data analytics requires high performance computing system, storage, and network resources. Hence, it is essential to design a high performance computing infrastructure with sufficient bandwidth which is capable to handle the big data processing in an efficient manner. However, the current network architectures in those infrastructures, with predefined network policies, do not allow for just-in-time reconfiguration of the networking infrastructure as demanded by big data analytics. In addressing these limitations, Software-Defined Networking (SDN) offers the means to dynamically configure the network parameters, dynamically provision the networks, and the network itself can be sliced in an on-demand manner. This research aims to characterize SDN with respect to the demands of big data analytics in Cluster, Grid, and Cloud Computing resources. The main motivation behind this research study is to design and develop an intelligent framework named as Big Data Analytics Management System (BDAMS) for collectively managing the compute, storage, and network resources in Cluster, Grid, and Cloud infrastructure for big data analytics.
PDR/INS/WiFi Integration Based on Handheld Devices for Indoor Pedestrian Navigation

Providing an accurate and practical navigation solution anywhere with portable devices, such as smartphones, is still a challenge, especially in environments where global navigation satellite systems (GNSS) signals are not available or are degraded. This paper proposes a new algorithm that integrates inertial navigation system (INS) and pedestrian dead reckoning (PDR) to combine the advantages of both mechanizations for micro-electro-mechanical systems (MEMS) sensors in pedestrian navigation applications. In this PDR/INS integration algorithm, a pseudo-velocity-vector, which is composed of the PDR-derived forward speed and zero lateral and vertical speeds from non-holonomic constraints (NHC), works as an update for the INS to limit the velocity errors. To further limit the drift of MEMS inertial sensors, trilateration-based WiFi positions with small variances are also selected as updates for the PDR/INS integrated system. The experiments illustrate that positioning error is decreased by 60%–75% by using the proposed PDR/INS integrated MEMS solution when compared with PDR. The positioning error is further decreased by 15%–55% if the proposed PDR/INS/WiFi integrated solution is implemented. The average accuracy of the proposed PDR/INS/WiFi integration algorithm achieves 4.5 m in indoor environments.
Using visual analytics to provide situation awareness for movement and communication data

Analyzing and correlating movement and communication data is challenging. To gather insights and gain knowledge out of such datasets, we propose a visual analytics system. We apply automated clustering techniques and propose a combination of various visualizations to provide overviews. We support the analyst in exploring the data to eventually enhance situational awareness in complex analysis scenarios. To evaluate our approach, we apply our techniques in the context of the VAST 2015 Grand Challenge (GC). Within this challenge, we successfully identify suspicious patterns and interesting groups of distinctive behavior among visitors of an amusement park, and correlate them with their respective communication patterns to gain insights.
TWO LEVEL FUZZY LATTICE (2L-FL) SUPERVISED CLUSTERING : A NEW METHOD FOR SOFT TISSUE IDENTIFICATION IN SURGERY

A new technique is presented towards the intelligent control of a surgical tool to be used for the epidural puncture. Specifically the problem of the accurate soft tissue identification from laser-based spectral data was dealt with successfully. The proposed new technique, namely two level fuzzy lattice (2LFL) supervised clustering, employs tools and methods from the novel theory of fuzzy lattices and it might be helpful towards a mechatronic solution to the problem of accurate tool placement through flexible tissues in surgery. 1 THE MEDICAL PROBLEM IN PERSPECTIVE The precision to which non-invasive measurements can be achieved in medicine through X-ray, high resolution computerized tomography and magnetic resonance imaging scans, has increased in many cases beyond the capabilities of the accurate deployment of surgical tools by manual methods. There is a need to be able to work with comparable accuracy in surgery, particularly as there is a move towards less invasive techniques. The more general problem of controlling precisely the position of a surgical tool in compliant tissues has been considered. Frequently in such situations the position of a tissue interface or of objects such as blood vessels and nerves will not be known in advance as most mediums are of unknown thickness and the position relative to the penetrating tool may change due to deformation of the medium, therefore the precise position will need to be determined in real-time. An approach which has been adopted by the authors, towards identifying accurately the location of a surgical tool, is to equip the penetrating tool with sensing components capable of identifying (or allowing the surgeon to identify) and reacting to the tissue type at the tool tip. The principal objective of this paper is to demonstrate a new and efficient tissue discrimination technique that might enable controlled penetration of soft tissues in surgery. A concrete soft tissue surgical procedure was considered, that is the epidural puncture. The epidural puncture to the spinal cavity involves the insertion of a needle through different layers of visco-elastic tissue into a fluid cavity and the delivery of anesthesia [8] as shown in Figure F1. This is currently a manual procedure relying on the interpretation of tactile force data to determine when the process is complete. The epidural puncture has been considered as a simplified version of procedures involving biopsies to soft tissue organs of the abdomen or discectomy within the spinal cord. In such, more complex situations it will be important to alert the user to the presence of nerve tissues and major blood vessels. An automated tool has been envisaged to control penetration through soft tissues. Hence, there is a need to investigate appropriate sensing and decision making methods for this purpose. 1 This work was supported in part by Brite Euram Project BE7470 (a) Lower back. indicates the approximate
RCA analysis of the polar codes and the use of feedback to aid polarization at short blocklengths

This paper uses an extension of Reciprocal Channel Approximation (RCA) to accurately and efficiently predict the frame error rate (FER) performance of polar codes by analyzing the probability density function (p.d.f) of log likelihood ratios (LLR) associated with information bits. A feedback scheme uses the RCA to predict the p.d. f of LLRs in conjunction with a repetition coding system to decrease the blocklength required for a target FER by a factor of 16. Using a rate-0.5 128-bit polar code as the initially transmitted code, the FER of the system with feedback is obtained by theoretical analysis and verified by simulation. Including the additional incremental transmissions the average blocklength for the system with feedback is 137.55 bits and the rate is 0.4653. Without feedback, a polar code with blocklength 2048 is required to achieve a comparable FER at a comparable rate. Intuitively, feedback allows the polar code to use fewer frozen bits in the initial transmission and then uses repetition codes to provide the needed reliability to resolve unreliable unfrozen bits identified by feedback.