Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuropsychological dissociations between priming and recognition: a single-system connectionist account.

A key claim of current theoretical analyses of the memory impairments associated with amnesia is that certain distinct forms of learning and memory are spared. A compelling example is that amnesic patients and controls are indistinguishable in repetition priming but amnesic patients are impaired at recognizing the study items. The authors show that this pattern of results is predicted by a single-system connectionist model of learning in which amnesia is simulated by a reduced learning rate. They also demonstrate that the model can reproduce the converse pattern in which priming but not recognition is impaired if the input is assumed to be additionally degraded in a priming test. The authors conclude that dissociations between priming and recognition do not require functionally or neurally distinct memory systems.
Reducing impact of data fragmentation caused by in-line deduplication

Deduplication results inevitably in data fragmentation, because logically continuous data is scattered across many disk locations. In this work we focus on fragmentation caused by duplicates from previous backups of the same backup set, since such duplicates are very common due to repeated full backups containing a lot of unchanged data. For systems with in-line dedup which detects duplicates during writing and avoids storing them, such fragmentation causes data from the latest backup being scattered across older backups. As a result, the time of restore from the latest backup can be significantly increased, sometimes more than doubled. We propose an algorithm called context-based rewriting (CBR in short) minimizing this drop in restore performance for latest backups by shifting fragmentation to older backups, which are rarely used for restore. By selectively rewriting a small percentage of duplicates during backup, we can reduce the drop in restore bandwidth from 12--55% to only 4--7%, as shown by experiments driven by a set of backup traces. All of this is achieved with only small increase in writing time, between 1% and 5%. Since we rewrite only few duplicates and old copies of rewritten data are removed in the background, the whole process introduces small and temporary space overhead.
Belief propagation-based cooperative localization for quasi-synchronous UWSNs with sound propagation speed uncertainty

Underwater wireless sensor networks (UWSNs) provide a promising approach for ocean monitoring. Locating the sensor nodes (SNs) in UWSNs is critical for annotating data samples. However, the global positioning system (GPS) is not available underwater. The locations of SNs are usually interpreted from anchor nodes which are deployed at known locations on the seafloor or sea surface. Due to the characteristics of UWSNs such as node sparsity, network asynchrony, energy restriction and sound propagation speed uncertainty, to locate the SN accurately is challenging. In this paper, we propose a factor graph framework and a Gaussian message-based belief propagation algorithm for the joint localization and synchronization problem. Particularly, the proposed algorithm jointly estimates the SN's position and clock offset as well as the sound propagation speed in a fully distributed fashion. Simulation results show that the proposed algorithm converges in a few iterations and achieves good performance.
Efficacy of OH-CATH30 and Its Analogs against Drug-Resistant Bacteria In Vitro and in Mouse Models

ABSTRACT Antimicrobial peptides (AMPs) have been considered alternatives to conventional antibiotics for drug-resistant bacterial infections. However, their comparatively high toxicity toward eukaryotic cells and poor efficacy in vivo hamper their clinical application. OH-CATH30, a novel cathelicidin peptide deduced from the king cobra, possesses potent antibacterial activity in vitro. The objective of this study is to evaluate the efficacy of OH-CATH30 and its analog OH-CM6 against drug-resistant bacteria in vitro and in vivo. The MICs of OH-CATH30 and OH-CM6 ranged from 1.56 to 12.5 μg/ml against drug-resistant clinical isolates of several pathogenic species, including Escherichia coli, Pseudomonas aeruginosa, and methicillin-resistant Staphylococcus aureus. The MICs of OH-CATH30 and OH-CM6 were slightly altered in the presence of 25% human serum. OH-CATH30 and OH-CM6 killed E. coli quickly (within 60 min) by disrupting the bacterial cytoplasmic membrane. Importantly, the 50% lethal doses (LD50) of OH-CATH30 and OH-CM6 in mice following intraperitoneal (i.p.) injection were 120 mg/kg of body weight and 100 mg/kg, respectively, and no death was observed at any dose up to 160 mg/kg following subcutaneous (s.c.) injection. Moreover, 10 mg/kg OH-CATH30 or OH-CM6 significantly decreased the bacterial counts as well as the inflammatory response in a mouse thigh infection model and rescued infected mice in a bacteremia model induced by drug-resistant E. coli. Taken together, our findings demonstrate that the natural cathelicidin peptide OH-CATH30 and its analogs exhibit relatively low toxicity and potent efficacy in mouse models, indicating that they may have therapeutic potential against the systemic infections caused by drug-resistant bacteria.
Adolescent females with NAFLD and PCOS have an adverse metabolic phenotype compared with other females and males Short Title : Adverse metabolic impact of NAFLD combined with PCOS

Background and aims: Nonalcoholic fatty liver disease (NAFLD) and polycystic ovary syndrome (PCOS) share risk associations of adiposity and insulin resistance. We examined the impact of a PCOS diagnosis on the metabolic phenotype of adolescent girls with NAFLD and compared this to girls without PCOS or NAFLD and to age-matched boys. Methods: Community-based adolescents from the Raine Cohort participated in assessments for NAFLD (572 girls and 592 boys) and PCOS (244 girls). 199 girls attended both assessments. Results: Amongst the 199 girls, PCOS was diagnosed in 16.1% and NAFLD in 18.6%. NAFLD was diagnosed in 10.1% of the boys. NAFLD was more prevalent in girls with PCOS than girls without PCOS (37.5% vs. 15.1%, p=0.003). Girls with NAFLD plus PCOS had greater adiposity (waist circumference, body mass index, suprailiac skinfold thickness [SST], serum androgens, high-sensitivity C-reactive protein (hsCRP), ferritin, homeostasis model assessment for insulin resistance (HOMA-IR), and lower serum sex hormone binding globulin levels than girls with NAFLD without a PCOS diagnosis (all p<0.05). Girls with NAFLD plus PCOS had similar adiposity, HOMA-IR and adiponectin levels to boys with NAFLD, but more adiposity, serum leptin and HOMA-IR than both girls and boys without NAFLD. PCOS (OR 2.99, 95% CI 1.01-8.82, P=0.048) and SST (OR 1.14, 95% CI 1.081.20, p<0.001) independently predicted NAFLD in adolescent girls, however serum androgens and HOMA-IR levels did not. Conclusions: Adolescent girls with NAFLD plus PCOS have a similar metabolic phenotype to boys with NAFLD. Increasing SST and pre-existing PCOS independently predict NAFLD in adolescent girls.
GENERATING ELECTRICITY USING PIEZOELECTRIC MATERIAL

This paper discusses the use of piezoelectric material to generate electricity. This includes the basic theoretical modelling of the electrical power generation mechanisms and optimization of the piezo-host system. It is shown that with proper configuration, a single piezo-film can generate enough electrical density that can be stored in a rechargeable battery for later usage.
Pacific Biosciences Sequencing and IMGT/HighV-QUEST Analysis of Full-Length Single Chain Fragment Variable from an In Vivo Selected Phage-Display Combinatorial Library

Phage-display selection of immunoglobulin (IG) or antibody single chain Fragment variable (scFv) from combinatorial libraries is widely used for identifying new antibodies for novel targets. Next-generation sequencing (NGS) has recently emerged as a new method for the high throughput characterization of IG and T cell receptor (TR) immune repertoires both in vivo and in vitro. However, challenges remain for the NGS sequencing of scFv from combinatorial libraries owing to the scFv length (>800 bp) and the presence of two variable domains [variable heavy (VH) and variable light (VL) for IG] associated by a peptide linker in a single chain. Here, we show that single-molecule real-time (SMRT) sequencing with the Pacific Biosciences RS II platform allows for the generation of full-length scFv reads obtained from an in vivo selection of scFv-phages in an animal model of atherosclerosis. We first amplified the DNA of the phagemid inserts from scFv-phages eluted from an aortic section at the third round of the in vivo selection. From this amplified DNA, 450,558 reads were obtained from 15 SMRT cells. Highly accurate circular consensus sequences from these reads were generated, filtered by quality and then analyzed by IMGT/HighV-QUEST with the functionality for scFv. Full-length scFv were identified and characterized in 348,659 reads. Full-length scFv sequencing is an absolute requirement for analyzing the associated VH and VL domains enriched during the in vivo panning rounds. In order to further validate the ability of SMRT sequencing to provide high quality, full-length scFv sequences, we tracked the reads of an scFv-phage clone P3 previously identified by biological assays and Sanger sequencing. Sixty P3 reads showed 100% identity with the full-length scFv of 767 bp, 53 of them covering the whole insert of 977 bp, which encompassed the primer sequences. The remaining seven reads were identical over a shortened length of 939 bp that excludes the vicinity of primers at both ends. Interestingly these reads were obtained from each of the 15 SMRT cells. Thus, the SMRT sequencing method and the IMGT/HighV-QUEST functionality for scFv provides a straightforward protocol for characterization of full-length scFv from combinatorial phage libraries.
Multichannel wireless scheduling under limited terminal capability

Emerging systems like OFDMA and MIMO systems require multichannel scheduling over a wireless link. In this paper, we focus on the case that the number of channels to be assigned to a mobile terminal is limited, which we call limited matching. This case often occurs over a MIMO downlink when the number of receive antennas at a mobile user is smaller than that of transmit antennas at the base station. When the system exploits channel-aware opportunistic scheduling based on the channel feedback, finding the sum of channel gains by the limited matching is an NP-complete problem. To solve it easily, we can use the Hungarian algorithm, but its complexity is still too high and not amenable to performance analysis. Hence, we develop a heuristic algorithm, and analyze its cell throughput. Also, we investigate its performance when the channel feedback information is partially available. For analytic simplicity, we consider proportionally fair (PF) scheduling that is widely accepted as an opportunistic scheduler. Numerical results demonstrate that our heuristic limited-matching scheduling algorithm works well with partial channel feedback.
Fatigue in primary Sjögren's syndrome is associated with lower levels of proinflammatory cytokines

Objectives This article reports relationships between serum cytokine levels and patient-reported levels of fatigue, in the chronic immunological condition primary Sjögren's syndrome (pSS). Methods Blood levels of 24 cytokines were measured in 159 patients with pSS from the United Kingdom Primary Sjögren's Syndrome Registry and 28 healthy non-fatigued controls. Differences between cytokines in cases and controls were evaluated using Wilcoxon test. Patient-reported scores for fatigue were evaluated, classified according to severity and compared with cytokine levels using analysis of variance. Logistic regression was used to determine the most important predictors of fatigue levels. Results 14 cytokines were significantly higher in patients with pSS (n=159) compared to non-fatigued healthy controls (n=28). While serum levels were elevated in patients with pSS compared to healthy controls, unexpectedly, the levels of 4 proinflammatory cytokines—interferon-γ-induced protein-10 (IP-10) (p=0.019), tumour necrosis factor-α (p=0.046), lymphotoxin-α (p=0.034) and interferon-γ (IFN-γ) (p=0.022)—were inversely related to patient-reported levels of fatigue. A regression model predicting fatigue levels in pSS based on cytokine levels, disease-specific and clinical parameters, as well as anxiety, pain and depression, revealed IP-10, IFN-γ (both inversely), pain and depression (both positively) as the most important predictors of fatigue. This model correctly predicts fatigue levels with reasonable (67%) accuracy. Conclusions Cytokines, pain and depression appear to be the most powerful predictors of fatigue in pSS. Our data challenge the notion that proinflammatory cytokines directly mediate fatigue in chronic immunological conditions. Instead, we hypothesise that mechanisms regulating inflammatory responses may be important.
A rapid electrophoretic method for separating rabbit skeletal muscle myosin heavy chains at high resolution

An electrophoretic method using sodium dodecyl sulfate‐polyacrylamide gel electrophoresis (SDS‐PAGE) was developed which gives a high‐resolution separation of the known myosin heavy chains of rabbit skeletal muscle with excellent reproducibility. The gel of 10 cm total length consists of (i) a first stacking gel of 3.5% total gel concentration (T) and pH 6.8, (ii) a first separating gel of 6.6%T and pH 8.8, (iii) a second stacking gel of 6.6%T and pH 6.8, and (iv) a second separating gel of 8.8%T and pH 8.8. With this composition, a minigel system allows separation of six myosin heavy‐chain (MHC) isoforms at room temperature without cooling and within 8h. In agreement with previous reports, the isoforms appear in the sequence MHCemb, MHC IIa, MHC IId, MHCneo, MHC IIb, MHC I. A special advantage is the detectability not only of the adult but also of the embryonic and neonatal isoforms MHCemb and MHCneo.
Exploring the Effect of Four Factors on Affirmative Action Programs for Women

Abstract Our study aims to explore determinants and mechanisms that support affirmative action programs (AAPs). Managing the programs and enhancing women's well-being in organizational life requires knowledge about support for AAPs. Although many studies have examined attitudes toward these, they have confined their scope of research to their particular theoretical disciplines. Our study analyzes the impact of four primary factors—gender, political factors, psychological factors, and social structure on support for the programs. These in turn consist of eight variables, as we shall see, and we examine the varieties of causal mechanisms that are concerned with moderation and mediation, as well as the causal relationships within the variables. Our findings suggest that the four factors have comparative strengths in explaining support for AAPs and have moderating, mediating, and causal impact on them.
Predicting Pronouns across Languages with Continuous Word Spaces

Predicting pronouns across languages from a language with less variation to one with much more is a hard task that requires many different types of information, such as morpho-syntactic information as well as lexical semantics and coreference. We assumed that continuous word spaces fed into a multi-layer perceptron enriched with morphological tags and coreference resolution would be able to capture many of the linguistic regularities we found. Our results show that the model captures most of the linguistic generalisations. Its macro-averaged F-score is among the top-3 systems submitted to the DiscoMT shared task reaching 56.5%.
Conformal Theories and Strings on Orbifolds

We propose correspondences between 4d quantum eld theories with N = 2; 1; 0 (super)conformal invariance and Type IIB string theory on various orbifolds. We argue using the spacetime string theory, and check using the beta functions (exactly for N = 2 ; 1 and so far at 1-loop for the gauge couplings in the N = 0 case), that these theories have confor-mal xed lines. The latter case potentially gives well-dened non-supersymmetric vacua of string theory, with a mechanism for making the curvature and cosmological constant small at nontrivial string coupling. February 1998
Distributed Intelligence for Smart Home Appliances

A main goal for the researchers designing smart health care technology is to develop strategies allowing both, early detection of and avoid problems that could lead to a decreased independence. Medical analysis, smart sensors, intelligent software agents, distributed control, wireless communication and internet resources are research areas implied in home automation delivering intelligent health care. Ubiquitous computing is the most promising technological approach that can meet this challenge if their non-intrusive and adaptable objectives are reached, meanwhile it is able to maintain privacy. Our work is concerned with the development of a smart home architecture allowing to integrate information from a wide variety of sensors and actuators: information recruited for these elements is processed into microprocessors implementing computational intelligence techniques; cooperative communication between units is implemented through a wireless net into the home; and internet resources allow to link the home with external services. Within this general infrastructure, the proposed architecture expands from the conversion of passive sensors into smart devices (we call them “intelligent hardware units”) by adding processing capabilities and using them like plug-and-play mechanisms. Performed experiments on a robotic pet platform show the viability of the present approach and they appoint interesting results to be applied on the entire home.
Automated Illustration of News Stories

Forgetting what one has just read is, in some cases, linked to insufficient attention. The reader might feel either bored or distracted by something more interesting – a common trace in children and the elderly. The challenge is: how can multimedia systems assist readers in reading and remembering stories? Several studies [2, 7, 14] showed that reading memory is improved by visual stimulus. In this paper we formulate the hypothesis that an automated multimedia system can help users in reading a story by stimulating their reading memory with adequate visual illustrations. These illustrations are intended to increase the readers’ attention towards the story and to help them recalling the story. The framework automatically creates a multimedia presentation of the news story by (1) rendering the news text in a sentence-by-sentence fashion, (2) providing mechanisms to select the best illustration for each sentence and (3) select the set of illustrations that guarantees the best sequence of illustrations. Users may also activate a text-to-speech functionality according to their preference or reading difficulties. Experiments used Flickr images to illustrate BBC news articles: a user survey with 23 users assessed positively the effectiveness of the system.
Positioning and Navigation of Meal Delivery Robot Using Magnetic Sensors and RFID

The main purpose of this paper is to study positioning and navigation of mobile robot with integrated magnetic sensor and RFID. With the use of RFID and the proposed progressive polling algorithm, process the data access and identification for each Tag in order to prevent conflicts between read of Tag and ID number to improve efficiency. Through actual experiment results, mobile robot will move stably along a magnetic stripe. When one's position offset, it will be corrected immediately. At this moment, RFID can exclude conflict between reads of Tags using progressive polling algorithm to keep the robot arrive accurately at the next station. Therefore, it provides a stability of platform to move the route, flexibility of the real-time correction of system platform, a high accuracy of positioning and navigation, and a high efficiency of Tag's recognition.
Feature subset selection by SVM ensemble

Feature selection (FS) has proven to be useful to improve the generalization performance of classifiers. For applications with a small number of instances but a large number of input features, FS methods based on single classifier evaluation are subject to instability.We propose a new FS algorithm based on SVM ensemble learning. First, an ensemble of SVM classifiers are trained with re-sampled subsets of the training data. Then, with a predefined feature ranking criterion, a new stability criterion is defined on the ranking criterion values among the classifiers to measure the relevance of a certain feature. This measure favors the features which have stable ranking criterion values over the features whose ranking criterion values are subject to large variations. The unstable features usually do not have much relative information to the class label, and can be removed to improve the generalization performance of the classifier. To rank the features, the method only requires a small number of SVM classifiers to be trained. It is very fast to solve feature selection problems with a large number of input features. Combined with a backward elimination procedure, this method is robust to feature selection problems with very small sample sizes. In this paper, we evaluate its performance on nonlinear selection tasks.
Understanding other minds : perspectives from autism

Part 1 Introduction: an introduction to the debate early understanding of mind - the normal case social development in autism - historical and clinical perspectives. Part 2 The theory-of-mind hypothesis of autism - the cognitive approach: from attention-goal psychology to belief-desire psychology - the development of a theory of mind and its dysfunction what autism teaches us about metarepresentation the theory-of-mind deficit in autism - rethinking the metarepresentation theory what language reveals about the understanding of minds in children with autism the theory-of-mind deficit in autism - evidence from deception. Part 3 The theory-of-minds hypothesis of autism - critical perspectives: the theory-of-mind and joint-attention deficits in autism understanding persons - the role of affect pretending and planning narrative language in autism and the theory-of-mind hypothesis - a wider perspective theories of mind and the problem of autism the complexity of social behaviour in autism the development of individuals with autism - implications for the theory-of-mind hypothesis. Part 4 Wider perspectives: the role of imitation in understanding persons and developing a theory of mind evolving a theory of mind - the nature of non-verbal mentalism in other primates the comparative study of early communication and theories of mind - ontogeny, phylogeny and pathology autism and the theory of mind - some philosophical perspectives desire and fantasy - a psychoanalytic perspective on the theory of mind and autism the theory-of-mind deficit in autism - some questions for teaching and diagnosis the place of this book in autism research.
Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid

Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classification tasks even when the conditional independence assumption on which they are based is violated. However, most studies were done on small databases. We show that in some larger databases, the accuracy of Naive-Bayes does not scale up as well as decision trees. We then propose a new algorithm, NBTree, which induces a hybrid of decision-tree classifiers and Naive-Bayes classifiers: the decision-tree nodes contain univariate splits as regular decision-trees, but the leaves contain Naive-Bayesian classifiers. The approach retains the interpretability of Naive-Bayes and decision trees, while resulting in classifiers that frequently outperform both constituents, especially in the larger databases tested.