Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing the harmful effects of last-level cache polluters with an OS-level, software-only pollute buffer

It is well recognized that LRU cache-line replacement can be ineffective for applications with large working sets or non-localized memory access patterns. Specifically, in last-level processor caches, LRU can cause cache pollution by inserting non-reuseable elements into the cache while evicting reusable ones. The work presented in this paper addresses last-level cache pollution through a dynamic operating system mechanism, called ROCS, requiring no change to underlying hardware and no change to applications. ROCS employs hardware performance counters on a commodity processor to characterize application cache behavior at run-time. Using this online profiling, cache unfriendly pages are dynamically mapped to a pollute buffer in the cache, eliminating competition between reusable and non-reusable cache lines. The operating system implements the pollute buffer through a page-coloring based technique, by dedicating a small slice of the last-level cache to store non-reusable pages. Measurements show that ROCS, implemented in the Linux 2.6. 24 kernel and running on a 2.3 GHz PowerPC 970FX, improves performance of memory-intensive SPEC CPU 2000 and NAS benchmarks by up to 34%, and 16% on average.
Unusual Codon Bias in Vinyl Chloride Reductase Genes of Dehalococcoides Species

ABSTRACT Vinyl chloride reductases (VC-RDase) are the key enzymes for complete microbial reductive dehalogenation of chloroethenes, including the groundwater pollutants tetrachloroethene and trichloroethene. Analysis of the codon usage of the VC-RDase genes vcrA and bvcA showed that these genes are highly unusual and are characterized by a low G+C fraction at the third position. The third position of codons in VC-RDase genes is biased toward the nucleotide T, even though available Dehalococcoides genome sequences indicate the absence of any tRNAs matching codons that end in T. The comparatively high level of abnormality in the codon usage of VC-RDase genes suggests an evolutionary history that is different from that of most other Dehalococcoides genes.
A New Radiosity Approach Using Area Sampling for Parametric Patches

A high precision illumination model is indispensable for lighting simulation and realistic image synthesis. For the purpose of improving realism, research on global illumination has been done, and several papers on radiosity methods have been presented. In the most recently proposed methods, the shapes of light sources and objects are restricted to polygons or simple curved surfaces. We present a more general method which can handle the kind of free‐form surfaces widely used in industrial products and in architecture. The method proposed here solves the problem of the interreflection of light (i.e., radiosities) between patches, and form‐factors, which play an important role in this process, are precisely calculated without aliasing through the use of an area sampling method (i.e., pyramid tracing). Furthermore the method can handle both non‐uniform intensity curved sources and non‐diffuse surfaces.
Argument Mining on Italian News Blogs

The goal of argument mining is to extract structured information, namely the arguments and their relations, from un-structured text. In this paper, we propose an approach to argument relation prediction based on supervised learning of linguistic and semantic features of the text. We test our method on the CorEA corpus of user comments to online newspaper articles, evaluating our system's performances in assigning the correct relation, i.e., support or attack, to pairs of arguments. We obtain results consistently better than a sentiment analysis-based base-line (over two out three correctly classified pairs), and we observe that sentiment and lexical semantics are the most informative features with respect to the relation prediction task.
On the X-Ray emission of Gamma Ray Bursts

Recent data gathered and triggered by the SWIFT satellite have greatly improved our knowledge of long-duration gamma ray bursts (GRBs) and X-ray flashes (XRFs). This is particularly the case for the X-ray data at all times. We show that the entire X-ray observations are in excellent agreement with the predictions of the `cannonball' model of GRBs and XRFs, which are based on simple physics and were published long before the launch of SWIFT. Two mechanisms underlie these predictions: inverse Compton scattering and synchrotron radiation, generally dominant at early and late times, respectively. The former mechanism provides a unified description of the gamma-ray peaks, X-ray flares and even the optical `humps' seen in some favourable cases; i.e. their very different durations, fluxes and peak-times are related precisely as predicted. The observed smooth or bumpy fast decay of the X-ray light curve is correctly described case-by-case, in minute detail. The `canonical' X-ray plateau, as well as the subsequent gradual steepening of the afterglow to an asymptotic power-law decay, are as foretold. So are the chromatic and achromatic properties of the light-curves.
Space Efficient Formats for Structure of Sparse Matrices Based on Tree Structures

Very large sparse matrices are often processed on massively parallel computer systems with distributed memory architectures consisting of tens or hundreds of thousands of processor cores. The problem occurs when we want or need to load/store these matrices from/to a distributed file system. This paper deals with the design of new formats for storing very large sparse matrices suitable for parallel I/O systems. The first one is based on arithmetic coding and the second one is based on binary tree format. We compare the space complexity of common storage formats and our new formats and prove that the latter are considerably more space efficient.
Non-linear regression algorithms for motor skill acquisition: a comparison

Endowing robots with the capability to learn is an important goal for the robotics re-search community. One part of this research is focused on learning skills, where usually two learning paradigms are used sequentially. First, a robot learns a motor primitive by demonstration (or imitation). Then, it improves this motor primitive with respect to some externally defined criterion. In this paper, we study how the representation used in the demonstration learning step can influence the performance of the policy improvement step. We provide a conceptual survey of different demonstration learning al-gorithms and perform an empirical comparison of their performance when combined with a subsequent policy improvement step.
Treating mental health problems in primary care: Discrepancies between therapist expectations and clients’ attendance

ABSTRACT Models of collaborative primary health care were introduced in Ontario in 2005 with the goal of providing comprehensive care, including that of mental health, to patients. While the success of this model of health care delivered through Family Health Teams (FHT) has been established, evidence based guidelines to guide mental health therapists regarding the number of sessions needed to treat common issues such as depression and anxiety seen within this model is lacking. The goal of this study was to gain a better understanding of how therapists conducted therapy, including type of intervention, under a physician determined eight session cap. Questionnaires completed by the therapists after the first session and again when therapy ended, a focus group and a chart review were conducted over an eight month period involving a total of 147 patients. Several key findings were identified including the unanticipated outcome of a discrepancy between how many sessions were initially thought to be required by the mental health therapist compared to how many sessions were determined helpful by the patient. Conversely, it was also identified that eight sessions was insufficient to treat more severe mental health issues such as trauma. Such results suggest that the treatment of mental health issues within Family Health remains a little understood and explored issue, with the authors suggesting better ways of negotiating session caps reflective of mental health issues addressed within this model of care.
Automatic brain portion extraction method for coronal T1-W Magnetic Resonance Images(MRI) of human head scans by double processing

This paper proposes a new brain portion extraction method for coronal T1-W Magnetic Resonance Images (MRI) of human head scans. In the proposed scheme we first find a rough brain portion using an existing method. This method is based on image diffusion, intensity thresholding, largest connected component analysis and morphological operations. After obtaining the brain portion we expand the brain boundary by few pixels. The brain region enclosed in the expanded boundary is then processed using intensity thresholding, and morphological operations. Application of this method on a coronal T1-W dataset from Internet Brain Service Repository (IBSR). The performance of the method is evaluated by computing Jaccard and Dice coefficients.
Self-Adapting Payoff Matrices in Repeated Interactions

Traditional iterated prisoner's dilemma (IPD) assumed a fixed payoff matrix for all players, which may not be realistic because not all players are the same in the real-world. This paper introduces a novel co-evolutionary framework where each strategy has its own self-adaptive payoff matrix. This framework is generic to any simultaneous two-player repeated encounter game. Here, each strategy has a set of behavioral responses based on previous moves, and an adaptable payoff matrix based on reinforcement feedback from game interactions that is specified by update rules. We study how different update rules affect the adaptation of initially random payoff matrices, and how this adaptation in turn affects the learning of strategy behaviors
A new model of intonation for use with speech synthesis and recognition

This paper describes a synthesis from analysis scheme for producing natural sounding intonation for speech synthesis. The paper presents a new method of describing F0 contours in terms of three basic phonetic intonation elements. Details are given of an automatic system for labelling F0 contours, which could be used for speech recognition purposes. Current work on extracting a phonological description from this phonetic description is discussed.
A comparative study on collectives of term weighting methods for extractive presentation speech summarization

This paper presents a comparative study of collectives of term weighting methods for extractive speech summarization of Mandarin Presentation Speech. The summarization process can be considered as a binary classification process. The collectives of different term weighting methods can provide better summarization performance than each of them with the same classification algorithm. Several different unsupervised and supervised term weighting methods and their collectives were evaluated with summarizer based on support vector machine (SVM) classifier. The majority vote strategy is used for handling the collectives. We show that the best result is provided with the vote of the collective of all term weighting methods. We also show that Term Relevance Ratio (TRR) gives more contribution for presentation speech summarization than other term weighting methods.
KISTI at CLEF eHealth 2015 Task 2

Laypeople (e.g., patients and their caregivers) usually use queries which de- scribe a sign, symptom or condition to obtain relevant medical information on the Web. They can fail to find useful information for diagnosing or understanding their health conditions because the search results delivered by existing medical search engines do not fit the information needs of users. To deliver useful medical information, we at- tempted to combine multiple ranking methods, explicit semantic analysis (ESA), a clus- ter-based external expansion model (CBEEM), and concept-based document centrality (CBDC), using external medical resources to improve retrieval performance. As a first step, initial documents are searched using a baseline method. Based on the initial docu- ments, ranking methods are selectively applied. Our experiments with combinations of ranking methods aim to find the best means of computing accurate similarity scores using different external medical resources. The best performance was obtained when the CBEEM and the CBDC were used together.
Detecting Spammers on Twitter

With millions of users tweeting around the world, real time search systems and dierent types of mining tools are emerging to allow people tracking the repercussion of events and news on Twitter. However, although appealing as mechanisms to ease the spread of news and allow users to discuss events and post their status, these services open opportunities for new forms of spam. Trending topics, the most talked about items on Twitter at a given point in time, have been seen as an opportunity to generate trac and revenue. Spammers post tweets containing typical words of a trending topic and URLs, usually obfuscated by URL shorteners, that lead users to completely unrelated websites. This kind of spam can contribute to de-value real time search services unless mechanisms to fight and stop spammers can be found. In this paper we consider the problem of detecting spammers on Twitter. We first collected a large dataset of Twitter that includes more than 54 million users, 1.9 billion links, and almost 1.8 billion tweets. Using tweets related to three famous trending topics from 2009, we construct a large labeled collection of users, manually classified into spammers and non-spammers. We then identify a number of characteristics related to tweet content and user social behavior, which could potentially be used to detect spammers. We used these characteristics as attributes of machine learning process for classifying users as either spammers or nonspammers. Our strategy succeeds at detecting much of the spammers while only a small percentage of non-spammers are misclassified. Approximately 70% of spammers and 96% of non-spammers were correctly classified. Our results also highlight the most important attributes for spam detection on Twitter.
Generating Learning Analytics to Improve Learners' Metacognitive Skills Using nStudy Trace Data and the ICAP Framework

Metacognition is integral in the cycle of self-regulated learning. Enhancing learner’s metacognitive skills is a focus of many studies. Offering learning analytics to learners about learning has also gained popularity as a means to improve their metacognitive skills. A key question is, What types of analytics can meaningfully prompt change? We propose Chi’s (2009; 2014) ICAP framework can lend meaning to analytics and guide learners to enhance metacognitive skills.
X-ray image analysis for cultural heritage investigations

Image processing of radiograms is one of the fields that have known a very fast development since the digitalization of the radiological imagery. Depending on the objects that are studied, different approaches have been developed. In Cultural Heritage investigations image processing, the image analysis can go from conversion of image types and classes, morphological filtering, deblurring, and other image enhancement tools, to image transforms or refinement of regions of interest. In the present work, historical aretefacts, that are not to be opened, will be investigated by X-ray, and then the images will be processed and enhanced. Also for the case studied it would be analyzed the possibility of 3D reconstruction of an object of interest, inside the studied object by an alternative method.
Methylene Blue Modulates Huntingtin Aggregation Intermediates and Is Protective in Huntington's Disease Models

Huntington's disease (HD) is a devastating neurodegenerative disorder with no disease-modifying treatments available. The disease is caused by expansion of a CAG trinucleotide repeat and manifests with progressive motor abnormalities, psychiatric symptoms, and cognitive decline. Expression of an expanded polyglutamine repeat within the Huntingtin (Htt) protein impacts numerous cellular processes, including protein folding and clearance. A hallmark of the disease is the progressive formation of inclusions that represent the culmination of a complex aggregation process. Methylene blue (MB), has been shown to modulate aggregation of amyloidogenic disease proteins. We investigated whether MB could impact mutant Htt-mediated aggregation and neurotoxicity. MB inhibited recombinant protein aggregation in vitro, even when added to preformed oligomers and fibrils. MB also decreased oligomer number and size and decreased accumulation of insoluble mutant Htt in cells. In functional assays, MB increased survival of primary cortical neurons transduced with mutant Htt, reduced neurodegeneration and aggregation in a Drosophila melanogaster model of HD, and reduced disease phenotypes in R6/2 HD modeled mice. Furthermore, MB treatment also promoted an increase in levels of BDNF RNA and protein in vivo. Thus, MB, which is well tolerated and used in humans, has therapeutic potential for HD.
Seismic Responses of a Cable-Stayed Bridge with Consideration of Uniform Temperature Load

The effects of temperature load on the dynamic responses of cable-stayed bridges have attracted the attention of researchers in recent years. However, these investigations mainly focus on the influence of temperature on the dynamic characteristics of structures, such as vibration mode and frequency. This paper discusses the effects of uniform temperature changes on the seismic responses of a cable-stayed bridge. A three dimensional finite element model of a cable-stayed bridge using OpenSees is established for nonlinear time history analysis, and uniform temperature load is applied to the prototype bridge before the conducting of seismic excitation. Three ground motion records are selected from the PEER strong motion database based on the design spectrum. Case studies are then performed considering the varying temperature and the connections between the deck and pylons of the bridge. The result shows that the seismic responses of the bridge are significantly increased with the consideration of temperature load. Meanwhile, the types between the deck and pylon also have notable impacts on the seismic responses of the bridge with and without temperature changes. This research could provide a reference for designers during the design phase of cable-stayed brides.
Traumatic brain injury and adrenal insufficiency: morning cortisol and cosyntropin stimulation tests

Introduction Adrenal insufficiency (AI) has a great impact on the prognosis of patients with traumatic brain injury. There is a lack of consensus regarding the diagnostic criteria of AI. In these patients with acute stress we compared fasting cortisol, low and high dose cosyntropin stimulation tests to assess adrenal function in patients with moderate to severe traumatic brain injury. Material and methods This multicenter, cross-sectional study recruited 50 consecutive patients (aged between 15 and 70 years old) with moderate to severe traumatic brain injury who survived more than 5 days after the event. The patients’ adrenal function was assessed using the fasting cortisol, 1 and 250-µg ACTH stimulation tests. Results More cases of AI were detected by the 1-µg ACTH stimulation test compared to those detected by the basal serum cortisol level and 250-µg ACTH stimulation test. The κ test showed no agreement between these tests. The incidence of AI in the first 10 days after traumatic brain injury varied from 34% to 82% according to the various definitions of AI. The incidence of hypotension and need for vasopressors was higher in the patients diagnosed by the 250-µg ACTH stimulation test (p < 0.0001). Conclusions The incidence of secondary AI in moderate to severe traumatic brain injury seems to be high. A combination of stimulation test (either 250 or 1 µg) and basal cortisol level may improve diagnostic ability compared to either test alone. Hence performing both tests for the assessment of adrenal function in patients with traumatic brain injury is recommended.
Encoding Human Sexual Chemosensory Cues in the Orbitofrontal and Fusiform Cortices

Chemosensory communication of affect and motivation is ubiquitous among animals. In humans, emotional expressions are naturally associated with faces and voices. Whether chemical signals play a role as well has hardly been addressed. Here, we use functional magnetic resonance imaging to show that the right orbitofrontal cortex, right fusiform cortex, and right hypothalamus respond to airborne natural human sexual sweat, indicating that this particular chemosensory compound is encoded holistically in the brain. Our findings provide neural evidence that socioemotional meanings, including the sexual ones, are conveyed in the human sweat.
OMR: An Opportunistic Multi-Path Reliable Routing Protocol in Wireless Sensor Networks

Motivated by realistic sensor network scenarios that have fading environment and opportunistic transmission, we propose a novel routing protocol, OMR, which integrates routing and MAC protocols to improve the packet delivery ratio, reduce the packet delivery latency, and decrease the energy consumption in multi-hop wireless sensor networks. OMR chooses a packet's route at each hop after the transmission to that hop and the choice is based on the fact that intermediate nodes actually receive the packet and can forward the packet to any nodes at the next hop with high probability. The nodes coordinate to guarantee hop-based reliability while avoiding redundant retransmission by blindly flooding. The timeout and sink initiated retransmission mechanisms by intermediate nodes in OMR guarantee the end-to-end reliability. Piggyback acknowledgement shares the packet and acknowledgement information among neighbors to mitigate the effects of lossy and asymmetric links. The results of intensive simulation using TOSSIM show that OMR performs well compared with traditional routing protocols.
Dual-Process Models of the Remember / Know Paradigm

Dual-process models of the remember/know paradigm are analyzed as consisting of two interrelated components a process theory and a task theory. A process theory is concerned with the relationship between experimental variables and hypothetical constructs of the model. A task theory is concerned with the relationship between these constructs and the observed proportions of remember and know responses. Three different task theories of the remember/know paradigm are compared the independence, redundancy, and exclusivity variants. The exclusivity model is shown to be logically inconsistent and three alternative versions are proposed including one based on the principles of fuzzy logic. The resulting six models are compared against existing data under the assumption that remember and know responses are based on the two processes of recollection, selectively affected by variation in age, level of processing, and divided attention, and familiarity, selectively affected by variation in stimulus modality. Data from 22 studies comprising 37 comparisons of age, level of processing, and divided attention are consistent with the independence and fuzzy logic models and inconsistent with the exclusivity and redundancy models. Data from three studies comprising six comparisons of stimulus modality fail to adjudicate between the models. The implications of these results for theories of the remember/know paradigm are discussed.