Miracle-dz/UniFAR-FTUs huggingface dataset:
DEPTH-AVERAGED SEDIMENT TRANSPORT MODEL TAKEN INTO ACCOUNT OF BEND FLOWS

A numerical experiment is carried out to investigate methods for depth-averaged two-dimensional (2D) modeling of suspended sediment transport in river bends. The hydrodynamic simulation includes the commonly-used dispersion stress terms arisen from secondary current by integration of the product of the discrepancy between the mean and the actual vertical velocity distribution. Two existing 2D depth-averaged models for lateral convection of suspended sediment induced by secondary current are investigated in this study. One is the bend flow model which includes the lateral convection term by incorporating the assumption of secondary current velocity profile and mean sediment concentration profile. The other is the conventional model neglects the lateral convection term induced by secondary current and use the Schmidt number as a calibration parameter for lateral dispersion. Both models are applied to actual rivers. Analysis of the simulation results confirms the importance of lateral convective transport of suspended sediment. The vertical sediment concentration profile is very important for simulating lateral convection of suspended sediment. Numerical experiments reveal that using common concentration profile sometimes leads to unreasonable lateral convection for coarse particles. For the falling velocity of suspended sediment particles is small, the vertical velocity of secondary current affects the concentration profile considerably. Considering vertical velocity of secondary current, the model can be improved.
HEDGE FUND PERFORMANCE 1990-2000 DO THE ‘ MONEY MACHINES ’ REALLY ADD VALUE ?

In this paper we investigate the claim that hedge funds offer investors a superior riskreturn trade-off. We do so using a continuous time version of Dybvig’s (1988a, 1988b) payoff distribution pricing model. The evaluation model, which does not require any assumptions with regard to the return distribution of the funds in question, is applied to the monthly returns of 77 hedge funds and 13 hedge fund indices over the period May 1990 – April 2000. The results show that as a stand-alone investment hedge funds do not offer a superior risk-return profile. We find 12 indices and 72 individual funds to be inefficient, with the average efficiency loss amounting to 2.76% per annum for indices and 6.42% for individual funds. Part of the inefficiency cost of individual funds can be diversified away. Funds of funds, however, are not the preferred vehicle for this as their performance appears to suffer badly from their double fee structure. Looking at hedge funds in a portfolio context results in a marked improvement in the evaluation outcomes. Seven of the 12 hedge fund indices and 58 of the 72 individual funds classified as inefficient on a stand-alone basis are capable of producing an efficient payoff profile when mixed with the S&P 500. The best results are obtained when 10-20% of the portfolio value is invested in hedge funds.
IDS/A: An Interface between Intrusion Detection System and Application

We describe a number of problems which may reduce the effectiveness of a conventional network intrusion detection system. These problems are the result of the IDS having to second-guess the components or applications it is protecting. We propose a bi-directional interface between IDS and application. Applications use this interface to describe their state and submit their actions for approval to the IDS. The IDS thus receives information first-hand and is able to block suspect actions immediately. Apart from eliminating some of the abovementioned problems, the interface may also make it possible to extend the uses of intrusion detection systems to such tasks as gradual, continuous authentication and automated least privilege enforcement.
An Efficiency Analysis of a Nature-Like Fishway for Freshwater Fish Ascending a Large Korean River

Using traps and passive integrated transponder (PIT) telemetry, we investigated the effectiveness of the nature-like fishway installed at Sangju Weir on the Nakdong River, Korea. In 11 regular checks over the study period, 1474 individuals classified into 19 species belonging to 5 families were collected by the traps, representing 66% of the species inhabiting the main channel of the Nakdong River. PIT tags were applied to 1615 individuals belonging to 22 species, revealing fishway attraction and passing rates of 20.7% and 14.5%, respectively. Interspecific differences were also shown. For 63.2% of fishes, it took more than a day to pass through the fishway. Some individuals spent a longer time (>28 days) inside the fishway, suggesting the fishway was also being used for purposes other than passage. In this study, we verified species diversity of fish using a nature-like fishway installed in a large river in Korea. The results of this study provide a useful contribution to the development of fishways suitable for fish species endemic to Korea and for non-salmonid fish species worldwide.
Speech emotion recognition via a max-margin framework incorporating a loss function based on the Watson and Tellegen's emotion model

This paper considers a method for speech emotion recognition by a max-margin framework incorporating a loss function based on a well-known model called theWatson and Tellegen's emotion model. Each emotion is modeled by a single-state hidden Markov model (HMM) that is trained by maximizing the minimum separation margin between emotions, and the margin is scaled by a loss function. The framework is optimized by the semi-definite programming. Experiments were performed to evaluate the framework using the Berlin database of emotional speech. The framework performed better than other conventional training criteria for HMM such as maximum likelihood estimation and maximum mutual information estimation.
Traumatic Brain Injury-Induced Hippocampal Neurogenesis Requires Activation of Early Nestin-Expressing Progenitors

It is becoming increasingly clear that brain injuries from a variety of causes stimulate neurogenesis within the hippocampus. It remains unclear, however, how robust this response may be and what primary cell types are involved. Here, using a controlled cortical impact model of traumatic brain injury on a previously characterized transgenic mouse line that expresses enhanced green fluorescent protein (eGFP) under the control of the nestin promoter, we demonstrate that it is the earliest type-1 quiescent progenitor cells that are induced to proliferate and migrate outside the subgranular layer of the dentate gyrus. This type-1 cell activation occurs at the same time that we observe adjacent but more differentiated doublecortin-expressing progenitors (type-2 cells) being eliminated. Also, although type-2 cells remain intact in the contralateral (uninjured) dentate gyrus, the type-1 cells there are also activated and result in increased numbers of the doublecortin-expressing type-2 cells. In addition, we have generated a novel mouse transgenic that expresses a modified version of the herpes simplex virus thymidine kinase along with eGFP that allows for the visualization and inducible ablation of early dividing progenitors by exposing them to ganciclovir. Using this transgenic in the context of traumatic brain injury, we demonstrate that these early progenitors are required for injury-induced remodeling to occur. This work suggests that injury-induced hippocampal remodeling following brain injury likely requires sustained activation of quiescent early progenitors.
A novel view on modulus-preserving rate-one space-time block codes

In this paper, we first provide an alternative construction of full-diversity rate-one space-time block codes (STBCs) based on linear constellation precoding (LCP) and circular delay techniques. Through explicit unitary diagonal precoding on symbol blocks, and implicit FFT precoding via circularly shifted transmissions over multiple antennas, the proposed design preserves the modulus property of the information symbols. We then demonstrate that existing quasi-orthogonal STBCs can be constructed by embedding modulus-preserving LCP designs into orthogonal structures, and interestingly, they are also equivalent to the space-time-frequency (STF) block codes, which have been developed for multi-antenna OFDM systems, up to unitary transformations.
A Murine Model to Study the Antibacterial Effect of Copper on Infectivity of Salmonella Enterica Serovar Typhimurium

This study investigated the effect of copper as an antibacterial agent on the infectivity of Salmonella enterica serovar Typhimurium. Mice were infected orally with a standardized dose of unstressed Salmonella Typhimurium and copper-stressed cells of Salmonella Typhimurium. Bacterial counts in ileum, blood, liver and spleen were observed up to 168 h under normal aerobic conditions. Serum sensitivity, phagocytosis, malondialdehyde levels and histopathology were studied for both set of animals. A decreased bacterial count in the organs with mild symptoms of infection and a complete recovery by 48 h was observed in mice infected with copper-stressed bacteria. Histopathological examination of ileum tissue demonstrated regeneration of damaged tissue post-infection with copper-stressed bacteria and no malondialdehyde levels were detected after 24 h in ileum, spleen and liver. Exposure to copper sensitized Salmonella Typhimurium to the lytic action of serum and intracellular killing by peritoneal macrophages. It can be concluded that copper stress confers a decrease in the infectivity of healthy Salmonella Typhimurium in normal mice. This study highlights the significance of use of copper as an antibacterial agent against Salmonella Typhimurium in reducing the risk of incidence of Salmonella infections from contaminated water.
Answering End-User Questions, Queries and Searches on Wikipedia and its History

Knowledge bases (KBs) encoded using RDF triples deliver many beneﬁts to applications and programmers that access the KBs on the web via SPARQL endpoints. In this paper, we describe and compare two user-friendly systems that seek to make the universal knowledge of Web KBs available to users who neither know SPARQL, nor the internals of the KBs. We ﬁrst describe CANaLI, that lets people enter Natural Language (NL) questions and translates them into SPARQL queries executed on DBpe-dia. CANaLI removes the ambiguities that are often present in NL communication by requiring the use of a Controlled NL and providing on-line knowledge-driven question-completion that shows alternate correct interpretations. While CANaLI is a very powerful NL system, which placed ﬁrst in the 2016 competition on Question Answering over Linked Data QALD-6, even more powerful user-friendly interfaces are available to users who enter questions and queries on web-browsers. In particular, the SWiPE sys-tem provides a wysiwyg interface that lets users specify powerful queries on the Infoboxes of Wikipedia pages in a query-by-example fashion. Thus, in addition to those supported in CANaLI, we now have queries with (i) complex aggregates, (ii) structured conditions combined with keyword-based searches, and (iii) temporal conditions on Cliopedia, a historical knowledge base that captures the evolution of Wikipedia entities and properties. These systems demonstrate that semi-curated web document corpora and their KBs are making possible the seamless integration through user-friendly interfaces of (i) NL question answering, (ii) structured DB queries, and (iii) information retrieval. These were once viewed as distinct functions supported by different enabling technologies.
Optimal Valuation and Timing of Price Benchmarks for IT Services Contracts

Information technology (IT) services contracts often contain provisions for benchmarking fixed prices to current market prices. Prices of IT services tend to be opaque, but can be revealed through third$part y benchmarks. Little research has been conducted on the value and timing of such benchmarks. We draw upon the theory of mortgage refinance and value$at$ risk analysis from financial economics, and the IT in$ vestment under uncertainty literature to create a model of the benchmarking decision for IT services con$ tracts. Our model permits the determination of the value and optimal timing of the benchmark. We provide conditions under which a client firm should consider one or multiple benchmark provisions. Our solution is robust to uncertainty surrounding benchmark forecasts. Firms can leverage market price uncertainties and exercise benchmarks even when the potential rate of declining IT prices does not reach a minimum threshold to benchmark.
’ s repository of research publications and other research outputs Self-training from labelled features for sentiment analysis

Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudolabeled examples and are used to train another classifier by constraining the model’s predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents. 2010 Elsevier Ltd. All rights reserved.
A Bayesian Approach for the Detection of Code and Design Smells

The presence of code and design smells can have a severe impact on the quality of a program. Consequently, their detection and correction have drawn the attention of both researchers and practitioners who have proposed various approaches to detect code and design smells in programs. However, none of these approaches handle the inherent uncertainty of the detection process. We propose a Bayesian approach to manage this uncertainty. First, we present a systematic process to convert existing state-of-the-art detection rules into a probabilistic model. We illustrate this process by generating a model to detect occurrences of the Blob antipattern. Second, we present results of the validation of the model: we built this model on two open-source programs, GanttProject v1.10. 2 and Xerces v2.7. 0, and measured its accuracy. Third, we compare our model with another approach to show that it returns the same candidate classes while ordering them to minimise the quality analysts' effort. Finally, we show that when past detection results are available, our model can be calibrated using machine learning techniques to offer an improved, context-specific detection.
Nanoengineered implant as a new platform for regenerative nanomedicine using 3D well-organized human cell spheroids

In tissue engineering, it is still rare today to see clinically transferable strategies for tissue-engineered graft production that conclusively offer better tissue regeneration than the already existing technologies, decreased recovery times, and less risk of complications. Here a novel tissue-engineering concept is presented for the production of living bone implants combining 1) a nanofibrous and microporous implant as cell colonization matrix and 2) 3D bone cell spheroids. This combination, double 3D implants, shows clinical relevant thicknesses for the treatment of an early stage of bone lesions before the need of bone substitutes. The strategy presented here shows a complete closure of a defect in nude mice calvaria after only 31 days. As a novel strategy for bone regenerative nanomedicine, it holds great promises to enhance the therapeutic efficacy of living bone implants.
Burst ORAM: Minimizing ORAM Response Times for Bursty Access Patterns

We present Burst ORAM, the first oblivious cloud storage system to achieve both practical response times and low total bandwidth consumption for bursty workloads. For real-world workloads, Burst ORAM can attain response times that are nearly optimal and orders of magnitude lower than the best existing ORAM systems by reducing online bandwidth costs and aggressively rescheduling shuffling work to delay the bulk of the IO until idle periods. We evaluate our design on an enterprise file system trace with about 7,500 clients over a 15 day period, comparing to an insecure baseline encrypted block store without ORAM. We show that when baseline response times are low, Burst ORAM response times are comparably low. In a 32TB ORAM with 50ms network latency and sufficient bandwidth capacity to ensure 90% of requests have baseline response times under 53ms, 90% of Burst ORAM requests have response times under 63ms, while requiring only 30 times the total bandwidth consumption of the insecure baseline. Similarly, with sufficient bandwidth to ensure 99.9% of requests have baseline responses under 70ms, 99.9% of Burst ORAM requests have response times under 76ms.
Dorsal Raphe Neuroinflammation Promotes Dramatic Behavioral Stress Dysregulation

Impulsivity, risk-taking behavior, and elevated stress responsivity are prominent symptoms of mania, a behavioral state common to schizophrenia and bipolar disorder. Though inflammatory processes activated within the brain are involved in the pathophysiology of both disorders, the specific mechanisms by which neuroinflammation drives manic behavior are not well understood. Serotonin cell bodies originating within the dorsal raphe (DR) play a major role in the regulation of behavioral features characteristic of mania. Therefore, we hypothesized that the link between neuroinflammation and manic behavior may be mediated by actions on serotonergic neurocircuitry. To examine this, we induced local neuroinflammation in the DR by viral delivery of Cre recombinase into interleukin (IL)-1βXAT transgenic male and female mice, resulting in overexpressing of the proinflammatory cytokine, IL-1β. For assertion of brain-region specificity of these outcomes, the prefrontal cortex (PFC), as a downstream target of DR serotonergic projections, was also infused. Inflammation within the DR, but not the PFC, resulted in a profound display of manic-like behavior, characterized by increased stress-induced locomotion and responsivity, and reduced risk-aversion/fearfulness. Microarray analysis of the DR revealed a dramatic increase in immune-related genes, and dysregulation of genes important in GABAergic, glutamatergic, and serotonergic neurotransmission. Behavioral and physiological changes were driven by a loss of serotonergic neurons and reduced output as measured by high-performance liquid chromatography, demonstrating inflammation-induced serotonergic hypofunction. Behavioral changes were rescued by acute selective serotonin reuptake inhibitor treatment, supporting the hypothesis that serotonin dysregulation stemming from neuroinflammation in the DR underlies manic-like behaviors.
Trust issues in open source software development

Open source software and the associated development model holds great promise, but the issue of trust is a major challenge. This applies to companies wishing to adopt the open source model but also within open source projects. We investigate this issue by data mining open source repositories to study two related phenomena: update propagation and distributed version control.
Turbocharged Speed Scaling: Analysis and Evaluation

In speed scaling systems, the execution speed of a processor can be adjusted dynamically under operating system control to provide tradeoffs between response time, fairness, and energy consumption. In this paper, we propose and evaluate an approach called envelope-based turbo charging, applied in conjunction with Fair Sojourn Protocol (FSP) scheduling and job-count-based speed scaling. This approach restores the strict dominance of FSP over Processor Sharing (PS) in speed scaling systems, and preserves fairness. We evaluate our new approach using analysis and simulation. The simulation results show that Turbocharged FSP (T-FSP) outperforms PS in response time, and often in energy consumption as well. Furthermore, the energy consumption of T-FSP is typically within 15% of optimal.
Generation of photoclinometric DTMs for application to transient changes on the surface of comet 67P/Churyumov-Gerasimenko

Context. The wide spatial and temporal coverage of 67P/Churyumov-Gerasimenko (67P) by the Rosetta mission has revealed a surface created by scattered large-scale changes and numerous small-scale changes. The many small-scale changes are of particular interest because they are unexpected and ubiquitous. As their topographic relief is often smaller than one meter, which is below the resolution of any shape models, we need higher resolution topography to analyze them properly. Aims. We describe a photoclinometry method that is able to retrieve surface elevations for a single OSIRIS image of the surface of 67P. With this method, we can provide accurate measures, along with error estimates, of the centimeter-scale topography of observed transient changes. Methods. Photoclinometry, or shape-from-shading, estimates heights by examining the light reflection of the surface as dictated by a photometric model under a specified set of viewing geometries. Assuming a standard photometric model for 67P, we can recreate the shading of a surface under specified viewing geometries. The output is a high-resolution height map that matches the original image pixel by pixel. We then provide estimates of the error in the retrieved heights and ensure that our method is valid with a series of checks. Results. We generate digital terrain models (DTMs) with a vertical resolution comparable to or smaller than the pixel scale. This allows us to accurately measure changes in the surface topography on centimeter scales. We find that most changes within the smooth terrains involve the transport or removal of material thinner than one meter.
Automatic Information Extraction in the Medical Domain by Cross-Lingual Projection

This research tackles the automatic annotation of texts written in a language L1 by exploiting resources and tools available for another language L2. Our approach involves the use of a parallel corpus (L1-L2) aligned at the level of sentences and words. To address the lack of annotated French corpus in the medical field, we focus on the French-English language pair to annotate French medical texts automatically. We focus in this article on Medical Entity Recognition (MER). We evaluate our MER method on the English corpus and the projection of the annotations on the French corpus. We also discuss the problem of scalability since we use a parallel corpus extracted from the Web and propose a statistical method to handle heterogeneous corpora.