Miracle-dz/UniFAR-FTUs huggingface dataset:
Crowdsourced Top-k Algorithms: An Experimental Evaluation

Crowdsourced top-k computation has attracted significant attention recently, thanks to emerging crowdsourcing platforms, e.g., Amazon Mechanical Turk and CrowdFlower. Crowdsourced top-k algorithms ask the crowd to compare the objects and infer the top-k objects based on the crowdsourced comparison results. The crowd may return incorrect answers, but traditional top-k algorithms cannot tolerate the errors from the crowd. To address this problem, the database and machine-learning communities have independently studied the crowdsourced top-k problem. The database community proposes the heuristic-based solutions while the machine-learning community proposes the learningbased methods (e.g., maximum likelihood estimation). However, these two types of techniques have not been compared systematically under the same experimental framework. Thus it is rather difficult for a practitioner to decide which algorithm should be adopted. Furthermore, the experimental evaluation of existing studies has several weaknesses. Some methods assume the crowd returns high-quality results and some algorithms are only tested on simulated experiments. To alleviate these limitations, in this paper we present a comprehensive comparison of crowdsourced top-k algorithms. Using various synthetic and real datasets, we evaluate each algorithm in terms of result quality and efficiency on real crowdsourcing platforms. We reveal the characteristics of different techniques and provide guidelines on selecting appropriate algorithms for various scenarios.
Bushmeat Consumption in Western Tanzania: A Comparative Analysis from the Same Ecosystem

Consumption of bushmeat is a problem around many protected areas, but successful mitigation programs are proving difficult to design, in part because anthropogenic pressures are often treated as uniform. In Eastern Africa, where bushmeat consumption has become a major problem, most studies focus on threats emerging from a single site or single ethnic group. In this study, we interviewed two groups of people, indigenous inhabitants of Mpimbwe and refugee dwellers in Katumba, who both hunt in the same ecosystem, in order to compare their consumption of bushmeat and their knowledge of hunters' activities. We related our findings to economic and other differences between them. More than four-fifths of the sampled population in Mpimbwe consumed bushmeat whereas just over half did in Katumba, and frequency of eating bushmeat was higher in the former. Mpimbwe residents hunted principally in the wet season, whereas Katumba residents hunted more in the dry season. We attributed greater consumption of bushmeat in Mpimbwe to a comparative shortage of eggs and pork there, to greater ease of access to large mammals, and to a lower price of bushmeat. Our study draws attention to the variation in bushmeat consumption between groups hunting in the same general area and demonstrates how site-specific data can be used to hone different strategies for reducing illegal hunting in the same region.
When Newton meets Descartes: a simple and fast algorithm to isolate the real roots of a polynomial

We introduce a novel algorithm denoted NewDsc to isolate the real roots of a univariate square-free polynomial f with integer coefficients. The algorithm iteratively subdivides an initial interval which is known to contain all real roots of f and performs exact (rational) operations on the coefficients of f in each step. For the subdivision strategy, we combine Descartes' Rule of Signs and Newton iteration. More precisely, instead of using a fixed subdivision strategy such as bisection in each iteration, a Newton step based on the number of sign variations for an actual interval is considered, and, only if the Newton step fails, we fall back to bisection. Following this approach, quadratic convergence towards the real roots is achieved in most iterations. In terms of complexity, our method induces a recursion tree of almost optimal size O(n·log(nτ)), where n denotes the degree of the polynomial and τ the bitsize of its coefficients. The latter bound constitutes an improvement by a factor of τ upon all existing subdivision methods for the task of isolating the real roots. We further provide a detailed complexity analysis which shows that NewDsc needs only Õ(n3τ) bit operations to isolate all real roots of f. In comparison to existing asymptotically fast numerical algorithms (e.g. the algorithms by V. Pan and A. Schönhage), NewDsc is much easier to access and, due to its similarities to the classical Descartes method, it seems to be well suited for an efficient implementation.
A Complete Synthesis Method for Block-Level Relaxation in Self-Timed Datapaths

Self-timed circuits present an attractive solution to the problem of process variation. However, implementing self-timed combinational logic can be complex and expensive. This paper presents a complete synthesis flow that generates self-timed combinational networks from conventional Boolean networks. The Boolean network is partitioned into small function blocks which are then synthesised using self-timed techniques. The procedure employs relaxation optimisations to distribute the overheads associated with self-timed networks between function-blocks. Relaxation is incorporated into the function block synthesis procedures, meaning the optimisations can be applied at a much finer granularity than previously possible. The new techniques are demonstrated on a range of benchmarks showing average reduction of 5% in area, 26% in latency and 48% in energy over gate-level relaxation techniques and 17% in area, 8% in latency and 20% in energy consumption over other block-level relaxation techniques.
The Yale cTAKES extensions for document classification: architecture and application

BACKGROUND Open-source clinical natural-language-processing (NLP) systems have lowered the barrier to the development of effective clinical document classification systems. Clinical natural-language-processing systems annotate the syntax and semantics of clinical text; however, feature extraction and representation for document classification pose technical challenges. METHODS The authors developed extensions to the clinical Text Analysis and Knowledge Extraction System (cTAKES) that simplify feature extraction, experimentation with various feature representations, and the development of both rule and machine-learning based document classifiers. The authors describe and evaluate their system, the Yale cTAKES Extensions (YTEX), on the classification of radiology reports that contain findings suggestive of hepatic decompensation. RESULTS AND DISCUSSION The F(1)-Score of the system for the retrieval of abdominal radiology reports was 96%, and was 79%, 91%, and 95% for the presence of liver masses, ascites, and varices, respectively. The authors released YTEX as open source, available at http://code.google. com/p/ytex.
Modeling the Static and the Dynamic Parts of the Environment to Improve Sensor-based Navigation

This paper addresses the modeling of the static and dynamic parts of the scenario and how to use this information within a real sensor-based navigation system. The contribution in the modeling aspect is a formulation of the Detection and Tracking of Mobile Objects and the Simultaneous Localization and Map Building in such a way that the nature (static/dynamic) of the observations is included in the estimation process. This is achieved by a set of filters tracking the moving objects and a map of the static structure constructed on line. In addition, this paper discusses how this modeling module is integrated in a real sensor-based navigation system taking advantage selectively of the dynamic and static information. The experimental results confirm that the complete navigation system is able to move a vehicle in unknown and dynamic scenarios. Furthermore, the system overcomes many of the limitations of previous systems associated to the ability to distinguish the nature of the parts of the scenario.
Optimization of Wet Pulp Mold Process and Reduction of Drying Energy

Pulp mold is one of the famous environmental friendly materials, which made from re cycled materials such as old newsprints through the environmental friendly processes. Furthermore, the used pulp mold can be easily recycled and the pulp mold itself is biodegradable. However, the higher cost and some deficiency in physical properties of pulp mold have been considered as issues to be overcome for a substitute for polymeric packaging materials such as EPS (Expandable Polystyrene). The way for the optimization of a pulp mold mill was proposed in this report. The possible reduction of drying energy was calculated by using a computer simulation method, which could Provide the detailed information about mass balance of overall process. The simulated results showed a great possible curtailment of production cost by improving the forming systems, for example, increasing the temperature and the dryness of a wet pulp mold.
Platelet‐derived growth factor‐BB induces cystathionine γ‐lyase expression in rat mesangial cells via a redox‐dependent mechanism

BACKGROUND AND PURPOSE So far, there is only limited information about the regulation of the endogenous synthesis of hydrogen sulfide (H2S), an important gaseous signalling molecule. This study was done to evaluate the redox‐dependent signalling events that regulate the expression of the H2S synthesising enzyme cystathionine‐γ‐lyase (CSE) in rat mesangial cells.
Towards single cell spectroscopy and refractometry in microfluidic chip platforms

This paper evaluates various strategies proposed for single cell refractometry and spectroscopy using fiber optic sensors and microfluidic chips. Details concerning design, fabrication and characterization of the chips will be addressed. Preliminary results obtained with alternative on-chip configurations using combination of fiber Bragg gratings with mirrored single mode and multimode fibers will be presented indicating the possibility of performing simultaneous assessment of cellular refractive index and absorption properties.
Application of Identical Degree of Set Pair Analysis on Software Refactoring

Set pair analysis posssses many virtues, including specific concept, convenience calculation and it covers comprehensive information as well. In course of software refactoring, traditional code locating method mostly relies on developer's observations and subjective perceptions, which brings with much inconvenience. Therefore, a new software refactoring scheme optimization model was built up based on identical degree of set pair analysis, in virtue of correlation indicators of software metrics, which offered a resolution to code locating in software refactoring. This paper applied variation coefficient method to calculate the weight of indicators, which improves the reliability of scheme evaluation. An experimental case argues that the set pair analysis model can locate the iffy code correctly, and furthermore, it can offer specific refactoring order of each scheme.
Development of Indonesian-Japanese statistical machine translation using lemma translation and additional post-process

Despite the fact that study of statistical machine translation has been growing rapidly to date, there has not been much research done about Indonesian-Japanese statistical machine translation. The previous research about Indonesian-Japanese statistical machine translation has shown several problems in translation process, such as low coverage corpus data, unknown words, and sentence reordering problem. In this research, we propose two methods to address these problems. The proposed methods are lemma translation with generated surface form and additional post-process. Lemma translation uses lemma and POSTAG of word in its translation process. Rule based katakana translation and unknown word substitution are also used for additional post-process. Experimental data was collected from JLPT (Japanese Language Proficiency Test) Level 3 with total 1132 sentences. Experimental results using these methods showed an improvement over the baseline system with a 116% increased BLEU score on Japanese to Indonesian translation and 26% increased BLEU score on Indonesian to Japanese translation.
Wavelet-based image decomposition by variational functionals

We discuss a wavelet based treatment of variational problems arising in the context of image processing, inspired by papers of Vese-Osher and Osher-Sole-Vese, in particular, we introduce a special class of variational functionals, that induce a decomposition of images in oscillating and cartoon components. Cartoons are often modeled by BV functions. In the setting of Vese et.el. and Osher et.al. the incorporation of BV penalty terms leads to PDE schemes that are numerically intensive. We propose to embed the problem in a wavelet framework. This provides us with elegant and numerically efficient schemes even though a basic requirement, the involvement of the space BV , has to be softened slightly. We show results on test images of our wavelet algorithm with a B11 (L1) penalty term, and we compare them with the BV restorations of Osher-Sole-Vese.
Inhibition of peroxisome fission, but not mitochondrial fission, increases yeast chronological lifespan

Mitochondria are key players in aging and cell death. It has been suggested that mitochondrial fragmentation, mediated by the Dnm1/Fis1 organelle fission machinery, stimulates aging and cell death. This was based on the observation that Saccharomyces cerevisiae Δdnm1 and Δfis1 mutants show an enhanced lifespan and increased resistance to cell death inducers. However, the Dnm1/Fis1 fission machinery is also required for peroxisome division. Here we analyzed the significance of peroxisome fission in yeast chronological lifespan, using yeast strains in which fission of mitochondria was selectively blocked. Our data indicate that the lifespan extension caused by deletion of FIS1 is mainly due to a defect in peroxisome fission and not caused by a block in mitochondrial fragmentation. These observations are underlined by our observation that deletion of FIS1 does not lead to lifespan extension in yeast peroxisome deficient mutant cells.
Column Selection via Adaptive Sampling

Selecting a good column (or row) subset of massive data matrices has found many applications in data analysis and machine learning. We propose a new adaptive sampling algorithm that can be used to improve any relative-error column selection algorithm. Our algorithm delivers a tighter theoretical bound on the approximation error which we also demonstrate empirically using two well known relative-error column subset selection algorithms. Our experimental results on synthetic and real-world data show that our algorithm outperforms non-adaptive sampling as well as prior adaptive sampling approaches.
In Vivo Inhibition of Serine Protease Processing Requires a High Fractional Inhibition of Cathepsin C

Inhibition of cathepsin C, a dipeptidyl peptidase that activates many serine proteases, represents an attractive therapeutic strategy for inflammatory diseases with a high neutrophil burden. We recently showed the feasibility of blocking the activation of neutrophil elastase, cathepsin G, and proteinase-3 with a single cathepsin C selective inhibitor in cultured cells. Here we measured the fractional inhibition of cathepsin C that is required for blockade of downstream serine protease processing, in cell-based assays and in vivo. Using a radiolabeled active site probe and U937 cells, a 50% reduction of cathepsin G processing required ∼50% of cathepsin C active sites to be occupied by an inhibitor. In EcoM-G cells, inhibition of 50% of neutrophil elastase activity required ∼80% occupancy. Both of these serine proteases were fully inhibited at full cathepsin C active site occupancy, whereas granzyme B processing in TALL-104 cells was partially inhibited, despite complete occupancy. In vivo, leukocytes from cathepsin C+/- mice exhibited comparable levels of neutrophil elastase activity to wild-type animals, even though their cathepsin C activity was reduced by half. The long-term administration of a cathepsin C inhibitor to rats, at doses that resulted in the nearly complete blockade of cathepsin C active sites in bone marrow, caused significant reductions of neutrophil elastase, cathepsin G and proteinase-3 activities. Our results demonstrate that the inhibition of cathepsin C leads to a decrease of activity of multiple serine proteases involved in inflammation but also suggest that high fractional inhibition is necessary to reach therapeutically significant effects.
Quantum Goethals-Preparata codes

We present a family of non-additive quantum codes based on Goethals and Preparata codes with parameters ((2m, 22m-5m+1, 8)). The dimension of these codes is eight times higher than the dimension of the best known additive quantum codes of equal length and minimum distance.
ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST

Automated prediction of subcellular localization of proteins is an important step in the functional annotation of genomes. The existing subcellular localization prediction methods are based on either amino acid composition or N-terminal characteristics of the proteins. In this paper, support vector machine (SVM) has been used to predict the subcellular location of eukaryotic proteins from their different features such as amino acid composition, dipeptide composition and physico-chemical properties. The SVM module based on dipeptide composition performed better than the SVM modules based on amino acid composition or physico-chemical properties. In addition, PSI-BLAST was also used to search the query sequence against the dataset of proteins (experimentally annotated proteins) to predict its subcellular location. In order to improve the prediction accuracy, we developed a hybrid module using all features of a protein, which consisted of an input vector of 458 dimensions (400 dipeptide compositions, 33 properties, 20 amino acid compositions of the protein and 5 from PSI-BLAST output). Using this hybrid approach, the prediction accuracies of nuclear, cytoplasmic, mitochondrial and extracellular proteins reached 95.3, 85.2, 68.2 and 88.9%, respectively. The overall prediction accuracy of SVM modules based on amino acid composition, physico-chemical properties, dipeptide composition and the hybrid approach was 78.1, 77.8, 82.9 and 88.0%, respectively. The accuracy of all the modules was evaluated using a 5-fold cross-validation technique. Assigning a reliability index (reliability index > or =3), 73.5% of prediction can be made with an accuracy of 96.4%. Based on the above approach, an online web server ESLpred was developed, which is available at http://www.imtech. res.in/raghava/eslpred/.
A functional-based segmentation of human body scans in arbitrary postures

This paper presents a general framework that aims to address the task of segmenting three-dimensional (3-D) scan data representing the human form into subsets which correspond to functional human body parts. Such a task is challenging due to the articulated and deformable nature of the human body. A salient feature of this framework is that it is able to cope with various body postures and is in addition robust to noise, holes, irregular sampling and rigid transformations. Although whole human body scanners are now capable of routinely capturing the shape of the whole body in machine readable format, they have not yet realized their potential to provide automatic extraction of key body measurements. Automated production of anthropometric databases is a prerequisite to satisfying the needs of certain industrial sectors (e.g., the clothing industry). This implies that in order to extract specific measurements of interest, whole body 3-D scan data must be segmented by machine into subsets corresponding to functional human body parts. However, previously reported attempts at automating the segmentation process suffer from various limitations, such as being restricted to a standard specific posture and being vulnerable to scan data artifacts. Our human body segmentation algorithm advances the state of the art to overcome the above limitations and we present experimental results obtained using both real and synthetic data that confirm the validity, effectiveness, and robustness of our approach.
An FNR-Type Regulator Controls the Anaerobic Expression of Hyn Hydrogenase in Thiocapsa roseopersicina

ABSTRACT The purple sulfur photosynthetic bacterium Thiocapsa roseopersicina BBS contains a heat-stable membrane-associated hydrogenase encoded by the hyn operon. Expression from the hyn operon regulatory region is up-regulated under anaerobic conditions. cis elements were mapped between positions −602 and −514 upstream from the hynS gene. Within this region two sequences that resemble DNA sites for FNR were recognized. The gene of an FNR homologue, FnrT, was identified in the genome of T. roseopersicina, and an fnrT knockout mutant was constructed. Anaerobic induction of hynS expression was abolished in the fnrT mutant, suggesting that FnrT is an activator of the hynS promoter. The T. roseopersicina hynS promoter could be activated in Escherichia coli, and this regulation was dependent on E. coli FNR. In vitro experiments with purified E. coli Ala154 FNR protein and purified E. coli RNA polymerase showed that FNR bound to two sites in the hyn regulatory region, that FNR could activate transcription initiation at the hynS promoter, and that FNR bound at the two target sites activated to different extents.
Vector Quantization for State-Action Map Compression &#8212; Comparison with Coarse Discretization Techniques and Efficiency Enhancement

We have proposed to use vector quantization (VQ) for compressing a decision making rule (a policy) on a multi-dimensional memory array. Our VQ method can reduce the amount of memory for recording a policy. In this paper, we compare this method with other techniques that economize the amount of memory in order to measure the ability of this method more rigidly than ever. One of the techniques is tile coding, which is frequently used for reinforcement learning. The other is the mere reduction of the resolution of a policy. Moreover, we try applying VQ to already compressed policies. As a result, vector quantized policies and double vector quantized policies could mark better performance than the others.
Effect of saliva on physical food properties in fat texture perception

ABSTRACT Sensory properties of food drive our food choices and it is generally accepted that lipids greatly contribute to the sensory properties of many foods and consequently to eating pleasure. Many studies have investigated the mechanisms of the fat perception. Unfortunately they used a variety of methods and products, thereby making generalization very difficult. The mechanism of fat perception in oral cavity is combined of several processes. Lipid composition and its properties strongly influence food structure. During consumption food is exposed to a range of in-mouth processing steps. Oral sensation of fat texture changes with time, from a first bite to chewing, while mixing with saliva, up to swallowing and even after swallowing. The present work reviews many aspects of fat texture perception from physical chemistry to physiology. Understanding the underlying mechanisms of in-mouth lipid processing would provide new concepts to produce low-fat food products with full-fat perception.