Miracle-dz/UniFAR-FTUs huggingface dataset:
Using reads to annotate the genome: influence of length, background distribution, and sequence errors on prediction capacity

Ultra high-throughput sequencing is used to analyse the transcriptome or interactome at unprecedented depth on a genome-wide scale. These techniques yield short sequence reads that are then mapped on a genome sequence to predict putatively transcribed or protein-interacting regions. We argue that factors such as background distribution, sequence errors, and read length impact on the prediction capacity of sequence census experiments. Here we suggest a computational approach to measure these factors and analyse their influence on both transcriptomic and epigenomic assays. This investigation provides new clues on both methodological and biological issues. For instance, by analysing chromatin immunoprecipitation read sets, we estimate that 4.6% of reads are affected by SNPs. We show that, although the nucleotide error probability is low, it significantly increases with the position in the sequence. Choosing a read length above 19 bp practically eliminates the risk of finding irrelevant positions, while above 20 bp the number of uniquely mapped reads decreases. With our procedure, we obtain 0.6% false positives among genomic locations. Hence, even rare signatures should identify biologically relevant regions, if they are mapped on the genome. This indicates that digital transcriptomics may help to characterize the wealth of yet undiscovered, low-abundance transcripts.
Spectrophotometric Evolution of Spiral Galaxies with Truncated Star Formation: An Evolutionary Link between Spirals and S0s in Distant Clusters

A one-zone chemospectrophotometric model is used to investigate the time evolution of disk galaxies whose star formation is truncated and to determine the dependence of this evolution on the previous star formation history and the truncation epoch. Truncated spirals show red colors ~1 Gyr after truncation and evolve spectrally from an e(b) type, down through the e(a), a+k, and k+a classes, to finally become a k type. The exact behavior in this phase depends on the truncation epoch and the star formation history prior to truncation. For example, earlier type disks show redder colors and do not show a+k-type spectra after truncation of star formation. We also discuss a possible evolutionary link between the k-type galaxies with spiral morphology found in distant clusters and present-day S0s by investigating whether truncated spirals reproduce the infrared color-magnitude relation of Coma galaxies. We suggest that only less luminous, later-type disk galaxies whose star formation is truncated at intermediate and high redshifts can reproduce the red I-K colors observed for S0s in the Coma cluster.
Model of secure P2P mobile instant messaging based on virtual network

Mobile Instant Messenger is one of the most application which use to exchange a message between Sender and Receiver over public network, so that it is very important building an application with good level of security aspect and fast communication. This paper propose a secure Mobile IM in excanging data over internet with an virtual network method. In this model, we propose a Mobile Instant Messenger with virtual network. It will implemented in JXTA protocols which establish a virtual network overlay on top of the Internet, allowing peers or device to directly interact and self-organize independently of their network connectivity and domain topology. This method developed with Pipes as virtual communication channels used to send and receive messages between services and applications, the pipe binding consists of searching and connecting the two or more ends of a pipe. This is new approach of IM communication method, that is message must be deliver over virtual network with good level security.
Prevalence of different types of gallstone in the patients with cholelithiasis at Kathmandu Medical College, Nepal.

BACKGROUND Gallstone disease known as cholelithiasis is the most common digestive surgical disorder and account for an important part of health care expenditure. Attempt was made to analyse the gallstone for typing depending upon the composition. AIMS & OBJECTIVES The main objective of this study was to see the prevalence of different types of gallstone in Nepal and to correlate them with the clinical findings. MATERIALS & METHODS Gallstones of 80 different patients who underwent cholecystectomy for cholelithiasis were collected from 20th January 2005 to 16th May 2006 in Department of Pathology, Kathmandu Medical College Teaching Hospital. Detailed history was taken. Stones were analyzed with chemical and enzymatic methods using clinical spectrophotometer. RESULTS & CONCLUSION The most commonly involved age group for cholelithiasis (32.5%) is found to be 30-39 years with a female predominance (M: F=1:3.2). Cholelithiasis was found more commonly among non-vegetarian with the vegetarian: non-vegetarian ratio 1:9. Mixed type stone was found to be the most common type of stone comprising 78.75%, followed by cholesterol stone 12.5%, Brown pigment stone 7.5% and Black pigment stone 1.25%.
Resolving Elided Scopes of Modality in OntoAgent

This paper describes how intelligent agents modeled within the OntoAgent cognitive architecture treat the linguistic phenomenon of modal scope ellipsis. The approach offers two innovative aspects with respect to cognitive modeling: (1) ellipsis treatment is distributed across processing modules in a psychologically plausible way and (2) agents are prepared to incorporate calculations of utility into their ellipsis resolution efforts. The latter means that agents can evaluate their confidence in each ellipsis resolution decision and, in cases of low confidence, determine whether or not it is worthwhile to pursue a clarification. Endowing agents with such decision making capabilities about language processing creates an environment in which it is feasible and useful to attempt even the more sophisticated aspects of language processing in the near term.
A fast approximate interprocedural analysis for speculative multithreading compilers

Speculative multithreading (SpMT) promises to be very effective for parallelizing non-numeric programs. Data dependences are perhaps the most important factor in the crucial step of deciding the thread boundaries, and SpMT compilers need to estimate data dependences as precisely as possible. Interprocedural points-to and side-effects information is essential to accurately determine data dependences. Determining this information has been an Achilles' heel in SpMT compilation. Existing interprocedural analyses are unsuitable for large programs, as they have high complexity or generate imprecise (very conservative) information that is not useful for aggressive speculative parallelization.This paper presents a novel interprocedural analysis scheme that is highly effective for use in SpMT compilers. It has running time and memory requirement almost linear in the number of procedures in the input program. The information it generates is comparable in accuracy with those generated by highly accurate context-sensitive interprocedural analyses, although it does not guarantee safe information (which is not a requirement for SpMT compilers any way). We implemented this interprocedural analysis in our SpMT compiler, and parallelized large non-numeric programs from the SPEC 2000 suite. Parallel execution of these threads in an SpMT simulator demonstrate good speedups for these non-numeric programs.
Continuous Parabolic Molecules

Decomposition systems based on parabolic scaling have in the last years garnered attention for their ability to answer questions regarding curvilinear singularities of functions. Well known examples of these systems are curvelets and shearlets. In recent years there has been a sufficient body of evidence to suggest that these systems are able to answer the same fundamental questions and it should thus be possible to consider them as parts of a broader framework. Thus far each such system required proofs of their properties that are tailored to their specific constructions, which is a predicament that can be avoided by focusing on the fundamental features they share. Another incentive is that while these systems exhibit same or similar properties, the specifics of their constructions might make a difference. For example, some systems are good for theoretical considerations whereas other systems might be better suited for implementations. In this paper we will construct a framework for parabolic molecules in the continuous setting, and show that it is wide enough to contain both the curveletand shearlet-type systems. Using almost-orthogonality we will show that some results of note (resolution of the wavefront set, microlocal Sobolev regularity) are universal for all suitable continuous parabolic molecules. The main tool we will use is that molecules are almost-orthogonal in a certain sense. Email: philipp.grohs@sam.math.ethz.ch :Email: zeljko.kereta@math.ethz.ch 1
Using Bag-of-Concepts to Improve the Performance of Support Vector Machines in Text Categorization

This paper investigates the use of concept-based representations for text categorization. We introduce a new approach to create concept-based text representations, and apply it to a standard text categorization collection. The representations are used as input to a Support Vector Machine classifier, and the results show that there are certain categories for which concept-based representations constitute a viable supplement to word-based ones. We also demonstrate how the performance of the Support Vector Machine can be improved by combining representations.
Learning Impairment and Cholinergic Deafferentation after Cortical Nerve Growth Factor Deprivation

Cholinergic basal forebrain (CBF ) neurons have been shown to respond in vivo to exogenous administration of NGF. Although neurotrophins and their receptors are widely expressed in the CNS, little data exist for the physiological significance of endogenous neurotrophin signaling in CNS neurons. To test directly whether cortically derived NGF is functionally required for the cholinergic functions mediated by the cerebral cortex, repeated injections of anti-NGF mAbs were locally applied into the insular cortex (IC) of rats. The biochemical results, using an in vivomicrodialysis technique, showed a dramatic lack of extracellular release of acetylcholine after high potassium stimulation compared with controls. Furthermore, by using small injections of the neurotracer fluorogold, we found a corresponding disruption in the connectivity between the IC and the CBF. Behavioral experiments showed that the NGF antibodies applied into the IC produced a significant disruption on the acquisition of conditioned taste aversion and inhibitory avoidance learning. However, the same animals were able to recall the taste aversion when the conditioning trial was established before injections of NGF antibodies. Given these results, it seems that cortical cholinergic functions are actively dependent on locally derived NGF in the adult normal brain, and that the cholinergic activity from the CBF is not necessary for recalling aversive stimuli, but is necessary for the acquisition of aversively motivated conditionings.
Entrepreneurial Signaling: Success Factor for Innovative Start-Ups

Innovative start-ups and their respective market partners are faced with severe problems of asymmetric information due to their lack of prior production history and reputation. We study whether entrepreneurial signaling can help solve these problems and thereby increase the potential success of innovative start-ups. We concentrate our analysis on the credit and labor market because they are crucial for the success of innovative start-ups and focus on the role of educational signals. We argue that entrepreneurs signal their quality to potential employees and creditors with certain characteristics of their educational history. According to our theoretical considerations we expect potential employees to use an entrepreneur's university degree as a quality signal when deciding whether to accept a job at an innovative start-up. And we expect banks to use a more precise indicator, namely the actual length of study in relation to a standard length, as a signal when deciding upon credits for an innovative founder. However, since asymmetric information problems and skill requirements are different for traditional start-ups we do not expect employees or banks to use the same signals for traditional start-ups. We empirically test our implications based on a dataset of more than 700 German start-ups collected in 1998/99. All implications are borne out in the data. So contrary to conventional wisdom, educational degrees and studying fast (not just studying) are even more important success factors for innovative than for traditional start-ups.
Weighted pairwise scatter to improve linear discriminant analysis

Linear Discriminant Analysis (LDA) aims to transform an original feature space to a lower dimensional space with as little loss in discrimination as possible. We introduce a novel LDA matrix computation that incorporates confusability information between classes into the transform. Our goal is to improve discrimination in LDA. In conventional LDA, a between class covariance matrix that is based on the scatter of class means around the global mean is used. By rewriting the between class covariance expression in a more revealing way, we unveil that each class pair is considered equally confusable in the conventional LDA. We introduce a weighting factor for each pairwise scatter that enables to integrate the confusability information into the between class covariance matrix. There are many possibilities to choose the weighting factors. We consider few of them that depend on Euclidean and Kullback-Leibler distances between classes when a single Gaussian approximation is used for each class. The method combined with speaker cluster based transformation decreases the error rate by about relative 10% on a large vocabulary speech recognition task using IBM’s speech recognition engine.
An intervention using digital social media to support academic writing of university students: a case study

Many university students, both native and non-native speakers of English, seek ongoing discipline-based academic writing support. However, this type of support within the university sector has yet to be broadly implemented due to cost and practical concerns. their overall academic performance. The effects of using digital social media as a platform in providing academic writing support for university students was investigated within this research. The research also intended to investigate university -efficacy beliefs in this context. It is believed that self-efficacy functions as a significant predictor of academic writing performance. This study included three main phases pre-intervention, intervention and post-intervention—with 25 university students from a regional Australian university. A case study method was incorporated that accompanied quantitative and qualitative methods within an overall qualitative design. Self-efficacy questionnaires, interviews and field notes were used to collect data in this study. The intervention phase provided the participants with academic writing support by the researcher via digital social media for four weeks. The analysed data of this case study showed improvement in the participants’ academic writing self-efficacy beliefs after the academic writing support they gained during the intervention phase. The reason for this improvement can be found in the use of a digital social media platform that enabled the provision of discipline-based academic writing support when the participants needed it most. The results suggest that digital social media may be a beneficial platform for providing ongoing discipline-based academic writing support for university students.
3D MAPPING TECHNIQUES USING A STEREO BOOM ON LOW-FLYING VEHICLES

The Unmanned Systems Lab at Virginia Tech has developed a calibrated 1.5 meter (5 feet) long stereo camera boom that can be attached to a low flying aerial vehicle in order to collect imagery for creating dense 3D reconstructions. To construct the 3D terrain maps, we use two separate techniques. The first is an altered version of the semi-global block matching (SGBM) algorithm. This implementation uses sequential stereo image frames to improve the resolution obtained by using SGBM through a bundle adjustment. This technique produces 3D reconstructions with height resolution greater than is possible using standard disparity mapping techniques. The second is an extension of the structure-from-motion program known as Bundler. The original Bundler uses a random set of images to determine camera locations and orientations in a local reference frame, and creates a sparse 3D model of the visible terrain. Our technique uses GPS data to remove the scale ambiguity of Bundler and to orient the models in the world reference frame. We then provide the Bundler results to PMVS software in order to build intricate models of the local terrain. The result of this process is a detailed map which is oriented in world coordinates. This paper describes the hardware, functionality, and limitations of the stereo boom system as well as describing the improvements to the 3D reconstruction algorithms.
Hybridization of appearance and symmetry for vehicle-logo localization

Vehicle logo detection is of great importance in Intelligent Transportation System (ITS) applications since it augments the traditional License Plate Recognition (LPR) based vehicle identification solutions. An algorithm based on the combination of feature appearance and symmetry is proposed to locate the vehicle logo in a digital image. The air-intake grille, as the most recognizable visual structure in the front view of the vehicle, can be efficiently detected and recognized thereby providing information about relative position of vehicle logos. Symmetry, a widely used feature for analyzing vehicle front-view images, has also been taken into consideration along with grille detection in this paper. This hybrid scheme facilitates more accurate vehicle logo detection rate in comparison with using isolated symmetry (which performs poorly on scenes with complex background) or edge statistics (which cannot handle non/small-grille vehicles). Experimental results on a large number of images validate the robustness and efficiency of the proposed algorithm.
Event-based prospective memory in patients with Parkinson’s disease: the effect of emotional valence

The present study investigated the effect of Parkinson’s disease (PD) on prospective memory (PM) tasks by varying the emotional content of the PM actions. Twenty-one older adults with PD and 25 healthy older adults took part in the present study. Participants performed three virtual days in the Virtual Week task. On each virtual day, participants performed actions with positive, negative or neutral content. Immediately following each virtual day, participants completed a recognition task to assess their retrospective memory for the various PM tasks. PD patients were less accurate than the control group at both PM accuracy and recognition task accuracy. The effect of emotional valence was also evident, indicating that all participants were more accurate on positive PM tasks than both negative and neutral. This study confirmed PM impairment in PD patients and extended previous research showing how positive emotional stimuli can influence PM performance.
A test of TMT: Mortality salience and avoidance of worldview threats

Imagine playing a game of catch with three people, and all the things that would influence to whom you throw. Were thoughts of death included as one of those factors? The hypothesis that mortality salience motivates avoidant behavior toward a worldviewthreatening target was empirically tested on a sample of 200 undergraduate Caucasian college students from a Midwestern university. I induced participants to write about either their own death or dental pain, and presented them with summer descriptions of three fictitious male African American targets that they believed they were going to interact with later in the study. Targets' descriptions were manipulated to be either consistent, neutral, or inconsistent with American stereotypes commonly associated with African Americans. Participants completed several personality and attitude measures about themselves and targets, and then played a simulated game of catch called cyberball over the internet, supposedly with the three targets who were in different locations. Participants demonstrated a preference to toss the ball more to a stereotype inconsistent African American male target regardless of mortality salience condition or participants' individual level of need for closure. Attitude and trait ratings mirrored the above results, with the exception of attitude ratings toward the stereotype consistent target, in which need for closure moderated the effects of mortality salience. Participants in the mortality salience condition who were high in need for closure rated the stereotype consistent target less favorably than both neutral and stereotype inconsistent targets. These results suggest that mortality salience may not demonstrate the prominent effects on attitudes and behaviors that terror management theory has suggested (Solomon, Greenberg, & Pyszczynski, 2004). However, the results do imply that stereotypes influence behavioral interaction with and favorability toward African Americans. In accordance with research on the effects of social exclusion, the increased avoidance of an African American based on his or her consistency with prominent African American stereotypes may in fact evoke behaviors that validate these negative stereotypes (i.e., hostility, social loafing, and decreased cognitive ability) and thus perpetuate the stereotypes associated with African Americans. A TEST OF TMT: MORTALITY SALIENCE AND AVOIDANCE OF WORLDVIEW THREATS
Neighbor Selection and Weighting in User-Based Collaborative Filtering: A Performance Prediction Approach

User-based collaborative filtering systems suggest interesting items to a user relying on similar-minded people called neighbors. The selection and weighting of these neighbors characterize the different recommendation approaches. While standard strategies perform a neighbor selection based on user similarities, trust-aware recommendation algorithms rely on other aspects indicative of user trust and reliability. In this article we restate the trust-aware recommendation problem, generalizing it in terms of performance prediction techniques, whose goal is to predict the performance of an information retrieval system in response to a particular query. We investigate how to adopt the preceding generalization to define a unified framework where we conduct an objective analysis of the effectiveness (predictive power) of neighbor scoring functions. The proposed framework enables discriminating whether recommendation performance improvements are caused by the used neighbor scoring functions or by the ways these functions are used in the recommendation computation. We evaluated our approach with several state-of-the-art and novel neighbor scoring functions on three publicly available datasets. By empirically comparing four neighbor quality metrics and thirteen performance predictors, we found strong predictive power for some of the predictors with respect to certain metrics. This result was then validated by checking the final performance of recommendation strategies where predictors are used for selecting and/or weighting user neighbors. As a result, we have found that, by measuring the predictive power of neighbor performance predictors, we are able to anticipate which predictors are going to perform better in neighbor-scoring-powered versions of a user-based collaborative filtering algorithm.