Miracle-dz/UniFAR-FTUs huggingface dataset:
Using appearance re-matching to improve real-time compressive tracking

A small number of randomly generated linear measurements can preserve most of the salient information of one compressible image according to compress sensing theory. Using these measurements as features can greatly improve the speed of detection based tracking methods, and deal with the problems caused by occlusion, illumination change, pose variation and motion blur to some extent. This paper addressed to improve the state-of-the-art real-time compressive object tracking algorithm, which extracted low-dimensional multistate features of object and background, then used naïve Bayesian classifier combined with online updating mechanism to track object in real-time under the compressed domain. On the basis of its tracking results, we rematch the first 30 candidate targets with online appearance model to search for the optimum tracking position. The experimental results in lot of challenging test sequences show that the proposed algorithm has promising potential.
The indirectly generated tainted truth effect: warning is not necessary to worsen the testimony of non-misled persons

ABSTRACT The purpose of the present research was to examine the indirectly generated tainted truth effect (TTE). The TTE refers to decreased accuracy on a memory test among persons who are erroneously warned about non-existent misinformation in material that describes an event. It was expected that the TTE would occur even without an explicit warning about misinformation, if one distinctive item of misinformation appeared in postevent material that also contained correct information that was consistent with the original material about the event (indirectly generated TTE: IGTTE). Experiment 1 produced the IGTTE and this effect was replicated in Experiment 2. Experiment 2 showed that the classic and indirect versions of the TTE usually occurred among persons who incorrectly monitored the source of information. Experiment 3 replicated the IGTTE using a procedure that included an additional phase between presenting the original and postevent material – initial testing. When the initial testing procedure was administered in Experiment 3, the TTE was smaller; however, this initial testing procedure had no effect on the IGTTE.
Visual-attention-based thumbnail using two-stage GrabCut

This paper proposes a new thumbnail generation method to improve the recognizability of visual attention objects on small displays. Previous methods such as simple scaling reduce the recognizability of original images because the visual attention objects become too small to recognize. When we view thumbnails on small displays such as those of mobile devices, recognizability is indispensable for handling many images simultaneously. To solve the problem of low recognizability of visual attention objects, we adopt GrabCut to extract visual attention objects from an original image and then divide the original image into visual attention objects and a background image. While the background image is reduced to fit the size of a thumbnail, the extracted visual attention objects are merged into the reduced background image to preserve their recognizability. In adopting GrabCut, we propose a two-stage GrabCut method to automate the extraction of attention objects; the extraction was performed by hand in previous methods. Our experimental results show that our proposed method is able to shorten the search time by 44% and improve the precision of the search by 19% in comparison with simple scaling.
GDI-Mediated Cell Polarization in Yeast Provides Precise Spatial and Temporal Control of Cdc42 Signaling

Cell polarization is a prerequisite for essential processes such as cell migration, proliferation or differentiation. The yeast Saccharomyces cerevisiae under control of the GTPase Cdc42 is able to polarize without the help of cytoskeletal structures and spatial cues through a pathway depending on its guanine nucleotide dissociation inhibitor (GDI) Rdi1. To develop a fundamental understanding of yeast polarization we establish a detailed mechanistic model of GDI-mediated polarization. We show that GDI-mediated polarization provides precise spatial and temporal control of Cdc42 signaling and give experimental evidence for our findings. Cell cycle induced changes of Cdc42 regulation enhance positive feedback loops of active Cdc42 production, and thereby allow simultaneous switch-like regulation of focused polarity and Cdc42 activation. This regulation drives the direct formation of a unique polarity cluster with characteristic narrowing dynamics, as opposed to the previously proposed competition between transient clusters. As the key components of the studied system are conserved among eukaryotes, we expect our findings also to apply to cell polarization in other organisms.
IDEPI: Rapid Prediction of HIV-1 Antibody Epitopes and Other Phenotypic Features from Sequence Data Using a Flexible Machine Learning Platform

Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals — a cure and a vaccine – remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.
Dealing With Ambiguous and Fluctuating Requirements of Embedded System Development: A Case-Study

This paper presents the findings of a case study that investigates how developers and managers deal with ambiguous and fluctuating requirements during an embedded system development in a structured process management environment. In particular, this paper focuses on improvisation and bricolage actions as a coping strategy by software developers and managers. This research adopts an interpretive approach that involves the collection and analysis of qualitative data. In this study, we observed a turbulent environment with situated improvisational and bricolage responses from developers and managers. The organizational structured process management framework was not sophisticated enough to deal with the existing challenges. Moreover, some improvisational and bricolage activities became institutionalized and, hence, became organizational routines of developers and managers. This paper indicates the value of reflexive practices as vital issues for strategic conduct in the event that improvisational and bricolage activities were deployed as a coping strategy.
Impact of Cluster Zone Leaf Removal on Grapes cv. Regent Polyphenol Content by the UPLC-PDA/MS Method

Leaf removal is known to enhance light exposure of clusters and therefore may affect grape composition. Owing to the risk of decreasing grape quality or sunburn as a consequence of improper sun exposure, it is crucial to determine the optimum leaf removal techniques adequate for the particular climate conditions of a vineyard area. Defoliation experiments on vine cv. Regent were conducted in two consecutive years (2014 and 2015). The effect of leaf removal treatment on the qualitative and quantitative composition of the polyphenol compounds in the grapes, with reference to the basic weather conditions of the vineyard area, located in Szczecin in the North-Western part of Poland, was assessed. Defoliation was performed manually in the cluster zone at three phenological plant stages: pre-flowering, berry-set and véraison. Leaf removal, especially early defoliation (pre-flowering), enhanced total polyphenol content, including the amount of anthocyanins, flavonols and flavan-3-ols and furthermore, it increased the amount of soluble solids, decreasing the titratable acidity in grapes. On the other hand, the treatments had a reducing impact on the phenolic acids in berries. Defoliation at earlier stages of cluster development appears to be an efficient strategy for improving berry quality in cool climate areas, however, additionally further weather data control is required to determine the effects on berry components.
Concurrency Control in Multi-Role Association

A role shows a job function in an enterprise. In a rolebased access control model, a role is a set of access rights. A subject doing jobs is granted roles showing the jobs. In addition, objects have to be consistent in presence of multiple conflicting transactions. A transaction issued by a subject is associated with a subset of roles granted to the subject, which is named purpose. A method with a more significant purpose is performed before another method with a less significant purpose. We discuss which purpose is more significant than another purpose. We discuss general role-ordering (GRO) schedulers so that multiple conflicting transactions are serializable in the significant order of subjects and purposes. We evaluate the GRO scheduler compared with the traditional two-phase locking protocol.
Entropy-constrained vector quantization

An iterative descent algorithm based on a Lagrangian formulation for designing vector quantizers having minimum distortion subject to an entropy constraint is discussed. These entropy-constrained vector quantizers (ECVQs) can be used in tandem with variable-rate noiseless coding systems to provide locally optimal variable-rate block source coding with respect to a fidelity criterion. Experiments on sampled speech and on synthetic sources with memory indicate that for waveform coding at low rates (about 1 bit/sample) under the squared error distortion measure, about 1.6 dB improvement in the signal-to-noise ratio can be expected over the best scalar and lattice quantizers when block entropy-coded with block length 4. Even greater gains are made over other forms of entropy-coded vector quantizers. For pattern recognition, it is shown that the ECVQ algorithm is a generalization of the k-means and related algorithms for estimating cluster means, in that the ECVQ algorithm estimates the prior cluster probabilities as well. Experiments on multivariate Gaussian distributions show that for clustering problems involving classes with widely different priors, the ECVQ outperforms the k-means algorithm in both likelihood and probability of error. >
Surface forms trump underlying representations in functional generalisations in speech perception: the case of German devoiced stops

ABSTRACT Listeners can adapt their perceptual categories for speech sounds in response to speakers’ unusual pronunciations. The present study tested whether this generalisation is sensitive to surface or underlying properties of speech by exploiting the devoicing of voiced stops in German. This allows us to assess whether learning on phonetically voiceless stops that are underlyingly voiced generalises to stops that share the same surface form (i.e. voiceless) or the same underlying representation (i.e. voiced). Our results showed only minimal generalisation: learning for (surface) voiceless stops in offset position that are underlyingly voiced generalises to surface and underlying voiceless stops in the same position but neither to voiced nor voiceless stops in intervocalic position. This suggests that listeners extract segments of sufficient acoustic similarity from the input and use them for generalisation of learning in speech perception. The units of perception thereby appear context sensitive rather than abstract phonemes or phonological/articulatory features.
Using the Mutual k-Nearest Neighbor Graphs for Semi-supervised Classification on Natural Language Data

The first step in graph-based semi-supervised classification is to construct a graph from input data. While the k-nearest neighbor graphs have been the de facto standard method of graph construction, this paper advocates using the less well-known mutual k-nearest neighbor graphs for high-dimensional natural language data. To compare the performance of these two graph construction methods, we run semi-supervised classification methods on both graphs in word sense disambiguation and document classification tasks. The experimental results show that the mutual k-nearest neighbor graphs, if combined with maximum spanning trees, consistently outperform the k-nearest neighbor graphs. We attribute better performance of the mutual k-nearest neighbor graph to its being more resistive to making hub vertices. The mutual k-nearest neighbor graphs also perform equally well or even better in comparison to the state-of-the-art b-matching graph construction, despite their lower computational complexity.
The three-dimensional distribution of nerves along the entire intrapulmonary airway tree of the adult rat and the anatomical relationship between nerves and neuroepithelial bodies.

Using airway microdissection and three-dimensional confocal microscopy techniques in combination with the immunomarkers protein gene product (PGP) 9.5 and calcitonin gene-related peptide (CGRP), we defined the distribution of small afferent nerves fibers and all nerves throughout the intrapulmonary airways, along with the distribution of airway neuroendocrine cells and neuroepithelial bodies. We found (i) the presence of CGRP-and PGP 9.5-positive structures along the entire intrapulmonary airway tree of adult rats, (ii) decreasing nerve density from more proximal to more distal generations of conducting airways, (iii) the presence of nerve fibers in terminal bronchioles, (iv) the asymmetrical distribution of nerves within a single generation of intrapulmonary airway with regard to associated vessels, (v) the frequent interchange of single nerve fibers across epithelial and sub-epithelial compartments without termination, and (vi) a definably intimate relationship between afferent nerves and neuroepithelial bodies (NEBs) (i.e., 58% of NEBs studied were observed to have nerve fibers coursing through them, indicating direct connections). We conclude that the distribution of nervous elements (nerve fibers and neuroendocrine cells) within the intrapulmonary airways is highly heterogeneous, varying between airway levels and locally within a specific airway level.
relifix: Automated Repair of Software Regressions

Regression occurs when code changes introduce failures in previously passing test cases. As software evolves, regressions may be introduced. Fixing regression errors manually is time-consuming and error-prone. We propose an approach of automated repair of software regressions, called relifix, that considers the regression repair problem as a problem of reconciling problematic changes. Specifically, we derive a set of code transformations obtained from our manual inspection of 73 real software regressions; this set of code transformations uses syntactical information from changed statements. Regression repair is then accomplished via a search over the code transformation operators - which operator to apply, and where. Our evaluation compares the repairability of relifix with GenProg on 35 real regression errors. relifix repairs 23 bugs, while GenProg only fixes five bugs. We also measure the likelihood of both approaches in introducing new regressions given a reduced test suite. Our experimental results shows that our approach is less likely to introduce new regressions than GenProg.
Scalable Traffic Dependence Analysis for Detecting Android Malware Activities

The openness of Android application development mechanism poses security challenges to smartphone users. Malicious apps (malware) may be created by repackaging popular apps. At runtime, they directly fetch and run code on-thefly without the user’s knowledge [6]. Thereafter, malicious apps may spy on the victim users, stealthily collect and exfiltrate user’s information. Therefore, they threaten the data confidentiality and system integrity on Android devices. Existing static analysis solutions inspect the source code, binaries or call sequences for anomalies. For example, SCSDroid [7] identifies the malicious apps by extracting the subsequences of system calls. However, dynamic code loading, Java reflection-based method invocation, data encryption, and self-verification of signatures are commonly seen in the malware code [4]. These types of code obfuscation make static analysis based detection challenging. Dynamic analysis, as a complementary to the static analysis, detects the runtime behaviors of the malicious apps (e.g., verifying the sensitive information flows through the apps [3]). In this work, we aim at identifying malicious apps by analyzing their dynamic behaviors, specifically the network traffic. Our goal is to profile the normalcy of the mobile traffic patterns of benign apps, and thus to detect the malicious network requests that are sent without user’s awareness. We propose a triggering relation model to formalize the causal relations of app-generated network requests in Android. The problem of dependency discovery on network data was first introduced in [12]. The dependency of network requests is defined as the triggering relationship (TR). The triggering relation of two network requests ri and rj exists if rj cannot be issued unless ri is sent out first. This temporal and causal relationship can be represented in a di-
A deep quasi-linear kernel composition method for support vector machines

In this paper, we introduce a data-dependent kernel called deep quasi-linear kernel, which can directly gain a profit from a pre-trained feedforward deep network. Firstly, a multi-layer gated bilinear classifier is formulated to mimic the functionality of a feed-forward neural network. The only difference between them is that the activation values of hidden units in the multi-layer gated bilinear classifier are dependent on a pre-trained neural network rather than a pre-defined activation function. Secondly, we demonstrate the equivalence between the multi-layer gated bilinear classifier and an SVM with a deep quasi-linear kernel. By deriving a kernel composition function, traditional optimization algorithms for a kernel SVM can be directly implemented to implicitly optimize the parameters of the multi-layer gated bilinear classifier. Experimental results on different data sets show that our proposed classifier obtains an ability to outperform both an SVM with a RBF kernel and the pre-trained feedforward deep network.
The effect of saliva decontamination procedures on dentin bond strength after universal adhesive curing

Objectives The purpose of this study was to investigate the effectiveness of multiple decontamination procedures for salivary contamination after curing of a universal adhesive on dentin bond strength according to its etch modes. Materials and Methods Forty-two extracted bovine incisors were trimmed by exposing the labial dentin surfaces and embedded in cylindrical molds. A universal adhesive (All-Bond Universal, Bisco) was used. The teeth were randomly divided into groups according to etch mode and decontamination procedure. The adhesive was applied according to the manufacturer's instructions for a given etch mode. With the exception of the control groups, the cured adhesive was contaminated with saliva for 20 sec. In the self-etch group, the teeth were divided into three groups: control, decontamination with rinsing and drying, and decontamination with rinsing, drying, and adhesive. In the etch-and-rinse group, the teeth were divided into four groups: control, decontamination with rinsing and drying, decontamination with rinsing, drying, and adhesive, and decontamination with rinsing, drying, re-etching, and reapplication of adhesive. A composite resin (Filtek Z350XT, 3M ESPE) was used for filling and was cured on the treated surfaces. Shear bond strength was measured, and failure modes were evaluated. The data were subjected to one-way analysis of variation and Tukey's HSD test. Results The etch-and-rinse subgroup that was decontaminated by rinse, drying, re-etching, and reapplication of adhesive showed a significantly higher bond strength. Conclusions When salivary contamination occurs after curing of the universal adhesive, additional etching improves the bond strength to dentin.
Hepatitis C Virus (HCV) Infection May Elicit Neutralizing Antibodies Targeting Epitopes Conserved in All Viral Genotypes

Anti-hepatitis C virus (HCV) cross-neutralizing human monoclonal antibodies, directed against conserved epitopes on surface E2 glycoprotein, are central tools for understanding virus-host interplay, and for planning strategies for prevention and treatment of this infection. Recently, we developed a research aimed at identifying these antibody specificities. The characteristics of one of these antibodies (Fab e20) were addressed in this study. Firstly, using immunofluorescence and FACS analysis of cells expressing envelope HCV glycoproteins, Fab e20 was able to recognize all HCV genotypes. Secondly, competition assays with a panel of mouse and rat monoclonals, and alanine scanning mutagenesis analyses located the e20 epitope within the CD81 binding site, documenting that three highly conserved HCV/E2 residues (W529, G530 and D535) are critical for e20 binding. Finally, a strong neutralizing activity against HCV pseudoparticles (HCVpp) incorporating envelope glycoproteins of genotypes 1a, 1b, 2a, 2b and 4, and against the cell culture-grown (HCVcc) JFH1 strain, was observed. The data highlight that neutralizing antibodies against HCV epitopes present in all HCV genotypes are elicited during natural infection. Their availability may open new avenues to the understanding of HCV persistence and to the development of strategies for the immune control of this infection.