Miracle-dz/UniFAR-FTUs huggingface dataset:
A Scalable Unsupervised Feature Merging Approach to Efficient Dimensionality Reduction of High-Dimensional Visual Data

To achieve a good trade-off between recognition accuracy and computational efficiency, it is often needed to reduce high-dimensional visual data to medium-dimensional ones. For this task, even applying a simple full-matrix-based linear projection causes significant computation and memory use. When the number of visual data is large, how to efficiently learn such a projection could even become a problem. The recent feature merging approach offers an efficient way to reduce the dimensionality, which only requires a single scan of features to perform reduction. However, existing merging algorithms do not scale well with high-dimensional data, especially in the unsupervised case. To address this problem, we formulate unsupervised feature merging as a PCA problem imposed with a special structure constraint. By exploiting its connection with k-means, we transform this constrained PCA problem into a feature clustering problem. Moreover, we employ the hashing technique to improve its scalability. These produce a scalable feature merging algorithm for our dimensionality reduction task. In addition, we develop an extension of this method by leveraging the neighborhood structure in the data to further improve dimensionality reduction performance. In further, we explore the incorporation of bipolar merging - a variant of merging function which allows the subtraction operation - into our algorithms. Through three applications in visual recognition, we demonstrate that our methods can not only achieve good dimensionality reduction performance with little computational cost but also help to create more powerful representation at both image level and local feature level.
Fighting bullying with the help of autonomous agents in a virtual school environment

Using empathic virtual agents for Personal and Social Education is a powerful and immersive way to sensitise students for the problems and dangers related to persistent aggressive behaviour among students, also known as bullying. The work reported here relates to the evaluation of applying such agents in schools in the UK, Portugal, and Germany as well as to consequences that can be drawn from these evaluation results. The Bullying Problem The problem of bullying is well-known to educators and parents across Europe. Among others, the enduring victimisation of one student by one or more other students may cause health problems, depression, truancy, and even suicide in the victim. It has been defined as “repeated action that occurs regularly over time, and usually involves an imbalance in strength, either real or perceived” (Olweus, 1999). Bullying means various types of aggressive behaviour including punching, kicking, hitting (direct physical bullying), teasing and cruel name-calling (direct verbal bullying) and rather indirect actions like social exclusion or deliberate withdrawal from friendship (indirect relational bullying). To fight the serious long-term effects, the EU sponsored project VICTEC (Virtual ICT with Empathic Characters) intended to deliver a software product based on episodic virtual drama to be used in schools with the aim to enhance students’ insight in the destructive nature of bullying and sensitise them for the harmful consequences for the victim. By using a 3D virtual environment for education purpose, students are encouraged to explore the problem of bullying in a save and exiting environment. The basic idea is to encourage empathic processes towards the virtual victims of bullying and hence create sensitivity towards the bullying problem.
Neural nets for adaptive filtering and adaptive pattern recognition

The adaptive linear combiner (ALC) is described, and practical applications of the ALC in signal processing and pattern recognition are presented. Six signal processing examples are given, which are system modeling, statistical prediction, noise canceling, echo canceling, universe modeling, and channel equalization. Adaptive pattern recognition using neural nets is then discussed. The concept involves the use of an invariance net followed by a trainable classifier. It makes use of a multilayer adaptation algorithm that descrambles output and reproduces original patterns. <<ETX>>
The Linkage Between Reporting Quality and Performance in Information Systems Projects

Recent research suggests that flawed status reporting is a serious concern in information systems projects. Several investigations have sought to understand the factors that lead project reporters to engage in misreporting. The main motivation for these studies has been the presumption that inaccurate reporting has a significant, negative impact on project performance. However, the linkage between reporting quality and project performance has not been empirically confirmed. The goal of this effort was to answer the following research question: Is reporting quality associated with project outcomes? Our investigation consisted of two complementary survey studies. The first study considered the perceptions of status report senders; the second study considered the perceptions of status report receivers. Both studies showed that reporting quality is positively associated with task and psychological outcomes. Moreover, the second study’s results suggest that reporting quality is also related to organizational outcomes.
Seamless formal verification of complex event processing applications

Despite proven successful in previous projects, the use of formal methods for enhancing quality of software is still not used in its full potential in industry. We argue that seamless support for formal verification in a high-level specification tool enhances the attractiveness of using a formal approach for increasing software quality. Commercial Complex Event Processing (CEP) engines often have support for modelling, debugging and testing CEP applications. However, the possibility of utilizing formal analysis is not considered. We argue that using a formal approach for verifying a CEP system can be performed without expertise in formal methods. In this paper, a prototype tool REX is presented with support for specifying both CEP systems and correctness properties of the same application in a high-level graphical language. The specified CEP applications are seamlessly transformed into a timed automata representation together with the high-level properties for automatic verification in the model-checker UPPAAL.
A Mac-error-warning method for SCTP congestion control over high BER wireless network

The problem of high BER (bit error rate) usually plagues the wireless connection, especially for some real time applications such as VoIP (voice over IP) and some military uses. The newly developed transport layer protocol SCTP (stream control transmission protocol) also has to face this problem. Though equipped with many new features, SCTP congestion control mechanism fails to distinguish wireless loss from congestion loss, thus its performance over high BER wireless network suffers from unnecessary congestion window decreasing. To improve the performance of SCTP in such a scenario, a Mac-error-warning method is proposed in this paper. Simulation experiments conducted through extended ns-2 validated that the proposed method could achieve higher throughput. The throughput improvement arrives at 946.22% when BER is 0.0005.
Crystal structures of Thermotoga maritima reverse gyrase: inferences for the mechanism of positive DNA supercoiling

Reverse gyrase is an ATP-dependent topoisomerase that is unique to hyperthermophilic archaea and eubacteria. The only reverse gyrase structure determined to date has revealed the arrangement of the N-terminal helicase domain and the C-terminal topoisomerase domain that intimately cooperate to generate the unique function of positive DNA supercoiling. Although the structure has elicited hypotheses as to how supercoiling may be achieved, it lacks structural elements important for supercoiling and the molecular mechanism of positive supercoiling is still not clear. We present five structures of authentic Thermotoga maritima reverse gyrase that reveal a first view of two interacting zinc fingers that are crucial for positive DNA supercoiling. The so-called latch domain, which connects the helicase and the topoisomerase domains is required for their functional cooperation and presents a novel fold. Structural comparison defines mobile regions in parts of the helicase domain, including a helical insert and the latch that are likely important for DNA binding during catalysis. We show that the latch, the helical insert and the zinc fingers contribute to the binding of DNA to reverse gyrase and are uniquely placed within the reverse gyrase structure to bind and guide DNA during strand passage. A possible mechanism for positive supercoiling by reverse gyrases is presented.
Adaptive CPU scheduling policies for mixed multimedia and best-effort workloads

As multimedia applications with real-time constraints rapidly invade today's desktops, it becomes increasingly important for the operating system to provide robust resource allocation mechanisms for both multimedia and traditional best-effort workloads. We present a flexible CPU scheduling policy that adjusts the CPU proportion allocated to each application class using recent history as a feedback mechanism. The algorithm quickly adapts to varying workload conditions and compares favorably with static proportional scheduling schemes for mixed workloads.
Multimodal Stereo Vision Using Mutual Information with Adaptive Windowing

This paper proposes a method for computing disparity maps from a multimodal stereovision system composed of an infrared and a visible camera pair. The method uses mutual information (MI) as the basic similarity measure where a segmentation-based adaptive windowing mechanism is proposed for greatly enhancing the results. On several datasets, we show that (i) our proposal improves the quality of existing MI formulation, and (ii) our method can provide depth comparable to the quality of Kinect depth data.
Effect of Cholesterol Anchoring Group on the Properties of G-Quadruplex-Based FRET Probes for Potassium Ion

In this study we report on the spectral properties and G-quadruplex folding ability of fluorescent oligonucleotide probes modified by the attachment of a cholesterol moiety. These probes were designed and studied in order to verify their potential as potassium-sensing devices that can be incorporated into the cellular membrane. The 19-meric guanine-rich deoxyoligonucleotide was labeled with reporter fluorescent FRET groups (FAM and TAMRA) and a cholesterol anchor was attached using different approaches. The probes exhibited abilities to fold into a quadruplex structure and to bind metal cations (Na+ and K+). In an unbound state, both termini of the oligonucleotide are separated, thus fluorophores do not interact with each other and the probe exhibits an unperturbed fluorescence spectrum. In the presence of K+, the quadruplex structure is developed such that it enables fluorophores to be arranged in close proximity, causing generation of a different fluorescence spectrum (FRET signal). Folding properties of probes and their spectral behavior were examined by recording the UV-Vis, fluorescence emission, and excitation spectra (FRET efficiency), and the temperature stability of G-quadruplex structures adopted by probes (melting profiles). Fluorescence energy transfer efficiency increased with increases in sodium or potassium ion concentrations in an aqueous solution, which indicated that the probes retained their cation-binding properties and could be promising candidates for potassium sensing at the cell membrane interface.
Netscape Plug-in for Wavelet Compressed Images

Current schemes for image transmission over Internet require getting the whole stream to display picture. We propose a new approach, based on wavelet decomposition and embedded zerotree algorithm, allowing users to decide which fraction of the whole stream is to be chosen in order to obtain the satisfactory quality of the image. We take advantage of the new plug-in technology, developed by Netscape, allowing users to include a new data type into a HTML document. Wavelet compressed images created by the plug-in appear to the user as another part of the HTML document.
Non-Viral Transfection Methods Optimized for Gene Delivery to a Lung Cancer Cell Line

Background Mehr-80 is a newly established adherent human large cell lung cancer cell line that has not been transfected until now. This study aims to define the optimal transfection conditions and effects of some critical elements for enhancing gene delivery to this cell line by utilizing different non-viral transfection Procedures. Methods In the current study, calcium phosphate (CaP), DEAE-dextran, superfect, electroporation and lipofection transfection methods were used to optimize delivery of a plasmid construct that expressed Green Fluorescent Protein (GFP). Transgene expression was detected by fluorescent microscopy and flowcytometry. Toxicities of the methods were estimated by trypan blue staining. In order to evaluate the density of the transfected gene, we used a plasmid construct that expressed the Stromal cell-Derived Factor-1 (SDF-1) gene and measured its expression by real-time PCR. Results Mean levels of GFP-expressing cells 48 hr after transfection were 8.4% (CaP), 8.2% (DEAE-dextran), 4.9% (superfect), 34.1% (electroporation), and 40.1% (lipofection). Lipofection had the highest intense SDF-1 expression of the analyzed methods. Conclusion This study has shown that the lipofection and electroporation methods were more efficient at gene delivery to Mehr-80 cells. The quantity of DNA per transfection, reagent concentration, and incubation time were identified as essential factors for successful transfection in all of the studied methods.
Automatic hand gesture segmentation for recognition of Vietnamese sign language

In this paper, we propose a new solution to identify hand gestures corresponding to alphabetic characters of Vietnamese Sign Language (VSL) via a sequence of images (video) collected from the depth sensor in a Microsoft Kinect. First, a preprocessing is performed to localize and separate the hand from each image and then remove possible noise. In the next stage, the object is extracted to select key frames, which support to represent a segment of the video. Each determined key frame is then converted to a binary image and estimate some biological information such as the hand boundary, finger positions and the palm center. The position of palm centre and fingertips are also localized in 3D space. The process of recognition is performed using Support Vector Machine (SVM) method. The experiments show that the proposed approach is promising since the recognition accuracy is about 91%.
Single Nucleotide Polymorphism Clustering in Systemic Autoimmune Diseases

Systemic Autoimmune Diseases, a group of chronic inflammatory conditions, have variable symptoms and difficult diagnosis. In order to reclassify them based on genetic markers rather than clinical criteria, we performed clustering of Single Nucleotide Polymorphisms. However naive approaches tend to group patients primarily by their geographic origin. To reduce this “ancestry signal”, we developed SNPClust, a method to select large sources of ancestry-independent genetic variations from all variations detected by Principal Component Analysis. Applied to a Systemic Lupus Erythematosus case control dataset, SNPClust successfully reduced the ancestry signal. Results were compared with association studies between the cases and controls without or with reference population stratification correction methods. SNPClust amplified the disease discriminating signal and the ratio of significant associations outside the HLA locus was greater compared to population stratification correction methods. SNPClust will enable the use of ancestry-independent genetic information in the reclassification of Systemic Autoimmune Diseases. SNPClust is available as an R package and demonstrated on the public Human Genome Diversity Project dataset at https://github.com/ThomasChln/snpclust.
In Vivo Imaging of Trypanosome-Brain Interactions and Development of a Rapid Screening Test for Drugs against CNS Stage Trypanosomiasis

Human African trypanosomiasis (HAT) manifests in two stages of disease: firstly, haemolymphatic, and secondly, an encephalitic phase involving the central nervous system (CNS). New drugs to treat the second-stage disease are urgently needed, yet testing of novel drug candidates is a slow process because the established animal model relies on detecting parasitemia in the blood as late as 180 days after treatment. To expedite compound screening, we have modified the GVR35 strain of Trypanosoma brucei brucei to express luciferase, and have monitored parasite distribution in infected mice following treatment with trypanocidal compounds using serial, non-invasive, bioluminescence imaging. Parasites were detected in the brains of infected mice following treatment with diminazene, a drug which cures stage 1 but not stage 2 disease. Intravital multi-photon microscopy revealed that trypanosomes enter the brain meninges as early as day 5 post-infection but can be killed by diminazene, whereas those that cross the blood-brain barrier and enter the parenchyma by day 21 survived treatment and later caused bloodstream recrudescence. In contrast, all bioluminescent parasites were permanently eliminated by treatment with melarsoprol and DB829, compounds known to cure stage 2 disease. We show that this use of imaging reduces by two thirds the time taken to assess drug efficacy and provides a dual-modal imaging platform for monitoring trypanosome infection in different areas of the brain.
Mollifying Networks

The optimization of deep neural networks can be more challenging than the traditional convex optimization problems due to highly non-convex nature of the loss function, e.g. it can involve pathological landscapes such as saddle-surfaces that can be difficult to escape from for algorithms based on simple gradient descent. In this paper, we attack the problem of optimization of highly non-convex neural networks objectives by starting with a smoothed -- or mollified -- objective function which becomes more complex as the training proceeds. Our proposition is inspired by the recent studies in continuation methods: similarly to curriculum methods, we begin by learning an easier (possibly convex) objective function and let it evolve during training until it eventually becomes the original, difficult to optimize objective function. The complexity of the mollified networks is controlled by a single hyperparameter that is annealed during training. We show improvements on various difficult optimization tasks and establish a relationship between recent works on continuation methods for neural networks and mollifiers.
A multistate dynamic site occupancy model for spatially aggregated sessile communities

Estimation of transition probabilities of sessile communities seems easy in principle but may still be difficult in practice because resampling error (i.e. a failure to resample exactly the same location at fixed points) may cause significant estimation bias. Previous studies have developed novel analytical methods to correct for this estimation bias. However, they did not consider the local structure of community composition induced by the aggregated distribution of organisms that is typically observed in sessile assemblages and is very likely to affect observations. We developed a multistate dynamic site occupancy model to estimate transition probabilities that accounts for resampling errors associated with local community structure. The model applies a nonparametric multivariate kernel smoothing methodology to the latent occupancy component to estimate the local state composition near each observation point, which is assumed to determine the probability distribution of data conditional on the occurrence of resampling error. By using computer simulations, we confirmed that an observation process that depends on local community structure may bias inferences about transition probabilities. By applying the proposed model to a real data set of intertidal sessile communities, we also showed that estimates of transition probabilities and of the properties of community dynamics may differ considerably when spatial dependence is taken into account. Results suggest the importance of accounting for resampling error and local community structure for developing management plans that are based on Markovian models. Our approach provides a solution to this problem that is applicable to broad sessile communities. It can even accommodate an anisotropic spatial correlation of species composition, and may also serve as a basis for inferring complex nonlinear ecological dynamics.
Tracking control of discrete-time affine nonlinear systems based on kernel-HDP algorithm

In the past decade, adaptive dynamic programming (ADP) has been widely used to realize online learning tracking control of dynamical systems, where neural networks with manually designed features are commonly used. In order to improve the generalization capability and learning efficiency of ADP, this paper presents a novel framework of ADP with sparse kernel machines by integrating kernel methods and approximately linear dependence (ALD) analysis into the critic module of ADP for the optimal tracking controller design. An ADP algorithm based on sparse kernel learning and heuristic dynamic programming (HDP) is proposed, that is, kernel HDP (KHDP). Based on KHDP, an experiment is established. By simulation, the effectiveness of proposed algorithm is demonstrated.
Multispace, Dynamic, Fixed-Radius, All Nearest Neighbours Problem

We present a solution to a specific version of one of the most fundamental computer science problem - the nearest neighbour problem (NN). The new, proposed variant of the NN problem is the multispace, dynamic, fixed-radius, all nearest neighbours problem, where the NN data structure handles queries that concern different subsets of input dimensions. In other words, solutions to this problem allow searching for closest points in terms of different features. This is an important issue in the context of practical applications of incremental state abstraction techniques for high dimensional Markov Decision Processes (MDP). The proposed solution is a set of simple, one-dimensional structures, that can handle range queries for arbitrary subset of input dimensions for the Chebyshev distance. We also provide version for other metrics, and a simplified version of the algorithm that yields approximate results but runs faster. The proposed approximation is deterministic in a way that ensures that the most important (in the context of the considered state abstraction task) parts of the result are returned with no accuracy loss. The presented experimental study demonstrates improvement in comparison to some state-of-the-art algorithms on uniformly random and MDP-generated data.