Miracle-dz/UniFAR-FTUs huggingface dataset:
Upregulation of Glutamate Receptors in Rat Cerebral Cortex with Neuronal Migration Disorders

Neuronal migration disorders (NMDs) constitute the main pathologic substrate of medically intractable epilepsy in human. This study is designed to investigate the changes in expression of glutamate receptor subtypes on radiation-induced NMD in rats. The lesion was produced by intrauterine irradiation (240 cGy) on E17 rats, and then 10 weeks old rats were used for the study. The pathologic and immunohistochemical findings for glutamate receptor subunit proteins on NMD cortex were correlated with development of behavioral seizures and EEG abnormality. Spontaneous seizures uncommonly occurred in NMD rats (5%); however, clinical stages of seizures were significantly increased in NMD rats by an administration of kainic acid. Brains taken from irradiated rats revealed gross and histopathologic features of NMD. Focal cortical dysplasia was identified by histopathology and immunohistochemistry with neurofilament protein (NF-M/H). Significantly strong NR1 and NR2A/B immunoreactivities were demonstrated in cytomegalic and heterotopic neurons of NMD rats. The results of the present study indicate that epileptogenesis of NMD might be caused by upregulation of glutamate receptor expression in dysplastic neurons of the rat cerebral cortex with NMDs.
Towards Cross Project Vulnerability Prediction in Open Source Web Applications

Building secure software is challenging, time-consuming, and expensive. Software vulnerability prediction models that identify vulnerable software components are usually used to focus security efforts, with the aim of helping to reduce the time and effort needed to secure software. Existing vulnerability prediction models use process or product metrics and machine learning techniques to identify vulnerable software components. Cross project vulnerability prediction plays a significant role in appraising the most likely vulnerable software components, specifically for new or inactive projects. Little effort has been spent to deliver clear guidelines on how to choose the training data for project vulnerability prediction. In this work, we present an empirical study aiming at clarifying how useful cross project prediction techniques in predicting software vulnerabilities. Our study employs the classification provided by different machine learning techniques to improve the detection of vulnerable components. We have elaborately compared the prediction performance of five well-known classifiers. The study is conducted on a publicly available dataset of several PHP open source web applications and in the context of cross project vulnerability prediction, which represents one of the main challenges in the vulnerability prediction field.
Tap Selection Based Multipath Channel Equalization For UWB Systems

In this paper, tap selection based Minimum Mean Square Error (MMSE) equalization technique is discussed for ultra wideband (UWB) systems in the presence of inter symbol interference (ISI) and multiple access interference (MAI). A novel tap selection technique based on matching pursuit (MP) algorithm is proposed. Given finite training sample support, diagonal loading (DL) technique is incorporated into MP algorithm to insure the robustness of tap selection. Moreover, a fast heuristic MP based tap selection technique is developed to facilitate the tradeoff between equalization performance and computational complexity. Simulation results show that the proposed tap selection based equalizer outperforms the RAKE receiver and the conventional MMSE equalizer significantly given the same amount of training symbols
DVB-T Passive Radar Signal Processing

This paper provides a detailed overview of the Digital Video Broadcasting Terrestrial (DVB-T) signal structure and the implications for passive radar systems that use these signals as illuminators of opportunity. In particular, we analyze the ambiguity function and explain its delay and Doppler properties in terms of the underlying structure of the DVB-T signal. Of particular concern for radar range-Doppler processing are ambiguities consistent in range and Doppler with targets of interest. In this paper we adopt a mismatched filtering approach for range-Doppler processing. We also recognize that while the structure of the DVB-T signal introduces ambiguities, the structure can also be exploited to better estimate the transmitted signal and channel, as well as any mismatch between transmitter and receiver (e.g., clock offsets). This study presents a scheme for pre-processing both the reference and surveillance signals obtained by the passive radar to mitigate the effects of the ambiguities and the clutter in range-Doppler processing. The effectiveness of our proposed scheme in enhancing target detection is demonstrated using real-world data from an (Australian) 8k-mode DVB-T system. A 29 dB reduction in residual ambiguity levels over existing techniques is observed, and a 36 dB reduction over standard matched filtering; with only a 1 dB reduction in the zero-delay, zero-Doppler peak.
A KD-Tree Based Non-Intrusive Speech Quality Evaluation for Telephony Systems

A new single-ended method for non-intrusive speech quality assessment for telephony applications is proposed and its performance evaluated. The method computes objective auditory distances between perception-based parametric vectors representing portions of the speech signal, whose quality is to be evaluated, and matching reference vectors extracted from a pre-formulated speech reference book or books. The reference books are constructed by efficiently partitioning a large number of perception-based parametric speech vectors extracted from a database of clean speech signals, using an optimised KD-tree data structure. The measured auditory distances are then mapped into objective listening quality scores. Reported evaluation results show that the method oifers sufficiently accurate and low-complexity speech quality evaluation method with potential for real time applications.
Unsupervised discriminative feature selection in a kernel space via L2,1-norm minimization

Traditional nonlinear feature selection methods map the data from an original space into a kernel space to make the data be separated more easily, then move back to the original feature space to select features. However, the performance of clustering or classification is better in the kernel space, so we are able to select the features directly in the kernel space and get the direct importance of each feature. Motivated by this idea, we propose a novel method for unsupervised feature selection directly in the kernel space. To do this, we utilize local discriminative information to find the best label for each instance with L2,1-norm minimization, then select the most important features in the kernel space using the labels predicted. Extensive experiments demonstrate the effectiveness of our method.
Quantification of left ventricular volumes using three-dimensional echocardiographic speckle tracking: comparison with MRI.

AIMS Although the utility of two-dimensional (2D) speckle tracking echocardiography (STE) to quantify left ventricular (LV) volume has been demonstrated, this methodology is limited by foreshortened views, geometric modelling, and the assumption that speckles can be tracked from frame to frame, despite their out of plane motion. To circumvent these limitations, a three-dimensional (3D) speckle tracking algorithm was recently developed. Our goal was to evaluate the accuracy of the new 3D-STE side by side with 2D-STE using cardiac magnetic resonance (CMR) as a reference. METHODS AND RESULTS Apical two- and four-chamber views (A2C and A4C) and real-time 3D datasets (Toshiba Artida 4D System) obtained in 43 patients with a wide range of LV size and function were analysed to measure LV end-systolic and end-diastolic volumes (ESV and EDV) using 2D and 3D-STE techniques. Short-axis CMR images (Siemens 1.5T scanner) acquired on the same day were analysed to obtain ESV and EDV reference values using the method of disks approximation. Reproducibility of both STE techniques was assessed using repeated measurements. While 2D-STE correlated well with CMR (r: 0.72-0.88), it underestimated LV volumes with relatively large biases (10-30 mL) and wide limits of agreement (SD: 36-51 mL), with A2C-derived measurements being worse than A4C values. The 3D-STE measurements showed higher correlation with CMR (0.87-0.92), and importantly smaller biases (1-16 mL) and narrower limits of agreement (SD: 28-37 mL). In addition, 3D-STE showed lower inter- and intra-observer variability (11-14% and 12-13%), than 2D-STE (16-17% and 12-16%, respectively). CONCLUSION This is the first study to validate the new 3D-STE technique for LV volume measurements and demonstrate its superior accuracy and reproducibility over previously used 2D-STE technique.
NextGenMap: fast and accurate read mapping in highly polymorphic genomes

SUMMARY When choosing a read mapper, one faces the trade off between speed and the ability to map reads in highly polymorphic regions. Here, we report NextGenMap, a fast and accurate read mapper, which reduces this dilemma. NextGenMap aligns reads reliably to a reference genome even when the sequence difference between target and reference genome is large, i.e. highly polymorphic genome. At the same time, NextGenMap outperforms current mapping methods with respect to runtime and to the number of correctly mapped reads. NextGenMap efficiently uses the available hardware by exploiting multi-core CPUs as well as graphic cards (GPUs), if available. In addition, NextGenMap handles automatically any read data independent of read length and sequencing technology. AVAILABILITY NextGenMap source code and documentation are available at: http://cibiv.github. io/NextGenMap/. CONTACT fritz.sedlazeck@univie.ac. at. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
BARGAINING VERSUS FIGHTING

I examine the determinants of conflict and settlement by embedding probabilistic contests in a bargaining framework. Different costly enforcement efforts (e.g. arming, litigation expenditures) induce different disagreement points and Pareto frontiers. After examining the incentives for settlement, I demonstrate how different division rules and bargaining norms have real, economic effects. I then analyze some sources of conflict. I emphasize long‐term, strategic considerations by examining an illustrative model and discussing particular historical examples.
Seasonality of synoptic eddy feedback and the AO/NAO

In this paper, by using a dynamic model with synoptic eddy and low‐frequency flow (SELF) interaction, we show that the SELF feedback is of significant importance for the formation of the AO/NAO in both winter and summer. Although background storm activity is significantly weaker in summer than that in winter, the positive SELF feedback associated with the AO/NAO mode in summer can be as strong as that in winter. The seasonal variations of the AO/NAO patterns are found to resemble those of the leading singular modes simulated by a linear dynamic system with the SELF interaction in different seasons, and the seasonal shifts in spatial patterns of the AO/NAO are the result of the seasonal changes in climatological basic state and synoptic eddy activity.
A Fused Multi-feature Based Co-training Approach for Document Clustering

Document clustering is a popular topic in data mining and information retrieval. Most models and methods for this problem are based on computing the similarity between pair documents modeled in a space of all terms, or a new feature space obtained by applying a topic modeling technique for a given corpus. In this paper, we regard these two ideas as clustering on term feature and on semantic feature, and have an assumption that they can contribute to each other in clustering. Also, we propose a co-training approach for spectral clustering taking two features into account. Experiments on four real-world datasets show the feasibility and efficacy of our proposed approach compared with a number of the baseline methods.
MDL Histogram Density Estimation

We regard histogram density estimation as a model selection problem. Our approach is based on the information-theoretic minimum description length (MDL) principle, which can be applied for tasks such as data clustering, density estimation, image denoising and model selection in general. MDLbased model selection is formalized via the normalized maximum likelihood (NML) distribution, which has several desirable optimality properties. We show how this framework can be applied for learning generic, irregular (variable-width bin) histograms, and how to compute the NML model selection criterion efficiently. We also derive a dynamic programming algorithm for finding both the MDL-optimal bin count and the cut point locations in polynomial time. Finally, we demonstrate our approach via simulation tests.
Instance-Driven Discovery of Ontological Relation Labels

An approach is presented to the automatic discovery of labels of relations between pairs of ontological classes. Using a hyperlinked encyclopaedic resource, we gather evidence for likely predicative labels by searching for sentences that describe relations between terms. The terms are instances of the pair of ontological classes under consideration, drawn from a populated knowledge base. Verbs or verb phrases are automatically extracted, yielding a ranked list of candidate relations. Human judges rate the extracted relations. The extracted relations provide a basis for automatic ontology discovery from a non-relational database. The approach is demonstrated on a database from the natural history domain.
ParadisEO: a framework for parallel and distributed biologically inspired heuristics

In this paper we present ParadisEO, an open source framework for flexible parallel and distributed design of hybrid metaheuristics. Flexibility means that the parameters such as data representation and variation operators can be evolved. It is inherited from the EO object-oriented library for evolutionary computation. ParadisEO provides different parallel and/or distributed models and allows a transparent multi-threaded implementation. Moreover, it supplies different natural hybridization mechanisms mainly for metaheuristics including evolutionary algorithms and local search methods. The framework is experimented here in the spectroscopic data mining field. The flexibility property allowed an easy and straightforward development of a geneticalgorithm-based attribute selection for models discovery in NIR spectroscopic data. Experiments on a cluster of SMPs (IBM SP3) show that a good speed-up is achieved by using the provided parallel distributed models and multi-threading. Furthermore, the hybridization of the GA with the efficient PLS method allows to discover high-quality models. Indeed, their accuracy and understandability are improved respectively by 37% and 88%.
Small-Stature Emergent Macrophytes and Crepuscular Sprinkler Disturbance Reduce Mosquito Abundance in Wetland Mesocosms

ABSTRACT: The impact of emergent macrophyte species and crepuscular sprinkler disturbance on mosquito abundance over a 2-year period was measured in wetland mesocosms. Mosquito oviposition and abundance of immature mosquitoes and aquatic invertebrates were monitored in monotypic plots of small-stature (height of mature stands <1.5 m) alkali bulrush (Schoenoplectus maritimus) and large-stature (height of mature stands > 2 m) California bulrush (Schoenoplectus californicus) without or with daily sprinkler showers to deter mosquito egg laying. Relative to wetlands without operational sprinklers, oviposition by culicine mosquitoes was reduced by > 99% and immature mosquito abundance was reduced by > 90% by crepuscular sprinkler applications. Mosquito abundance or distribution in wetlands did not differ between the two bulrush species subjected to the sprinkler treatment. Alkali bulrush wetlands without daily sprinkler treatments contained more egg rafts but significantly fewer mosquito larvae than did California bulrush wetlands. Predaceous damselfly naiads were 3–5 times more abundant in alkali bulrush than in California bulrush. Stem density, rate of spread, and autumnal mortality of alkali bulrush were higher than for California bulrush. Replacement of large emergent macrophytes by smaller species may enhance the efficacy of integrated mosquito management programs to reduce mosquito-transmitted disease cycles associated with multipurpose constructed wetlands used worldwide for water reclamation and habitat restoration.
Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval

In this paper, we propose a novel image indexing and retrieval algorithm using local tetra patterns (LTrPs) for content-based image retrieval (CBIR). The standard local binary pattern (LBP) and local ternary pattern (LTP) encode the relationship between the referenced pixel and its surrounding neighbors by computing gray-level difference. The proposed method encodes the relationship between the referenced pixel and its neighbors, based on the directions that are calculated using the first-order derivatives in vertical and horizontal directions. In addition, we propose a generic strategy to compute nth-order LTrP using (n - 1)th-order horizontal and vertical derivatives for efficient CBIR and analyze the effectiveness of our proposed algorithm by combining it with the Gabor transform. The performance of the proposed method is compared with the LBP, the local derivative patterns, and the LTP based on the results obtained using benchmark image databases viz., Corel 1000 database (DB1), Brodatz texture database (DB2), and MIT VisTex database (DB3). Performance analysis shows that the proposed method improves the retrieval result from 70.34%/44.9% to 75.9%/48.7% in terms of average precision/average recall on database DB1, and from 79.97% to 85.30% and 82.23% to 90.02% in terms of average retrieval rate on databases DB2 and DB3, respectively, as compared with the standard LBP.
Perceptual Control Theory A Model for Understanding the Mechanisms and Phenomena of Control

Perceptual Control Theory (PCT) provides a general theory of functioning for organisms. At the conceptual core of the theory is the observation that living things control the perceived environment by means of their behavior. Consequently, the phenomenon of control takes center stage in PCT, with observable behavior playing an important but supporting role. The first part of the paper explains how the PCT model works. This explanation includes a definition of “control” as well as the basic equations from which one can see what is required for control to be possible. The second part of the paper describes demonstrations that the reader can download from the Internet and run, so as to learn the basics of control by experiencing and verifying the phenomenon directly. The third part of the paper shows examples of the application of PCT to different areas of psychological research including learning, developmental psychology, social psychology, and psychotherapy. This summary of the current state of the field celebrates the 50th Anniversary of the first major publication in PCT (Powers, Clark & MacFarland, 1960).
Supporting precise garbage collection in Java Bytecode-to-C ahead-of-time compiler for embedded systems

A Java bytecode-to-C ahead-of-time compiler (AOTC) can improve the performance of a Java virtual machine (JVM) by translating bytecode into C code, which is then compiled into machine code via an existing C compiler. Although AOTC is effective in embedded Java systems, a bytecode-to-C AOTC could not easily employ precise garbage collection (GC) due to a difficulty in making a GC map, which keeps information on where each root live object is located when GC occurs. This is one of the reasons why all previous JVMs using a bytecode-to-C AOTC employed conservative GC, which can lead to poorer GC performance with more frequent memory shortage, especially in embedded systems where memory is tight.In this paper, we propose a way of allowing precise GC in a bytecode-to-C AOTC by generating additional C code which collects GC map-equivalent information at runtime. In order to reduce this runtime overhead, we also propose two optimization techniques which remove unnecessary C code. Our experimental results on Sun's CVM indicate that we can support precise GC for bytecode-to-C AOTC with a relatively low overhead.
Improving software model checking on program backbone within distributed system

Model checking technique currently has been applied to a wide range of problem domains. Among them, verifying the reliability of software systems becomes much more significant. However, as to software with complex structure and large scale, the verification process suffers from the state space explosion, thus leading to the resource exhaustion and low efficiency. In this paper, we propose a method of improving software model checking in both foreground and background of ANSI-C source program to verify the properties. In the foreground stage, we directly dispose of program itself by pruning the program with respect to the assertion property and compressing the circular paths to extract the program backbone. Subsequently, the program backbone is used to generate a simple CTL automaton model which will be applied afterwards. In the background stage, we redesign the CTL state automaton's data structure and improve the model checking algorithm to adapt the MapReduce framework in distributed system. The set of states which are satisfied with CTL property is output and checked for satisfiability based on the CTL automaton model. The example in each part illustrates the validity of the whole method, and the experiments show this method improves the efficiency of program verification substantially.
Protocol Log Analysis with Constraint Programming

Testing a telecommunication protocol often requires protocol log analysis. A protocol log is a sequence of messages with timestamps. Protocol log analysis involves checking that the content of messages and timestamps are correct with respect to the protocol specification. We model the protocol specification using constraint programming (MiniZinc), and we present an approach where a constraint solver is used to perform protocol log analysis. Our case study is the Public Warning System service, which is a part of the Long Term Evolution (LTE) 4G standard.