Miracle-dz/UniFAR-FTUs huggingface dataset:
Organizing and visualizing software repositories using the growing hierarchical self-organizing map

A software repository, a place where reusable components are stored and searched for, is a key ingredient for instituting and popularizing software reuse. It is vital that a software repository should be well-organized and provide efficient tools for developers to locate reusable components that meet their requirements. The growing hierarchical self-organizing map (GHSOM), an unsupervised learning neural network, is a powerful data mining technique for the clustering and visualization of large and complex data sets. The resulting maps, serving as retrieval interfaces, can be beneficial to developers in obtaining better insight into the structure of a software repository and increasing their understanding of the relationships among software components. The GHSOM, which is an improvement over the basic self-organizing map (SOM), can adapt its architecture during its learning process and expose the hierarchical structure that exists in the original data. In this paper, we demonstrate the potential of the GHSOM for the organization and visualization of a collection of reusable components stored in a software repository, and compare the results with the ones obtained by using the traditional SOM.
Promoting Access to White Rose Research Papers Cross-lingual Document Retrieval, Categorisation and Navigation Based on Distributed Services

(2004) Cross-lingual document retrieval categorisation and navigation based on distributed services. Abstract The widespread use of the Internet across countries has increased the need for access to document collections that are often written in languages different from a user's native language. In this paper we describe Clarity, a Cross Language Information Retrieval (CLIR) system for English, Finnish, Swedish, Latvian and Lithuanian. Clarity is a fully-fledged retrieval system that supports the user during the whole process of query formulation, text retrieval and document browsing. We address four of the major aspects of Clarity: (i) the user-driven methodology that formed the basis for the iterative design cycle and framework in the project, (ii) the system architecture that was developed to support the interaction and coordination of Clarity's distributed services, (iii) the data resources and methods for query translation, and (iv) the support for Baltic languages. Clarity is an example of a distributed CLIR system built with minimal translation resources and, to our knowledge, the only such system that currently supports Baltic languages.
Structural insight into the specificity of the B3 DNA-binding domains provided by the co-crystal structure of the C-terminal fragment of BfiI restriction enzyme

The B3 DNA-binding domains (DBDs) of plant transcription factors (TF) and DBDs of EcoRII and BfiI restriction endonucleases (EcoRII-N and BfiI-C) share a common structural fold, classified as the DNA-binding pseudobarrel. The B3 DBDs in the plant TFs recognize a diverse set of target sequences. The only available co-crystal structure of the B3-like DBD is that of EcoRII-N (recognition sequence 5′-CCTGG-3′). In order to understand the structural and molecular mechanisms of specificity of B3 DBDs, we have solved the crystal structure of BfiI-C (recognition sequence 5′-ACTGGG-3′) complexed with 12-bp cognate oligoduplex. Structural comparison of BfiI-C–DNA and EcoRII-N–DNA complexes reveals a conserved DNA-binding mode and a conserved pattern of interactions with the phosphodiester backbone. The determinants of the target specificity are located in the loops that emanate from the conserved structural core. The BfiI-C–DNA structure presented here expands a range of templates for modeling of the DNA-bound complexes of the B3 family of plant TFs.
Ketamine Dysregulates the Amplitude and Connectivity of High-Frequency Oscillations in Cortical-Subcortical Networks in Humans : Evidence from Resting-State MEG-Recordings

Hypofunctioning of the N-methyl-D-aspartate (NMDA)-receptor (NMDA-R) has been prominently implicated in the pathophysiology of schizophrenia (ScZ). The current study tested the effects of ketamine, a dissociative anesthetic and NMDA-R antagonist, on resting-state activity recorded with magnetoencephalography (MEG) in healthy volunteers. In a single-blind cross-over design, each participant (n = 12) received, on two different sessions, a subanesthetic dose of S-ketamine (0.006 mg/Kg) and saline injection. MEG-data were analyzed at sensorand sourcelevel in the beta (13-30 Hz) and gamma (30-90 Hz) frequency ranges. In addition, connectivity analysis at source-level was performed using transfer entropy (TE). Ketamine increased gamma-power while beta-band activity was decreased. Specifically, elevated 30-90 Hz activity was pronounced in subcortical (thalamus and hippocampus) and cortical (frontal and temporal cortex) regions, whilst reductions in beta-band power were localized to the precuneus, cerebellum, anterior cingulate, temporal and visual cortex. TE analysis demonstrated increased information transfer in a thalamo-cortical network after ketamine administration. The findings are consistent with the pronounced dysregulation of highfrequency oscillations following the inhibition of NMDA-R in animal models of ScZ as well as with evidence from EEG-data in ScZ-patients and increased functional connectivity during early illness stages. Moreover, our data highlight the potential contribution of thalamo-cortical connectivity patterns towards ketamine-induced neuronal dysregulation, which may be relevant for the understanding of schizophrenia as a disorder of disinhibition of neural circuits.
Printed multiband antenna for mobile and wireless communications

In this paper, a low profile printed multiband antenna is numerically investigated and experimentally measured. The antenna is proposed to be used for GSM-850, GSM-900, UMTS-2100, WiMAX-2.3 and WiMAX-3.3. It is developed from the existing tripleband microstrip antenna that works on frequency of 2GHz, 3.5GHz and 5.5GHz. To improve the coverage of frequency bands, the existing antenna structure is totally redesigned using FR-4 Epoxy substrate with permittivity of 4.3 and thickness of 0.8mm to produce a new antenna configuration that works at desired frequency bands. To have the proposed antenna to be as compact as possible, the meandering strip technique is applied to minimize the antenna dimension. Hence, to be possible being fed in planar, the microstrip line technique is used to feed the antenna. Prior to the fabrication, the proposed antenna is numerically designed in which some parameters of antenna including return loss, VSWR, bandwidth, gain, and radiation pattern are used as design indicators. Based on the design result, the antenna with total dimension of 32mm × 32mm is then realized to be experimentally characterized. From the measured result, it is shown that the proposed antenna has working bandwidths (VSWR<2) for frequency ranges of 800–920MHz, 1900–2400MHz and 3300–3420MHz that corresponds to frequency ranges of GSM-850, GSM-900, UMTS-2100, WiMAX-2.3 and WiMAX-3.3, respectively. In addition, the measurement results of antenna parameters show good agreements with the numerical results.
A Hybrid Conditional Random Field for Estimating the Underlying Ground Surface From Airborne LiDAR Data

Recent advances in airborne light detection and ranging (LiDAR) technology allow rapid and inexpensive generation of digital surface models (DSMs), 3-D point clouds of buildings, vegetations, cars, and natural terrain features over large regions. However, in many applications, such as flood modeling and landslide prediction, digital terrain models (DTMs), the topography of the bare-Earth surface, are needed. This paper introduces a novel machine learning approach to automatically extract DTMs from their corresponding DSMs. We first classify each point as being either ground or nonground, using supervised learning techniques applied to a variety of features. For the points which are classified as ground, we use the LiDAR measurements as an estimate of the surface height, but, for the nonground points, we have to interpolate between nearby values, which we do using a Gaussian random field. Since our model contains both discrete and continuous latent variables, and is a discriminative (rather than generative) probabilistic model, we call it a hybrid conditional random field. We show that a Maximum a Posteriori estimate of the surface height can be efficiently estimated by using a variant of the Expectation Maximization algorithm. Experiments demonstrate that the accuracy of this learning-based approach outperforms the previous best systems, based on manually tuned heuristics.
Wheat Genomics: Present Status and Future Prospects

Wheat (Triticum aestivum L.), with a large genome (16000 Mb) and high proportion (∼80%) of repetitive sequences, has been a difficult crop for genomics research. However, the availability of extensive cytogenetics stocks has been an asset, which facilitated significant progress in wheat genomic research in recent years. For instance, fairly dense molecular maps (both genetic and physical maps) and a large set of ESTs allowed genome-wide identification of gene-rich and gene-poor regions as well as QTL including eQTL. The availability of markers associated with major economic traits also allowed development of major programs on marker-assisted selection (MAS) in some countries, and facilitated map-based cloning of a number of genes/QTL. Resources for functional genomics including TILLING and RNA interference (RNAi) along with some new approaches like epigenetics and association mapping are also being successfully used for wheat genomics research. BAC/BIBAC libraries for the subgenome D and some individual chromosomes have also been prepared to facilitate sequencing of gene space. In this brief review, we discuss all these advances in some detail, and also describe briefly the available resources, which can be used for future genomics research in this important crop.
SPEED OF ADAPTATION AND GENOMIC FOOTPRINTS OF HOST–PARASITE COEVOLUTION UNDER ARMS RACE AND TRENCH WARFARE DYNAMICS

Coevolution between hosts and their parasites is expected to follow a range of possible dynamics, the two extreme cases being called trench warfare (or Red Queen) and arms races. Long‐term stable polymorphism at the host and parasite coevolving loci is characteristic of trench warfare, and is expected to promote molecular signatures of balancing selection, while the recurrent allele fixation in arms races should generate selective sweeps. We compare these two scenarios using a finite size haploid gene‐for‐gene model that includes both mutation and genetic drift. We first show that trench warfare do not necessarily display larger numbers of coevolutionary cycles per unit of time than arms races. We subsequently perform coalescent simulations under these dynamics to generate sequences at both host and parasite loci. Genomic footprints of recurrent selective sweeps are often found, whereas trench warfare yield signatures of balancing selection only in parasite sequences, and only in a limited parameter space. Our results suggest that deterministic models of coevolution with infinite population sizes do not predict reliably the observed genomic signatures, and it may be best to study parasite rather than host populations to find genomic signatures of coevolution, such as selective sweeps or balancing selection.
Toward Feedback-Based Web Search Engine

The capability to easily find useful information on the Web becomes increasingly difficult as the available content increases. To assist users in finding relevant information Web Search Engines are developed. Those engines apply different strategies to measure the relevance of a Web page (its rank). However, page ranking is mainly conducted by relying on automatic assessment criteria. Hence, a gap is created between the effective relevance of a content and the computed one. To reduce this gap, we introduce a framework for feedback based web search engine development. To illustrate the effectiveness and the use of the proposed framework, we developed a web search engine prototype called Social Seeker.
Influence of phase on Cramer-Rao lower bounds for joint time delay and Doppler stretch estimation

The Cramer-Rao lower bounds for joint time delay and Doppler stretch estimation have been obtained for wideband signals with known phase already. However, the carrier phase is usually unknown in non-coherent receivers in the applications such as radar and sonar. And accuracy of the joint estimation of time delay and Doppler stretch will be affected by this unknown phase. In this paper, starting with a general wideband signal model with unknown phase, the Cramer-Rao lower bounds for joint time delay and Doppler stretch estimation are derived by using Fisher information matrix. The bounds obtained under unknown and known phases are compared and the relations between them are offered. Further explanations about the relations are also given from the view of information theory. Simulations are provided to support the theoretical results.
A Framework for Tuning Posterior Entropy in Unsupervised Learning

We present a general framework for unsupervised and semi-supervised learning containing a graded spectrum of Expectation Maximization (EM) algorithms. We call our framework Unified Expectation Maximization (UEM.) UEM allows us to tune the entropy of the inferred posterior distribution during the E-step to impact the quality of learning. Furthermore, UEM covers existing algorithms like standard EM and hard EM as well as constrained versions of EM such as Constraint-Driven Learning (Chang et al., 2007) and Posterior Regularization (Ganchev et al., 2010). Within the UEM framework, we can adapt the learning procedure to the data, initialization point, and supervision signals like constraints. Experiments on POS tagging, information extraction, and wordalignment show that often the best performing algorithm in the UEM family is a new algorithm that wasn’t available earlier, exhibiting the benefits of the UEM framework.
Annotating Geographical Entities on Microblog Text

This paper presents a discussion of the problems surrounding the task of annotating geographical entities on microblogs and reports the preliminary results of our efforts to annotate Japanese microblog texts. Unlike prior work, we not only annotate geographical location entities but also facility entities, such as stations, restaurants, shopping stores, hospitals and schools. We discuss ways in which to build a gazetteer, the types of ambiguities that need to be considered, reasons why the annotator tends to disagree, and the problems that need to be solved to automate the task of annotating the geographical entities. All the annotation data and the annotation guidelines are publicly available for research purposes from our web site.
Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection

Nowadays, we see more and more cyber-attacks on major Internet sites and enterprise networks. Intrusion Detection System (IDS) is a critical component of such infrastructure defense mechanism. IDS monitors and analyzes networks' activities for potential intrusions and security attacks. Machine-learning (ML) models have been well accepted for signature-based IDSs due to their learn ability and flexibility. However, the performance of existing IDSs does not seem to be satisfactory due to the rapid evolution of sophisticated cyber threats in recent decades. Moreover, the volumes of data to be analyzed are beyond the ability of commonly used computer software and hardware tools. They are not only large in scale but fast in/out in terms of velocity. In big data IDS, the one must find an efficient way to reduce the size of data dimensions and volumes. In this paper, we propose novel feature selection methods, namely, RF-FSR (Random Forest-Forward Selection Ranking) and RF-BER (Random Forest-Backward Elimination Ranking). The features selected by the proposed methods were tested and compared with three of the most well-known feature sets in the IDS literature. The experimental results showed that the selected features by the proposed methods effectively improved their detection rate and false-positive rate, achieving 99.8% and 0.001% on well-known KDD-99 dataset, respectively.
The Annular Report on Non-Critical String Theory

Recent results on the annulus partition function in Liouville field theory are applied to non-critical string theory, both below and above the critical dimension. Liouville gravity coupled to $c\le 1$ matter has a dual formulation as a matrix model. Two well-known matrix model results are reproduced precisely using the worldsheet formulation: (1) the correlation function of two macroscopic loops, and (2) the leading non-perturbative effects. The latter identifies the eigenvalue instanton amplitudes of the matrix approach with disk instantons of the worldsheet approach, thus demonstrating that the matrix model is the effective dynamics of a D-brane realization of $d\le 1$ non-critical string theory. In the context of string theory above the critical dimension, i.e. $d\ge 25$, Liouville field theory realizes two-dimensional de Sitter gravity on the worldsheet. In this case, appropriate D-brane boundary conditions on the annulus realize the S-matrix for two-dimensional de Sitter gravity.
Detecting tactics patterns for archiving tennis video clips

Video processing has found many applications in sports such as slow motion replay, pattern analysis, statistics collection, video archiving, etc. This paper presents an automatic archiving system for tennis games. It extracts ball trajectory using calibrated cameras. The trajectory is then used to classify tennis video clips into 58 tactic patterns. Semantic annotation is then attached to each clip so future query can be made easily. The annotated video clips can be used for training, sports analysis, broadcasting, etc. It can also be used for online browsing and streaming.
A novel LED arrangement to reduce SNR fluctuation for multi-user in visible light communication systems

The arrangement with 4 LEDs positioned in the corners and 12 LEDs spread evenly on a circle makes the fluctuation of received SNR reduce to 0.85dB. The BER performance does not suffer severely from ISI, where ZF equalization only provides 0.28dB power reduction to achieve BER of 10−3 when the receiver locates in the corner.
Alpha channel digital image watermarking method

In this paper, a method for embedding a digital watermark onto a host imagepsilas alpha channel is described. Blending factors which form an alpha channel define the proportion of foreground and background colors which in turn are combined to result in an actual color for each pixel. Here, the blending factors corresponding to areas along edges in the host image is of interest. The host image is first divided into blocks of subimages. A dominant edge found in any subimage is used to divide the pixels on that block into three groups:the group of foreground-color only pixels, the group of background-color only pixels, and the group of foreground-background blended color pixels. The blending factors for the pixels in the last group are modified to embed each bit of a watermarking pattern. Because the modified pixels belong to the area around an image edge, change to the original image due to watermark embedding is less perceptible.
The Dorsal Attention Network Mediates Orienting toward Behaviorally Relevant Stimuli in Spatial Neglect

Experimental neurophysiology and functional neuroimaging have identified a dorsal attention network that encodes neural signals related to the behavioral significance of a stimulus. The core anatomical areas of this network are the frontal eye fields and the posterior parietal cortex, which are interconnected by the superior longitudinal fasciculus. Here, we show that damage or disconnection of this network predicts the extent to which task-relevant stimuli capture attention of human stroke patients with spatial neglect. Healthy volunteers, right-hemisphere-damaged control participants, and patients with left neglect reacted to peripheral targets defined by their color, which were preceded by a brief distracter stimulus. The position of the distracter and its relevance for the current trial were independently varied. In neglect patients with damage including the frontal eye fields and the superior longitudinal fasciculus, ipsilesional distracters impaired orienting into contralesional space regardless of their relevance for the current task. In contrast, patients with sparing of these regions were only impaired when distracters were task-relevant. These findings indicate that the dorsal attention network controls spatial orienting by modulating the saliency of distracter stimuli according to current action goals.
Reshaped Wirtinger Flow and Incremental Algorithm for Solving Quadratic System of Equations

We study the phase retrieval problem, which solves quadratic system of equations, i.e., recovers a vector $\boldsymbol{x}\in \mathbb{R}^n$ from its magnitude measurements $y_i=|\langle \boldsymbol{a}_i, \boldsymbol{x}\rangle|, i=1,. , m$. We develop a gradient-like algorithm (referred to as RWF representing reshaped Wirtinger flow) by minimizing a nonconvex nonsmooth loss function. In comparison with existing nonconvex Wirtinger flow (WF) algorithm \cite{candes2015phase}, although the loss function becomes nonsmooth, it involves only the second power of variable and hence reduces the complexity. We show that for random Gaussian measurements, RWF enjoys geometric convergence to a global optimal point as long as the number $m$ of measurements is on the order of $n$, the dimension of the unknown $\boldsymbol{x}$. This improves the sample complexity of WF, and achieves the same sample complexity as truncated Wirtinger flow (TWF) \cite{chen2015solving}, but without truncation in gradient loop. Furthermore, RWF costs less computationally than WF, and runs faster numerically than both WF and TWF. We further develop the incremental (stochastic) reshaped Wirtinger flow (IRWF) and show that IRWF converges linearly to the true signal. We further establish performance guarantee of an existing Kaczmarz method for the phase retrieval problem based on its connection to IRWF. We also empirically demonstrate that IRWF outperforms existing ITWF algorithm (stochastic version of TWF) as well as other batch algorithms.
VAST: The illusion of a large memory space for GPUs

Heterogeneous systems equipped with traditional processors (CPUs) and graphics processing units (GPUs) have enabled processing large data sets. With new programming models, such as OpenCL and CUDA, programmers are encouraged to offload data parallel workloads to GPUs as much as possible in order to fully utilize the available resources. Unfortunately, offloading work is strictly limited by the size of the physical memory on a specific GPU. In this paper, we present Virtual Address Space for Throughput processors (VAST), an automatic GPU memory management system that provides an OpenCL program with the illusion of a virtual memory space. Based on the available physical memory on the target GPU, VAST does the following: automatically partitions the data parallel workload into chunks; efficiently extracts the precise working set required for the divided workload; rearranges the working set in contiguous memory space; and, transforms the kernel to operate on the reorganized working set. With VAST, the programmer is responsible for developing a data parallel kernel in OpenCL without concern for physical memory space limitations of individual GPUs. VAST transparently handles code generation dealing with the constraints of the actual physical memory and improves the re-targetability of the OpenCL with moderate overhead. Experiments demonstrate that a real GPU, NVIDIA GTX 760 with 2 GB of memory, can compute any size of data without program changes achieving 2.6× speedup over CPU exeuction, which is a realistic alternative for large data computation.