Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the PLDA based speaker verification in limited microphone data conditions

A significant amount of speech data is required to develop a robust speaker verification system, but it is difficult to find enough development speech to match all expected conditions. In this paper we introduce a new approach to Gaussian probabilistic linear discriminant analysis (GPLDA) to estimate reliable model parameters as a linearly weighted model taking more input from the large volume of available telephone data and smaller proportional input from limited microphone data. In comparison to a traditional pooled training approach, where the GPLDA model is trained over both telephone and microphone speech, this linear-weighted GPLDA approach is shown to provide better EER and DCF performance in microphone and mixed conditions in both the NIST 2008 and NIST 2010 evaluation corpora. Based upon these results, we believe that linear-weighted GPLDA will provide a better approach than pooled GPLDA, allowing for the further improvement of GPLDA speaker verification in conditions with limited development data.
Test suite reduction analysis with enhanced tie-breaking techniques

Test suite minimization techniques try to remove redundant test cases of a test suite. However, reducing the size of a test suite might reduce its ability to reveal faults. Most of prior works which address this problem affect some extent of suite size reduction. In this paper, we present a novel approach for test suite reduction that uses additional testing criterion to break the ties in the minimization process. We performed an experiment with the Siemens suite subject programs. The experiment results show that, compared to existing approaches, the proposed approach can improve the fault detection effectiveness of reduced suites with negligible increase in the size of the suites. Besides, the proposed approach can also accelerate the process of minimization.
Multivariate NIR Studies of Seed-Water Interaction in Scots Pine Seeds (Pinus sylvestris L.)

This thesis describes seed-water interaction using near infrared (NIR) spectroscopy, multivariate regression models and Scots pine seeds. The presented research covers classification of seed viability, prediction of seed moisture content, selection of NIR wavelengths and interpretation of seed-water interaction modelled and analysed by principal component analysis, ordinary least squares (OLS), partial least squares (PLS), bi-orthogonal least squares (BPLS) and genetic algorithms. The potential of using multivariate NIR calibration models for seed classification was demonstrated using filled viable and non-viable seeds that could be separated with an accuracy of 98-99%. It was also shown that multivariate NIR calibration models gave low errors (0.7% and 1.9%) in prediction of seed moisture content for bulk seed and single seeds, respectively, using either NIR reflectance or transmittance spectroscopy. Genetic algorithms selected three to eight wavelength bands in the NIR region and these narrow bands gave about the same prediction of seed moisture content (0.6% and 1.7%) as using the whole NIR interval in the PLS regression models. The selected regions were simulated as NIR filters in OLS regression resulting in predictions of the same quality (0.7 % and 2.1%). This finding opens possibilities to apply NIR sensors in fast and simple spectrometers for the determination of seed moisture content. Near infrared (NIR) radiation interacts with overtones of vibrating bonds in polar molecules. The resulting spectra contain chemical and physical information. This offers good possibilities to measure seed-water interactions, but also to interpret processes within seeds. It is shown that seed-water interaction involves both transitions and changes mainly in covalent bonds of O-H, C-H, C=O and N-H emanating from ongoing physiological processes like seed respiration and protein metabolism. I propose that BPLS analysis that has orthonormal loadings and orthogonal scores giving the same predictions as using conventional PLS regression, should be used as a standard to harmonise the interpretation of NIR spectra.
Sparse Combinatorial Autoencoders

Recent research has shown that employing unsupervised pretraining often produces well-conditioned neural network initializations that lead to better local optima attained during training. One commonly used pretraining method involves hierarchically stacking sparse autoencoders (SAs) and learning the network parameters layer by layer using unlabeled data. Large network sizes and the amount of data required to properly pretrain a deep network make pretraining computationally intensive and the training bottleneck. To alleviate this problem, we propose a novel warm-start procedure for the SA capable of rapidly initializing large SAs in parameter regions yielding fast convergence to good local optima. At the heart of our approach lies the sparse combinatorial autoencoder (SCA), a novel method to regularize neural networks that allows us to train an SA withH features in O( √ H) time. We present a comprehensive series of experiments demonstrating the effectiveness of the warm-start procedure, called fast initialization with SCAs (FISCA), on the STL-10 and the MNIST datasets. Our experiments consider untied sigmoid and tied soft-rectified SAs of various sizes and demonstrate that FISCA ultimately yields significantly reduced training times compared to widely prevalent initialization techniques. For example, on the MNIST dataset, FISCAinitialized soft-rectified SAs with 10K hidden neurons converge over 20× faster to notably better local optima than SAs initialized with alternate methods.
Variants of the Lamin A / C ( LMNA ) Gene in Non-Valvular Atrial Fibrillation Patients A Possible Pathogenic Role of the Thr 528 Met Mutation

Background and Objective: Lamin A/C (LMNA) gene mutations cause dilated cardiomyopathy, often accompanied by conduction disturbances. Our aim was to search for LMNA mutations in individuals with atrial fibrillation. Methods: A cohort of Polish subjects (N = 103) with non-valvular atrial fibrillation with a high (48.5%) prevalence of conduction system disturbances was screened for LMNA variants by direct DNA sequencing. Results:We found a single non-synonymous variant (Thr528Met) in a 72-year-old patient with normal left ventricular function and episodes of advanced atrioventricular block. One of his two mutation-carrying daughters had episodes of type I second-degree atrioventricular block on a 24-hour Holter ECG and peak exercise arrhythmia. Interpretation of cardiac anomalies observed in the other daughter was complicated by thyroid insufficiency. A Thr528Met weak pathogenic effect was supported by transient transfections of C2C12mouse myoblasts and computationally. Another interesting variant was Ile26Ile (c.78C>T), found in a NewYork Heart Association class III patient with a depressed left ventricular ejection fraction (30%), left bundle branch block, and a family history of heart disease. Ile26Ile was absent in 246 healthy individuals and was computationally predicted to interfere with splicing. Conclusion: LMNAmutations are not a frequent cause of atrial fibrillation even when conduction disease is present. Unlike the majority of LMNA mutations clearly associated with a severe clinical phenotype and a poor prognosis, Thr528Met results in a more subtle pathogenic effect, while Ile26Ile should be considered as a variant of unknown significance.
Non-uniform Illumination Representation based on HDR Light Probe Sequences

This paper presents a method to represent the complicated illumination in the real world by using HDR light probe sequences. The illumination representations proposed in this paper employ non-uniform structure instead of uniform light field to simulate lighting with spatial and angular variation, which turns out to be more efficient and accurate. The captured illuminations are divided into direct and indirect parts that are modeled respectively. Both integrated with global illumination algorithm easily, the direct part is organized as an amount of clusters on a virtual plane, which can solve the lighting occlusion problem successfully, while the indirect part is represented as a bounding mesh with HDR texture. This paper demonstrates the technique that captures real illuminations for virtual scenes, and also shows the comparison with the renderings using traditional image based lighting.
A method of dynamic hand gesture detection based on local background updates and skin color model

Aim at the faults of the traditional background difference method based on Bayes Rule when detects motion hand gestures, such as the existence of interference regions, slow computing speed and so on. A method of dynamic hand gesture detection based on local background updates and skin color model is proposed in this paper. The skin color region in a single image is extracted through skin color model to remove interference objects, and computing speed is improved through local background updates. The experimental results show that this method can preferably detect out motion hand gestures even in complex background.
Learn2Dance: Learning Statistical Music-to-Dance Mappings for Choreography Synthesis

We propose a novel framework for learning many-to-many statistical mappings from musical measures to dance figures towards generating plausible music-driven dance choreographies. We obtain music-to-dance mappings through use of four statistical models: 1) musical measure models, representing a many-to-one relation, each of which associates different melody patterns to a given dance figure via a hidden Markov model (HMM); 2) exchangeable figures model, which captures the diversity in a dance performance through a one-to-many relation, extracted by unsupervised clustering of musical measure segments based on melodic similarity; 3) figure transition model, which captures the intrinsic dependencies of dance figure sequences via an n-gram model; 4) dance figure models, capturing the variations in the way particular dance figures are performed, by modeling the motion trajectory of each dance figure via an HMM. Based on the first three of these statistical mappings, we define a discrete HMM and synthesize alternative dance figure sequences by employing a modified Viterbi algorithm. The motion parameters of the dance figures in the synthesized choreography are then computed using the dance figure models. Finally, the generated motion parameters are animated synchronously with the musical audio using a 3-D character model. Objective and subjective evaluation results demonstrate that the proposed framework is able to produce compelling music-driven choreographies.
Measurement of G protein-coupled receptor surface expression

Abstract The quantity of G protein-coupled receptors (GPCRs) expressed on the cell surface is an important factor regulating receptor signaling. Maturation, internalization, recycling and degradation together determine the net amount of receptor surface expression. Understanding every aspect of the receptor lifecycle will facilitate the development of therapeutic applications. A number of assays for measuring the surface expression of GPCRs are currently available. This minireview summarizes the currently available assays and their suitability and usage for measuring GPCR surface expression.
Coherent structures and isolated spectrum for Perron–Frobenius cocycles

Abstract We present an analysis of one-dimensional models of dynamical systems that possess ‘coherent structures’: global structures that disperse more slowly than local trajectory separation. We study cocycles generated by expanding interval maps and the rates of decay for functions of bounded variation under the action of the associated Perron–Frobenius cocycles. We prove that when the generators are piecewise affine and share a common Markov partition, the Lyapunov spectrum of the Perron–Frobenius cocycle has at most finitely many isolated points. Moreover, we develop a strengthened version of the Multiplicative Ergodic Theorem for non-invertible matrices and construct an invariant splitting into Oseledets subspaces. We detail examples of cocycles of expanding maps with isolated Lyapunov spectrum and calculate the Oseledets subspaces, which lead to an identification of the underlying coherent structures. Our constructions generalize the notions of almost-invariant and almost-cyclic sets to non-autonomous dynamical systems and provide a new ensemble-based formalism for coherent structures in one-dimensional non-autonomous dynamics.
ThreadMon: a tool for monitoring multithreaded program performance

This paper describes ThreadMon, a monitoring tool for improving the performance of multithreaded programs, and how we have used it to examine various aspects of the many-to-many (or two-level) threads implementation model. We run unmodified binary subject code, insert software probes to collect data, and analyze and present the results in real time on another machine. We show that the behavior of multithreaded programs, particularly those running on multiprocessors, often defies intuition when the many-to-many threads implementation model is used.
Using Bisimulation for Policy Transfer in MDPs

Knowledge transfer has been suggested as a useful approach for solving large Markov Decision Processes. The main idea is to compute a decision-making policy in one environment and use it in a different environment, provided the two are ”close enough”. In this paper, we use bisimulation-style metrics (Ferns et al., 2004) to guide knowledge transfer. We propose algorithms that decide what actions to transfer from the policy computed on a small MDP task to a large task, given the bisimulation distance between states in the two tasks. We demonstrate the inherent ”pessimism” of bisimulation metrics and present variants of this metric aimed to overcome this pessimism, leading to improved action transfer. We also show that using this approach for transferring temporally extended actions (Sutton et al., 1999) is more successful than using it exclusively with primitive actions. We present theoretical guarantees on the quality of the transferred policy, as well as promising empirical results.
Enhanced training algorithms, and integrated training/architecture selection for multilayer perceptron networks

The standard backpropagation-based multilayer perceptron training algorithm suffers from a slow asymptotic convergence rate. Sophisticated nonlinear least-squares and quasi-Newton optimization techniques are used to construct enhanced multilayer perceptron training algorithms, which are then compared to the backpropagation algorithm in the context of several example problems. In addition, an integrated approach to training and architecture selection that uses the described enhanced algorithms is presented, and its effectiveness illustrated in the context of synthetic and actual pattern recognition problems.
Compact 1 mJ fiber MOPA for space-based laser-ablation resonant ionization mass spectrometry (LARIMS)

A 1064 nm, 1 mJ pulsed fiber MOPA module, housed in 16”x14”x2.5” package for application in a lunar and planetary in-situ surface dating instrument is demonstrated. The module is based on a three-stage MOPA with a 60 μm core tapered fiber terminal amplifier. The master oscillator and first two preamplifier stages, which generate 20 μJ pulses, are all contained on a 13”x11”x1” board. Several improvements to the electronic signal control were instrumental to the laser development, including bipolar drive of the phase modulator for SBS suppression, shaping of the seed pulse to compensate pulse steepening, and pulsed operation of the power amplifier pump to reduce spontaneous emission at low pulse repetition frequency. The packaged laser runs at a repetition rate of 10 kHz and generates 10 ns pulses at 1 mJ with a 40 GHz linewidth, an M2 ~ 1.2 beam quality, and an 18 dB polarization extinction ratio. The modular design enables seven independent lasers to be stacked in a 20”x18”x16.25” enclosure, supporting a path towards a fiber laser based LARIMS for advanced materials characterization and chronological dating in harsh and remote environments.
Termite : a Lisp for Distributed Computing

Termite is a language and system offering a simple and powerful tool for expressing distributed computation. It is based on a message-passing model of concurrency inspired by Erlang, and on a variant of the functional language Scheme. Our system is an appropriate tool for building custom protocols and abstractions for distributed computation. Its open network model allows for the building of non-centralized distributed applications. The possibility of failure is reflected in the model, and ways to handle them are proposed. The existence of first-class continuations is exploited in order to allow the expression of high-level concepts such as transparent migration of processes. We describe the Termite model with its implications and describe sample applications built with Termite. We conclude with a discussion of the current implementation and its performance. GeneralTerms Distributed computing in Lisp
Chloroplast Ribosomal Protein S7 of Chlamydomonas Binds to Chloroplast mRNA Leader Sequences and May Be Involved in Translation Initiation

Certain mutations isolated in the 5′untranslated region (5′UTR) of the chloroplastrps7gene in Chlamydomonas reduce expression of reporter genes. Second site suppressors in this 5′UTR sequence restore reporter expression. 5′UTR sequences with the original mutations fail to bind a 20-kD protein, one of five proteins that bind to leaders of several chloroplast genes. However, 5′UTRs from suppressed mutants restore binding to this protein but do not bind a 47-kD protein present on the wild type and the original mutant 5′UTRs. The 20-kD protein was shown to be the S7 protein of the chloroplast ribosomal small subunit encoded byrps7, whereas the 47-kD protein was shown to be RB47, a poly(A) binding protein. Our data are consistent with the hypothesis that the S7 protein plays either a general or a specific regulatory role in translation initiation in the chloroplast.
Ca2+/calmodulin protein kinase II and memory: learning‐related changes in a localized region of the domestic chick brain

The role of calcium/calmodulin‐dependent protein kinase II (CaMKII) in the recognition memory of visual imprinting was investigated. Domestic chicks were exposed to a training stimulus and learning strength measured. Trained chicks, together with untrained chicks, were killed either 1 h or 24 h after training. The intermediate and medial hyperstriatum ventrale/mesopallium (IMHV/IMM), a forebrain memory storage site, was removed together with a control brain region, the posterior pole of the neostriatum/nidopallium (PPN). Amounts of membrane total αCaMKII (tCaMKII) and Thr286‐autophosphorylated αCaMKII (apCAMKII) were measured. For the IMHV/IMM 1 h group, apCaMKII amount and apCAMKII/tCaMKII increased as chicks learned. The magnitude of the molecular changes were positively correlated with learning strength. No learning‐related effects were observed in PPN, or in either region at 24 h. These results suggest that CaMKII is involved in the formation of memory but not in its maintenance.
Ultrasonic Echo Image Adaptive Watermarking Using the Just-Noticeable Difference Estimation

Most of the image watermarking methods, using the properties of the human visual system (HVS), have been proposed in literature. The component of the visual threshold is usually related to either the spatial contrast sensitivity function (CSF) or the visual masking. Especially on the contrast masking, most methods have not mention to the effect near to the edge region. Since the HVS is sensitive what happens on the edge area. This paper proposes ultrasound image watermarking using the visual threshold corresponding to the HVS in which the coefficients in a DCT-block have been classified based on the texture, edge, and plain area. This classification method enables not only useful for imperceptibility when the watermark is insert into an image but also achievable a robustness of watermark detection. A comparison of the proposed method with other methods has been carried out which shown that the proposed method robusts to blockwise memoryless manipulations, and also robust against noise addition. Keywords—Medical image watermarking, Human Visual System, Image Adaptive Watermark
ICDAR 2011 Chinese Handwriting Recognition Competition

In the Chinese handwriting recognition competition organized with the ICDAR 2011, four tasks were evaluated: offline and online isolated character recognition, offline and online handwritten text recognition. To enable the training of recognition systems, we announced the large databases CASIA-HWDB/OLHWDB. The submitted systems were evaluated on un-open datasets to report character-level correct rates. In total, we received 25 systems submitted by eight groups. On the test datasets, the best results (correct rates) are 92.18% for offline character recognition, 95.77% for online character recognition, 77.26% for offline text recognition, and 94.33% for online text recognition, respectively. In addition to the evaluation results, we provide short descriptions of the recognition methods and have brief discussions.
Video text detection with text edges and convolutional neural network

Text and captions in videos provide useful information for content analysis and understanding. In this paper, we present an approach to detecting video text in a coarse-to-fine strategy. In the coarse phase we propose an efficient method to detect multi-scale candidate text regions with high recall. Then the candidate text regions are segmented and sent to the fine phase where a convolutional neural network(CNN) is applied to generate a confidence map for each candidate text region. Finally, the candidate text regions are further refined and partitioned into text lines by projection analysis. The CNN classifier in the fine phase enables feature sharing and robustly identifies text regions. The coarse phase sharply reduce the number of windows needed to be scanned by the CNN. The combination endows the proposed method with both efficiency and robustness when detecting video text. It was verified by experiment results on two publicly testing datasets and a dataset created by us.
Enhancing Automated Detection of Vulnerabilities in Java Components

Java-based systems are built from components from various providers that are integrated together. Generic coding best practices are gaining momentum, but no tool is availableso far that guarantees that the interactions between these components are performed in a secure manner. We propose the 'Weak Component Analysis' (WCA) tool, which performs static analysis of the component code to identify exploitable vulnerabilities. Three types of classes can be identified in Java components, that each can be exploited through specific vulnerabilities. Internal classes which are not available for other components can be abused in an indirect manner. Shared classes which are provided by libraries can be abused through class-level vulnerabilities. Shared objects, i.e. instantiated classes, which are made available as local services in Service-oriented Programming platforms such as OSGi, Spring and Guice can be abused through object-level vulnerabilities in addition to class-level vulnerabilities.
Denoising based on wavelets and deblurring via self-organizing map for Synthetic Aperture Radar images

This work deals with unsupervised image deblurring. We present a new deblurring procedure on images provided by low-resolution synthetic aperture radar (SAR) or simply by multimedia in presence of multiplicative (speckle) or additive noise, respectively. The method we propose is defined as a two-step process. First, we use an original technique for noise reduction in wavelet domain. Then, the learning of a Kohonen self-organizing map (SOM) is performed directly on the denoised image to take out it the blur. This technique has been successfully applied to real SAR images, and the simulation results are presented to demonstrate the effectiveness of the proposed algorithms.