Miracle-dz/UniFAR-FTUs huggingface dataset:
DIFFERENTIAL EFFECTS OF STIMULUS VARIABILITY AND LEARNERS' PRE-EXISTING PITCH PERCEPTION ABILITY IN LEXICAL TONE LEARNING BY NATIVE ENGLISH SPEAKERS

We examined the role of stimulus variability in learning non-native phonetic contrasts (suprasegmentals) for word identification by adults, considering whether all learners benefit from high-variability training. Forty-seven English-speakers were trained to use Mandarin lexical tones to identify 18 English pseudowords with stimuli produced by either four talkers (multi talker training) or only one of the four talkers (single-talker training). Subjects’ pre-training ability to identify the pitch patterns in a non-lex ical context was also measured. Subjects with high pitch-identification ability learned more successfully than those with lower pitchidentification ability. Importantly, multi-talker training was beneficial only for learners with high pitch-identification ability, whereas learners with low pitch-identification ability benefited more from single-talker training. These findings provide support for phonetic-phonology continuity in adult sound-to-word learning and suggest that in order for high-variability training to be beneficial, learners first need to be able to successfully enco de phonetic details.
A Distributed Cloud Robotics Middleware Architecture for Trust-minimized Storage and Computation

We introduce a novel, distributed architecture utilizing consensual, blockchain-secured computation and verification that enables a scalable, transparent, and semantically interoperable cloud robotics middleware, capable of powering an emerging internet of robots in an automated way, without human intervention. Our architecture combines a set of modular key technologies that distinguishes it from existing, centralized robotics middleware approaches whilst enhancing their value. This includes technologies to (i) render data available across a distributed network; (ii) enact re-usability of data and applications, and their interoperability; (iii) universally execute data-manipulating applications yielding reproducible results, independently of the underlying infrastructure; and (iv) enable trust-minimized verification of derived data and the computations generating it. On an implementation level, the proposed middleware architecture integrates with ontologies, contentaddressable distributed storage, a blockchain database, and software containerization. In this paper, we motivate and justify the need for such a system and all underlying building blocks. Design and interplay of the components are explained in detail and embody the main contribution of our work. The benefits of our approach are highlighted by means of a sample application.
SSM: Secure-Split-Merge data distribution in cloud infrastructure

With the advancement of cloud storage services the vulnerabilities associated with cloud-related data has increased. It affects the cloud distributed data servers that are not only used for data storage, but also used for managing a huge amount of data with distributed file structures in collaborative sharing. The data sharing processes of cloud computing infrastructure are susceptible to various kinds of attacks where many traditional cryptographic techniques fail to provide a proper and efficient authentication mechanism. In this paper, a novel approach namely Secure-Split-Merge (SSM) is introduced for the security of data. The proposed SSM scheme uses a unique mechanism of performing splitting of data using AES 128 bit encryption key. The chunks of encrypted splits are being maintained on various rack servers of different types of cloud zones. The comparative analysis shows that the proposed system gives effective outcomes as compared to various existing and traditional security standards.
An Overview of Game Theory and its Applications in Communication Networks

Due to its capability to solve the situations of conflict and competition, Game Theory has been used as a mathematical tool in economics, politics, biology and human psychology. Nash Equilibrium , being the solution of a non-cooperative game, gives a stable state in a sense that no agent/player have any positive incentive to deviate from its current adopted strategy, when all others players of the game stick to their current moves. The Pareto Efficiency is the solution of a game where the utility gained by a player by deviating from the current strategy causes at least one other player worst off. In Communication Networks, the cooperation to follow a certain protocol cannot be taken as for granted, keeping in view the selfish nature of now a day's network entities. To cope with the selfish and competitive behavior of the network entities, Game Theory provides a feasible solution for resource utilization and service provisioning. This paper presents the detailed overview of the Game Theory concepts and its applications in the Communication Networks, both from cooperative and non-cooperative perspectives.
A programmable gate driver for power semiconductor switching loss characterization

This paper presents a programmable gate driver unit for power semiconductors such as IGBTs and MOSFETs which allows to adjust the gate voltage and gate resistance for turn-on and turn-off of the power semiconductor independently. As properties of the gate driver have a major influence on the switching behavior of power semiconductors and thus on switching losses and EMI, it is highly desirable to characterize the influence of gate driver parameters on the switching behavior of power semiconductors experimentally. Contrary to other approaches, the presented circuit is optimized for device characterization in double pulse or similar experiments. The gate voltage range covers most gate-voltage controlled power semiconductors, including GaN and SiC MOSFETs. An internal connection of multiple output stages allows to vary the effective gate resistance over a wide range. As the gate driver parameters can be set remotely during operation, an automated characterization of the gate-driver-dependent switching behavior is enabled.
Influence of initial conditions on capillary discharge device Capex 2

The first time we have used McPherson's XUV grazing incidence spectrograph for measurement of a soft X-ray emission from the capillary discharge device Capex 2 and this spectrum is presented. Recently we have started to study an influence of pre-pulse capillary current on the soft X-ray emission in the fast capillary discharge and the latest results are shown as well. It was found that a very short intense spike that had been measured by a PIN diode doesn't necessarily correspond to a laser pulse.
A MULTILEVEL ADAPTIVE MESH GENERATION SCHEME USING KD-TREES

We introduce a mesh refinement strategy for PDE based simu- lations that benefits from a multilevel decomposition. Using Harten's MRA in terms of Schroder-Pander linear multiresolution analysis (20), we are able to bound discontinuities in R. This MRA is extended to R n in terms of n-orthogonal linear transforms and utilized to identify cells that contain a codimension-one discontinuity. These refinement cells become leaf nodes in a balanced Kd-tree such that a local dyadic MRA is produced in R n , while maintaining a minimal computational footprint. The nodes in the tree form an adaptive mesh whose density increases in the vicinity of a discontinuity.
Articulatory Feature Extraction Using Temporal Flow Neural Networks

A number of interesting properties of articulatory features motivate its incorporation into speech recognition systems. This work explores the possibility of extracting articulatory features from pre-processed speech signals (modulation spectrogram) using a special temporal ow model (TFM) neural network. Good performance was obtained with the TFMs on each feature category, and the results were comparable to or better than that of the multilayer perceptron (MLP) with much larger number of parameters on the same tasks. We also show results of applying the TFM outputs in the estimation of posterior phoneme probabilities with another MLP. Reasonable phoneme frame accuracy were obtained, and the TFM/MLP system gave complimentary resultls to that of an MLP-only system. Combination of the results from the TFM/MLP system and MLP-only system gained signi cant improvements in the phoneme frame accuracy. Analysis on the trained TFM network gave interesting node activation correlation patterns, corresponding to useful cues for articulatory feature extraction. We also discuss the e ect of recurrent links and mutiple time-delayed links of TFM on the context window size.
Single cell expression profiling reveals major disruption of DNA repair capacity in incipient Alzheimer's Disease

Understanding the pathogenesis in the early stages of late-onset Alzheimer's disease (LOAD) can help in gaining important mechanistic insights into this devastating neurodegenerative disorder. Alzheimer's disease (AD) is characterised by extensive cell death with disease progression. In this paper laser capture microdissection (LCM) based gene expression profiling, which is able to profile gene expression in a single cell type, is employed to analyse the gene expression regulation of incipient AD. Our analysis shows that LCM based gene expression profiling of neurons has a critical advantage over the conventional gene expression profiling method which uses samples of mixed cell types and does not account for cell loss. A natural question that arises in this situation is how can we say that a gene is "down-regulated" when it just may not be present due to the absence of cells? Our investigation on expression regulation also resulted in 1633 significant genes, from which we identified 15 DNA repair genes that had elevated or lowered levels of mRNA expression. Many key players involved in the defense against DNA damage were present in this list, and many genes were involved in the base-excision repair pathway. Our results indicate that the base excision repair pathway, a predominant pathway in neurons, fails to handle the DNA damage in an efficient manner and is a compelling explanation for many of the pathological features of LOAD. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Single cell expression profiling reveals major disruption of DNA repair capacity in incipient Alzheimer’s Disease Monika Ray, Weixiong Zhang1,2∗ Washington University School of Engineering, Dept. of Computer Science and Engineering, St. Louis, MO 63130 Washington University School of Medicine, Dept. of Genetics, St. Louis, MO 63110 ∗ Corresponding author
Title On insensitivity of the chi-square model test to non-linear misspecification in structural equation models

In this note we show that for some structural equation models (SEM), the classical chi-square goodness-of-fit test is unable to detect the presence of interaction (non-linear) terms in the model. Not only the model test has zero power against that type of misspecifications, but even the theoretical (chi-square) distribution of the test is not distorted when severe interaction term misspecification is present in the postulated model. We explain this phenomenon by exploiting results on asymptotic robustness (AR) in structural equation models. The importance of this paper is to warn against the conclusion that if a proposed linear model fits the data well according to the chi-quare goodness-of-fit test, then the underlying model is linear indeed; it will be shown that the underlying model may in fact be be severely nonlinear. In addition, the present paper shows that such insensitivity to interaction terms is only a particular instance of a more general problem, namely, the incapacity of the classical chi-square goodness-of-fit test to detect deviations from zero correlation among exogenous regressors (either being them observable, or latent) when the structural part of the model is just saturated.
A Framework for Interactive Physical Simulations on Remote HPC Clusters

In this work, we introduce the framework for visualization and interactivity for physics engines in realtime, for short VIPER. It is able to execute various physical simulations, visualize the simulation results in real-time and offer computational steering. Especially interesting in this context are simulations running on remotely accessible HPC clusters. As an example, we present a particulate flow simulation consisting of a coupled rigid body and CFD simulation, the chosen visualization strategy and steering possibilities. Additionally, performance evaluations and a performance prediction model concerning the update rate for remote simulations in the context of the VIPER framework are given.
Universität Regensburg Mathematik Elastic flow with junctions : Variational approximation and application to nonlinear splines

We consider stable semidiscrete approximations of parameterized curve networks for gradient flows of elastic type functionals. Here meaningful and relevant conditions at junction points, such as double and triple junctions, need to be derived and suitably discretized. Examples for double junction types are C0 attachment and C1 continuity. We develop strong and weak formulations for the elastic flow for curve networks with such junction points. For junctions with three or more curves the conditions at the junctions are derived here for the first time. Possible applications include a simplified one-dimensional model of geometric biomembranes, as well as nonlinear splines in two and higher dimensions. The numerical results presented in this paper demonstrate the practicality of the introduced finite element approximations.
The Application of Bayesian Heuristic for Scheduling in Real-Time Private Clouds

The emergence of Cloud data centers has revolutionized the IT industry. Private Clouds in specific provide Cloud services for certain group of customers/businesses. In a real-time private Cloud each task that is given to the system has a deadline that desirably should not be violated. Scheduling tasks in a real-time private CLoud determine the way available resources in the system are shared among incoming tasks. The aim of the scheduling policy is to optimize the system outcome which for a real-time private Cloud can include: energy consumption, deadline violation, execution time and the number of host switches. Different scheduling policies can be used for scheduling. Each lead to a sub-optimal outcome in a certain settings of the system. A Bayesian Scheduling strategy is proposed for scheduling to further improve the system outcome. The Bayesian strategy showed to outperform all selected policies. It also has the flexibility in dealing with complex pattern of incoming task and has the ability to adapt. Keywords—Bayesian, cloud computing, real-time private cloud, scheduling.
Remote sensing image enhancement via bilateral filtering

Remote sensing image enhancement methods have to increase the contrast and emphasize the edges, while preserving the color. In this study, an enhancement method based on bilateral filtering is proposed. We propose to extract the details of the image by a multiscale bilateral filter and add these details to the original image using a weighting scheme. Visual results and evaluation metrics show that the proposed method, enhance the image better than the former methods while it better preserves the original color information.
Investigations into the lymphocyte phenotypes and the presence of rheumatoid factor and antinuclear antibody in the peripheral blood of 515 dogs

The levels of rheumatoid factor (RF), antinuclear antibody (ANA), and composition of peripheral lymphocyte subsets in 515 dogs were examined. Of these sample cases, 33 cases were diagnosed as immune-mediated fever that presented with high C-reactive protein (CRP), 31 cases were diagnosed with Hansen's Type 1 disc hernia - tion and the remaining 415 cases were clinically healthy dogs, and served as controls. In the cases diagnosed with immune-mediated fever, 84% of the dogs tested positive to either RF or ANA (RF positive 60.6%; ANA positive 24.2%). By contrast, 16.2% of the healthy dogs were positive for either RF or ANA (RF positive 14.9%; ANA 1.3%). The CD4/CD8 ratio for peripheral lymphocyte was high for all analysed cases diagnosed with immune-mediated fever, and was significantly higher than those of healthy controls. These results indicate that the abnormal levels of lymphocytes may be an effective indicator for immune-mediated disease coupled to immune-mediated fever.
Simulation of polar stratospheric clouds : 1 . Microphysical characteristics

Microphysical properties of stratospheric aerosol and polar stratospheric clouds (PSCs) are simulated based on the up-to-date numerical model of formation and transformation of aerosol particles. Overall, the analyzed ensemble totals 255949 realizations and may be considered as a broad representative ensemble of stratospheric aerosol states and PSC under polar and midlatitude winter conditions of the northern hemisphere (45–90°N). The means and covariance matrices of the size distribution function (SDF) for different scenarios of PSC transformation are constructed. Possibilities of optimally parameterization of SDF and its higher-order moments are explored. It is shown that, to describe all SDF realizations with the error no larger than 5–10%, it is sufficient to use just 6 expansion coefficients, rather than specifying SDF a priori in 39 bins. Use of “foreign” vectors as a basis in parameterizations by use of other models insignificantly changes the relative errors of SDF parameterization.
Fusion based Multimodal Biometric Security for Social Networks Communication

modern means of communication increase in their potential and receptiveness, they instigate additional demands in terms of security. Therefore, communication between users via social network becomes complicated, that increases possibility of threats with respect to user authentication. With the objective of ensuring security in social networks, different user authentication and cryptographic mechanism were designed. But, with different heuristic and computational algorithm, user authentication through single modal biometric is easily broken and vulnerabilities of multimodal approaches in social network still remain unexplored. This paper proposes a novel Fusion based Multimodal Biometric Security (FMBS) method utilizing Face and Fingerprint features of human individuals on social networks. Feature extraction for FMBS is performed utilizing combination of Binomial Feature Distribution Algorithm and Neighborhood Dominant Attribute Identification for both face and fingerprint features. Then, dominant attributes are stored in spatial vector for both the modalities to form biometric fusion template. Finally, Structural Biometric Fusion Template Matching algorithm designed to compute matching accuracy of test data to available training data. Experimental evaluation with Biometric Research Repositories is conducted. Performance evaluation show that the method significantly improve matching accuracy of human biometric samples, compared to conventional biometrics user authentication that only make use of single modal biometrics. The result shows that the method has low social network authentication time and network space complexity suited for deployment in real time social network sites.
Inducing Multiple Reactions with a Single Photon Using an optical cavity to couple several molecules can potentially set up a chemical chain reaction that requires just one photon to initiate

M any fundamental processes in nature, such as photosynthesis or human vision, are due to chemical reactions that are triggered by light. In a simplified picture of such processes we assume that photons merely provide the necessary energy to the chemical system to overcome a certain reaction barrier. Besides this energy contribution, the photons supposedly do not influence the system any further. Such a perturbative picture of the interaction between light and matter is reasonable in many situations of photochemistry and justifies what is known as the Stark-Einstein law [1]: for each absorbed quantum of light, no more than one molecule undergoes a chemical reaction.
Application of Rough Set and Fuzzy Comprehensive Evaluation Method in Water Quality Assessment

In view of the existing fuzziness of water types and grading standards in water assessment, the rough set theory and fuzzy comprehensive evaluation method are combined and a water quality assessment model is constructed. This paper focuses on the construction process of the model and put forward an idea of using rough set theory to reduce attribute, that is to eliminate factors that is not important or irrelevant in water evaluation. The model is applied to Jia-lu River and the evaluation result is consistent with the adding notes scoring method’s which is recommended by the Environment Quality Standards for Surface Water. So the model is reliable and reasonable.
Droplet Formation Utilizing Controllable Moving-Wall Structures for Double-Emulsion Applications

The formation of microscale single- and double-emulsion droplets with various sizes is crucial for a variety of industrial applications. In this paper, we report a new microfluidic device which can actively fine-tune the size of single- and double-emulsion droplets in liquids by utilizing controllable moving-wall structures. Moreover, various sizes of external and internal droplets for double emulsions are also successfully formed by using this device. Three pneumatic side chambers are placed at a T-junction and flow-focusing channels to construct the controllable moving-wall structures. When compressed air is applied to the pneumatic side chambers, the controllable moving-wall structures are activated, thus physically changing the width of the microchannels. The size of the internal droplets at the intersection of the T-junction channel is then fine-tuned due to the increase in the shear force. Then, the internal droplets are focused into a narrow stream hydrodynamically and finally chopped into double-emulsion droplets using another pair of moving-wall structures downstream. For single emulsions, oil-in-water droplets can be actively fine-tuned from 50.07 to 21.80 under applied air pressures from 10 to 25 psi with a variation of less than 3.53%. For a water-in-oil single emulsion, droplets range from 50.32 to 14.76 with a variation of less than 4.62% under the same applied air pressures. For double emulsions, the sizes of the external and internal droplets can be fine-tuned with external/internal droplet diameter ratios ranging from 1.69 to 2.75. The development of this microfluidic device is promising for a variety of applications in the pharmaceutical, cosmetics, and food industries.
Improving SMT with Morphology Knowledge for Baltic Languages

In the recent years, several machine translation systems have been built for the Baltic languages. Besides Google and Microsoft machine translation engines and research experiments with statistical MT for Latvian [1] and Lithuanian, there are both English-Latvian [2] and English-Lithuanian [3] rulebased MT systems available. Both Latvian and Lithuanian are morphologically rich languages with quite free word order. In combination with the limited availability of parallel corpora for these languages, it poses a sparseness problem for phrase-based SMT. This research is a part of a project to build the best general-purpose phrase-based SMT using publicly available and proprietary corpora and tools. During the project we added language-specific knowledge to assess the possible improvement of translation quality. This paper reports on implementation, as well as automatic and human evaluation of EnglishLatvian and Lithuanian-English statistical machine translation systems. Results of human evaluation show that integrating morphology knowledge into SMT gives significant improvement of translation quality compared to baseline SMT.