Miracle-dz/UniFAR-FTUs huggingface dataset:
Motivation Modulates Brain Networks in Response to Faces Varying in Race and Status: A Multivariate Approach

Abstract Previous behavioral and neuroimaging work indicates that individuals who are externally motivated to respond without racial prejudice tend not to spontaneously regulate their prejudice and prefer to focus on nonracial attributes when evaluating others. This fMRI multivariate analysis used partial least squares analysis to examine the distributed neural processing of race and a relevant but ostensibly nonracial attribute (i.e., socioeconomic status) as a function of the perceiver’s external motivation. Sixty-one white male participants (Homo sapiens) privately formed impressions of black and white male faces ascribed with high or low status. Across all conditions, greater external motivation was associated with reduced coactivation of brain regions believed to support emotion regulation (rostral anterior cingulate cortex), introspection (middle cingulate), and social cognition (temporal pole, medial prefrontal cortex). The reduced involvement of this network irrespective of target race and status suggests that external motivation is related to the participant’s overall approach to impression formation in an interracial context. The findings highlight the importance of examining network coactivation in understanding the role of external motivation in impression formation, among other interracial social processes.
On the Capacity of D2D Social Networks With Fractal Communications

The maximum capacity, i.e., the maximum bit rate per second of each user, of a D2D (device-to-device) social network with fractal interactions is studied in this paper. Specifically, the self-similarity of a fractal network is mathematically formulated by a power-law degree distribution P(k), and the social connection feature between two users with degree <tex>$k_{1}$</tex> and <tex>$k_{2}$</tex> is captured by a joint probability distribution <tex>$P(k_{1},\ k_{2})$</tex>. It is proved that if the source user communicates with one of his contacts randomly, the maximum capacity is <tex>$\Theta(\frac{1}{\sqrt{n\log n}})$</tex>, where <tex>$n$</tex> is the number of users in the network. On ‘the other hand, if two users with distance <tex>$d$</tex> communicate according to the probability <tex>$d^{-\beta}$</tex>, the maximum capacity can reach up to <tex>$\Theta(\frac{1}{\log n})$</tex> ‘ where <tex>$\beta$</tex> is the frequency parameter.
Energy Efficient and Accurate Tracking and Detection of Continuous Objects in Wireless Sensor Networks

Continuous objects such as mud flow, oil spill, forest fire and toxic gases detection and localization is a fundamental and significant subject in Wireless sensor networks (WSNs). A continuous object is usually blowout over a large area and therefore a large number of sensor nodes are required for tracking and detection. The nodes exchange control messages with each other and with sink node to report their detection status. For accurate tracking and detection nodes at the phenomenon boundary needs to be carefully selected. In this paper, we use four proximity graphs and different spatial interpolation methods for accurate boundary estimation and boundary nodes selection in a dense duty-cycled wireless sensor network. Accurate and reliable nodes are carefully chosen to be awakened when their sensual data is more applicable and consistent to be boundary node. To reduce overall network energy consumption and increase system lifetime, we propose sensor node scheduling scheme. Extensive simulation result shows that the proposed continuous objects detection and tracking technique is more energy efficient and the boundary area can be enhanced more accurately.
MIMO Radar Target Detection Using Low-Complexity Receiver

This paper studies reduced complexity target detection using multiple-input-multiple-output (MIMO) radar with lower complexity. To reduce either hardware or software complexity, some parts of the test statistic are eliminated in the proposed method. For the general case where clutter-plus-noise and reflection coefficients are correlated, the test statistic requires the computation of a set of matched filters (MF-s). These MFs correlate the clutter-plus-noise-free signal received at one receiver due to the signal transmitted from some transmit antenna with the signal received at another receiver. For a special case of uncorrelated clutter-pIus-noise and reflection coefficients and orthogonal waveforms, the proposed method is equivalent to choosing a subset of transmitters to maximize detection probability. In this case we prove that selecting the transmitters at each receiver corresponding to the largest signal-to-clutter-plus-noise ratio (SCNRs) leads to the best detection performance. In the more general case our algorithm picks the best of these MFs to implement under the constraint that the total number of these MFs that one can implement at each receiver is limited.
Graph Regularized Tensor Factorization for Single-Trial EEG Analysis

This study proposes a tensor factorization algorithm for electroencephalographies (EEGs) that incorporates the geometric structure of the electrode location. The purpose is removing noise caused by EEG activities which are irrelevant to stimuli presented to a subject from single-trial event-related potential (ERP) data. Canonical polyadic decomposition (CPD) is extended by adding a regularization term that controls the spatial smoothness of the decomposed components on a scalp. An initialization method using geometrical information is also proposed. The geometric structure of an EEG signal is expressed as an undirected graph where the similarities between electrodes are defined by their relative distances on a scalp. The effectiveness is demonstrated in a noise-removing experiment using pseudo-ERP, where the proposed method achieved better performance than the conventional CPD.
Guided Image Filtering with Arbitrary Window Function

In this paper, we propose an extension of guided image filtering to support arbitrary window functions. The guided image filtering is a fast edge-preserving filter based on a local linearity assumption. The filter supports not only image smoothing but also edge enhancement and image interpolation. The guided image filter assumes that an input image is a local linear transformation of a guidance image, and the assumption is supported in a local finite region. For realizing the supposition, the guided image filtering consists of a stack of box filtering. The limitation of the guided image filtering is flexibilities of kernel shape setting. Therefore, we generalize the formulation of the guide image filter by using the idea of window functions in image signal processing to represent arbitrary kernel shapes. Also, we reveal the relationship between the guided image filtering and the variants of this filter.
Relative Level of Bacteriophage Multiplication in vitro or in Phyllosphere May Not Predict in planta Efficacy for Controlling Bacterial Leaf Spot on Tomato Caused by Xanthomonas perforans

Following analysis of eight phages under in vitro, growth chamber and greenhouse conditions with the bacterial spot of tomato pathogen Xanthomonas perforans, there was no correlation between disease control efficacy and in vitro phage multiplication, in vitro bacterial suppression, or in vivo phage multiplication in the presence of the host, but there was a low correlation between phage persistence on the leaf surface and disease control. Two of the 8 virulent phages (ΦXv3-21 and ΦXp06-02) were selected for in depth analysis with two X. perforans (Xp06-2-1 and Xp17-12) strains. In in vitro experiments, phage ΦXv3-21 was equally effective in infecting the two bacterial strains based on efficiency of plating (EOP). Phage ΦXp06-02, on the other hand, had a high EOP on strain Xp06-2-1 but a lower EOP on strain Xp17-12. In several growth chamber experiments, ΦXv3-21 was less effective than phage ΦXp06-02 in reducing disease caused by strain Xp06-2-1, but provided little or no disease control against strain Xp17-12. Interestingly, ΦXp06-02 could multiply to significantly higher levels on the tomato leaf surface than phage ΦXv3-21. The leaf surface appears to be important in terms of the ability of certain bacteriophages to multiply in the presence of the bacterial host. ΦXv3-21, when applied to grapefruit leaves in combination with a bacterial host, was unable to multiply to high levels, whereas on tomato leaflets the phage multiplied exponentially. One plausible explanation is that the leaf surface may be an important factor for attachment of certain phages to their bacterial host.
Acoustic Modeling of Speech Waveform Based on Multi-Resolution, Neural Network Signal Processing

Recently, several papers have demonstrated that neural networks (NN) are able to perform the feature extraction as part of the acoustic model. Motivated by the Gammatone feature extraction pipeline, in this paper we extend the waveform based NN model by a second level of time-convolutional element. The proposed extension generalizes the envelope extraction block, and allows the model to learn multi-resolutional representations. Automatic speech recognition (ASR) experiments show significant word error rate reduction over our previous best acoustic model trained in the signal domain directly. Although we use only 250 hours of speech, the data-driven NN based speech signal processing performs nearly equally to traditional handcrafted feature extractors. In additional experiments, we also test segment-level feature normalization techniques on NN derived features, which improve the results further. However, the porting of speech representations derived by a feed-forward NN to a LSTM back-end model indicates much less robustness of the NN front-end compared to the standard feature extractors. Analysis of the weights in the proposed new layer reveals that the NN prefers both multi-resolution and modulation spectrum representations.
Structural Features of Amyloid Fibrils Formed from the Full-Length and Truncated Forms of Beta-2-Microglobulin Probed by Fluorescent Dye Thioflavin T

The persistence of high concentrations of beta-2-microglobulin (β2M) in the blood of patients with acute renal failure leads to the development of the dialysis-related amyloidosis. This disease manifests in the deposition of amyloid fibrils formed from the various forms of β2M in the tissues and biological fluids of patients. In this paper, the amyloid fibrils formed from the full-length β2M (β2m) and its variants that lack the 6 and 10 N-terminal amino acids of the protein polypeptide chain (ΔN6β2m and ΔN10β2m, respectively) were probed by using the fluorescent dye thioflavin T (ThT). For this aim, the tested solutions were prepared via the equilibrium microdialysis approach. Spectroscopic analysis of the obtained samples allowed us to detect one binding mode (type) of ThT interaction with all the studied variants of β2M amyloid fibrils with affinity ~104 M−1. This interaction can be explained by the dye molecules incorporation into the grooves that were formed by the amino acids side chains of amyloid protofibrils along the long axis of the fibrils. The decrease in the affinity and stoichiometry of the dye interaction with β2M fibrils, as well as in the fluorescence quantum yield and lifetime of the bound dye upon the shortening of the protein amino acid sequence were shown. The observed differences in the ThT-β2M fibrils binding parameters and characteristics of the bound dye allowed to prove not only the difference of the ΔN10β2m fibrils from other β2M fibrils (that can be detected visually, for example, by transmission electron microscopy (TEM), but also the differences between β2m and ΔN6β2m fibrils (that can not be unequivocally confirmed by other approaches). These results prove an essential role of N-terminal amino acids of the protein in the formation of the β2M amyloid fibrils. Information about amyloidogenic protein sequences can be claimed in the development of ways to inhibit β2M fibrillogenesis for the treatment of dialysis-related amyloidosis.
Adversarial Multilingual Training for Low-Resource Speech Recognition

This paper proposes an adversarial multilingual training to train bottleneck (BN) networks for the target language. A parallel shared-exclusive model is also proposed to train the BN network. Adversarial training is used to ensure that the shared layers can learn language-invariant features. Experiments are conducted on IARPA Babel datasets. The results show that the proposed adversarial multilingual BN model outperforms the baseline BN model by up to 8.9% relative word error rate (WER) reduction. The results also show that the proposed parallel shared-exclusive model achieves up to 1.7% relative WER reduction when compared with the stacked share-exclusive model.
Motor Imagery for Eeg Biometrics Using Convolutional Neural Network

This paper deals with electroencephalography (EEG)-based biometric identification, using a motor imagery task, specifically performing imaginary arms and legs movements. Deep learning methods such as convolutional neural network (CNN) is used for automatic discriminative feature extraction and person identification. An extensive set of experimental tests, performed on a large database comprising EEG data collected from 40 subjects over two different sessions taken at a week distance, shows the existence of repeatable discriminative characteristics in individuals' brain signals.
TSh Vibrations of Limited Size Plates of Rotated Y-cut Quartz with Development of Matrix-operation Method for Solving Equations

The paper deals with development of ma-trix-operation method of solving equations of thickness-shear (TSh) vibrations of piezo-electric quartz plates for rotated Y-cut with the aim of analytic description of vibrations when the plate has a limited length. The basic equations of TSh - vibrations for arbitrary boundary conditions are obtained.
A Self-contained Teleoperated Quadrotor: On-Board State-Estimation and Indoor Obstacle Avoidance

Indoor operation of unmanned aerial vehicles (UAV s) poses many challenges due to the lack of GPS signal and cramped spaces. The presence of obstacles in an unfamiliar environment requires reliable state estimation and active algorithms to prevent collisions. In this paper, we present a teleoperated quadrotor UAV platform equipped with an onboard miniature computer and a minimal set of sensors for this task. The platform is capable of highly accurate state-estimation, tracking of desired velocity commanded by the user and ensuring collision-free navigation. The robot estimates its linear velocity through a Kalman filter integration of inertial and optical flow (OF) readings with corresponding distance measurements. An RGB-D camera serves the purpose of providing visual feedback to the operator and depth measurements to build a probabilistic, robo-centric obstacle model, allowing the robot to avoid collisions. The platform is thoroughly validated in experiments in an obstacle rich environment.
HTTP/2 Push-Based Low-Delay Live Streaming Over Mobile Networks With Stream Termination

HTTP adaptive streaming (HAS) has become popular for delivering videos over the Internet. However, most HAS services are currently based on the traditional HTTP/1.1, which has limitations for live streaming. Meanwhile, the new HTTP/2 was standardized in 2015 with many improvements. In this letter, we present a novel HTTP/2-based adaptation scheme for low-delay live streaming over mobile networks. The proposed scheme leverages two HTTP/2 features called server push and stream termination to enable a shorter camera-to-display delay. Experimental results with small buffer sizes show that our scheme can provide users with high video bitrate and stable buffer level. Furthermore, the buffer size in live streaming can be reduced to two sec without sacrificing video quality.
Digital-Analog Superposition Coding for Ofdm Channels with Application To Video Transmission

A new approach to hybrid digital-analog (HDA) video transmission over orthogonal frequency division multiplexing (OFDM) channels is presented. The goal is to achieve the best video quality by optimal power allocation, when the OFDM sub-channels have unequal and time-varying signal-to-noise ratios (SNR). In this method, the quantization error of a video encoder is superimposed on digital quadrature amplitude modulation (QAM) symbols. A solution to the power allocation problem is presented. Experimental comparisons with layered video coding and adaptive modulation shows that the proposed HDA approach is able to achieve a better video quality most of the time, particularly whenever there is a high motion content.
Constructing a Knowledge Base for Al-Qur'an Utilizing Principles of Human Communication

The knowledge behind the data is of utmost importance. It is necessary to develop meaningful, expressive and comprehensive knowledge base for every domain in order to use the knowledge. The repositories need to be designed considering natural language interactions for better performance. A knowledge base with such features seems to be non-existent for Al-Qur'an. The current work aims to develop an efficient and comprehensive knowledge base for Al-Qur'an based on principles of logic, linguistics and semantic networks, etc. The developed knowledge base is expressive, comprehensive, meaningful and efficient, establishing and maintaining semantic relations. The developed knowledge base can be used for varied purposes such as expert systems, etc. contributing to the efficiency of these systems based on its unique design.
Singing Voice Correction Using Canonical Time Warping

Expressive singing voice correction is an appealing but challenging problem. A robust time-warping algorithm which synchronizes two singing recordings can provide a promising solution. We thereby propose to address the problem by canonical time warping (CTW) which aligns amateur singing recordings to professional ones. A new pitch contour is generated given the alignment information, and a pitch-corrected singing is synthesized back through the vocoder. The objective evaluation shows that CTW is robust against pitch-shifting and time-stretching effects, and the subjective test demonstrates that CTW prevails the other methods including DTW and the commercial auto-tuning software. Finally, we demonstrate the applicability of the proposed method in a practical, real-world scenario.
Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning

In this paper, we implement an information-theoretic approach to travel behaviour analysis by introducing a generative modelling framework to identify informative latent characteristics in travel decision making. It involves developing a joint tri-partite Bayesian graphical network model using a Restricted Boltzmann Machine (RBM) generative modelling framework. We apply this framework on a mode choice survey data to identify abstract latent variables and compare the performance with a traditional latent variable model with specific latent preferences – safety, comfort, and environmental. Data collected from a joint stated and revealed preference mode choice survey in Quebec, Canada were used to calibrate the RBM model. Results show that a significant impact on model likelihood statistics and suggests that machine learning tools are highly suitable for modelling complex networks of conditional independent behaviour interactions.
Improving Physical Layer Security in Two-Way Relay Systems

Ahstract- This paper studies the physical layer security in a two-way relay system (TWRS) consisting of two transceivers, one eavesdropper, and multiple relays when the channel state information (CSI) of the eavesdropper is available. We consider the problem of maximizing the secrecy sum rate by optimizing the relay beamforming vector under relays' total power constraint. The optimization problem has been formulated in the literature as a product of three Rayleigh quotients, which is very difficult to solve, and the best proposed solution was a suboptimal solution to the null space beamforming. Here, we propose two approaches: 1) Optimal solution to the null space beamforming approach, and 2) Ignoring one Rayleigh quotient (IORQ) approach (which has not been proposed before). In the first approach, we convert the nonconvex product of two Rayleigh quotients into a convex problem with one dimensional search using semidefinite programming (SDP). Then we significantly reduce the complexity of solving the problem using the generalized eigenvalues. In the second approach, we look for the beamforming vector that does not eliminate the complete information signal at the eavesdropper aiming to increase the whole secrecy sum rate. Numerical results show that the proposed algorithms provide higher secrecy sum rates than the existing algorithms.
Using physical models and failure data to estimate reliability with uncertainty

This paper presents a method for estimating the reliability resulting from a design change to an existing system by using a combination of physics-based modeling and simulation (M&S) and failure data for the existing system. Physics-based M&S is a powerful tool for understanding the impact of environmental and internal stressors during actual usage. This understanding allows for estimates of reliability to be developed prior to any test data being available for a new design. The results can give an early indication of the expected change in reliability due to the new design, and they may serve to aid in decision making regarding potential design modifications.
Towards Light-weight Annotations: Fuzzy Interpolative Reasoning for Zero-shot Image Classificaiton

Despite the recent popularity of Zero-shot Learning (ZSL) techniques, existing approaches rely on ontological engineering with heavy annotations to supervise the transferable attribute model that can go across seen and unseen classes. Moreover, existing cross-sourcing, expert-based, or data-driven attribute annotations (e.g. Word Embeddings) cannot guarantee sufficient description to the visual features, which leads to significant performance degradation. In order to circumvent the expensive attribute annotations while retaining the reliability, we propose a Fuzzy Interpolative Reasoning (FIR) algorithm that can discover inter-class associations from light-weight Simile annotations based on visual similarities between classes. The inferred representation can better bridge the visual-semantic gap and manifest state-of-the-art experimental results.
TSV Assignment of Thermal and Wirelength Optimization for 3D-IC Routing

The 3D integrated circuit is a complex structure composed of chips in multi-layers fabricated vertically and horizontally. Signal through-silicon-via (TSV) is vertical electric connection enabling to communicate and compact the functional bulk. And thermal TSV is effective to amplify the heat dissipation and reduce temperature by establishing the heat dissipation path. In this work we introduce conditional grid extension method to Integrated Multi-Commodity Min-Cost (IMCMC) problem to relieve capacity limit. Moreover, we propose thermal increase model to simulate thermal distribution and realize temperature reduction. Compared with previous work, several routing revision is adopted to make the result more accurate and reduce wire capacitance. The experimental results show the effectiveness of our algorithm on reducing temperature and optimizing congestion.