Miracle-dz/UniFAR-FTUs huggingface dataset:
Reward Only Training of Encoder-Decoder Digit Recognition Systems Based on Policy Gradient Methods

Zero resource speech recognition is getting attention for engineering as well as scientific purposes. Based on the existing unsupervised learning frameworks using only speech input, however, it is impossible to associate automatically found linguistic units with spellings and concepts. In this paper, we propose an approach that uses a scalar reward that is assumed to be given for each decoding result of an utterance. While the approach is straightforward using reinforcement learning, the difficulty is to obtain a convergence without the help of supervised learning. Focusing on encoder-decoder based speech recognition, we explore several neural network architectures, optimization methods, and reward definitions, seeking a suitable configuration for policy gradient reinforcement learning. Experiments were performed using connected digit utterances from the TIDIGITS corpus as training and evaluation sets. While it is challenging, we show that learning a connected digit recognition system is possible achieving 13.6% of digit error rate. The success largely depends on the configurations and we reveal the appropriate condition that is largely different from supervised training.
Deep Learning based on CNN for Pedestrian Detection: An Overview and Analysis

Pedestrian detection methods based on deep learning can automatically learn features in unsupervised or supervised fashions and are capable to learn qualified high level feature representations to detect pedestrian. Recently, deep learning methods chiefly algorithms based on DCNN (Deep Convolutional Neural Network) have made outstanding achievements on pedestrian detection. The aim of this paper is to review the state-of-the-art in pedestrian detection algorithms based on DCNN. Also, we emphasize contributions and challenges of DCNN in pedestrian detection from the recent researches. In fact, this paper first overviews a number of well-known training approaches based on DCNN and their recent development, and then briefly describes recent algorithms based on these approaches. Ultimately, the paper analyzes deep pedestrian detection algorithms from training approach, categorization, and deep model points of view, and proposes a novel training method for deep pedestrian detection based on the analysis.
Nonlinear Stabilization: Control Contraction Metric Approach via Sum of Square Programming Technique

The study in this article focuses on the stability and performance of nonlinear uncertain system. Control contraction metric (CCM)approach is the latest ongoing trend in the field of design and stability of nonlinear systems. Unlike other nonlinear stability and design approach, contraction analysis deals with stability that is defined incrementally between two arbitrary independent trajectories. In this approach, CCM is found via sum of square (SOS)programming using SOSTOOLS and semi definite programming solver SeDuMi. The said design is validated on the nonlinear model of Moore-Greitzer jet engine. The said approach ensures better overall transient performance compared to that of linear quadratic regulator (LQR)controller independent of initial conditions confirming universal stability of CCM. Also, with the increase in the degree of polynomial of contraction metric, system performance is found to be better compared to that of lower degree polynomial.
Classification of Chronic Kidney Disease using Logistic Regression, Feedforward Neural Network and Wide & Deep Learning

Chronic kidney disease (CKD) is a global health burden that affects approximately 10% of the adult population in the world. It is also recognized as the top 20 causes of death worldwide. Unfortunately, there is no cure for CKD however, it is possible to slow down its progression and mollify the damage by early diagnosis of the disease. Due to a limited number of nephrologists, the early diagnosis of CKD is often not possible for most of the people. Therefore the use of modern computer-aided methods is necessary to aid the traditional CKD diagnosis system to be more efficient and accurate. In this research, our primary focus was to apply 3 modern machine learning techniques namely logistic regression, feedforward neural networks and wide & deep learning to diagnose CKD as well as finding the best performing technique by evaluating their diagnosis performance. To evaluate their performance, f1-score, precision, recall and AUC score was used for logistic regression and an additional loss score was considered for the feedforward neural networks and wide & deep model. We found the feedforward neural network as the best performing technique for CKD diagnosis with 0.99 f1-score, 0.97 precision, 0.99 recall and 0.99 AUC score. Logistic regression produced the lowest result among all and the wide & deep learning with a larger number of hidden layers and neurons found to be effective for larger datasets.
An Efficient Bi-directional DC/DC Charger for Electric Vehicle Battery Charging

The choice of bi-directional dc/dc converter plays a vital role in the battery chargers of electrical vehicles (EVs), In this paper, a bidirectional battery charging and discharging topology without snubber circuitry is proposed. A different frequency operation is performed in step-up and step-down mode to achieve zero voltage switching in both the directions. The battery side converter is selected as a current-fed half bridge boost converter to achieve 2X voltage gain from primary to secondary of transformer. losses of soft switching from full load to no load and vice-versa are identified and rectified by using MATLAB/Simulink for 1KW battery connected system.
The A2B adenosine receptor modulates pulmonary hypertension associated with interstitial lung disease

Development of pulmonary hypertension is a common and deadly complication of interstitial lung disease. Little is known regarding the cellular and molecular mechanisms that lead to pulmonary hypertension in patients with interstitial lung disease, and effective treatment options are lacking. The purpose of this study was to examine the adenosine 2B receptor (A2BR) as a regulator of vascular remodeling and pulmonary hypertension secondary to pulmonary fibrosis. To accomplish this, cellular and molecular changes in vascular remodeling were monitored in mice exposed to bleomycin in conjunction with genetic removal of the A2BR or treatment with the A2BR antagonist GS‐6201. Results demonstrated that GS‐6201 treatment or genetic removal of the A2BR attenuated vascular remodeling and hypertension in our model. Furthermore, direct A2BR activation on vascular cells promoted interleukin‐6 and endothelin‐1 release. These studies identify a novel mechanism of disease progression to pulmonary hypertension and support the development of A2BR antagonists for the treatment of pulmonary hypertension secondary to interstitial lung disease. —Karmouty‐Quintana, H. , Zhong, H. , Acero, L. , Weng, T. , Melicoff, E. , West, J. D. , Hemnes, A. , Grenz, A. , Eltzschig, H. K. , Blackwell, T. S. , Xia, Y. , Johnston, R. A. , Zeng, D. , Belardinelli, L. , Blackburn, M. R. The A2B adenosine receptor modulates pulmonary hypertension associated with interstitial lung disease. FASEB J. 26, 2546–2557 (2012). www.fasebj. org
Application of Beta Random Variables to Category-based Collaborative Filtering

Memory-based Collaborating Filtering (MbCF) is one of the most common techniques utilized by the recommender system. Despite its many advantages and wide applicability, this technique suffers high computational complexity, especially in the presence of scalability issues. Category-based Collaborative Filtering is an approach to MbCF for alleviating its mentioned drawbacks by categorizing the users’ ratings under a few categories. In this paper, we propose a new Category-based Collaborative Filtering method with linear computational complexity by which the category information is modeled by the Beta random variable. Beta random variable requires only two parameters for intelligent data modeling of the bipolar (negative/positive) emotional patterns. Then, the parameters of the corresponding probability distribution function are estimated by a Bayesian estimator with linear computational complexity. The experiments on the MovieLens 1M dataset prove that utilizing the mentioned method and correspondingly compressing the information by the proposed probabilistic model improves the prediction accuracy and time in comparison with other state-of-the-art MbCF algorithms.
16.8 A 25.4-to-29.5GHz 10.2mW Isolated Sub-Sampling PLL Achieving -252.9dB Jitter-Power FoM and -63dBc Reference Spur

Recent mm-wave PLLs have explored different architectures to enhance their jitter performance at low power. Without noisy loop components, the injection-locked PLL in [1] using a GHz reference (REF=2.25 GHz) can effectively suppress the integrated jitter ($86fs _{\mathrm{rms}})$, resulting in a better jitter-power FoM (-247.2dB). Yet, high-frequency REF injection leads to large spur (-32dBc), entailing continuous frequency tracking to withstand the PVT variations. Also, at the system level, the GHz REF has to be generated on-chip (i.e., cascaded PLLs). The power overhead, e.g., additional 20mW in [2], and unwanted coupling between the two VCOs become inevitable. To this end, direct-synthesis mm-wave PLLs using a MHz REF are of higher interest, despite the challenge of a large division ratio (N). An example is a Type-II mm-wave PLL reported in [3] that achieves $115fs_{\mathrm{rms}}$ integrated jitter, but the involved divider, charge pump (CP), and VCO totally draw 31mW to suppress the in-band and out-of-band phase noise (PN).
Stochastic DNN-HMM Training for Robust ASR

Since the introduction of deep neural network (DNN)-based acoustic model to automatic speech recognition (ASR), robust ASR using DNN are being in research. However, most DNN-based techniques are performed without consideration of the reliability of the estimates and this degrades the ASR performance especially in the training-test mismatch conditions. In this paper, we propose a novel deep learning-based acoustic modeling technique which measures and takes account of the reliability using a single DNN. The proposed approach describes the mapping between the noisy input and clean features as a stochastic process. Therefore, a statistical modeling is applied to the DNN-based acoustic model in predicting the posterior distribution of the clean speech features given a distorted input data. Also, by attempting the two different probabilistic models in clean feature distribution assumption, we investigate which distribution is more proper on various environment conditions. It has been shown that the proposed technique outperforms the conventional DNN-based techniques on Aurora-4 DB and mismatched noise conditions.
Performance Analyses on Logging Policies of Log-structured File System

Log-structured filesystems have suffered from garbage collection overheads when they have lack of free space. To address the problem, previous research suggested threaded logging that reuses invalidated blocks without a cleaning process. However, threaded logging incurs random writes that can degrade the performance of upcoming read operations, since the invalid blocks can be discontiguous. In this paper, we show the performance of the two logging policies and analyze their performance characteristics.
Abnormal brain detection and analysis of EEG signals

The Electroencephalogram(EEG) is a representative signal enclosing information about the function of the brain. EEG signals can be utilized to study an automatic diagnosis of epilepsy. This later is characterized by a spontaneous and an unexpected that occurs seizures. In this paper, we propose an automatic detection of seizure disease using a support vector machine (SVM) classifier for the classification of EEG signals from three classes like healthy, pre-ictal and epileptic patients. Five statistical time domain features are extracted namely Root Mean Square (RMS), Sekwens (Sk), kurtosis (Ku), crest factor (CF) and clearance indicator (CLI). The Generalized Discriminant Analysis (GDA) is applied on statistical time domain features to reduce the dimensionality. These features with reduced dimensionality are utilized as input to a support vector machine (SVM) classifier. Our results in terms of classification accuracy out performs other existing methods and achieve the best classification rates with an average of 98.80%. Our approach is effective to diagnosis and detect epileptic seizure.
Image Recognition Based on Convolutional Neural Networks Using Features Generated from Separable Lattice Hidden Markov Models

An image recognition method based on convolutional neural networks (CNNs) using features generated from separable lattice hidden Markov models (SLHMMs) is proposed. A major problem in image recognition is that the recognition performance is degraded by geometric variations in the size and position of the object to be recognized. To solve this problem, SLHMMs have been proposed as an extension of HMMs with size and locational invariances based on state transitions. Although SLHMMs are generative models that can represent the generation processes of observations well, there is a possibility that they are not specialized for discrimination compared to discriminative models. Our method integrates SLHMMs that extract features invariant to geometric variations with CNNs that build an accurate classifier based on discriminative models with the extracted features. Face recognition experiments showed that the proposed method improves recognition performance.
Modeling and Model-free Fuzzy Control of a Continuum Robotic Arm

Continuum robotic arms have many advantages compared to traditional manipulators, which motivate further researches in this field. Dynamics modeling and controlling these systems are challenging areas due to the complicated nonlinearities and uncertainties. In this paper, a three-dimensional dynamics model of such manipulators is developed through appropriate system identification method that is validated by experimental data, and then it is used to design a PID-fuzzy controller. This is a model-free controller and does not require the exact model of the system for controlling the position of the end-effector. Next, the performance in reaching different positions of the workspace is investigated. Obtained results show the merits of the proposed controller.
Yeast Pyruvate Dehydrogenase Complex Is Regulated by a Concerted Activity of Two Kinases and Two Phosphatases*

The activity of yeast pyruvate dehydrogenase complex is regulated by reversible phosphorylation. Recently we identified two enzymes that are involved in the phosphorylation (Pkp1p) and dephosphorylation (Ppp1p) of Pda1p, the α-subunit of the pyruvate dehydrogenase complex. Here we provide evidence that two additional mitochondrial proteins, Pkp2p (Ygl059wp) and Ppp2p (Ycr079wp), are engaged in the regulation of this complex by affecting the phosphorylation state of Pda1p. Our data indicate complementary activities of the kinases and a redundant function for the phosphatases. Both proteins are associated with the complex. We propose a model for the role of the regulatory enzymes and the phosphorylation state of Pda1p in the assembly process of the pyruvate dehydrogenase complex.
Distributed MPPT Nonlinear Control Algorithm for Boost DC-DC Converters in Cascaded Connection

In this paper, a bank of nonlinear controllers is proposed for boost dc-dc converters in cascade connection (well- known as optimizers). Each optimizer is stabilized at its maximum power point by using a feedback linearization technique plus a conventional proportional and derivative controller. The proof of the internal stability is given by taking into account an electrical representation of the internal dynamics. The innovative nature of the proposed methodology features an improvement of the transient state of the system with a minimal steady state error even though conventional maximum power point tracking algorithm (MPPT) is used. Co-simulation tests (MATLAB and PSIM) are carried out by considering partial shading conditions in order to evaluate the performance of the controllers for the boost dc-dc converters in distributed maximum power point tracking (DMPPT) applications.
Mutational analysis of Brh2 reveals requirements for compensating mediator functions

Brh2, a member of the BRCA2 family of proteins, governs homologous recombination in the fungus Ustilago maydis through interaction with Rad51. Brh2 serves at an early step in homologous recombination to mediate Rad51 nucleoprotein filament formation and also has the capability to function at a later step in recombination through its inherent DNA annealing activity. Rec2, a Rad51 paralogue, and Rad52 are additional components of the homologous recombination system, but the absence of either is less critical than Brh2 for operational activity. Here we tested a variety of mutant forms of Brh2 for activity in recombinational repair as measured by DNA repair proficiency. We found that a mutant of Brh2 deleted of the non‐canonical DNA‐binding domain within the N‐terminal region is dependent upon the presence of Rad52 for DNA repair activity. We also determined that a motif first identified in human BRCA2 as important in binding DMC1 also contributes to DNA repair proficiency and cooperates with the BRC element in Rad51 binding.
4.5 A 13.5dBm Fully Integrated 200-to-255GHz Power Amplifier with a 4-Way Power Combiner in SiGe:C BiCMOS

In order to efficiently utilize the frequency band above 200GHz for radar and communication applications, enough transmitted output power is essential to overcome the elevated path loss. For silicon-based technologies, the available output power at this frequency range is limited. The enlarging of the transistor size and so the corresponding power handling leads to a reduced output impedance. This causes the impedance transformation ratio to increase allowing only for narrow-band designs. Power combining of multiple single power amplifiers (PAs) is a common approach to achieve high output power alleviating the need to enlarge the devices size [1]. In this paper, we present a fully integrated PA that combines the power of 4 differential PA units using a 4-way zero-degree combiner and a 4-way active splitter to feed the 4 PA units.
Speaker Identity Tracing Using Fingerprint Data Hiding against Telecommunications Fraud

One of the problems in telecommunication fraud investigation is speaker identity tracing and verification. It is difficult to trace identities due to the anonymity of speaker. Therefore the identity tracing of the anonymous user has been identified as a significant problem to assist the investigation. In this paper, a speaker identity tracing scheme using fingerprint data hiding is proposed for preventing telecommunication fraud. First, the fingerprint data of the speaker as the identity information is hidden into the recorded speech signal in the process of calling by smartphone, and then the OFDM of 4G wireless communication is used to modulate the speech signal containing the speaker identity information. Once telecommunication fraud occurs, the speaker can be successfully traced by extracting the fingerprint data from the speech signal. Experimental results show the proposed scheme can accurately trace the speaker identity in the remote speech communications, and prevent telecommunication fraud to some extent.
A Branch Cut Algorithm for 180-Degree Ambiguity Resolution in 2D Vector Fields and Its Application to Single-Frame Fringe Projection Profilometry

The 180-degree ambiguity resolution is an estimation problem of correct signs of discrete samples of a two-dimensional (2D) vector field. To estimate the correct signs at 2D lattice points, several optimization approaches are proposed. These approaches assume that the true vector felid changes smoothly between many neighboring lattice points, and solve a combinatorial optimization problem on sign matrices, where the objective function is given by the sum of costs for neighboring signs. This objective function is non-submodular, and hence the optimization problem is NP-hard. For this NP-hard problem, we propose a branch cut type solver which is inspired by Goldstein's approach for 2D phase unwrapping. In application to single-frame fringe projection profilometry, we show the effectiveness of the proposed branch cut algorithm.
Multi-Modal Detection Fusion on a Mobile UGV for Wide-Area, Long-Range Surveillance

We introduce a self-contained, mobile surveillance system designed to remotely detect and track people in real time, at long ranges, and over a wide field of view in cluttered urban and natural settings. The system is integrated with an unmanned ground vehicle, which hosts an array of four IR and four high-resolution RGB cameras, navigational sensors, and onboard processing computers. High-confidence, low-false-alarm-rate person tracks are produced by fusing motion detections and single-frame CNN person detections between co-registered RGB and IR video streams. Processing speeds are increased by using semantic scene segmentation and a tiered inference scheme to focus processing on the most salient regions of the 43° x 7.8° composite field of view. The system autonomously produces alerts of human presence and movement within the field of view, which are disseminated over a radio network and remotely viewed on a tablet computer. We present an ablation study quantifying the benefits that multi-sensor, multi-detector fusion brings to the problem of detecting people in challenging outdoor environments with shadows, occlusions, clutter, and variable weather conditions.
Detection of Double JPEG Compression with the Same Quantization Matrix Based on Convolutional Neural Networks

The detection of double JPEG compression with the same quantization matrix is a challenging problem in image forensics. In this paper, a CNN framework is proposed to solve this problem. This framework contains a preprocessing layer and a well-designed CNN. In the preprocessing layer, the rounding and truncation error images are extracted from continuous recompressed input samples and then fed into the following CNN. In the design of the CNN architecture, several advanced techniques are carefully considered to prevent overfitting, such as $1\times 1$ convolutional kernel and global average pooling layer. The performance of proposed framework is evaluated on the public available image dataset (BOSSbase) with various quality factors (QF). Experimental results have shown the proposed CNN framework performs better than the state-of-the-art method based on hand-crafted features.