Miracle-dz/UniFAR-FTUs huggingface dataset:
Report Genetic analysis of the role of G protein–coupled receptor signaling in

Kingdom ells display chemotaxis and electrotaxis by migrating directionally in gradients of speciﬁc chemicals or electrical potential. Chemotaxis in Dictyostelium discoideum is mediated by G protein–coupled receptors. The unique G (cid:2) is essential for all chemotactic responses, although different chemoattractants use different receptors and G (cid:3) subunits. Dictyostelium amoebae show striking electrotaxis in an applied direct current electric ﬁeld. Per-haps electrotaxis and chemotaxis share similar signaling mechanisms? Null mutation of G (cid:2) and cAMP receptor 1 and G (cid:3) 2 did not abolish electrotaxis, although G (cid:2) -null mutations showed suppressed electrotaxis. By C G protein signaling plays an essential role in chemotaxis. G protein–coupled receptor signaling was monitored with PHcrac–green ﬂuorescent protein, which translocates to inositol phospholipids at the leading edge of cells during chemotaxis. There was no intracellular gradient of this protein during electrotaxis. However, F-actin was polymerized at the leading edge of cells during electrotaxis. We conclude that reception and transduction of the electrotaxis signal are largely independent of G protein–coupled receptor signaling and that the pathways driving chemotaxis and electrotaxis intersect downstream of heterotrimeric G proteins to invoke cytoskeletal
On the Relation between Brauer Groups and Discrete Logarithms

In this article we want to make evident that Brauer groups of local and global fields play an important role in public key cryptopgraphy. In the first section we show that all ideal class groups resp. divisor class groups attached to curves over finite fields Fq taken into consideration for DL-systems can be mapped to such Brauer groups via the Tate-Lichtenbaum pairing. It is well known that this pairing applied to elements of order n can can be computed with complexity polynomial in j Fq(„n) j where „n is the group of roots of unity of order n. The image under this map leads to cyclic algebras over local fields with residue field Fq(„n). In the next section we study such algebras and explain the connection with discrete logarithms in finite fields as well as with elements in the Brauer group over global fields. This leads to a possibility to apply index-calculus methods described in the third section. As result we get subexponential algorithms for the computation of discrete logarithms in finite fields as well as for the computation of the Euler totient function. For the convenience of the reader we list the needed definitions and results about Galois cohomology groups in an appendix. ⁄ This article is based on a lecture given at the conference WartaCrypt 2004. The author would like to thank the organizers of the conference for generous support, an interesting program and last but not least for a very inspiring and pleasant atmosphere, and T. Lange for fruitful discussions on questions related to this work.
ischemic stroke ◼ acute renal Acute Renal Failure Is Associated With Higher Death and Disability in Patients With Acute Ischemic Stroke Analysis of Nationwide Inpatient Sample

years; P <0.0001) and predominantly men (52%; P <0.0001). Comorbid conditions, such as hypertension, diabetes mellitus, atrial fibrillation, and congestive heart failure, were significantly higher in patients with ARF (Table I in the online-only Background and Purpose —Acute renal failure (ARF) in setting of acute ischemic stroke (AIS) is associated with worse outcome. We sought to determine the prevalence of ARF and effect on outcomes of patients with AIS. Methods —Data from all patients admitted to US hospitals between 2002 and 2010 with a primary discharge diagnosis of ischemic stroke and secondary diagnosis of ARF were included. The effect of ARF on rates of intracerebral hemorrhage and discharge outcomes was analyzed after adjusting for potential confounders using logistic regression analysis. 0.5%; P in-hospital of to 95% 1.3–1.4; intracerebral 2.2; 95%
A Hybrid Multilevel Storage Architecture for Electric Power Dispatching Big Data

Electric power dispatching is the center of the whole power system. In the long run time, the power dispatching center has accumulated a large amount of data. These data are now stored in different power professional systems and form lots of information isolated islands. Integrating these data and do comprehensive analysis can greatly improve the intelligent level of power dispatching. In this paper, a hybrid multilevel storage architecture for electrical power dispatching big data is proposed. It introduces relational database and NoSQL database to establish a power grid panoramic data center, effectively meet power dispatching big data storage needs, including the unified storage of structured and unstructured data fast access of massive real-time data, data version management and so on. It can be solid foundation for follow-up depth analysis of power dispatching big data.
Frequency Identification of a Memory Polynomial Model for PA Modeling

In this paper, we describe the validation of the modeling of a power amplifier. The model used is a memory polynomial model. We propose a method of identification in the frequency domain, from characteristic points (in amplitude and in phase) which are the fundamental and the inter-modulation tones. We then describe the realization of a test-bench suitable for the validation of this model.
Improvement of Visual Odometry Using Classic Features by Semantic Information

Visual odometry is a key technology for the lo-calization of autonomous robots in real-world environments. In particular, when a robot adopts a camera as an external sensor, visual odometry can provide useful information for fine-grained localization. Many approaches have been developed for this task; however, this study proposes a novel scheme that uses traditional feature extraction and tracking to realize computationally efficient but sufficiently accurate for practical use when used with a visual navigation scheme based on semantic segmentation. The key features of the proposed scheme can be summarized as follows: the elimination of feature points on moving obstacles, feature point extraction on object boundaries, feature tracking, and outlier elimination, considering semantic information. The proposed scheme reduced the estimation error of visual odometry to 12% of the existing scheme in the best case, according to experimental results, using datasets created with the CARLA simulator.
On the use of Deep Learning and Scattering Transform for Pathological voices recognition

In the last few decades, Deep Neural Networks (DNNs) has shown outstanding performance in speech recognition applications. We demonstrate that the improved accuracy obtained by Deep Convolutional Neural Network (DCNN) arose from their capacity to extract discriminative representations which are robust to various sources of variability in speech signals. By this study, we propose a new algorithm, named Scattering Transform-Deep Convolutional Neural Network CNN: ST-DCNN to identify normal and pathological voices. The effectiveness of advances in speech features have been proven to be the root for an efficient pathological voices classification. The proposed algorithm involved two stages: First, scatter wavelet features are extracted. Then, DCNN is used to classify the voices samples. We evaluated the robustness of the proposed system in silent environments. The experimental results indicates that it achieves better performance with scattering wavelet and DCNN with the clean data within 99.62 % of recognition rate.
PVT Analysis for RRAM and STT-MRAM-based Logic Computation-in-Memory

Emerging non-volatile resistive memories like Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM) and Resistive RAM (RRAM) are in the focus of today’s research. They offer promising alternative computing architectures such as computation-in-memory (CiM) to reduce the transfer overhead between CPU and memory, usually referred to as the memory wall, which is present in all von Neumann architectures. A multitude of architectures with CiM capabilities are based on these devices, due to their inherent resistive behavior and thus their ability to perform calculation directly within the memory, and thus without invoking the CPU at all. However, emerging memories are sensitive to Process, Voltage and Temperature (PVT) variations. This sensitivity has an even larger impact on CiM architectures. In this paper, we analyze and compare the impact of PVT variations on STT-MRAM and RRAM-based CiM architectures. We perform a sensitivity analysis to identify which parts of the CiM structure are most susceptible to PVT variations, for each technology. Based on these analyses, we recommend that STT-MRAM is used in high-performance CiM, while RRAM is used for edge CiM.
Adaptive control of a biped robot mobilized by linear actuators considering articular restrictions

This study is summarising the design of an output hybrid feedback controller for biped robots where the motion range for each joint is being considered. The design considers the hybrid nature of biped device when it is developing an entire gait cycle including the interaction with the environment touching the support floor. A simplified hybrid model is proposed to represent the dynamics of the robotic device in a realistic form. The proposed biped device is driven using linear actuators with a motion transfer system. A hybrid formulation for the control with adaptive state dependent gains regulates the articulations motion considering the limits of actuator motion. The dynamics of the adaptive gains is obtained with the application of a control barrier-like Lyapunov function for hybrid systems. The explicit structure of these gains are derived in a formal way. A set of numerical simulations is used to demonstrate the applicability of the developed controller analysing the tracking of bio-inspired reference trajectories obtained from reported biomechanical information. The numerical simulations used a virtual model of a biped robotic device where the interaction with the environment was considered for analysing the effect of hybrid evolution. A comparison between trajectories produced by the hybrid controller and a traditional state feedback offers a class of validation for the application of the restricted barrier inspired output feedback control strategy,
Parallel Network - A Deep Learning Approach for Blood Vessel Segmentation in Retinal fundus Images

In this modern era, computerized Retinal Blood Vessel (RVS) segmentation plays major role in diagnosis of various diseases like Diabetic Retinopathy (DR), Neovascularization, Hemorrhage. Early detection of retinal diseases can aid in the preservation of the patient’s vision. Deep learning based a new modified Convolution Neural Network (CNN) architecture is proposed for RVS. The Proposed architecture has two layers in which the layer one is for detecting the blood vessels of size thick and layer two for small vessel detection. Then the output of two layers has been combined to get the desired output. The proposed method is tested on the generally accepted public databases for this research field, DRIVE and STARE. In addition, various pre-processed methods are studied to investigate network performance enhancement. The pre-processing of the raw input image is much needed for better segmented output of blood vessels. In this work CLAHE, normalization and morphological operation of opening are done in the pre-processing stage. The proposed parallel architecture is then used to segment the retinal vessels. Accuracy, specificity, and sensitivity achieved with this proposed network are 98.02, 98.02, 88.04 respectively. Regardless of vessel thickness, the developed system performs better in terms of vessel extraction. The architecture can also be used to identify blood vessels that are frequently obstructed by factors such as lesions and hemorrhages, regardless of vessel thickness.
Implementation of Logistic Map based Differential Evolution for Channel Distribution in Cognitive Radio Networks

In the present work, a logistic map is used in Differential Evolution (DE) algorithm is used to generate the random numbers which are helpful to escape from the local optimal solutions. The proposed logistic map-based DE (LDE) is used to optimize the network benefit functions to increase spectrum resources and will be effectively utilized among the licensed and unlicensed users. The proposed algorithm is evaluated by realizing the network performance benefit functions. The results shown that the LDE improved the reduction of run time by 74.8% and 238.5%, standard deviation is diminished by 37.5% and 102%, benefit value 8.3% and 23.2% compared to the DE and PSO (Particle Swarm Optimization) algorithms. As it is important that the proposed technique needs to be implemented on any embedded platform to realize the performance of the proposed technique as hardware Intellectual Property. It is developed by using Xilinx Vivado HLS tool and interfaced to Zynq (ZC-702) embedded platform.
Implementation of a wetting and drying algorithm in a finite element model

In numerical modelling of water movement wetting and drying is a well known problem. The governing equations are not valid in the dry part of the computational domain which may result in problems with mass conservation, negative water depths and artificially enlarged gradients. A method is proposed that allows for the surface elevation to become negative while strict positivity is demanded from the water depth. In this way mass conservation is guaranteed. It is investigated whether this procedure is mass conservative, efficient and robust. To this end a simple finite element discretization of the inviscid shallow water equations (SWE) is derived. The resulting procedure is validated with several one- and two-dimensional analytical solutions for: 1) a one dimensional dam break, 2) flow over a long crested weir, 3) a one-dimensional oscillating water surface in a parabolic basin, 4) the runup of long waves on a beach, 4) a two-dimensional standing wave in a parabolic basin and 5) the spreading of a parabolic flood wave in two dimensions. In addition two laboratory experiments are used for validation. One experiment with solitary wave runup on a conical island and one experiment with a two dimensional dam break. In general the methods performance is good. However in two-dimensions it can be beneficial, in case of small gradients at the wet/dry interface, to use a lumped mass matrix at partially dry elements too. The wetting and drying iteration converges on average in 2 to 3 iteration steps. In some cases bifurcations and mass errors can occur. However mass errors are caused by rounding errors and can be resolved by using double precision. The occurrence of bifurcations is much less frequent in case of calculations in double precision and can be minimized by adjusting the BiCGSTAB settings.
A Non-Isolated Bidirectional DC-DC Converter with High Conversion Ratio

In this paper, a non-isolated bidirectional dc-dc converter with a high voltage conversion ratio is implemented by using coupled inductor and switched capacitor. The voltage stress on the switch can be reduced by a clamped circuit, and the leakage energy is recycled. Moreover, the conduction losses and current ripple can be significantly reduced to improve the system efficiency in stepup stage because of two continuous current paths. A prototype with low voltage side voltage of 48–60 V, high voltage side voltage of 400 V, and rated power of 1 kW is built to verify the performance. The maximum efficiency and full load efficiency are 95.1% and 93.5% in step-up stage, respectively; 95.6% and 94.8% in the step-down stage, respectively.
Detection and isolation of sensor faults under external disturbances

The paper considers the problem of sensor faults detection and isolation under external disturbances for a linear multi-output system. A special disturbance observer is used for its value online estimation on the base of input signal and measured output. Adaptive modification provides disturbance generator identification. Disturbance estimates are used for synthesis of plant full state observer. Fault detection is based on the residual bank that consists of differences between plant state estimates and measured outputs. Fault isolation is based on directional residual set. Robustness with respect to the noises, non-regular terms of disturbance and parametric uncertainties is provided by thresholds and directional relationships technique. The simulation results confirm the performance of the proposed approach for the third order plant.
Design of Transimpedance Amplifier using CMOS 180nm Technology

This paper presents design of an Transimpedance Amplifier using 180nm technology. Post schematic design, simulation results are obtained through Cadence Virtuoso tool. In this particular design authors have used power supply of 1.8 volts, input voltage set to 1.5 volts and frequency range lies in between 1Hz to 10GHz. Various parameters are obtained using 20µA bias current, different aspect ratio W/L and the trade-off among parameters such as open loop gain, Phase margin are measured. The gain of this Transimpedance amplifier is obtained as 75dB. The simulation results are reported in this paper. This design has been carried out in Cadence design tools lab.
Passivity-Based Design for High-Order Harmonic Voltage Emulation of Grid Emulators

The high-order $(\mathbf{i}.\mathbf{e}., 50^{\mathbf{th}}\sim 100^{\mathbf{th}})$ harmonic voltage emulation is increasingly required for future grid emulators. To mitigate adverse control interactions between grid emulator and converter under test, this paper proposes a passivity-based design method for the harmonic resonant controllers of grid emulators. This method selects the phase compensation angles of resonant controllers based on the theory of constrained piecewise linear fitting. Compared to conventional passivity-based design, the proposed method avoids complicated mathematical derivations and achieves precise harmonic voltage control with the passive harmonic impedance till the critical frequency of voltage control loop. The effectiveness of the proposed method is validated by experimental results.
Android Feature Selection based on Permissions, Intents, and API Calls

Android is a platform that hosts roughly 99% of known mobile malware to date and is thus the focus of much research efforts in mobile malware detection. One of the main tools used in this effort is supervised machine learning. While a decade of work has made a lot of progress in detection accuracy, there is an obstacle that each stream of research is forced to overcome, feature selection, i.e., determining which attributes of Android are most effective as inputs into machine learning models. This research tackles the feature selection problem by providing the community with an exhaustive analysis of the three primary types of Android features used by researchers: Permissions, Intents and API Calls. We applied a wide spectrum of feature selection techniques including eleven different algorithms which consisted of filter methods, wrapper methods and embedded methods. Results were evaluated with three different supervised learning classifiers, Random Forest, Support Vector Machine and Neural Network, on a dataset with over 119K Android apps and over 400 features. The results showed that using a combination of Permissions, Intents and API Calls produced higher accuracy than using any of those alone or in any other combination. The results also showed that feature selection should be performed on the combined dataset, not by feature type and then combined and that the negative effects of not doing so are more pronounced the larger the feature set.
Metrological foundations of emotional valence measurement through an EEG-based system

A contribution regarding the measurability of emotions and a reflection on its main related issues, are provided. The proposed use case is an electroencephalography-based detection system of positive and negative valence states. Some metrological characteristics of the proposed system were considered, i.e. reproducibility, sensitivity, and resolution. The issues concerning the measurability of emotions as lack of experimental, cross-subject, and within-subject reproducibility, as well as uncertainty induced by the adopted stimuli, were highlighted. A theoretical reference model was first identified, namely the circumplex model of affect, which is based on an interval scale. A standardized stimuli set known as Oasis was exploited. Furthermore, an initial screening of the participants was carried out to manage the bias of depressive disorders, and a compatibility analysis was conducted between the experimental sample and the sample exploited by the standardized dataset. The effectiveness of the emotion induction was maximized by choosing a polarized subset of stimuli and an implicit-more controlled mood induction procedure. A Self-Assessment Manikin was employed to verify the effectiveness of the induction procedure. The validity of the proposed method was experimentally proved. EEG signals from 25 healthy subjects were acquired through a 8-channel device. As a result, an average accuracy of 96.1 % in the within-subject case and an average accuracy equal to 80.2 % in the cross-subject case, were obtained.
Defect Detection of Integrated Circuit Based on YOLOv5

In Integrated Circuit (IC) manufacturing, defect detection is a necessary task. The small size and high density of IC, and the complex characteristics of various defects, which burdens the defect detection system. It is difficult for the existing detection methods to accurately detect various types of defects while ensuring the detection speed. We implement a deep convolutional neural network in IC defect detection, based on YOLOv5, we add a prediction head to detect objects at different scales. In addition, we also integrate the Squeeze-and-Excitation layer (SELayer) to help the detection network find attention region on scenarios with dense objects, extract key features, enhance the network’s ability to detect difficult-to-detect samples, and generally improve the accuracy of the detection network. Extensive experiments on the IC defect dataset made by us show that the AP result of YOLOv5 with integrated attention module in IC defect detection is 95.4%, which is 0.5% better than the network without SELayer. Compared with other detection networks, it has obvious accuracy advantage, which is encouraging and promising competitive.
DC Power Filter Design for a Neutral-Point Clamped Hybrid Multilevel Converter

The neutral point clamped AC-side cascaded H-bridge (NPC-AC-CHB) converter has been proposed as a low volume alternative to the Modular Multilevel Converter (MMC) for medium voltage DC networks. It is known that due to the slow switching of the main bridge large current distortion is observed on the DC-side and thus require a DC-side filter, usually given as a passive LC filter in the literature. However, design and dimensioning of the required DC filters has not been addressed in the literature. This paper considers the design of a passive LC filter topology and shows that such a solution would requires a very high DC capacitance to achieve a suitably low harmonic current on the DC-side. This large DC filter undermines the stated low volume benefits of the NPC-AC-CHB and therefore the DC filter topology must be reconsidered. Thus this paper investigates the use of both tuned passive filters and active filters to reduce the DC filter passive component size. It is shown that both designs provide significant improvements over the LC filter case, but the active filter provides the greatest reduction. Finally, simulation results are presented for verification of the design analysis.