Miracle-dz/UniFAR-FTUs huggingface dataset:
Chromosome-Level Assembly of the Chinese Seabass (Lateolabrax maculatus) Genome

1 State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen, China, 2 Shenzhen Research Institute of Xiamen University, Shenzhen, China, 3 The Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao, China, 4 State-Province Joint Engineer Laboratory of Marine Bioproducts and Technology, College of Ocean and Earth Sciences, Xiamen University, Xiamen, China, 5 Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
Mental Workload Classification By Eye Movements In Visual Search Tasks

This paper presents a method to objectively evaluate mental workload by analyzing the changes of eye movements characteristics in different visual search tasks. Eye movements data were collected by the eye tracking device called Eye Tracking Core+ while subjects were performing four different visual search tasks produced by NASA’s Multi-Attribute Task Battery (MATB) on the screen of computer. By varying the difficulty of visual search tasks, the eye movements were measured to examine whether they could be used to classify the mental workload. As a result, the five indexes (Saccades Amplitude, Saccades Velocity, Fixation Duration, Blink Duration and Pupil Diameter) showed significant differences under low and high workload of visual search tasks. Moreover, with the increase of task workload, Saccades Amplitude, Saccades Velocity, and Blink Duration decreased significantly, while Fixation Duration and Pupil Diameter increased gradually.
Investigation of the Meyer-Neldel Rule in Si MOSFETs

We show that a Meyer-Neldel (MN) regime, usually reported for disordered materials, is found in state-of-the-art monocrystalline Si MOSFETs. This unexpected result is explained via device simulation in terms of the self-consistency between the mobile charge and the device electrostatics. The dependence on device parameters is then discussed, showing that neglecting this effect can lead to interpretation errors for data evaluated at low activation energies.
“Automatic Control Knowledge Repository” – A Computational Approach for Simpler and More Robust Reproducibility of Results in Control Theory

As many other disciplines, control theory to some degree suffers from a reproducibility crisis. In particular, since computational methods like simulation, numeric approximation or computer algebra play an important role, the reproducibility of results relies on implementation details, which are typically out of scope for written papers. While some publications do reference the source code of the respective software, this is by far not standard in industry and academia. Additionally, having access to the source code does not imply reproducibility due to dependency issues w. r. t. hardware and software components. This paper proposes a three-component approach to mitigate the problem: a) an open repository with a suitable data structure to publish formal problem specifications and problem solutions (each represented as source code) along with necessary metadata, b) a web service that automatically checks the solution methods against the problem specifications and auxiliary software for local testing and c) a peer-oriented process scheme to organize both the contribution process to that repo and formal quality assurance. The proposed concept offers simplified and more robust reproducibility, as well as increased visibility of published results and a community-curated set of reference implementations for control-related methods.
Voltage Regulation of DC-DC Boost Converter using H-infinity Controller

In this paper, the output voltage of a DC-DC boost converter with pulse-width modulator (PWM) has been controlled using an output feedback H-infinity (H∞) controller. The H∞ controller for the converter has been designed applying the concept of Linear Matrix Inequality (LMI) technique. The main target of this study is to regulate the output voltage of the non-linear boost converter in presence of parameter variations and external noise. The designed H∞ controller regulates different reference voltages in presence of sensor noise and parameter variations. The proposed H∞ controller is also very simple and does not require any inner current loop with the outer voltage loop like the other designed controller reported earlier.
Luminance Ratios for Stable SIFT Keypoint Detection in Challenging Illuminations Scenarios

Invariance to changes in scene illumination is a desirable feature in computer vision, but its accomplishment is still a challenge even for modern systems. This paper proposes a novel way to detect keypoints in scale-space by exploring the concept of luminance ratio first introduced in Wallach’s Lightness Constancy Theory, instead of using linear difference operators. The hypothesis is that an operator based on the concept of luminance ratio may provide more robust results with respect to illumination changes. In order to test this hypothesis, the SIFT keypoint detector was adapted to the proposed approach with minimum implementation effort. Experimental results show that the luminance ratio approach indeed yields more stable keypoint detection than the original SIFT based on linear difference operators, specially in lower illumination conditions. Existing SIFT implementations can be easily adapted to use the luminance ratio concept for improved operation in environments with uncontrolled lighting conditions.
MicroRNA-1224 Inhibits Tumor Metastasis in Intestinal-Type Gastric Cancer by Directly Targeting FAK

Intestinal-type gastric cancer (GC) of the Lauren classification system has specific epidemiological characteristics and carcinogenesis patterns. MicroRNAs (miRNAs) have prognostic significance, and some can be used as prognostic biomarkers in GC. In this study, we identified miR-1224 as a potential survival-related miRNA in intestinal-type GC patients by The Cancer Genome Atlas (TCGA) analysis. Using quantitative real-time PCR (qRT-PCR), we showed that the relative expression of miR-1224 was significantly decreased in intestinal-type GC tissues compared to matched adjacent normal mucosa tissues (p < 0.01). We found that high miR-1224 expression was associated with no lymph-node metastasis (p < 0.05) and good prognosis (p = 0.028) in 90 intestinal-type GC tissues. Transfection of intestinal-type GC cells with miR-1224 mimics showed that miR-1224 suppressed cell migration in vitro (wound healing assay and Transwell migration assay), whereas the transfection of cells with miR-1224 inhibitor promoted cell migration in vitro. miR-1224 also suppressed intestinal-type GC cell metastasis in a xenograft mouse model. Furthermore, bioinformatics, luciferase reporter, Western blotting, and immunohistochemistry (IHC) studies demonstrated that miR-1224 directly bound to the focal adhesion kinase (FAK) gene, and downregulated its expression, which decreased STAT3 and NF-κB signaling and subsequent the epithelial-to-mesenchymal transition (EMT). Repression of FAK is required for the miR-1224-mediated inhibition of cell migration in intestinal-type GC. The present study demonstrated that miR-1224 is downregulated in intestinal-type GC. miR-1224 inhibits the metastasis of intestinal-type GC by suppressing FAK-mediated activation of the STAT3 and NF-κB pathways, and subsequent EMT. miR-1224 could represent an important prognostic factor in intestinal-type GC.
An Efficient Degraded Deductive Fault Simulator for Small-Delay Defects

An efficient degraded deductive simulator for small delay defects is proposed. The proposed method takes into account the conditions of re-convergence sensitization and hazard-based detection, providing fast and accurate simulation results for small delay defects. Separate simulation strategies for faults with different fault effects are proposed. For faults on fault effects re-convergent fan-out stems, the serial simulation technique is applied. For other faults, a deductive simulation technique is proposed to accelerate the simulation. Different from previous works, serial simulations are carried out no longer for all faults on fan-out re-convergent stems, but only for fault effects re-convergences, and the other faults are parallel simulated with the degraded deductive technique, which eliminates “AND” operation and the propagation of fault-list is simpler than conventional deductive ones. Experimental results demonstrate that the proposed simulator that can further accelerate the fault simulation in efficiency. It achieves a 28.3X speedup on average compared with the serial simulation method, and a 3.92X speedup on average compared with the critical path tracing based method.
XOR-CIM: Compute-In-Memory SRAM Architecture with Embedded XOR Encryption

Compute-in-memory (CIM) is a promising approach that exploits the analog computation inside the memory array to speed up the vector-matrix multiplication (VMM) for deep neural network (DNN) inference. SRAM has been demonstrated as a mature candidate for CIM architecture due to its availability in advanced technology node. However, as the weights of the DNN model are stationary in the memory cells, it causes potential threats and vulnerabilities for inference engine such as model leaking. This work aims at developing a lightweight yet effective countermeasure to protect the DNN model in CIM architecture. We modify the 6-transistor SRAM bit cell with dual wordlines to implement XOR cipher without sacrificing the parallel computation's efficiency. The evaluations at 28 nm show that XOR-CIM could provide enhanced security and achieve 1.4× energy efficiency improvement and no throughput loss, with only 2.5% area overhead compared to the normal-CIM design without encryption.
G-entropy Analysis of LTI Continuous-Time Systems with Stochastic External Disturbance in Time Domain

This paper deals with disturbance attenuation capabilities of continuous linear time-invariant systems with respect to stochastic input disturbance with bounded 2 or power norm (for generality called N norm). A set of the input disturbances is bounded by the scalar parameter s which is determined by entropy integral and called σ-entropy. This set is connected with system bandwidth at low frequencies. S-entropy norm of the continuous linear system can be defined as a supremum ratio between N norms of the system’s output and input signals. It characterizes system gain of the stochastic input disturbance. In the paper a state-space solution to σ-entropy norm calculation is given.
Consensus-Based Public Acceptance and Mapping of Nuclear Energy Investments Using Spherical and Pythagorean Fuzzy Group Decision Making Approaches

The purpose of the study is to identify the ways to improve the public acceptance for nuclear energy investment in energy importing countries. In this framework, 4 different factors are identified that have an influence on this situation. After that, consensus model is applied for obtaining the fuzzy preference relations at the most consistency level. Next, the criteria are weighted by using DEMATEL (Decision Making Trial and Evaluation Laboratory) methodology based on the Spherical fuzzy sets. Additionally, another evaluation has also been made by considering the Pythagorean fuzzy sets to check the consistency and coherency of the analysis results. It is identified that the results of both Spherical and Pythagorean fuzzy sets are almost the same. This situation gives information about the consistency of the analysis results. Also, sensitivity analysis is also applied by using the different values of $\delta $ with 4 cases. It is determined that there is significant coherency in the consensus-based group decision-making process. The findings indicate that security conditions play the most crucial role to improve the public acceptance for nuclear energy investments in energy importing countries. In addition, it is also identified that economic benefit and environmental issues also have importance influence on this situation. It is recommended that countries should be able to convince their citizens that nuclear energy is safe. Within this context, necessary measures should be taken for security in nuclear power plants and this situation should be clearly explained to citizens. It is obvious that priority should be mainly given to the security issues to improve public perception of nuclear energy investments in energy importing countries. One of the issues most worried by the public is the possibility of an explosion of the nuclear reactor. Hence, for this purpose, the nuclear power plant should be open to international inspection.
Proposing a Hybrid RPL Protocol for Rank and Wormhole Attack Mitigation using Machine Learning

Internet of Things have profoundly transformed the way technology is deployed today in different domains of life. However, its widescale implementation has also caused major security concerns in context of data communication because of escalating interconnectivity of resource-constrained smart devices. Due to the exacerbating security attack vulnerability, it has become necessary to address the issue of insecure routing in these devices. Low-power and lossy IoT networks on which they run commonly use RPL for routing due to its lightweight nature and compatibility for data transmission. However, RPL is prone to both WSN-inherited and RPL-specific attacks. Several existing solutions have addressed the detection of some of them. However, lack of mitigation techniques is observed which can extenuate attacks of both types such as wormhole as well as rank attack; when they are launched on an RPL-based network. Therefore, the aim of this study is to introduce RPL, its vulnerability to the two attacks, and the proposition that machine learning techniques like support vector machines can be effectively used to develop a secure and improved version of RPL for mitigation of both WSN-inherited and RPL-specific attacks in an RPL-based IoT network.
Educational note: causal decomposition of population health differences using Monte Carlo integration and the g-formula

Abstract One key objective of the population health sciences is to understand why one social group has different levels of health and well-being compared with another. Whereas several methods have been developed in economics, sociology, demography, and epidemiology to answer these types of questions, a recent method introduced by Jackson and VanderWeele (2018) provided an update to decompositions by anchoring them within causal inference theory. In this paper, we demonstrate how to implement the causal decomposition using Monte Carlo integration and the parametric g-formula. Causal decomposition can help to identify the sources of differences across populations and provide researchers with a way to move beyond estimating inequalities to explaining them and determining what can be done to reduce health disparities. Our implementation approach can easily and flexibly be applied for different types of outcome and explanatory variables without having to derive decomposition equations. We describe the concepts of the approach and the practical steps and considerations needed to implement it. We then walk through a worked example in which we investigate the contribution of smoking to sex differences in mortality in South Korea. For this example, we provide both pseudocode and R code using our package, cfdecomp. Ultimately, we outline how to implement a very general decomposition algorithm that is grounded in counterfactual theory but still easy to apply to a wide range of situations.
Creating Cover Photos (Thumbnail) for Movies and TV Series with Convolutional Neural Network

The latest version and the use of video streaming platforms is increasing day by day, adding new ones. Competition is also increasing in the development of platforms for expanding film and TV series. The purpose of replicating these platforms is to achieve more, better quality on some platforms, and to keep them on one platform even better. Before that, film and TV series platforms use artificial intelligence algorithms. In this study, the aim is to create more attractive cover photos for the user by finding suitable frames from a movie or TV series and converting these frames into cover/thumbnail images on the platform. It is based on eliminating frames that are useless according to closed eyes, blurred frames or non-face images. Also, deep learning used for labeling images with objects and face’s emotion and identity.
Location Specificity of Transcranial Electrical Stimulation on Neuronal Electrodynamics: A Mathematical Model of Ion Channel Gating Dynamics and Ionic Flux Due to Neurostimulation

Transcranial Electrical Stimulation (TES) continues to demonstrate success as a medical intervention for individuals with neurodegenerative diseases. Despite promising results from these neuromodulation modalities, the cellular level mechanisms by which this neurotherapy operates are not fully comprehended. In particular, the effects of TES on ion channel gating and ion transport are not known. Using the Poisson-Nernst-Planck model of electrodiffusion, coupled with a Hodgkin-Huxley based model of cellular ion transport, we present a model of TES that, for the first time, integrates electric potential energy, individualized ion species, voltage-gated ion channels, and transmembrane ionic flux during TES administration. Computational simulations are executed on a biologically-inspired domain with medically-based TES treatment parameters and quantify neuron-level electrical processes resulting from this form of neurostimulation. Results confirm prior findings that show that TES polarizes the cell membrane, however, these are extended as simulations in this paper show that polarization occurs in a location specific manner, where the type and degree of polarization depends on the position on the membrane within a node of Ranvier. In addition, results demonstrate that TES causes ion channel gating variables to change in a location specific fashion and, as a result, transmembrane current from distinct ion species depends on both time and membrane location. Another simulation finding is that intracellular calcium concentrations increase significantly due to a TES-induced calcium influx. As cytosolic calcium is critical in intracellular signaling pathways that govern proper neurotransmitter secretion as well as support cell viability, this alteration in calcium homeostasis suggests a possible mechanism by which TES operates at the neuronal level to achieve neurotherapeutic success.
Hardware Acceleration of Robot Scene Perception Algorithms

Hybrid machine learning algorithms that combine deep learning with probabilistic inference techniques provide highly accurate scene perception for robot manipulation. In particular, a 2-stage approach that combines object detection using convolutional neural networks with Monte-Carlo sampling for pose estimation has been shown to perform particularly well under adversarial scenarios. Unfortunately, this accuracy comes at the cost of high computational complexity, which affects runtime, resource utilization, and energy consumption. This paper describes various challenges in developing complexity-aware techniques for robust robot perception and presents a novel hardware accelerator that addresses these challenge. Experimental results show our design is at least 30% faster and consumes 97% less energy compared to an implementation on a high-end GPU. Compared to a low-power GPU implementation, our design is 95% faster while consuming 96% less energy, demonstrating that accurate, energy-efficient scene perception is possible in real time with targeted hardware acceleration.
Relevance and Recent Developments of Chitosan in Peripheral Nerve Surgery

Developments in tissue engineering yield biomaterials with different supporting strategies to promote nerve regeneration. One promising material is the naturally occurring chitin derivate chitosan. Chitosan has become increasingly important in various tissue engineering approaches for peripheral nerve reconstruction, as it has demonstrated its potential to interact with regeneration associated cells and the neural microenvironment, leading to improved axonal regeneration and less neuroma formation. Moreover, the physiological properties of its polysaccharide structure provide safe biodegradation behavior in the absence of negative side effects or toxic metabolites. Beneficial interactions with Schwann cells (SC), inducing differentiation of mesenchymal stromal cells to SC-like cells or creating supportive conditions during axonal recovery are only a small part of the effects of chitosan. As a result, an extensive body of literature addresses a variety of experimental strategies for the different types of nerve lesions. The different concepts include chitosan nanofibers, hydrogels, hollow nerve tubes, nerve conduits with an inner chitosan layer as well as hybrid architectures containing collagen or polyglycolic acid nerve conduits. Furthermore, various cell seeding concepts have been introduced in the preclinical setting. First translational concepts with hollow tubes following nerve surgery already transferred the promising experimental approach into clinical practice. However, conclusive analyses of the available data and the proposed impact on the recovery process following nerve surgery are currently lacking. This review aims to give an overview on the physiologic properties of chitosan, to evaluate its effect on peripheral nerve regeneration and discuss the future translation into clinical practice.
Intelligent Detection for CT Image of COVID-19 using Deep Learning

Purpose: Use artificial intelligence technology to identify the characteristics of covid-19 in CT images, quickly screen COVID-19 patients, achieve rapid diversion and treatment of suspected patients, reduce the risk of infection and control the spread of the disease.Materials and methods: This article combines deep learning target detection and image classification methods to study the CT images of COVID-19 patients. By extracting and analyzing the features of lesions in different periods, a new detection model of covid-19 based on time-spatial sequence convolution is obtained. The algorithm is based on a recurrent neural network structure and a 2D convolutional layer structure.Results: The spatiotemporal sequence convolution kernel based on time and space attributes can effectively extract the latent image semantic features of multiple image data of COVID-19 patients. By comparing with Faster RCNN, YOLO3 and SSD algorithm models, the detection method proposed in this paper can obtain more accurate comprehensive detection results.Conclusion: The time-spatial sequence convolution model can quickly complete the automatic detection of COVID-19 and improve the efficiency of preliminary diagnosis. By correlating images from different stages of the same patient, more accurate auxiliary preliminary screening results can be obtained.
An Adaptive Image Processing Model of Plant Disease Diagnosis and Quantification Based on Color and Texture Histogram

In this paper, a new approach for the detection and classification of potato plant disease is implemented using computer vision techniques. Most of the existing algorithms based on plant disease detection and classification are limited to common types of feature extraction methods. However, feature extraction is an important area as the classification of diseases of any leaf. The proposed method is based on color and texture features. The implemented method processed in four steps- In the preprocessing and segmentation, LAB color space and Delta E color difference method are applied. Later, features are extracted based on RGB, HSV and Local Binary Patterns (LBP). The extracted patterns are finally classified by Multi Support Vector Machine (SVM). Moreover, we compare the results of feature subsets of RGB and HSV color features with the addition of LBP texture features and found a classification difference of 3.6% between RGB and HSV color feature extractors. The overall results show our method outperforms as compared to existing techniques.