Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Factors That Shape Patterns of Nucleotide Diversity in the House Mouse Genome

A major goal of population genetics has been to determine the extent to which selection at linked sites influences patterns of neutral nucleotide diversity in the genome. Multiple lines of evidence suggest that diversity is influenced by both positive and negative selection. For example, in many species there are troughs in diversity surrounding functional genomic elements, consistent with the action of either background selection (BGS) or selective sweeps. In this study, we investigated the causes of the diversity troughs that are observed in the wild house mouse genome. Using the unfolded site frequency spectrum (uSFS), we estimated the strength and frequencies of deleterious and advantageous mutations occurring in different functional elements in the genome. We then used these estimates to parameterize forward-in-time simulations of chromosomes, using realistic distributions of functional elements and recombination rate variation in order to determine if selection at linked sites can explain the observed patterns of nucleotide diversity. The simulations suggest that BGS alone cannot explain the dips in diversity around either exons or conserved non-coding elements (CNEs). A combination of BGS and selective sweeps, however, can explain the troughs in diversity around CNEs. This is not the case for protein-coding exons, where observed dips in diversity cannot be explained by parameter estimates obtained from the uSFS. We discuss the extent to which our results provide evidence of sweeps playing a role in shaping patterns of nucleotide diversity and the limitations of using the uSFS for obtaining inferences of the frequency and effects of advantageous mutations. Author Summary We present a study examining the causes of variation in nucleotide diversity across the mouse genome. The status of mice as a model organism in the life sciences makes them an excellent model system for studying molecular evolution in mammals. In our study, we analyse how natural selection acting on new mutations can affect levels of nucleotide diversity through the processes of background selection and selective sweeps. To perform our analyses, we first estimated the rate and strengths of selected mutations from a sample of wild mice and then use our estimates in realistic population genetic simulations. Analysing simulations, we find that both harmful and beneficial mutations are required to explain patterns of nucleotide diversity in regions of the genome close to gene regulatory elements. For protein-coding genes, however, our approach is not able to fully explain observed patterns and we think that this is because there are strongly advantageous mutations that occur in protein-coding genes that we were not able to detect.
Optimum Selection of DNN Model and Framework for Edge Inference

This paper describes a methodology to select the optimum combination of deep neural network and software framework for visual inference on embedded systems. As a first step, benchmarking is required. In particular, we have benchmarked six popular network models running on four deep learning frameworks implemented on a low-cost embedded platform. Three key performance metrics have been measured and compared with the resulting 24 combinations: accuracy, throughput, and power consumption. Then, application-level specifications come into play. We propose a figure of merit enabling the evaluation of each network/framework pair in terms of relative importance of the aforementioned metrics for a targeted application. We prove through numerical analysis and meaningful graphical representations that only a reduced subset of the combinations must actually be considered for real deployment. Our approach can be extended to other networks, frameworks, and performance parameters, thus supporting system-level design decisions in the ever-changing ecosystem of embedded deep learning technology.
A Spatial Pyramid Pooling Convolutional Neural Network for Smoky Vehicle Detection

This paper investigates the automated recognition of smoky vehicles in surveillance videos. Most current methods recognize the smoky vehicle based on additional sensors. It is a challenging task to recognize smoky vehicle from images due to complex background and the movement of the vehicles. In this paper, we develop a spatial pyramid pooling convolutional neural network (SPPCNN) for smoky vehicle detection according to the image processing algorithm. Resizing the image will lead to shape changes of the smoke plume, and reducing the recognition rate. The spatial pyramid pooling layer is used to overcome this problem. The input of CNN is raw pictures of the tail of the vehicles which captured with method of foreground-background segmentation. This model extracts features from both the vehicles and the smoke by performing convolutions, and regarding the vehicle information as a prior. Experimental results show that this method achieved very low false alarm rates below 1% with detection rates above 80% on real video sequences.
Fault detection in time-varying dynamic process using recursive sparse dynamic PCA

Principal component analysis is one of the most popular data-driven methods, which is widely used in data compression, modeling, singular value detection, and classification. However, the PCA method is effective under the assumption that the normally distributed process variables have no any serial correlation, which is not reasonable in real industrial practice as some process is not only dynamic but also time-varying because of the time-delays or some disturbances. To address this issue, this article proposes a recursive sparse dynamic PCA method to monitor the time-varying dynamic process. The sparse system matrices varying across time are estimated using an optimization method based on accelerated projected gradient descent and a recursive Lasso method. By employing the $l_{2,1}$ norm term to the optimization method, the system matrix is able to achieve row-wise sparsity, which highly reduces the calculation in subsequent operation. Meanwhile, the use of recursive Lasso ensures affordable calculation complexity in recursively updating the time-varying matrices. The efficiency of the proposed method is verified through a simulation example comparing to the dynamic PCA method.
Differential Gene Expression Common to Acquired and Intrinsic Resistance to BRAF Inhibitor Revealed by RNA-Seq Analysis

Melanoma cells have been shown to respond to BRAF inhibitors; however, intrinsic and acquired resistance limits their clinical application. In this study, we performed RNA-Seq analysis with BRAF inhibitor-sensitive (A375P) and -resistant (A375P/Mdr with acquired resistance and SK-MEL-2 with intrinsic resistance) melanoma cell lines, to reveal the genes and pathways potentially involved in intrinsic and acquired resistance to BRAF inhibitors. A total of 546 differentially expressed genes (DEGs), including 239 up-regulated and 307 down-regulated genes, were identified in both intrinsic and acquired resistant cells. Gene ontology (GO) analysis revealed that the top 10 biological processes associated with these genes included angiogenesis, immune response, cell adhesion, antigen processing and presentation, extracellular matrix organization, osteoblast differentiation, collagen catabolic process, viral entry into host cell, cell migration, and positive regulation of protein kinase B signaling. In addition, using the PANTHER GO classification system, we showed that the highest enriched GOs targeted by the 546 DEGs were responses to cellular processes (ontology: biological process), binding (ontology: molecular function), and cell subcellular localization (ontology: cellular component). Ingenuity pathway analysis (IPA) network analysis showed a network that was common to two BRAF inhibitor-resistant cells. Taken together, the present study may provide a useful platform to further reveal biological processes associated with BRAF inhibitor resistance, and present areas for therapeutic tool development to overcome BRAF inhibitor resistance.
Cognition and emotion: Neuroscience and behavioural perspectives

Ever since 1879, when Wilhelm Wundt was credited with the founding of psychology as a separate discipline, experimental psychologists have been studying what today we term ‘‘cognitive psychology’’. However, even though neural pathways associated with cognitive processing and emotional processing have many known intersections, there has been surprisingly little effort to study cognition and emotion as integrated systems. We have known for some time that human involuntary emotions and voluntary cognition can affect motor behaviour in qualitatively different ways. In ‘‘central facial weakness’’, for example, stroke patients cannot voluntarily move the side of their face opposite to the stroke when instructed by another person to smile (Cytowic, 1996). Despite the paralysis of voluntary (cognitive) movements, this same side of the face can ‘‘smile’’ when activated involuntarily by a funny (emotional) movie. This phenomenon illustrates the distinction between the pyramidal (voluntary) and extrapyramidal (involuntary) motor systems and the separability of emotional and cognitive processing (Cytowic, 1996). Although affective and cognitive systems sometimes operate separately, it is now clear that they are not completely independent. Papez (1937) has been credited with developing the neuroscience foundation for this idea by showing that emotions are indeed processed in the ‘‘cognitive’’ neocortex via what has been termed the ‘‘Papez circuit’’, although he did not yet comprehend the contribution of the amygdala to these pathways (see LeDoux, 2007b). To further complicate matters, many neural pathways involving what we refer to as emotions are part of ‘‘implicit pathways’’ (especially the amygdala) that involve associative learning, but emotions can also enter ‘‘explicit pathways’’ (emphasising the medial temporal lobes and the prefrontal cortex; LeDoux, 2007a). Finally, there is no single ‘‘emotional pathway’’ for all emotions (LeDoux, 2007a); emotions such as fear hold survival value, but others such as happiness appear to be higher level emotions. Our field of cognitive psychology has made many wonderful advances in the past 50 years, but a precise understanding of how emotions and cognitions are integrated in humans from a neuroscience perspective is still a work in progress. Because of the massive interconnectivity of the different ‘‘cognitive’’ and ‘‘emotional’’ neural systems at both implicit and explicit levels, we suggest that the best way of going about this endeavour is not to push for a ‘‘primacy’’ position for either cognition or emotion, but instead, to work towards a better understanding of how these different systems interact under a wide variety of different contexts. The goal of this Special Issue on Cognition and Emotion is therefore to provide a deeper and more
The Bald Soprano

SCENE: A middle-class English interior, with English armchairs. An English evening. Mr. Smith, an Englishman, seated in his English armchair and wearing English slippers, is smoking his English pipe and reading an English newspaper, near an English fire. He is wearing English spectacles and a small gray English mustache. Beside him, in another English armchair, Mrs. Smith, an Englishwoman, is darning some English socks. A long moment of English silence. The English clock strikes 17 English strokes.
Participants’ experiences of an Internet-based cognitive behavioural therapy intervention for tinnitus

Abstract Objective: This study aimed to explore participants’ experiences after undertaking an Internet-based cognitive behavioural therapy intervention (ICBT) for tinnitus. Design: Semi-structured telephone interviews were conducted 6–8 months after participants undertook the ICBT intervention. Qualitative thematic analysis was used to interpret the interview data. Study sample: A purposeful sampling strategy was used to identify a diverse range of participants. Semi-structured interviews were carried out with 15 participants. The mean age was 58.5 years, 7 men and 8 women participated. Results: The analysis generated the following main themes: (1) expectations and motivation for doing the intervention, (2) experiences of the intervention, (3) intervention engagement and (4) intervention effects. Most participants’ expectations were hopeful that the intervention would lessen the impact of their tinnitus. Aspects of the intervention that were beneficial, as well as difficult, were identified together with the impact they had on engagement. Intervention effects were evident on both tinnitus and activities of daily life. Conclusions: The benefits described by participants indicate the potential of ICBT as an alternate form of intervention delivery. The difficulties that hampered engagement need to be addressed to enhance the application and to optimise the clinical acceptability of ICBT for tinnitus.
Effect of Radiation & HEI on Flicker noise in 0.5 μm CMOS transistor

The ubiquitous flicker or 1/f noise deteriorates low-frequency (LF) signals. For high reliability in CMOS devices, factors leading to degradation of SNR in LF domain need to be understood and minimized, especially for devices processing low-frequency signals. With transistors scaling down, power consumption decreasing and advancement in system integration, portable integrated CMOS sensors are being used in labs, hospitals, space applications etc. In such field applications, continuous radiation exposure degrades the sensors as well as deteriorates the signal to noise ratio. Also, in certain applications, continuous operation of sensors with high voltage stress on transistors causes hot electron injection (HEI) which also deteriorates the SNR by increasing the trap density in the gate oxide and making the sensor unreliable over time. To address the issue of SNR deterioration and to understand the reliability aspects of the sensors due to such operational stresses, we present a study of the impact of γ$\textbf{radiation exposure and hot electron injection on flicker noise of CMOS transistors. While flicker noise has been characterized in other processes, we chose the commonly available, low cost 0.5 um CMOS process for our study.
A Multi-Model of Classification for Electric-Power Industrial Customer Based on Big Data

Aiming to help Electric-Power Industry to fast recognize customer's features, a multi-model of customer classification is proposed in this paper. On basis of big data presented by CCF competition sponsor in China, with some excellent technology or algorithm such as JieBa, SFFS and so on, we extract many important features and successfully draw a portrait for customers who pay close attention to electricity charges. Furthermore, machine learning algorithm and its the strategy selection model are investigated. Eventually, a multi-model is achieved, which can effective draw customer portrait and recognize targeted customer from big data. The result of experiment indicate that the multi-model has precision of 84%, good recall and excellent generalization.
Evaluation of cupboard door sensors for improving activity recognition in the kitchen

Smart home systems are becoming increasingly relevant with every passing year, but while the technology is more available than ever, other issues such as cost and intrusiveness are becoming more apparent. To this end, we consider the types of sensors which are most useful for fine-grained activity recognition in the kitchen in terms of cost, intrusiveness, durability and ease of installation. We install sensors into a conventional residence for testing, and propose a system which meets the design challenges such an environment presents. We show that cupboard door sensors produce useful data about access to certain non mechanical processes and items, while being cheap and simple. We also show that they positively impact the activity recognition performance of our model through their addition, while providing information that we can make use of in future studies.
Attack Vulnerability of Online Social Networks

In order to protect existing systems and design new systems, attack vulnerability of online social networks is studied in this paper. Firstly, directed online social networks are constructed, and the topological quantities (i.e. degree, betweenness centrality) are investigated. Then, the ability of online social network to resist attacks is studied by removing specific classes of important nodes with highest-degree or highest betweenness centrality. Network performance is quantitatively measured by characteristic path length, global efficiency and the size of the largest connected subgraph. Experimental results show that removals by recalculated betweenness centrality are more harmful than attack strategies based on the initial network, suggesting that online social network is highly vulnerable to deliberate attacks on vital nodes keeping the connectivity of the network.
An Interference-Aware Resource Allocation Scheme for Connectivity Improvement in Vehicular Networks

In vehicular networks, the interference is brought about by the rapid growth of various services due to the scarcity of radio resources, which poses great challenges for the network connectivity. Unlike the connectivity that is solely determined by the signal strength or the distance between pairwise vehicles, the connectivity considered in this paper is influenced by the interference among multiple links. To improve the network connectivity by interference mitigation, a connectivity optimization problem based on resource allocation is modeled using graph theory. First, the definitions of the connectivity metrics affected by interference in the vehicular networks are given. Second, the number of resources that guarantee the network connectivity is analyzed. Finally, a resource allocation algorithm based on the minimum spanning tree is proposed to improve the network connectivity that is constrained by limited resources. The simulation results confirm that the proposed algorithm approximates the optimal performance and improves the connectivity better than similar algorithms.
Tin nanoparticles modified nano-silver paste for pressureless low-temperature sintering process

Silver nanoparticles (Ag-NPs) with the average size of 9 nm and tin nanoparticles (Sn-NPs) with the average size of 30 nm were synthesized via chemical reduction in aqueous solutions, respectively. Then, a novel nano-sliver paste modified by tin nanoparticles (NSP-TNP) was developed and the influences of Sn-NPs content, sintering time and temperature on bonding strength of NSP-TNP joints were studied. After sintering the nano-silver paste modified by 3 wt. % Sn-NPs at 206 °C for 30 min without externally applied pressure, the bonding strength of sintered NSP-TNP joints reaches 34 MPa. The enhanced mechanical strength of the sintered NSP-TNP joints is owing to the denser structure and solid-solution strengthening when Sn-NPs were added to the nano-silver paste. The NSP-TNP is promising to be used in power semiconductor (e.g., SiC) packaging due to some advantages such as higher bonding strength, lower processing temperature and reduced cost.
Complete Synchronization of Finance Chaotic System with Disturbance by Using ADRC

This article investigates the complete synchronization of a three-dimensional finance chaotic system. Firstly, the controlled system is transformed into the canonical form of active disturbance rejection control(ADRC) according to differential geometric theory, then ADRC is used to realize the complete synchronization between the master and slave systems with external disturbance. When the disturbance does not exist, ADRC method still can deal with synchronization problems of finance chaotic systems. Simulation results testify the validity of the proposed method.
Automatic Panoramic UAV Image Mosaic Using ORB Features and Robust Transformation Estimation

Panoramic image mosaic, which aims to take many regular photographic or video images in order to cover the entire viewing space, plays an important role in many remote sensing tasks including map updating, change detection, environmental monitoring and surveillance. Typical mosaic methods involve four steps, namely, feature extraction, feature matching, transformation estimation, and blending. In this study, we introduce several novel strategies in these steps for the fast and automatic construction of unmanned aerial vehicle (UAV) panoramic image mosaic. First, we analyze and test several existing feature extraction techniques and propose to use oriented FAST and rotated BRIEF (ORB) because of its efficiency and capability to generate high-quality feature points. Second, we introduce a fast and robust feature matching strategy based on descriptor similarity along with a locality preserving geometric constraint. Third, we model the spatial transformation between a UAV image pair with an affine function and introduce a robust Bayesian framework to estimate this transformation from the ORB feature matches even if these matches are contaminated by false ones. Finally, we propose a gradual fading method to fuse and blend the matched images to create an attractive panorama. The qualitative and quantitative results of an image set demonstrate that our method exhibits superior performance over existing methods in terms of accuracy and efficiency.
Accuracy of Multimodal Pedestrian Localisation in Different Terminal Contexts

We present first results from extensive indoor localisation measurements. Our main objective is to understand the importance of different sensor modalities in high-accuracy localisation and tracking of modern terminal equipment such as smartphones. In our measurements, we have gathered Wi-Fi signal strength, accelerometer, magnetometer and compass readings over several trajectories in a typical office building, together with the ground truth locations of the user. We process all the combinations of these together with possibly known environmental constraints using state-of-the-art Bayesian filtering to estimate achievable localisation accuracy. Our initial results show that while relatively good median accuracy can be achieved by the use of, e.g., signal strength data alone, fusion of different data sources significantly reduces worst case errors and improves the topological correctness of the estimated user trajectories.
Micro-Dispensing of Graphene Oxide Based Capacitive Tactile Sensors for Human Pressure-Pulse Detection

This paper reports a novel capacitive pressure-pulse sensor array based on drop-dispensed graphene oxide (GO) sensing elements. The utilization of drop dispensing technology enables us a low cost, flexible and precise method to fabricate multiple capacitive sensing elements. The printed droplets volume (GO aqueous dispersion) were around 33.5 to 65.4 pL with droplet diameter ranging 40 to 50um. The size (i.e., footprint and dielectric thickness) of a sensing element can be controlled by the total GO dispersed. In this paper, we report on the fabrication process and preliminary characterization of these printed GO capacitive sensors. Thus far, we have shown that these sensors have a sensitivity of ~10−3kPa-1, with a relative permittivity of the printed GO being ~6 to 7 (measured at a frequency of 600kHz). We have also demonstrated that the printed sensing elements can be used for human pulse sensing, which means that these sensors could potentially be used in wearable electronics and healthcare applications.
CEP Support for Detection of Application Layer Attacks

Application Layer attacks are continuously targeted by new techniques of distributed denial of service attacks (DDoS) causing service disruption and considerable financial damage. While some of the DDoS attacks exploit sof tware vulnerabilities, most take advantage of the openness of the Internet and its ability to deliver packets of data from nearly any source to any destination. The aim of the research work is an overview of detection of application layer attacks for the support of Cardinality Estimation Problem (CEP) to propose new solutions, which take advantage of early detection of attacks.