Miracle-dz/UniFAR-FTUs huggingface dataset:
Dim Target Trajectory Recognition Method in Infrared Sequence Images Based on Deep Learning

Aiming at the false targets appearing in the detection of moving weak targets in infrared images, this paper proposes a target trajectory recognition method in infrared sequence images based on deep learning. The method compares the motion law of the real target with the false target, and then uses the deep learning convolutional neural network (CNN) algorithm to train the data set to accurately judge the real target trajectory. The convolutional neural network model used in this paper is AlexNet, which has good performance in image feature extraction and can effectively identify the target trajectory. Compared with the common traditional false target rejection method, this method has the characteristics of high flexibility, high speed and high accuracy.
Research on Influencing Factors of Consumer Behavior Based on Wechat Stores

With the continuous improvement of Internet technology, Internet users growing, online shopping consumers continue to increase, electric business platform number and size are also on the increase, the ensuing competition is more and more teeth. One of the shopping platforms has its unique advantages. Nonetheless, in each big electric business enterprise in order to make a massive increase in sales and profit, WeChat store platform to meet consumer demand, in the huge competition draw consumer attention in order to get more profit. Based on WeChat store platform of consumer shopping behavior related research on the basis of the summary of the seven factors, from the perspective of WeChat store consumers, using SPSS software, the influencing factors of consumer purchase behavior data analysis and research, for WeChat shop platform and WeChat shop stores provide some enlightenment for the establishment of marketing strategy.
Application of Internet of Things Technology in Agricultural Production

In recent years, the Internet of Things technology has developed rapidly. The main monitoring method of the Internet of Things is to realize its functions through the nodes of the sensor. The monitoring network of the Internet of Things transmits the monitoring data back to the database for further analysis through the computer and mobile phone platform. More detailed management and arrangement are also important for energy conservation and emission reduction. The continuous development of Internet of Things technology is, to a certain extent, the premise and foundation of agricultural development, and the ultimate goal and the biggest trend of China's agricultural development. This paper outlines the application scope of Internet of Things technology, studies the framework model of agricultural internet of things and its model prediction, and proposes the practical application of Internet of Things technology in agricultural production by studying the impact of Internet of Things technology on modern agriculture.
Summarization with Highway Condition Radom Pointer-Generator Network

Nowadays, Neural sequence-to-sequence models have become the common approach for abstractive text summarization. In this way, the abstract is closer to the natural language, not just the excerpt and recomposition of the original text. However, these models still have some shortcomings: They can't handle out of vocabulary(OOV) words well and they tend to repeat themselves or become confused. What's more, because of the selfcharacteristics of the recurrent neural network, it begins to degenerate when the depth is low (only two layers), which is not conducive to extracting deeper features to improve the accuracy of the model. In order to solve these problems, we have proposed a Highway Condition Radom Pointer-Generator Network(HCRPGN). We introduce the CRF layer to solve the duplication problem and use highway recurrent cell to optimize the neuron structure and prevent model degradation. We apply our model to the CNN/Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 1 ROUGE points.
End-to-End FusVAE for Face Image Fusion

Face fusion, which have widely applied in various of applications, has been receiving much attention. At present, the main face fusion method based on facial feature point matching is adopted, which rely on a large number of feature engineering based on the face. It has achieved good results but complex and time-consuming. Based on Variational Auto-encoding (VAE), this paper propose a new end-to-end solution for generating a merged-face. Our method achieves the fusion of multiple face image information by mixing the hidden vectors of face images extracted by VAE.
Online Social Spammer Detection Based on Semi-supervised Learning

Social networks, like Twitter and Sina Weibo have become popular platforms for internet users to disseminate and share information. However, there is a massive number of spammers who have been conducting social spamming on normal users. Social spammers have severely influenced the user experience and the healthy development of the website. Therefore, detecting spammers on social networks has become an urgent problem. Most of the existing methods for spammer detection is based on supervised learning, which is inappropriate for implementing in practice, as labeling sufficient training data costs too much resources. In this paper, we propose a semi-supervised solution for effective spammer detection which combines co-training with social graph model. First, we utilize the behavior features and content features to train two original classifiers. Then we select confident users judged by their credibility scores which is based on the social graph model. Afterwards, these users selected are added to the training set to retrain the two classifiers. The two steps are repeated till the classifiers cannot be refined any more. Experimental results prove that this method is capable of detecting social spammers effectively without sufficient labeled data.
Effects of vibrotactile signals coupled with pressure detection or timer when interacting with a touch screen in automobile

The aim of our study was to determine the benefit of vibrotactile feedback when interacting with the Lane Change Task (LCT) simulated driving display, but also to compare three modes of driving interaction: the pressure, the timer and the capacitive during two types of tasks: the slider and the push. The thirty-three participants were initially separated into two groups, depending on the type of task (slider or push) and thus conducted under two levels of cognitive load modulated by the OSPAN task (operation-word-span) which solicits working memory where the participant is asked to check the accuracy of a calculation or retain a word while interacting with a touch screen and performed these tasks in a driving simulator. The subjective results showed that i) for the slider, the pressure is preferred over the capacitive and timer modes. However, this is not verified for the pushes, ii) The subjective questionnaires confirmed the interest of the vibrotactile return as well in terms of preference, feeling of security and confidence regarding the touch screen. Conversely, the results obtained via the Driving Activity Load Index (DALI) questionnaire did not show any significant results.
VIZARD: Reliable Visual Localization for Autonomous Vehicles in Urban Outdoor Environments

Changes in appearance is one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present VIZARD, a visual localization system for urban outdoor environments. By combining a local localization algorithm with the use of multi-session maps, a high localization recall can be achieved across vastly different appearance conditions. The fusion of the visual localization constraints with wheel-odometry in a state estimation framework further guarantees smooth and accurate pose estimates. In an extensive experimental evaluation on several hundreds of driving kilometers in challenging urban outdoor environments, we analyze the recall and accuracy of our localization system, investigate its key parameters and boundary conditions, and compare different types of feature descriptors. Our results show that VIZARD is able to achieve nearly 100% recall with a localization accuracy below 0.5m under varying outdoor appearance conditions, including at night-time.
Evidence for Highly Variable, Region-Specific Patterns of T-Cell Epitope Mutations Accumulating in Mycobacterium tuberculosis Strains

Vaccines that confer protection through induction of adaptive T-cell immunity rely on understanding T-cell epitope (TCE) evolution induced by immune escape. This is poorly understood in tuberculosis (TB), an ancient, chronic disease, where CD4 T-cell immunity is of recognized importance. We probed 905 functionally validated, curated human CD4 T cell epitopes in 79 Mycobacterium tuberculosis (Mtb) whole genomes from India. This screen resulted in identifying 64 mutated epitopes in these strains initially using a computational pipeline and subsequently verified by single nucleotide polymorphism (SNP) analysis. SNP based phylogeny revealed the 79 Mtb strains to cluster to East African Indian (EAI), Central Asian Strain (CAS), and Beijing (BEI) lineages. Eighty-nine percent of the mutated T-cell epitopes (mTCEs) identified in the 79 Mtb strains from India has not previously been reported. These mTCEs were encoded by genes with high nucleotide diversity scores including seven mTCEs encoded by six antigens in the top 10% of rapidly divergent Mtb genes encoded by these strains. Using a T cell functional assay readout, we demonstrate 62% of mTCEs tested to significantly alter CD4 T-cell IFNγ and/or IL2 secretion with associated changes in predicted HLA-DR binding affinity: the gain of function mutations displayed higher predicted HLA-DR binding affinity and conversely mutations resulting in loss of function displayed lower predicted HLA-DR binding affinity. Most mutated antigens belonged to the cell wall/cell processes, and, intermediary metabolism and respiration families though all known Mtb proteins encoded mutations. Analysis of the mTCEs in an SNP database of 5,310 global Mtb strains identified 82% mTCEs to be significantly more prevalent in Mtb strains isolated from India, including 36 mTCEs identified exclusively in strains from India. These epitopes had a significantly higher predicted binding affinity to HLA-DR alleles that were highly prevalent in India compared to HLA-DR alleles rare in India, highlighting HLA-DR maybe an important driver of these mutations. This first evidence of region-specific TCE mutations potentially employed by Mtb to escape host immunity has important implications for TB vaccine design.
Capacity allocation analysis of neural networks: A tool for principled architecture design

Designing neural network architectures is a task that lies somewhere between science and art. For a given task, some architectures are eventually preferred over others, based on a mix of intuition, experience, experimentation and luck. For many tasks, the final word is attributed to the loss function, while for some others a further perceptual evaluation is necessary to assess and compare performance across models. In this paper, we introduce the concept of capacity allocation analysis, with the aim of shedding some light on what network architectures focus their modelling capacity on, when used on a given task. We focus more particularly on spatial capacity allocation, which analyzes a posteriori the effective number of parameters that a given model has allocated for modelling dependencies on a given point or region in the input space, in linear settings. We use this framework to perform a quantitative comparison between some classical architectures on various synthetic tasks. Finally, we consider how capacity allocation might translate in non-linear settings.
Classifying tangible user interfaces with cladistics: criteria and forms for a collaborative inventory

Tangible user interfaces take various forms : their design is often unique. As the number of systems is increasing, as understanding the whole becomes difficult. Yet, open innovation approaches require identifying and being inspired by previous solutions. By taking into account the line of descent of technological systems, we use a classification method from biology in HCI : the cladistic classification. The aim is to develop an original approach to characterize the variety of tangible interfaces. This paper presents our approach to develop a list of criteria and to develop a web application for the purpose of a collaborative inventory.
Usability Assessment of a Cable-Driven Exoskeletal Robot for Hand Rehabilitation

Study design: Case series. Background: Robot-assisted rehabilitation mediated by exoskeletal devices is a popular topic of research. The biggest difficulty in the development of rehabilitation robots is the consideration of the clinical needs. This study investigated the usability of a novel cable-driven exoskeletal robot specifically designed for hand rehabilitation. Methods: The study consists of three steps, including prototype development, spasticity observation, and usability evaluation. First, we developed the prototype robot DexoHand to manipulate the patient's fingers based on the clinical needs and the cable-driven concept established in our previous work. Second, we applied DexoHand to patients with different levels of spasticity. Finally, we obtained the system usability scale (SUS) and assessed its usability. Results: Two healthy subjects were recruited in the pre-test, and 18 patients with stroke and four healthy subjects were recruited in the formal test for usability. The total SUS score obtained from the patients and healthy subjects was 94.77 ± 2.98 (n = 22), indicating an excellent level of usability. The satisfaction score was 4.74 ± 0.29 (n = 22), revealing high satisfaction with DexoHand. The tension profile measured by the cables showed the instantaneous force used to manipulate fingers among different muscle tone groups. Conclusions: DexoHand meets the clinical needs with excellent usability, satisfaction, and reliable tension force monitoring, yielding a feasible platform for robot-assisted hand rehabilitation.
On-Body tangible interaction: using the body to support tangible manipulations for immersive visualization

Recent technological advancements in augmented and mixed reality devices opened up many opportunities to users to interact in their environments. However, there are many unanswered questions about the definition of these interaction paradigms. In this article, we present a new approach to interact in these environments: using the body as a physical support to constrain the movement of a multiple degrees of freedom device (3D Mouse). This mouse offers enough degrees of freedom as well as a necessary accuracy to support the interaction. The use of the body as a support for the interaction allows the user to move in his environment and avoids the inherent fatigue of mid-air interactions. Thus, we propose a new design space for tangible interactions on the body, and specifically on the forearm. To validate the feasibility of such an approach, we present an experiment aimed at establishing the range, stability and comfort of gestures performed with a multiple degrees of freedom mouse on the forearm.
WideDTA: prediction of drug-target binding affinity

Motivation: Prediction of the interaction affinity between proteins and compounds is a major challenge in the drug discovery process. WideDTA is a deep-learning based prediction model that employs chemical and biological textual sequence information to predict binding affinity. Results: WideDTA uses four text-based information sources, namely the protein sequence, ligand SMILES, protein domains and motifs, and maximum common substructure words to predict binding affinity. WideDTA outperformed one of the state of the art deep learning methods for drug-target binding affinity prediction, DeepDTA on the KIBA dataset with a statistical significance. This indicates that the word-based sequence representation adapted by WideDTA is a promising alternative to the character-based sequence representation approach in deep learning models for binding affinity prediction, such as the one used in DeepDTA. In addition, the results showed that, given the protein sequence and ligand SMILES, the inclusion of protein domain and motif information as well as ligand maximum common substructure words do not provide additional useful information for the deep learning model. Interestingly, however, using only domain and motif information to represent proteins achieved similar performance to using the full protein sequence, suggesting that important binding relevant information is contained within the protein motifs and domains.
Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus

The distributed nature of information processing in the brain creates a complex variety of decision making behavior. Likewise, computational models of saccadic decision making behavior are numerous and diverse. Here we present a generative model of saccadic action selection in the context of competitive decision making in the superior colliculus (SC) in order to investigate how independent neural signals may converge to interact and guide saccade selection, and to test if systematic variations can better replicate the variability in responses that are part of normal human behavior. The model was tasked with performing pro- and anti-saccades in order to replicate specific attributes of healthy human saccade behavior. Participants (ages 18–39) were instructed to either look toward (pro-saccade, well-practiced automated response) or away from (anti-saccade, combination of inhibitory and voluntary responses) a peripheral visual stimulus. They generated express and regular latency saccades in the pro-saccade task. In the anti-saccade task, correct reaction times were longer and participants occasionally looked at the stimulus (direction error) at either express or regular latencies. To gain a better understanding of the underlying neural processes that lead to saccadic action selection and response inhibition, we implemented 8 inputs inspired by systems neuroscience. These inputs reflected known sensory, automated, voluntary, and inhibitory components of cortical and basal ganglia activity that coalesces in the intermediate layers of the SC (SCi). The model produced bimodal reaction time distributions, where express and regular latency saccades had distinct modes, for both correct pro-saccades and direction errors in the anti-saccade task. Importantly, express and regular latency direction errors resulted from interactions of different inputs in the model. Express latency direction errors were due to a lack of pre-emptive fixation and inhibitory activity, which aloud sensory and automated inputs to initiate a stimulus-driven saccade. Regular latency errors occurred when the automated motor signals were stronger than the voluntary motor signals. While previous models have emulated fewer aspects of these behavioral findings, the focus of the simulations here is on the interaction of a wide variety of physiologically-based information integration producing a richer set of natural behavioral variability.
FT/TFL1: Calibrating Plant Architecture

There is a very large diversity in plant architecture in nature. Over the past few years, novel theoretical concepts and analytical methods have emerged as powerful tools to understand important aspects of plant architecture. Plant architecture depends on the relative arrangement of three types of organs: leaves, shoots, and flowers. During plant development, the architecture is modulated by the balance of two homologous proteins: FLOWERING LOCUS T (FT) and TERMINAL FLOWER 1 (TFL1). The FT/TFL1 balance defines the plant growth habit as indeterminate or determinate by modulating the pattern of formation of vegetative and reproductive structures in the apical and axillary meristems. Here, we present a summarized review of plant architecture and primarily focus on the FT/TFL1 balance and its effect on plant form and development. We also propose passion fruit as a suitable model plant to study the effect of FT/TFL1 genes on plant architecture.
The Transcription Factor TCF1 Preserves the Effector Function of Exhausted CD8 T Cells During Chronic Viral Infection

The long-term persistence of viral antigens drives virus-specific CD8 T cell exhaustion during chronic viral infection. Yet exhausted, CD8 T cells are still endowed with certain levels of effector function, by which they can keep viral replication in check in chronic infection. However, the regulatory factors involved in regulating the effector function of exhausted CD8 T cell are largely unknown. Using mouse model of chronic LCMV infection, we found that the deletion of transcription factor TCF-1 in LCMV-specific exhausted CD8 T cells led to the profound reduction in cytokine production and degranulation. Conversely, ectopic expression of TCF-1 or using agonist to activate TCF-1 activities promotes the effector function of exhausted CD8 T cells. Mechanistically, TCF-1 fuels the functionalities of exhausted CD8 T cells by promoting the expression of an array of key effector function-associated transcription regulators, including Foxo1, Zeb2, Id3, and Eomes. These results collectively indicate that targeting TCF-1 mediated transcriptional pathway may represent a promising immunotherapy strategy against chronic viral infections by reinvigorating the effector function of exhausted virus-specific CD8 T cells.
Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior

Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity for mental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer from low positive predictive values. More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice.
A new approach for spatio-temporal data mining

Spatio-temporal datasets (STD) are generated daily. Generally, their visualization includes 2D renderings or animated visualizations that do not fit to the exploration of such complex data. In this paper, we present an alternative approach to current STD visualizations. Our approach uses tangible and visual tools such as mini-robots and a tactile interaction screen to facilitate access, interpretation and manipulation of this kind of data. While tangible elements have been introduced to represent information, our system provides a new approach to explore multidimensional datasets that conventional tools would not allow.