Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-Wide Characterization of the sHsp Gene Family in Salix suchowensis Reveals Its Functions under Different Abiotic Stresses

Small heat shock proteins (sHsps) function mainly as molecular chaperones that play vital roles in response to diverse stresses, especially high temperature. However, little is known about the molecular characteristics and evolutionary history of the sHsp family in Salix suchowensis, an important bioenergy woody plant. In this study, 35 non-redundant sHsp genes were identified in S. suchowensis, and they were divided into four subfamilies (C, CP, PX, and MT) based on their phylogenetic relationships and predicted subcellular localization. Though the gene structure and conserved motif were relatively conserved, the sequences of the Hsp20 domain were diversified. Eight paralogous pairs were identified in the Ssu-sHsp family, in which five pairs were generated by tandem duplication events. Ka/Ks analysis indicated that Ssu-sHsps had undergone purifying selection. The expression profiles analysis showed Ssu-Hsps tissue-specific expression patterns, and they were induced by at least one abiotic stress. The expression correlation between two paralogous pairs (Ssu-sHsp22.2-CV/23.0-CV and 23.8-MT/25.6-MT) were less than 0.6, indicating that they were divergent during the evolution. Various cis-acting elements related to stress responses, hormone or development, were detected in the promoter of Ssu-sHsps. Furthermore, the co-expression network revealed the potential mechanism of Ssu-sHsps under stress tolerance and development. These results provide a foundation for further functional research on the Ssu-sHsp gene family in S. suchowensis.
Subjective ratings of age-of-acquisition: exploring issues of validity and rater reliability

Abstract This study aimed to investigate concerns of validity and reliability in subjective ratings of age-of-acquisition (AoA), through exploring characteristics of the individual rater. An additional aim was to validate the obtained AoA ratings against two corpora – one of child speech and one of adult speech – specifically exploring whether words over-represented in the child-speech corpus are rated with lower AoA than words characteristic of the adult-speech corpus. The results show that less than one-third of participating informants’ ratings are valid and reliable. However, individuals with high familiarity with preschool-aged children provide more valid and reliable ratings, compared to individuals who do not work with or have children of their own. The results further show a significant, age-adjacent difference in rated AoA for words from the two different corpora, thus strengthening their validity. The study provides AoA data, of high specificity, for 100 child-specific and 100 adult-specific Swedish words.
Relevance Estimation with Multiple Information Sources on Search Engine Result Pages

Relevance estimation is among the most important tasks in the ranking of search results because most search engines follow the Probability Ranking Principle. Current relevance estimation methodologies mainly concentrate on text matching between the query and Web documents, link analysis and user behavior models. However, users judge the relevance of search results directly from Search Engine Result Pages (SERPs), which provide valuable signals for reranking. Morden search engines aggregate heterogeneous information items (such as images, news, and hyperlinks) to a single ranking list on SERPs. The aggregated search results have different visual patterns, textual semantics and presentation structures, and a better strategy should rely on all these information sources to improve ranking performance. In this paper, we propose a novel framework named Joint Relevance Estimation model (JRE), which learns the visual patterns from screenshots of search results, explores the presentation structures from HTML source codes and also adopts the semantic information of textual contents. To evaluate the performance of the proposed model, we construct a large scale practical Search Result Relevance (SRR) dataset which consists of multiple information sources and 4-grade relevance scores of over 60,000 search results. Experimental results show that the proposed JRE model achieves better performance than state-of-the-art ranking solutions as well as the original ranking of commercial search engines.
A Network-embedding Based Method for Author Disambiguation

Most existing author disambiguation work relies heavily on feature engineering or cannot use multiple paper relationships. In this work, we propose a network-embedding based method for author disambiguation. For each ambiguous name, we construct networks among papers sharing an ambiguous name, and connect papers with multiple relationships (e.g., co-authoring a paper). We focus on maximizing the gap between positive paper edges and negative edges, and propose a graph coarsening technique to learn global information. Further, we design a clustering algorithm which partitions paper representations into disjoint sets such that each set contains all papers of a unique author. Through extensive experiments, we show that our method is significantly better than the state-of-the-art author disambiguation and network-embedding methods.
Comparative Transcriptome Analysis of Megacopta cribraria (Hemiptera: Plataspidae) in Response to High-Temperature Stress

Abstract Kudzu bug, Megacopta cribraria (Fabricius), is mainly distributed in southern China and has been considered an invasive species in the southeastern United States. Megacopta cribraria is a soybean pest with high-temperature resistance, but the molecular mechanisms underlying its thermal adaptation are largely unknown. Here, we performed comparative transcriptome analysis to unravel the molecular response of M. cribraria toward high-temperature stress. Following RNA-seq, we identified 93,959 assembled unigenes, 14,073 of which were annotated in M. cribraria transcriptome libraries. In addition, 127 differentially expressed unigenes (DEGs) were detected, 88 of them were significantly upregulated, whereas the remaining 39 genes were significantly downregulated. Functional classification revealed that the pathways of metabolic process, cellular processes, and single-organism processes were considered to be significantly enriched. In the COG classification, DEGs were mainly localized into O: post-translational modification, protein turnover, chaperone. Moreover, protein processing in endoplasmic reticulum and linoleic acid metabolism were significantly enriched among the 38 KEGG pathways. Further gene annotation analysis indicated that nine heat shock protein-related genes were significantly upregulated. Finally, five HSP DEGs were selected for real-time quantitative polymerase chain reaction validation and demonstrated a similar upregulation trend with RNA-seq expression profiles.Taken altogether, these findings provide new insights into the molecular mechanisms of thermal adaptation in M. cribraria.
Recurrent Spatio-Temporal Point Process for Check-in Time Prediction

We introduce a new problem, namely, check-in time prediction where the goal is to predict the time when a given user will check-in to a location of interest. We design a novel Recurrent Spatio-Temporal Point Process (RSTPP) model for check-in time prediction. RSTPP addresses two key challenges: 1) Data scarcity due to uneven distribution of check-ins among users/locations. 2) User trajectories contain valuable information that is ignored by standard temporal point process which only considers historical event times. RSTPP is designed to learn the latent dependencies of event times over both historical events and spatio-temporal information about locations a user visited before check-in to the location of interest. We evaluate RSTPP on several real-world datasets, and it significantly outperforms state-of-the-art event time predicting techniques. Our work derives a set of practical implications that can benefit a wide spectrum of applications.
Robotic Process Automation - Creating Value by Digitalizing Work in the Private Healthcare?

Organizations are applying digitalization to the constantly increasing amounts of different organizational processes [2]. The healthcare sector is also changing and actively seeking better ways to enhance performance, especially in the private healthcare sector [7]. Automation of workflow processes, e.g., Robotic Process Automation (RPA), in organizations has been emerging as a solution to this demand [3, 4]. To meet this clear demand, automation of workflow processes in organizations has been a rising trend during the past few years [3]. We analyze the value creating functions of the RPA potential in the private healthcare industry sector, using modified Walter et al.'s function-oriented value analysis as our theoretical lens for identifying the potential of RPA.
Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction

In this paper, we propose to incorporate information of related corporations of a target company for its stock price prediction. We first construct a graph including all involved corporations based on investment facts from real market and learn a distributed representation for each corporation via node embedding methods applied on the graph. Two approaches are then explored to utilize information of related corporations based on a pipeline model and a joint model via graph convolutional neural networks respectively. Experiments on the data collected from stock market in Mainland China show that the representation learned from our model is able to capture relationships between corporations, and prediction models incorporating related corporations' information are able to make more accurate predictions on stock market.
Direction-aware Neural Style Transfer

Neural learning methods have been shown to be effective in style transfer. These methods, which are called NST, aim to synthesize a new image that retains the high-level structure of a content image while keeps the low-level features of a style image. However, these models using convolutional structures only extract local statistical features of style images and semantic features of content images. Since the absence of low-level features in the content image, these methods would synthesize images that look unnatural and full of traces of machines. In this paper, we find that direction, that is, the orientation of each painting stroke, can capture the soul of image style preferably and thus generates much more natural and vivid stylizations. According to this observation, we propose a Direction-aware Neural Style Transfer (DaNST) with two major innovations. First, a novel direction field loss is proposed to steer the direction of strokes in the synthesized image. And to build this loss function, we propose novel direction field loss networks to generate and compare the direction fields of content image and synthesized image. By incorporating the direction field loss in neural style transfer, we obtain a new optimization objective. Through minimizing this objective, we can produce synthesized images that better follow the direction field of the content image. Second, our method provides a simple interaction mechanism to control the generated direction fields, and further control the texture direction in synthesized images. Experiments show that our method outperforms state-of-the-art in most styles such as oil painting and mosaic.
Bioactive Compounds in Kimchi Improve the Cognitive and Memory Functions Impaired by Amyloid Beta

This study investigated the abilities of kimchi and its bioactive compounds to ameliorate amyloid beta (Aβ)-induced memory and cognitive impairments. Mice were given a single intracerebroventricular injection of Aβ25-35, followed by a daily oral administration of capsaicin (10 mg·kg-bw–1), 3-(4′-hydroxyl-3′,5′-dimethoxyphenyl)propionic acid (50 mg/kg bw), quercetin (50 mg/kg bw), ascorbic acid (50 mg/kg bw), or kimchi methanol extract (KME; 200 mg/kg bw) for 2 weeks (n = 7 per group). Carboxymethylcellulose was used as a vehicle for the normal and control groups. Behavioral task tests showed that the learning and memory abilities were significantly waned by the injected Aβ25-35, but these cognitive deficits were recovered by the administrated KME and kimchi bioactive compounds (p < 0.05). The reactive oxygen species, peroxynitrite, and thiobarbituric acid reactive substances levels were lower, and the glutathione level was higher, in the KME and bioactive compound groups than in the control group (p < 0.05). In the KME and bioactive compound groups, the protein expression levels of antioxidant enzymes (nuclear factor (erythroid-derived 2)-like 2-regulated superoxide dismutase-1 and glutathione peroxidase) were increased, whereas those of inflammation-related enzymes (nuclear factor-kappaB -regulated inducible nitric oxide synthase and cyclooxygenase-2) were decreased (p < 0.05). Thus, the antioxidative and anti-inflammatory properties of bioactive compounds-rich kimchi might help to attenuate the symptoms of Alzheimer’s disease.
The complexity of temporal constraint satisfaction problems

A temporal constraint language is a set of relations that has a first-order definition in (b Q,<), the dense linear order of the rational numbers. We present a complete complexity classification of the constraint satisfaction problem (CSP) for temporal constraint languages: if the constraint language is contained in one out of nine temporal constraint languages, then the CSP can be solved in polynomial time; otherwise, the CSP is NP-complete. Our proof combines model-theoretic concepts with techniques from universal algebra, and also applies the so-called product Ramsey theorem, which we believe will be useful in similar contexts of constraint satisfaction complexity classification.
Temporal Hierarchical Attention at Category- and Item-Level for Micro-Video Click-Through Prediction

Micro-video sharing gains great popularity in recent years, which calls for effective recommendation algorithm to help user find their interested micro-videos. Compared with traditional online (e.g. YouTube) videos, micro-videos contributed by grass-root users and taken by smartphones are much shorter (tens of seconds) and more short of tags or descriptive text, making the recommendation of micro-videos a challenging task. In this paper, we investigate how to model user's historical behaviors so as to predict the user's click-through of micro-videos. Inspired by the recent deep network-based methods, we propose a Temporal Hierarchical Attention at Category- and Item-Level (THACIL) network for user behavior modeling. First, we use temporal windows to capture the short-term dynamics of user interests; Second, we leverage a category-level attention mechanism to characterize user's diverse interests, as well as an item-level attention mechanism for fine-grained profiling of user interests; Third, we adopt forward multi-head self-attention to capture the long-term correlation within user behaviors. Our proposed THACIL network was tested on MicroVideo-1.7M, a new dataset of 1.7 million micro-videos, coming from real data of a micro-video sharing service in China. Experimental results demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art solutions.
Distinguishing Trajectories from Different Drivers using Incompletely Labeled Trajectories

We consider a scenario that occurs often in the auto insurance industry. We are given a large collection of trajectories that stem from many different drivers. Only a small number of the trajectories are labeled with driver identifiers, and only some drivers are used in labels. The problem is to label correctly the unlabeled trajectories with driver identifiers. This is important in auto insurance to detect possible fraud and to identify the driver in, e.g., pay-as-you-drive settings when a vehicle has been involved in an incident. To solve the problem, we first propose a Trajectory-to-Image( T2I) encoding scheme that captures both geographic features and driving behavior features of trajectories in 3D images. Next, we propose a multi-task, deep learning model called T2INet for estimating the total number of drivers in the unlabeled trajectories, and then we partition the unlabeled trajectories into groups so that the trajectories in a group belong to the same driver. Experimental results on a large trajectory data set offer insight into the design properties of T2INet and demonstrate that T2INet is capable of outperforming baselines and the state-of-the-art method.
The Realization of Load Balancing Algorithm in Cloud Computing

Due1 to the large number, the length and the processing time of cloud tasks, the cloud task scheduling is unbalanced and it has the problem that the cloud computing service response will be delayed and inefficient in the cloud computing environment. This paper mainly studies the bee colony load balancing algorithm(BCLB) and establishes the mathematical model of the bee colony load balancing algorithm. The mathematical model of the Bee colony balancing algorithm is established and applied in cloud computing. It designs the algorithm implementation process and cloud task scheduling scheme. Finally, through the simulation in the CloudSim, the application of bee colony load balancing algorithm is realized in cloud computing, the experiments show that the BCLB algorithm has greater advantages than random algorithms and greedy load balancing algorithm.
OSMO: Online Specific Models for Occlusion in Multiple Object Tracking under Surveillance Scene

With demands of the intelligent monitoring, multiple object tracking (MOT) in surveillance scene has become an essential but challenging task. Occlusion is the primary difficulty in surveillance MOT, which can be categorized into the inter-object occlusion and the obstacle occlusion. Many current studies on general MOT focus on the former occlusion, but few studies have been conducted on the latter one. In fact, there are useful prior knowledge in surveillance videos, because the scene structure is fixed. Hence, we propose two models for dealing with these two kinds of occlusions. The attention-based appearance model is proposed to solve the inter-object occlusion, and the scene structure model is proposed to solve the obstacle occlusion. We also design an obstacle map segmentation method for segmenting obstacles from the surveillance scene. Furthermore, to evaluate our method, we propose four new surveillance datasets that contain videos with obstacles. Experimental results show the effectiveness of our two models.
The Construction of Large Graph Data Structures in a Scalable Distributed Message System

A large-scale distributed graph data substrate is the foundation of graph databases and knowledge base systems. Existing systems either scale up the capacity of a single compute node or build on distributed relational models (table based systems). The former limits the scalability of the graph system, and the latter can constrain the performance. We have designed a new graph data system based on a scalable message system. The new data model enables most of existing graph algorithms designed for a monolithic computer to run smoothly on a plurality of distributed nodes, and achieves high performance on complex graph operations. The implementation supports 100 million vertices and one billion arcs on a commodity computer cluster, and provides scalability to potentially operate on tens of billions of vertices.
Design and Experimental Validation of a Synchronous Wireless Acquisition System of EMG and Muscle Impedance Signals

Electromyogram (EMG)1 and muscle impedance signals are two important physiological parameters that reflect the state of muscles. They can be used to evaluate the motor function status of muscles, and thus their synchronous acquisition would be very significant in the fields of the rehabilitation medicine and rehabilitation engineering. In this paper, a Bluetooth data acquisition system was designed by using ADS1292R, STM8 and HJ-580X Bluetooth chips for recording EMG and muscle impedance signal simultaneously and wirelessly. The system is composed of two parts: the host computer and the slave microcontroller. The slave microcontroller is in charge of data acquisition and wireless transmission, while the host computer performs data receiving, displaying, processing and saving. The system was applied to the measurement of subcutaneous EMG and muscle impedance signals in rats. Our results showed that the system could accurately collect the signals of EMG and muscle impedance, which would be utilized to characterize the muscle activity.
Network structural dependency in the human connectome across the life-span

Principles of network topology have been widely studied in the human connectome. Of particular interest is the modularity of the human brain, where the connectome is divided into subnetworks from which changes with development, aging or disease can be investigated. We present a weighted network measure, the Network Dependency Index (NDI), to identify an individual region’s importance to the global functioning of the network. Importantly, we utilize NDI to differentiate four subnetworks (Tiers) in the human connectome following Gaussian mixture model fitting. We analyze the topological aspects of each subnetwork with respect to age and compare it to rich club-based subnetworks (rich club, feeder, and seeder). Our results first demonstrate the efficacy of NDI to identify more consistent, central nodes of the connectome across age groups, when compared with the rich club framework. Stratifying the connectome by NDI led to consistent subnetworks across the life-span, revealing distinct patterns associated with age where, for example, the key relay nuclei and cortical regions are contained in a subnetwork with highest NDI. The divisions of the human connectome derived from our data-driven NDI framework have the potential to reveal topological alterations described by network measures through the life-span.
Learning and Fusing Multimodal Deep Features for Acoustic Scene Categorization

Convolutional Neural Networks (CNNs) have been widely applied to audio classification recently where promising results have been obtained. Previous CNN-based systems mostly learn from two-dimensional time-frequency representations such as MFCC and spectrograms, which may tend to emphasize more on the background noise of the scene. To learn the key acoustic events, we introduce a three-dimensional CNN to emphasize on the different spectral characteristics from neighboring regions in spatial-temporal domain. A novel acoustic scene classification system based on multimodal deep feature fusion is proposed in this paper, where three CNNs have been presented to perform 1D raw waveform modeling, 2D time-frequency image modeling, and 3D spatial-temporal dynamics modeling, respectively. The learnt features are shown to be highly complementary to each other, which are next combined in a feature fusion network to obtain significantly improved classification predictions. Comprehensive experiments have been conducted on two large-scale acoustic scene datasets, namely the DCASE16 dataset and the LITIS Rouen dataset. Experimental results demonstrate the effectiveness of our proposed approach, as our solution achieves state-of-the-art classification rates and improves the average classification accuracy by 1.5% - 8.2% compared to the top ranked systems in the DCASE16 challenge.