Miracle-dz/UniFAR-FTUs huggingface dataset:
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions

Motivation Accurately predicting drug-target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks. Results Inspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks (CNNs) to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g., compound-protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning. Availability and implementation The source code and data used in NeoDTI are available at: https://github.com/FangpingWan/NeoDTI. Contact zengjy321@tsinghua.edu. cn Supplementary information Supplementary data are available at Bioinformatics online.
Systematic analysis and evaluation of visual conceptual modeling language notations

In systems analysis and design it is common to refer to some widely used de-facto industry standards like Unified Modeling Language (UML) and Business Process Model and Notation (BPMN). Albeit the wide adoption of such standard modeling languages, only limited research focuses on the techniques in which these standards are specified and the quality they provide. Most research focuses on case studies of applying standards, ways of extending standards to domain-specific requirements, e.g., by means of profiling, or evaluations of single modeling languages, e.g., using questionnaires or semiotic theories. By contrast, this paper critically reflects on the current state of modeling standards with a focus on their graphical representation (notation). The contribution of this paper is threefold: First, a systematic analysis is performed thereby investigating how different modeling standards specify notational aspects. Second, an evaluation is performed by applying Moody's Physics of Notation theory to the identified standards. Third, based on the findings, recommendations are given to improve modeling standard specifications in the future w.r. t. their notational aspects.
Betaine and Choline Improve Lipid Homeostasis in Obesity by Participation in Mitochondrial Oxidative Demethylation

We investigated the metabolic effects of betaine (Bet) supplementation on CTP:phosphoethanolamine cytidylyltransferase/Pcyt2 heterozygous mice (HET). HET received either no treatment or were allowed access to 1% Bet supplemented water for 8 weeks. As we previously showed with choline (Cho), Bet improved hypertriglyceridemia, and hepatic steatosis in HET. The protection from obesity associated with reduced hepatic steatosis and increased lipid breakdown in adipocytes was attributed to increased energy requirements for metabolism and elimination of supplemented Bet and Cho. 1H-NMR-based profiling revealed metabolic changes caused by Bet and Cho supplementation. Cho increased the citric acid cycle intermediate succinic acid while reducing isoleucine, valine, threonine, and lysine. Bet increased α-ketoglutaric acid and did not stimulate catabolism of amino acids. Increased histidine and alanine are specific biomarkers for Bet treatment. Cho and Bet caused glycerol accumulation and reduced sarcosine, taurine, acetate, and β-hydroxybutyrate levels. These data provide new insights on how Cho and Bet supplementation can aid in treatment of obesity related disorders due to their positive effects on lipolysis, the citric acid cycle, and mitochondrial oxidative demethylation.
PERFORMANCE OF A QUANTUM DOT PASSIVELY MODE-LOCKED LASER UNDER OPTICAL FEEDBACK AND TEMPERATURE CONTROL

The effect of external optical feedback on a packaged monolithic quantum dot passively mode-locked laser is investigated. The radio-frequency (RF) linewidth narrows from 8 KHz in the free-running situation to a value as low as 170 Hz under relatively low feedback and temperature control. The RF linewidth characterization under resonant feedback at a multiple of the laser cavity length agrees well with the published theory. The narrow RF linewidth with external optical feedback makes the chip-scale quantum dot mode-locked laser an attractive source for ultra low noise applications such as optical clocking and optoelectronic oscillators.
Robust deep learning features for face recognition under mismatched conditions

In this paper, we addressed the problem of face recognition under mismatched conditions. In the proposed system, for face representation, we leveraged the state-of-the-art deep learning models trained on the VGGFace2 dataset. More specifically, we used pretrained convolutional neural network models to extract 2048 dimensional feature vectors from face images of International Challenge on Biometric Recognition in the Wild dataset, shortly, ICB-RW 2016. In this challenge, the gallery images were collected under controlled, indoor studio settings, whereas probe images were acquired from outdoor surveillance cameras. For classification, we trained a nearest neighbor classifier using correlation as the distance metric. Experiments on the ICB-RW 2016 dataset have shown that the employed deep learning models that were trained on the VGGFace2 dataset provides superior performance. Even using a single model, compared to the ICB-RW 2016 winner system, around 15% absolute increase in Rank-1 correct classification rate has been achieved. Combining individual models at feature level has improved the performance further. The ensemble of four models achieved 91.8% Rank-1, 98.0% Rank-5 identification rate, and 0.997 Area Under the Curve of Cumulative Match Score on the probe set. The proposed method significantly outperforms the Rank-1, Rank-5 identification rates, and Area Under the Curve of Cumulative Match Score of the best approach at the ICB-RW 2016 challenge, which were 69.8%, 85.3%, and 0.954, respectively.
Is There a Processing Preference for Object Relative Clauses in Chinese? Evidence From ERPs

A consistent finding across head-initial languages, such as English, is that subject relative clauses (SRCs) are easier to comprehend than object relative clauses (ORCs). However, several studies in Mandarin Chinese, a head-final language, revealed the opposite pattern, which might be modulated by working memory (WM) as suggested by recent results from self-paced reading performance. In the present study, event-related potentials (ERPs) were recorded when participants with high and low WM spans (measured by forward digit span and operation span tests) read Chinese ORCs and SRCs. The results revealed an N400-P600 complex elicited by ORCs on the relativizer, whose magnitude was modulated by the WM span. On the other hand, a P600 effect was elicited by SRCs on the head noun, whose magnitude was not affected by the WM span. These findings paint a complex picture of relative clause processing in Chinese such that opposing factors involving structural ambiguities and integration of filler-gap dependencies influence processing dynamics in Chinese relative clauses.
Design and implementation of an automated and decentralized pollution monitoring system with blockchains, smart contracts, and LoRaWAN

This work proposes an IoT- and Blockchain-based, distributed system, for automated measuring, storing, and monitoring of water and air quality in environments such as lakes, mountains, urban areas, or factories. Comparable state-of-the-art solutions, require human interaction to access the data or require high power consumption or space requirements, or they are based on centralized architectures. The proposed pollution monitoring system here, on one hand, employs LoRa to address the high power consumption and long-range transmission challenges of IoT protocols. On the other hand, it is designed to be fully decentralized by using the Ethereum Blockchain to store and retrieve the data recorded by IoT sensors. Thus, data integrity is provided without the need for a Trusted Third Party (TTP) and data is collected and captured automatically without any manual operations needed. Observations on the four different types of sensors for measuring Potential Hydrogen (PH), Turbidity, Carbon monoxide (CO), and Carbon dioxide (CO2), revealed a high accuracy with the expected time-lines of measurements, non-falsified experimental values collected and can be used as reliable evidence of presence of pollution.
Computing optimal drone positions to wirelessly recharge IoT devices

In an Internet of Things (IoT) environment such as the wireless sensor network the nodes are usually powered by batteries. When the nodes are deployed in a harsh environment the battery replacement may be a hard task or the cost of the maintenance may be very high. In this paper, we explore the possibility of recharging the nodes using drones equipped with chargers that can wirelessly transfer energy to the nodes. We examine the problem of minimizing the number of drone positions by adjusting the drones' altitude since the harvesting power is related to the altitude the drones fly. Indeed, the higher the altitude, the larger the observed area but the lower the harvesting power. The aim is to find the minimum possible drone locations such that all the nodes are charged before the drones run out of energy. We show that this problem can be reduced to the set-cover problem which is NP-Complete. We propose a time-efficient heuristic which can run on devices with low processing capabilities. We, also, solve the problem using Integer Linear Programming (ILP) capable of providing the optimal solution. The evaluation using a set of Monte Carlo simulations shows significant performance gains in terms of execution time compared to the ILP solution while keeping the number of positions close to the optimal.
A comparison of Hadoop, Spark and Storm for the task of large scale image classification

An image-based retrieval system (IRS) finds the most relevant images of a query among all the images in the database. When the size of the database exceeds the storage capacity of a single machine, conventional systems are not adequate. Hadoop provides a solution by distributing data and processing over any available commodity hardware. It works well for batch processing and when low-latency is not required. When online processing and low-latency is needed Spark and Storm offer solutions. In this paper, we perform two comparisons regarding Hadoop, Spark and Storm frameworks. In the first one, Hadoop MapReduce (M/R) and Spark are analysed for the image indexing task. The results show that Hadoop MapReduce (M/R) performs better than Spark in case we have no iterative operations on data (e.g., indexing) since no intermediate disk writes are needed. On the other hand, Spark performs better when it comes to iterative operations (e.g., Word Count). In the second comparison, Spark and Storm are compared for the task of classification. Storm yields better latency than Spark while both methods are quite stable as the number of queries increases. This analysis could be useful for researchers and developers of distributed image processing systems.
An efficient feature selection technique for gene expression data

An efficient feature selection technique has been developed for selecting important genes in gene expression data. Feature selection in gene expression data plays a crucial role since the number of features/genes is extremely large as compared to number of samples. The mismatch in dimension poses a challenge for efficient classification. The paper discusses a feature selection technique to remove the drawbacks of effective range based gene selection technique [4] to take into account the redundancy between features. Redundancy of features is computed using inter feature effective range overlap over all classes. The overlap is computed between features having higher weight and all other features. On benchmark gene expression datasets, it has been shown to be far superior compared to the existing feature selection techniques.
A multidisciplinary task-based perspective for evaluating the impact of AI autonomy and generality on the future of work

This paper presents a multidisciplinary task approach for assessing the impact of artificial intelligence on the future of work. We provide definitions of a task from two main perspectives: socio-economic and computational. We propose to explore ways in which we can integrate or map these perspectives, and link them with the skills or capabilities required by them, for humans and AI systems. Finally, we argue that in order to understand the dynamics of tasks, we have to explore the relevance of autonomy and generality of AI systems for the automation or alteration of the workplace.
Energy models for NFV and service provisioning on fog nodes

The demand for high-bandwidth, low-latency services is rapidly increasing. Content Distribution Networks (CDNs) have addressed this by providing content from within or close to the Internet Service Provider (ISP). Still, the most common bottleneck for high service quality is the 'last mile' between ISP and end user. Serving content from small caches on end-user devices promises to increase service quality of the respective content. Similarly, fog computing promises to provide low-latency services from arbitrary nodes within the network. Both require additional functionality provided by network functions virtualization (NFV), redirecting traffic to the appropriate destinations. Still, the cost and performance of possible solutions are not well analyzed. Hence, this paper analyzes the forwarding and computing performance of a number of single-board computers (SBCs) from which models for the performance and energy cost of different loads are derived. Furthermore, the development of energy efficiency gains over the last years is analyzed, confirming Koomey's law also for SBCs, leading to an increase of computational efficiency by a factor of 5.5 to 7.5 over the course of four years, which relates to a doubling time of 1.62 years.
Deep Co-Clustering for Unsupervised Audiovisual Learning

The seen birds twitter, the running cars accompany with noise, people talks by face-to-face, etc. These naturally audiovisual correspondences provide the possibilities to explore and understand the outside world. However, the mixed multiple objects and sounds make it intractable to perform efficient matching in the unconstrained environment. To settle this problem, we propose to adequately excavate audio and visual components and perform elaborate correspondence learning among them. Concretely, a novel unsupervised audiovisual learning model is proposed, named as Deep Co-Clustering (DCC), that synchronously performs sets of clustering with multimodal vectors of convolutional maps in different shared spaces for capturing multiple audiovisual correspondences. And such integrated multimodal clustering network can be effectively trained with max-margin loss in the end-to-end fashion. Amounts of experiments in feature evaluation and audiovisual tasks are performed. The results demonstrate that DCC can learn effective unimodal representation, with which the classifier can even outperform human. Further, DCC shows noticeable performance in the task of sound localization, multisource detection, and audiovisual understanding.
Subgraphs and motifs in a dynamic airline network

How does the small-scale topological structure of an airline network behave as the network evolves? To address this question, we study the dynamic and spatial properties of small undirected subgraphs using 15 years of data on Southwest Airlines' domestic route service. We find that this real-world network has much in common with random graphs, and describe a possible power-law scaling between subgraph counts and the number of edges in the network, that appears to be quite robust to changes in network density and size. We use analytic formulae to identify statistically over-and under-represented subgraphs, known as motifs and anti-motifs, and discover the existence of substantial topology transitions. We propose a simple subgraph-based node ranking measure, that is not always highly correlated with standard node centrality, and can identify important nodes relative to specific topologies; and investigate the spatial "distribution" of the triangle subgraph using graphical tools. Our results have implications for the way in which subgraphs can be used to analyze real-world networks. * We are grateful to Karim Abadir, Gergana Bounova, Pascal Lezaud, Chantal Roucolle and Miguel Urdanoz for helpful comments and suggestions. We also thank Patrick Senac for supporting this project: Agasse-Duval was partially funded by an ENAC summer research grant. Correspondence can be addressed to Steve Lawford, ENAC (DEVI),. The visualization, subgraph analysis, and motif detection tools used in this paper were coded by the authors in Python 2.7. The usual caveat applies.
Robust Multi-Frame Super-Resolution Based on Spatially Weighted Half-Quadratic Estimation and Adaptive BTV Regularization

Multi-frame image super-resolution focuses on reconstructing a high-resolution image from a set of low-resolution images with high similarity. Combining image prior knowledge with fidelity model, the Bayesian-based methods have been considered as an effective technique in super-resolution. The minimization function derived from maximum a posteriori probability (MAP) is composed of a fidelity term and a regularization term. In this paper, based on the MAP estimation, we propose a novel initialization method for super-resolution imaging. For the fidelity term in our proposed method, the half-quadratic estimation is used to choose error norm adaptively instead of using fixed $L_{1}$ and $L_{2}$ norms. Besides, a spatial weight matrix is used as a confidence map to scale the estimation result. For the regularization term, we propose a novel regularization method based on adaptive bilateral total variation (ABTV). Both the fidelity term and the ABTV regularization guarantee the robustness of our framework. The fidelity term is mainly responsible for dealing with misregistration, blur, and other kinds of large errors, while the ABTV regularization aims at edge preservation and noise removal. The proposed scheme is tested on both synthetic data and real data. The experimental results illustrate the superiority of our proposed method in terms of edge preservation and noise removal over the state-of-the-art algorithms.
Real-Time and Spatio-Temporal Crowd-Sourced Social Network Data Publishing with Differential Privacy

Nowadays gigantic crowd-sourced data from mobile devices have become widely available in social networks, enabling the possibility of many important data mining applications to improve the quality of our daily lives. While providing tremendous benefits, the release of crowd-sourced social network data to the public will pose considerable threats to mobile users’ privacy. In this paper, we investigate the problem of real-time spatio-temporal data publishing in social networks with privacy preservation. Specifically, we consider continuous publication of population statistics and design RescueDP—an online aggregate monitoring framework over infinite streams with <inline-formula> <tex-math notation="LaTeX">$w$</tex-math><alternatives><inline-graphic xlink:href="wang-ieq1-2599873.gif"/> </alternatives></inline-formula>-event privacy guarantee. Its key components including adaptive sampling, adaptive budget allocation, dynamic grouping, perturbation and filtering, are seamlessly integrated as a whole to provide privacy-preserving statistics publishing on infinite time stamps. Moreover, we further propose an enhanced RescueDP with neural networks to accurately predict the values of statistics and improve the utility of released data. Both RescueDP and the enhanced RescueDP are proved satisfying <inline-formula><tex-math notation="LaTeX">$w$</tex-math> <alternatives><inline-graphic xlink:href="wang-ieq2-2599873.gif"/></alternatives></inline-formula>-event privacy. We evaluate the proposed schemes with real-world as well as synthetic datasets and compare them with two <inline-formula> <tex-math notation="LaTeX">$w$</tex-math><alternatives><inline-graphic xlink:href="wang-ieq3-2599873.gif"/> </alternatives></inline-formula>-event privacy-assured representative methods. Experimental results show that the proposed schemes outperform the existing methods and improve the utility of real-time data sharing with strong privacy guarantee.
Learning Multi-Instance Deep Ranking and Regression Network for Visual House Appraisal

This paper presents a weakly supervised regression model for the visual house appraisal problem, which aims to predict the value of a house from its photos and textual descriptions (e.g., number of bedrooms). The key idea of our approach is a multi-layer neural network, called multi-instance Deep Ranking and Regression (MiDRR) net, which jointly solves two coupled tasks: ranking and regression, in the multiple instance setting. The network is trained using weakly supervised data, which do not require intensive human annotations. We also design a set of human heuristics to promote deep features through imposing constraints over the solution space, e.g., a house with three bedrooms often has a higher value than that with only two bedrooms. While these constraints are specific to the studied problem, the developed formula can be easily generalized to the other regression applications. For test and evaluation purposes, we collect a comprehensive house image benchmark that includes 900,000 photos from 30,000 houses recently traded in the USA, and apply the proposed MiDRR net to predict house values. Extensive evaluations with comparisons demonstrate that additional usage of imagery data as well as human heuristics can significantly boost system performance and that the proposed MiDRR net clearly outperforms the alternative methods.
Robust Object Tracking via Local Sparse Appearance Model

In this paper, we propose a novel local sparse representation-based tracking framework for visual tracking. To deeply mine the appearance characteristics of different local patches, the proposed method divides all local patches of a candidate target into three categories, which are stable patches, valid patches, and invalid patches. All these patches are assigned different weights to consider the different importance of the local patches. For stable patches, we introduce a local sparse score to identify them, and discriminative local sparse coding is developed to decrease the weights of background patches among the stable patches. For valid patches and invalid patches, we adopt local linear regression to distinguish the former from the latter. Furthermore, we propose a weight shrinkage method to determine weights for different valid patches to make our patch weight computation more reasonable. Experimental results on public tracking benchmarks with challenging sequences demonstrate that the proposed method performs favorably against other state-of-the-art tracking methods.