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关键词： Big Data   Genomics   Computer science   Theory and methods  

摘要： The management of the exponential growth of data that Next Generation Sequencing techniques produce has become a challenge for researchers that are forced to delve into an ocean of complex data in order to extract new insights to unravel the secrets of human diseases. Initially, this can be faced as a Big Data-related problem, but the genomic data have particular and relevant challenges that make them different from other Big Data working domains. Genomic data are much more heterogeneous;they are spread in hundreds of repositories, represented in multiple formats, and have different levels of quality. In addition, getting meaningful conclusions from genomic data requires considering all of the relevant surrounding knowledge that is under continuous evolution. In this scenario, the precise identification of what makes Genome Data Management so different is essential in order to provide effective Big Data-based solutions. Genomic projects require dealing with the technological problems associated with data management, nomenclature standards, and quality issues that only robust Information Systems that use Big Data techniques can provide. The main contribution of this paper is to present a Big Data-driven approach for managing genomic data, that is adapted to the particularities of the domain and to show its applicability to improve genetic diagnoses, which is the core of the development of accurate Precision Medicine. (C) 2021 The Author(s). Published by Elsevier Inc.

关键词： Neural networks   Sensors   Semantics   Recommender systems   Bipartite graph   Aggregates   Computer science   Graph neural network   multi-view attention   path embedding   path-generation  

摘要： Recommender systems are ubiquitous and play an important role in most service-based applications. Since rich semantics lie in interactions between user and item, various models based on Graph Neural Network (GNN) are widely adopted to model the interactions and achieve excellent recommendation performance. Nevertheless, these models mainly learn nodes in the path between user and item separately. The path is also selected randomly, which would bring in low-quality paths and lead to suboptimal performance. To address the above issues, this paper proposes a model named Multi-View Attention neural network with high-quality paths (HP-MA). The path between user and item is viewed as an entirety, and a neural network is designed to distill path semantic embedding. Meanwhile, a multi-view attention model is provided to learn the influences between embedding of the path and user/item. Besides, we provide a method to construct HP-MA between target user and item, which would consider the length and inner details of the path. Moreover, a generalized version of our model (HP-MA*) is proposed to consider several kinds of multi-hop paths, simultaneously. Lastly, extensive experiments are conducted on several public datasets, the results demonstrate that our model can achieve better performance than the state-of-the-art models.

关键词： Applied mathematics   Statistics   Geography   Computer science  

摘要： The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. Finally, we show how flows generated by Deep Gravity may be explained in terms of the geographic features and highlight crucial differences among the three considered countries interpreting the model's prediction with explainable AI techniques. The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. Here, the authors use deep neural networks to discover non-linear relationships between geographical variables and mobility flows.

关键词： Geophysics   Tectonics   Computer science  

摘要： A deep neural network is developed to automatically extract ground deformation from Interferometric Synthetic Aperture Radar time series. Applied to data over the North Anatolian Fault, the method can detect 2 mm deformation transients and reveals a slow earthquake twice as extensive as previously recognized. Systematically characterizing slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here, we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault's location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California.

关键词： Computer science   Geography  

摘要： Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19. We evaluate the model on 14 forecast dates between 2020/10/25 and 2021/01/24 over one- to four-week prediction horizons. Comparing our predictions with a Baseline model developed by the COVID-19 Forecast Hub indicates an average 6.46% improvement in prediction Mean Absolute Errors (MAE) over the two-week prediction horizon up to 20.22% improvement in the four-week prediction horizon, pointing to the strong predictive power of our model in the longer prediction horizons. Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19. In this study, the authors develop a spatiotemporal machine learning model to predict county level new cases in the US using a variety of predictive features.

关键词： Computer science   Quantum physics   Science   Humanities and Social Sciences   multidisciplinary  

摘要： A zero-knowledge proof, which can be used to verify secret information, is reported here with security that is enforced by the laws of special relativity. Protecting secrets is a key challenge in our contemporary information-based era. In common situations, however, revealing secrets appears unavoidable;for instance, when identifying oneself in a bank to retrieve money. In turn, this may have highly undesirable consequences in the unlikely, yet not unrealistic, case where the bank's security gets compromised. This naturally raises the question of whether disclosing secrets is fundamentally necessary for identifying oneself, or more generally for proving a statement to be correct. Developments in computer science provide an elegant solution via the concept of zero-knowledge proofs: a prover can convince a verifier of the validity of a certain statement without facilitating the elaboration of a proof at all(1). In this work, we report the experimental realization of such a zero-knowledge protocol involving two separated verifier-prover pairs(2). Security is enforced via the physical principle of special relativity(3), and no computational assumption (such as the existence of one-way functions) is required. Our implementation exclusively relies on off-the-shelf equipment and works at both short (60 m) and long distances (>= 400 m) in about one second. This demonstrates the practical potential of multi-prover zero-knowledge protocols, promising for identification tasks and blockchain applications such as cryptocurrencies or smart contracts(4).

关键词： Computer science  

摘要： Entering your personal identification number using the keypad of a cash machine is notoriously insecure. A clever application of the special theory of relativity could make identification safer. Identification protocol uses Einstein’s theory to evade fakes.

关键词： Biomedical engineering   Computer science  

摘要： Electro-physiological sensing devices are becoming increasingly common in diverse applications. However, designing such sensors in compact form factors and for high-quality signal acquisition is a challenging task even for experts, is typically done using heuristics, and requires extensive training. Our work proposes a computational approach for designing multi-modal electro-physiological sensors. By employing an optimization-based approach alongside an integrated predictive model for multiple modalities, compact sensors can be created which offer an optimal trade-off between high signal quality and small device size. The task is assisted by a graphical tool that allows to easily specify design preferences and to visually analyze the generated designs in real-time, enabling designer-in-the-loop optimization. Experimental results show high quantitative agreement between the prediction of the optimizer and experimentally collected physiological data. They demonstrate that generated designs can achieve an optimal balance between the size of the sensor and its signal acquisition capability, outperforming expert generated solutions. Though skin-conformable electro-physiological sensors are attractive for epidermal electronics, their optimal design remains a challenge. Here, the authors report a computational design approach for realizing multi-modal electro-physiological sensors that optimizes electrode layout design.

关键词： 新工科   西藏高校   工程应用型   人才培养模式  

摘要： 社会主义新西藏的现代化建设离不开互联网、工业智能产业的布局和发展,必然需要大量新工科人才作支撑。西藏高校是为西藏建设输送中高端人才的重要阵地。本文以西藏大学计算机科学与技术专业为例,描述了西藏新工科人才培养现状,分析了西藏高校新工科人才培养的目标定位,提出了一种西藏高校工程应用型人才培养的教育改革新模式。

关键词： Security   Cloud computing   Games   Servers   Forgery   Computer science   Data privacy   Cloud computing   provable data possession   storage-as-a-service   privacy preserving   security  

摘要： Provable Data Possession is viewed as an important technique to check the integrity of the data stored in remote servers. Recently, a new provable data possession scheme [Secure and Efficient Privacy Preserving Provable Data Possession in Cloud Storage, IEEE Transactions on Services Computing, (2018) Doi: 10.1109/TSC.2018.2820713] was proposed. The authors claimed this scheme can guarantee the storage correction. In this paper, we show this scheme cannot satisfy this fundamental security. Specifically, we demonstrate the malicious cloud can generate a proof to pass the third party auditor's verification even if it does not store the user's whole file.