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Data-driven landscape across finance, government, and healthcare, the continuous generation of information demands robust solutions for secure storage, efficient dissemination, and fine-grained access control. Blockchain technology emerges as a significant tool, offering decentralized storage while upholding the tenets of data security and accessibility. However, on-chain and off-chain strategies are still confronted with issues such as untrusted off-chain data storage, absence of data ownership, limited access control policy for clients, and a deficiency in data privacy and auditability. To solve these challenges, we propose a permissioned blockchain-based privacy-preserving fine-grained access control on-chain and off-chain system, namely FACOS. We applied three fine-grained access control solutions and comprehensively analyzed them in different aspects, which provides an intuitive perspective for system designers and clients to choose the appropriate access control method for their systems. Compared to similar work that only stores encrypted data in centralized or non-fault-tolerant IPFS systems, we enhanced off-chain data storage security and robustness by utilizing a highly efficient and secure asynchronous Byzantine fault tolerance (BFT) protocol in the off-chain environment. As each of the clients needs to be verified and authorized before accessing the data, we involved the Trusted Execution Environment (TEE)-based solution to verify the credentials of clients. Additionally, our evaluation results demonstrated that our system offers better scalability and practicality than other state-of-the-art designs.

With the growing demand for vision applications and deployment across edge devices, the development of hardware-friendly architectures that maintain performance during device deployment becomes crucial. Neural architecture search (NAS) techniques explore various approaches to discover efficient architectures for diverse learning tasks in a computationally efficient manner. In this paper, we present the next-generation neural architecture design for computationally efficient neural architecture distillation - DONNAv2 . Conventional NAS algorithms rely on a computationally extensive stage where an accuracy predictor is learned to estimate model performance within search space. This building of accuracy predictors helps them predict the performance of models that are not being finetuned. Here, we have developed an elegant approach to eliminate building the accuracy predictor and extend DONNA to a computationally efficient setting. The loss metric of individual blocks forming the network serves as the surrogate performance measure for the sampled models in the NAS search stage. To validate the performance of DONNAv2 we have performed extensive experiments involving a range of diverse vision tasks including classification, object detection, image denoising, super-resolution, and panoptic perception network (YOLOP). The hardware-in-the-loop experiments were carried out using the Samsung Galaxy S10 mobile platform. Notably, DONNAv2 reduces the computational cost of DONNA by 10x for the larger datasets. Furthermore, to improve the quality of NAS search space, DONNAv2 leverages a block knowledge distillation filter to remove blocks with high inference costs., Comment: Accepted at ICCV-Workshop on Resource-Efficient Deep Learning, 2023

Our research explores the use of natural language processing (NLP) methods to automatically classify entities for the purpose of knowledge graph population and integration with food system ontologies. We have created NLP models that can automatically classify organizations with respect to categories associated with environmental issues as well as Standard Industrial Classification (SIC) codes, which are used by the U.S. government to characterize business activities. As input, the NLP models are provided with text snippets retrieved by the Google search engine for each organization, which serves as a textual description of the organization that is used for learning. Our experimental results show that NLP models can achieve reasonably good performance for these two classification tasks, and they rely on a general framework that could be applied to many other classification problems as well. We believe that NLP models represent a promising approach for automatically harvesting information to populate knowledge graphs and aligning the information with existing ontologies through shared categories and concepts., Comment: Presented at IFOW 2023 Integrated Food Ontology Workshop at the Formal Ontology in Information Systems Conference (FOIS) 2023 in Sherbrooke, Quebec, Canada July 17-20th, 2023

The ESA's Mars Express solar corona experiments were performed at two solar conjunctions in the years 2015 and 2017 by a number of radio telescopes in the European VLBI Network. This paper presents the methods to measure the frequency and phase fluctuations of the spacecraft radio signal, and the applications to study the characteristics of the plasma turbulence effects on the signal at a single station and at multiple stations via cross-correlation. The power spectra of the frequency fluctuations observed between 4.9 and 76.3 $\rm R_{s}$ have a power-law shape close to a Kolmogorov spectrum over the frequency interval $ \nu_{lo}< \nu <\nu_{up}$, where the nominal value of $\nu_{lo}$ is set to 3 mHz and $\nu_{up}$ is in the range of 0.03 $\sim$ 0.15 Hz. The RMS of the frequency fluctuations is presented as a function of the heliocentric distance. Furthermore, we analyse the variations of the electron column density fluctuations at solar offsets 4.9 $\rm{R_{s}}$ and 9.9 $\rm{R_{s}}$ and the cross-correlation products between the VLBI stations. The power density of the differential fluctuations between different stations decreases at $\nu < 0.01$ Hz. Finally, the fast flow speeds of solar wind $>700$ $\rm{km~s^{-1}}$ are derived from the cross-correlation of frequency fluctuations at $\nu < 0.01$ Hz. The fast flow speeds of solar wind correspond to the high heliolatitude of the coronal region that the radio rays passed. The VLBI observations and analysis methods can be used to study the electron column density fluctuations and the turbulence at multiple spatial points in the inner solar wind by providing multiple lines of sight between the Earth and the spacecraft.

Graph-based multi-view clustering has become an active topic due to the efficiency in characterizing both the complex structure and relationship between multimedia data. However, existing methods have the following shortcomings: (1) They are inefficient or even fail for graph learning in large scale due to the graph construction and eigen-decomposition. (2) They cannot well exploit both the complementary information and spatial structure embedded in graphs of different views. To well exploit complementary information and tackle the scalability issue plaguing graph-based multi-view clustering, we propose an efficient multiple graph learning model via a small number of anchor points and tensor Schatten p-norm minimization. Specifically, we construct a hidden and tractable large graph by anchor graph for each view and well exploit complementary information embedded in anchor graphs of different views by tensor Schatten p-norm regularizer. Finally, we develop an efficient algorithm, which scales linearly with the data size, to solve our proposed model. Extensive experimental results on several datasets indicate that our proposed method outperforms some state-of-the-art multi-view clustering algorithms.

Shanghai Astronomical Observatory has upgraded its DiFX cluster to 420 CPU cores and a 432-TB storage system at the end of 2014. An international network connection for the raw data transfer has also been established. The routine operations for IVS sessions including CRF, AOV, and APSG series began in early 2015. In addition to the IVS observations, the correlator is dedicated to astrophysical and astrometric programs with the Chinese VLBI Network and international joint VLBI observations. It also worked with the new-built Tianma 65-m radio telescope and successfully found fringes as high as at X/Ka and Q bands in late 2015. A more powerful platform is planned for the high data rate and massive data correlation tasks in the future., Comment: 4 pages, 5 figures, conference paper

We present here the results of the first part of the VLBI Ecliptic Plane Survey (VEPS) program. The goal of the program is to find all compact sources within $7.5^\circ$ of the ecliptic plane which are suitable as calibrators for anticipated phase referencing observations of spacecraft and determine their positions with accuracy at the 1.5~nrad level. We run the program in two modes: the search mode and the refining mode. In the search mode, a complete sample of all sources brighter than 50 mJy at 5 GHz listed in the Parkes-MIT-NRAO (PMN) and Green Bank 6~cm (GB6) catalogs, except those previously detected with VLBI, is observed. In the refining mode, the positions of all ecliptic plane sources, including those found in the search mode, are improved. By October 2016, thirteen 24-hr sessions that targeted all sources brighter than 100~mJy have been observed and analyzed. Among 3320 observed target sources, 555 objects have been detected. We also conducted a number of follow-up VLBI experiments in the refining mode and improved the positions of 249 ecliptic plane sources., Comment: 12 pages, 5 figures, 5 tables, accepted by The Astrophysical Journal Supplement Series (ApJS)