Integrating Overlapping Structures and Background Information of Words Significantly Improves Biological Sequence Comparison

Word-based models have achieved promising results in sequence comparison. However, as the important statistical properties of words in biological sequence, how to use the overlapping structures and background information of the words to improve sequence comparison is still a problem. This paper proposed a new statistical method that integrates the overlapping structures and the background information of the words in biological sequences. To assess the effectiveness of this integration for sequence comparison, two sets of evaluation experiments were taken to test the proposed model. The first one, performed via receiver operating curve analysis, is the application of proposed method in discrimination between functionally related regulatory sequences and unrelated sequences, intron and exon. The second experiment is to evaluate the performance of the proposed method with f-measure for clustering Hepatitis E virus genotypes. It was demonstrated that the proposed method integrating the overlapping structures and the background information of words significantly improves biological sequence comparison and outperforms the existing models

Exploiting wireless received signal strength indicators to detect evil-twin attacks in smart homes

Evil-twin is becoming a common attack in Smart Home environments where an attacker can set up a fake AP to compromise the security of the connected devices. To identify the fake APs, The current approaches of detecting Evil-twin attacks all rely on information such as SSIDs, the MAC address of the genuine AP or network traffic patterns. However, such information can be faked by the attacker, often leading to low detection rates and weak protection. This paper presents a novel evil-twin attack detection method based on the received signal strength indicator (RSSI). Our key insight is that the location of the genuine AP rarely moves in a home environment and as a result the RSSI of the genuine AP is relatively stable. Our approach considers the RSSI as a fingerprint of APs and uses the fingerprint of the genuine AP to identify fake ones. We provide two schemes to detect a fake AP in two different scenarios where the genuine AP can be located at either a single or multiple locations in the property, by exploiting the multipath effect of the WIFI signal. As a departure from prior work, our approach does not rely on any professional measurement devices. Experimental results show that our approach can successfully detect 90% of the fake APs, at the cost of an one-off, modest connection delay

Observed deep energetic eddies by seamount wake

Despite numerous surface eddies are observed in the ocean, deep eddies (a type of eddies which have no footprints at the sea surface) are much less reported in the literature due to the scarcity of their observation. In this letter, from recently collected current and temperature data by mooring arrays, a deep energetic and baroclinic eddy is detected in the northwestern South China Sea (SCS) with its intensity, size, polarity and structure being characterized. It remarkably deepens isotherm at deep layers by the amplitude of ~120 m and induces a maximal velocity amplitude about 0.18 m/s, which is far larger than the median velocity (0.02 m/s). The deep eddy is generated in a wake when a steering flow in the upper layer passes a seamount, induced by a surface cyclonic eddy. More observations suggest that the deep eddy should not be an episode in the area. Deep eddies significantly increase the velocity intensity and enhance the mixing in the deep ocean, also have potential implication for deep-sea sediments transport

Repurposing Niclosamide as a Novel Anti-SARS-CoV-2 Drug by Restricting Entry Protein CD147

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the global coronavirus disease 2019 (COVID-19) pandemic, and the search for effective treatments has been limited. Furthermore, the rapid mutations of SARS-CoV-2 have posed challenges to existing vaccines and neutralizing antibodies, as they struggle to keep up with the increased viral transmissibility and immune evasion. However, there is hope in targeting the CD147-spike protein, which serves as an alternative point for the entry of SARS-CoV-2 into host cells. This protein has emerged as a promising therapeutic target for the development of drugs against COVID-19. Here, we demonstrate that the RNA-binding protein Human-antigen R (HuR) plays a crucial role in the post-transcriptional regulation of CD147 by directly binding to its 3′-untranslated region (UTR). We observed a decrease in CD147 levels across multiple cell lines upon HuR depletion. Furthermore, we identified that niclosamide can reduce CD147 by lowering the cytoplasmic translocation of HuR and reducing CD147 glycosylation. Moreover, our investigation revealed that SARS-CoV-2 infection induces an upregulation of CD147 in ACE2-expressing A549 cells, which can be effectively neutralized by niclosamide in a dose-dependent manner. Overall, our study unveils a novel regulatory mechanism of regulating CD147 through HuR and suggests niclosamide as a promising therapeutic option against COVID-19

Maximizing frequency security margin via conventional generation dispatch and battery energy injection

To quantitatively evaluate the frequency stability margin during primary frequency control period following an under-frequency event, this paper presents a dynamic frequency response constrained optimal power flow (OPF) model. In this model, frequency security margin is defined and maximized by adjusting pre-disturbance generation outputs of conventional units and injections of battery energy storage system (BESS) immediately after a disturbance. Two nonlinear characteristics in speed-governing systems are considered and described as smooth and differentiable formulations to facilitate their incorporations into the proposed optimization model. A graphical tool is also provided to enable region-wise frequency security assessment based on the obtained maximum frequency security margin. Simulation results on WSCC 3-machine 9-bus system and New England 10-machine 39-bus system validate the suggested margin metric and the effectiveness of the proposed method

Postprocessing for quantum random number generators: entropy evaluation and randomness extraction

Quantum random-number generators (QRNGs) can offer a means to generate information-theoretically provable random numbers, in principle. In practice, unfortunately, the quantum randomness is inevitably mixed with classical randomness due to classical noises. To distill this quantum randomness, one needs to quantify the randomness of the source and apply a randomness extractor. Here, we propose a generic framework for evaluating quantum randomness of real-life QRNGs by min-entropy, and apply it to two different existing quantum random-number systems in the literature. Moreover, we provide a guideline of QRNG data postprocessing for which we implement two information-theoretically provable randomness extractors: Toeplitz-hashing extractor and Trevisan's extractor.Comment: 13 pages, 2 figure

Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization

Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS. In this paper, we propose a neural approach for Chinese word segmentation which can exploit both lexicon and unlabeled data. Our approach is based on a variant of posterior regularization algorithm, and the unlabeled data and lexicon are incorporated into model training as indirect supervision by regularizing the prediction space of CWS models. Extensive experiments on multiple benchmark datasets in both in-domain and cross-domain scenarios validate the effectiveness of our approach.Comment: 7 pages, 11 figures, accepted by the 2019 World Wide Web Conference (WWW '19