Single-Channel Speech Dereverberation using Subband Network with A Reverberation Time Shortening Target

This work proposes a subband network for single-channel speech dereverberation, and also a new learning target based on reverberation time shortening (RTS). In the time-frequency domain, we propose to use a subband network to perform dereverberation for different frequency bands independently. The time-domain convolution can be well decomposed to subband convolutions, thence it is reasonable to train the subband network to perform subband deconvolution. The learning target for dereverberation is usually set as the direct-path speech or optionally with some early reflections. This type of target suddenly truncates the reverberation, and thus it may not be suitable for network training, and leads to a large prediction error. In this work, we propose a RTS learning target to suppress reverberation and meanwhile maintain the exponential decaying property of reverberation, which will ease the network training, and thus reduce the prediction error and signal distortions. Experiments show that the subband network can achieve outstanding dereverberation performance, and the proposed target has a smaller prediction error than the target of direct-path speech and early reflections.Comment: Submitted to INTERSPEECH 202

A Practical Platform for Cube-Attack-like Cryptanalyses

Recently, various cryptanalysis methods related to Cube Attack have attracted a lot of interest. We designed a practical platform to perform such cryptanalysis attacks. We also developed a web-based application at \url{http://cube-attack.appspot.com/}, which is open to public for simple testing and verification. In this paper, we focus on linearity testing and try to verify the data provided in several papers. Some interesting results produced in our work indicate certain improper assumptions were made in these papers

Accurate magneto-optical determination of radius of topological nodal-ring semimetals

The shape of the Fermi surface of topological nodal-ring semimetals at low carrier concentrations is characterized by the ring radius b/ℏvF. This peculiar topological property may not have a clear signature in measurable physical quantities. We demonstrate an accurate and definitive method to determine the radius of topological nodal-ring semimetals. Under a magnetic field along the ring axis, the axial magneto-optical response (σzz) has a giant peak. The position of this ultrastrong response is at the frequency of exactly 2b and is independent of the strength of the magnetic field. The amplitude of the peak response is many times stronger than that of any other inter-Landau level transitions if the magnetic energy is greater than b and is similar strength if b is greater than the magnetic energy. The origin of the ultrastrong response is that the axial magnetic transition is governed by selection rules completely different to those governing σxx where the giant response is absent [R. Y. Chen et al., Phys. Rev. Lett. 115, 176404 (2015)]. The present work provides a method to accurately determine parameters of the topological properties of semimetals

Joint Entity and Relation Extraction with a Hybrid Transformer and Reinforcement Learning Based Model

Joint extraction of entities and relations is a task that extracts the entity mentions and semantic relations between entities from the unstructured texts with one single model. Existing entity and relation extraction datasets usually rely on distant supervision methods which cannot identify the corresponding relations between a relation and the sentence, thus suffers from noisy labeling problem. We propose a hybrid deep neural network model to jointly extract the entities and relations, and the model is also capable of filtering noisy data. The hybrid model contains a transformer-based encoding layer, an LSTM entity detection module and a reinforcement learning-based relation classification module. The output of the transformer encoder and the entity embedding generated from the entity detection module are combined as the input state of the reinforcement learning module to improve the relation classification and noisy data filtering. We conduct experiments on the public dataset produced by the distant supervision method to verify the effectiveness of our proposed model. Different experimental results show that our model gains better performance on entity and relation extraction than the compared methods and also has the ability to filter noisy sentences

The aftermath of broken links: Resilience of IoT systems from a networking perspective

© 2018 IEEE. Internet of things (IoT) is expected to provide a fully informative and controllable environment that features networking, automation, and intelligence by interconnecting physical systems to cyber world. Such a correlation opens the interdependence between the two, upon which a single incident in one domain, e.g., a broken communication link, or an out-of-battery device, can cause a cascade-of-failures across physical and cyber domains. To understand the resilience of IoT systems against such detrimental cascades, this paper studies the aftermath of edge and jointly-induced cascades, that is, a sequence of failures induced by randomly broken physical links (and simultaneous failing cyber nodes) by answering how many nodes will survive the cascade with a newly defined node yield metric. Specifically, we construct a framework to establish self-consistent equations of node yield through an auxiliary graph, without requiring the exact network topology. Then two algorithms are proposed to numerically calculate node yield for interdependent networks with arbitrary degree distributions. For random graph with Poisson degree distributions, we prove the existence of a critical initial edge disconnecting probability Φcr, under which an edge-induced cascade will result in dissolving the network topology, derive the closed form solution for Φcr, and find that Φcr increases sub-linearly with the mean degree of the physical network

Identification of Hub Genes and Biological Mechanisms Associated with Non-Alcoholic Fatty Liver Disease and Triple-Negative Breast Cancer

The relationship between non-alcoholic fatty liver disease (NAFLD) and triple-negative breast cancer (TNBC) has been widely recognized, but the underlying mechanisms are still unknown. The objective of this study was to identify the hub genes associated with NAFLD and TNBC, and to explore the potential co-pathogenesis and prognostic linkage of these two diseases. We used GEO, TCGA, STRING, ssGSEA, and Rstudio to investigate the common differentially expressed genes (DEGs), conduct functional and signaling pathway enrichment analyses, and determine prognostic value between TNBC and NAFLD. GO and KEGG enrichment analyses of the common DEGs showed that they were enriched in leukocyte aggregation, migration and adhesion, apoptosis regulation, and the PPAR signaling pathway. Fourteen candidate hub genes most likely to mediate NAFLD and TNBC occurrence were identified and validation results in a new cohort showed that ITGB2, RAC2, ITGAM, and CYBA were upregulated in both diseases. A univariate Cox analysis suggested that high expression levels of ITGB2, RAC2, ITGAM, and CXCL10 were associated with a good prognosis in TNBC. Immune infiltration analysis of TNBC samples showed that NCF2, ICAM1, and CXCL10 were significantly associated with activated CD8 T cells and activated CD4 T cells. NCF2, CXCL10, and CYBB were correlated with regulatory T cells and myeloid-derived suppressor cells. This study demonstrated that the redox reactions regulated by the NADPH oxidase (NOX) subunit genes and the transport and activation of immune cells regulated by integrins may play a central role in the co-occurrence trend of NAFLD and TNBC. Additionally, ITGB2, RAC2, and ITGAM were upregulated in both diseases and were prognostic protective factors of TNBC; they may be potential therapeutic targets for treatment of TNBC patients with NAFLD, but further experimental studies are still needed

Frailty Risk Prediction Model among Older Adults: A Chinese Nation-Wide Cross-Sectional Study

Objectives: Numerous studies have been performed on frailty, but rarely do studies explore the integrated impact of socio-demographic, behavioural and social support factors on frailty. This study aims to establish a comprehensive frailty risk prediction model including multiple risk factors. Methods: The 2018 wave of the Chinese Longevity and Health Longitudinal Survey was used. Univariate and multivariate logistic regressions were performed to identify the relationship between frailty and multiple risk factors and establish the frailty risk prediction model. A nomogram was utilized to illustrate the prediction model. The area under the receiver operating characteristic curve (AUC), Hosmer–Lemeshow test and calibration curve were used to appraise the prediction model. Results: Variables from socio-demographic, social support and behavioural dimensions were included in the final frailty risk prediction model. Risk factors include older age, working as professionals and technicians before 60 years old, poor economic condition and poor oral hygiene. Protective factors include eating rice as a staple food, regular exercise, having a spouse as the first person to share thoughts with, doing physical examination once a year and not needing a caregiver when ill. The AUC (0.881), Hosmer–Lemeshow test (p = 0.618), and calibration curve showed that the risk prediction model was valid. Conclusion: Risk factors from socio-demographic, behavioural and social support dimensions had a comprehensive effect on frailty, further supporting that a comprehensive and individualized intervention is necessary to prevent frailty