Exploring Continuous Integrate-and-Fire for Adaptive Simultaneous Speech Translation

Simultaneous speech translation (SimulST) is a challenging task aiming to translate streaming speech before the complete input is observed. A SimulST system generally includes two components: the pre-decision that aggregates the speech information and the policy that decides to read or write. While recent works had proposed various strategies to improve the pre-decision, they mainly adopt the fixed wait-k policy, leaving the adaptive policies rarely explored. This paper proposes to model the adaptive policy by adapting the Continuous Integrate-and-Fire (CIF). Compared with monotonic multihead attention (MMA), our method has the advantage of simpler computation, superior quality at low latency, and better generalization to long utterances. We conduct experiments on the MuST-C V2 dataset and show the effectiveness of our approach.Comment: Submitted to INTERSPEECH 202

Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites

[[abstract]]In this research, we propose recurrent neural networks (RNNs) to build a relationship between rainfalls and water level patterns of an urban sewerage system based on historical torrential rain/storm events. The RNN allows signals to propagate in both forward and backward directions, which offers the network dynamic memories. Besides, the information at the current time-step with a feedback operation can yield a time-delay unit that provides internal input information at the next time-step to effectively deal with time-varying systems. The RNN is implemented at both gauged and ungauged sites for 5-, 10-, 15-, and 20-min-ahead water level predictions. The results show that the RNN is capable of learning the nonlinear sewerage system and producing satisfactory predictions at the gauged sites. Concerning the ungauged sites, there are no historical data of water level to support prediction. In order to overcome such problem, a set of synthetic data, generated from a storm water management model (SWMM) under cautious verification process of applicability based on the data from nearby gauging stations, are introduced as the learning target to the training procedure of the RNN and moreover evaluating the performance of the RNN at the ungauged sites. The results demonstrate that the potential role of the SWMM coupled with nearby rainfall and water level information can be of great use in enhancing the capability of the RNN at the ungauged sites. Hence we can conclude that the RNN is an effective and suitable model for successfully predicting the water levels at both gauged and ungauged sites in urban sewerage systems.[[incitationindex]]SCI[[booktype]]紙

Raumplanerisches Konzept zur Entwicklung des ländlichen Raums in Taiwan: als Beitrag für eine integrierte Politik der ländlichen Entwicklung

Die Zielsetzung der vorliegenden Schrift ist die Entwicklung eines raumplanerischen Konzepts zur Entwicklung des ländlichen Raums in Taiwan als Beitrag für eine integrierte Politik der ländlichen Entwicklung. In Anknüpfung an funktionale Raumkategorien des Raumordnungsgesetzentwurfs wird das Taiwanische Ländliche Raummodell (TLRM) erarbeitet. Dieses fungiert als Leitbild für die Gestaltung eigenständiger, mit maximaler Effizienz auszustattender, ländlicher Regionen auf der Grundlage anthropogen gesteuerter Ökosysteme. Eine besondere Berücksichtigung finden energetische und stoffliche Nutzungspotentiale von Siedlungs- und Freiflächen

Topological Magnetoelectric Effect as Probed by Nanoshell Plasmonic Modes

Axion electrodynamics is applied to study the response of a plasmonic nanoshell with a core made of topological insulator (TI) materials. The electric polarizability of such a system is calculated in the long wavelength limit via the introduction of two scalar potentials satisfying the various appropriate boundary conditions. Our focus is on the topological magneto-electric effect (TME) as manifested in the coupled plasmonic resonances of the nanoshell. It is found that for a TI with broken time-reversal symmetry, such TME will lead to observable red-shifts in the coupled plasmonic modes, with more significant manifestation of such shifts for the bonding modes of a metallic nanoshell. It is speculated that such universal red-shift could be a manifestation of the fundamental dual symmetry as generalized for axion electrodynamics

Exposure to Secondhand Smoke and Risk of Tuberculosis: Prospective Cohort Study

Background: Prospective evidence on the association between secondhand-smoke exposure and tuberculosis is limited. Methods: We included 23,827 never smokers from two rounds (2001 and 2005) of Taiwan National Health Interview Survey. Information on exposure to secondhand smoke at home as well as other sociodemographic and behavioral factors was collected through in-person interview. The participants were prospectively followed for incidence of tuberculosis through cross-matching the survey database to the national tuberculosis registry of Taiwan. Results: A total of 85 cases of active tuberculosis were identified after a median follow-up of 7.0 years. The prevalence of exposure to secondhand smoke at home was 41.8% in the study population. In the multivariable Cox proportional hazards analysis, secondhand smoke was not associated with active tuberculosis (adjusted hazard ratio [HR], 1.03; 95% CI, 0.64 to 1.64). In the subgroup analysis, the association between secondhand smoke and tuberculosis decreased with increasing age; the adjusted HR for those = 18 and = 40 and = 60 years old was 8.48 (0.77 to 93.56), 2.29 (0.75 to 7.01), 1.33 (0.58 to 3.01), and 0.66 (0.35 to 1.23) respectively. Results from extensive sensitivity analyses suggested that potential misclassification of secondhand-smoke exposure would not substantially affect the observed associations. Conclusions: The results from this prospective cohort study did not support an overall association between secondhand smoke and tuberculosis. However, the finding that adolescents might be particularly susceptible to secondhand smoke's effect warrants further investigation

Investigation of Force-Sensor-Integrated Motorized Spindles and Diagnosis of Unbalanced Tool Operation

The motorized spindles play an important role in high precision and high speed metal cutting, and increase productivity benefit in die and mode, medical, and aerospace industries. As a result, improper high speed operation of bearings and system causes lower reliability. Lot of technology and sensors are development to monitor the failure of improper operation of motorized spindle. In this paper, one piezo-electric force sensor is developed. And, One Class SVM is investigated to monitoring and detects the unbalanced tool operation. The result shows the monitoring system is validated successfully

An Analysis System for Integrating High-Throughput Transcript Abundance Data with Metabolic Pathways in Green Algae

As the most important non-vascular plants, algae have many research applications, including high species diversity, biofuel sources, adsorption of heavy metals and, following processing, health supplements. With the increasing availability of next-generation sequencing (NGS) data for algae genomes and transcriptomes, an integrated resource for retrieving gene expression data and metabolic pathway is essential for functional analysis and systems biology in algae. However, gene expression profiles and biological pathways are displayed separately in current resources, and making it impossible to search current databases directly to identify the cellular response mechanisms. Therefore, this work develops a novel AlgaePath database to retrieve gene expression profiles efficiently under various conditions in numerous metabolic pathways. AlgaePath, a web-based database, integrates gene information, biological pathways, and next-generation sequencing (NGS) datasets in Chlamydomonasreinhardtii and Neodesmus sp. UTEX 2219-4. Users can identify gene expression profiles and pathway information by using five query pages (i.e. Gene Search, Pathway Search, Differentially Expressed Genes (DEGs) Search, Gene Group Analysis, and Co-Expression Analysis). The gene expression data of 45 and 4 samples can be obtained directly on pathway maps in C. reinhardtii and Neodesmus sp. UTEX 2219-4, respectively. Genes that are differentially expressed between two conditions can be identified in Folds Search. Furthermore, the Gene Group Analysis of AlgaePath includes pathway enrichment analysis, and can easily compare the gene expression profiles of functionally related genes in a map. Finally, Co-Expression Analysis provides co-expressed transcripts of a target gene. The analysis results provide a valuable reference for designing further experiments and elucidating critical mechanisms from high-throughput data. More than an effective interface to clarify the transcript response mechanisms in different metabolic pathways under various conditions, AlgaePath is also a data mining system to identify critical mechanisms based on high-throughput sequencing

Shilling Black-box Review-based Recommender Systems through Fake Review Generation

Review-Based Recommender Systems (RBRS) have attracted increasing research interest due to their ability to alleviate well-known cold-start problems. RBRS utilizes reviews to construct the user and items representations. However, in this paper, we argue that such a reliance on reviews may instead expose systems to the risk of being shilled. To explore this possibility, in this paper, we propose the first generation-based model for shilling attacks against RBRSs. Specifically, we learn a fake review generator through reinforcement learning, which maliciously promotes items by forcing prediction shifts after adding generated reviews to the system. By introducing the auxiliary rewards to increase text fluency and diversity with the aid of pre-trained language models and aspect predictors, the generated reviews can be effective for shilling with high fidelity. Experimental results demonstrate that the proposed framework can successfully attack three different kinds of RBRSs on the Amazon corpus with three domains and Yelp corpus. Furthermore, human studies also show that the generated reviews are fluent and informative. Finally, equipped with Attack Review Generators (ARGs), RBRSs with adversarial training are much more robust to malicious reviews