A Research Growth Study in Big Data field

Responding to the diffusion and growth of big data research, this study adopted the bibliometric approach to describe the growth of the literatures, the distribution of journals, publication countries and subject area. This study collected the relative literature by querying the Social Science Citation Index (SSCI) of ISI Web of knowledge database, where we could collect the big data literatures in academic papers, systematically. Data from citation indexes can be analyzed to determine the popularity and impact of specific articles, authors, and publications. The results provided the distribution of core journals, and described the trends and feature of big data research for researchers interested in this field

CasNet: Investigating Channel Robustness for Speech Separation

Recording channel mismatch between training and testing conditions has been shown to be a serious problem for speech separation. This situation greatly reduces the separation performance, and cannot meet the requirement of daily use. In this study, inheriting the use of our previously constructed TAT-2mix corpus, we address the channel mismatch problem by proposing a channel-aware audio separation network (CasNet), a deep learning framework for end-to-end time-domain speech separation. CasNet is implemented on top of TasNet. Channel embedding (characterizing channel information in a mixture of multiple utterances) generated by Channel Encoder is introduced into the separation module by the FiLM technique. Through two training strategies, we explore two roles that channel embedding may play: 1) a real-life noise disturbance, making the model more robust, or 2) a guide, instructing the separation model to retain the desired channel information. Experimental results on TAT-2mix show that CasNet trained with both training strategies outperforms the TasNet baseline, which does not use channel embeddings.Comment: Submitted to ICASSP 202

Non-Typhoidal Salmonella and the Risk of Kawasaki Disease: A Nationwide Population-Based Cohort Study

ObjectiveThe aim of this study was to investigate the relationship between non-typhoidal Salmonella (NTS) infection and the risk of Kawasaki disease (KD) by using a nationwide population-based data set in Taiwan.MethodsIn this retrospective cohort study, we enrolled 69,116 patients under 18 years of age, with NTS from January 1st, 2000, to December 31st, 2013, using the population-based National Health Insurance Research Database of Taiwan. A comparison group without NTS was matched (at a 1:4 ratio) by propensity score. The two cohorts were followed from the initial diagnosis of NTS until the date of KD development or December 31st, 2013. Cox proportional hazard regression analysis was conducted to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) after adjusting for covariates. Also, we conducted sensitivity analyses to examine our findings.ResultsAfter adjusting for covariates, the risk of KD for the children with NTS was significantly higher than that of the comparison group (hazard ratio = 1.31; 95% confidence interval = 1.03-1.66; p < 0.01). Stratified analysis showed that the associated risk of the investigated outcome was significant in children aged ≤2 years (aHR= 1.31, 95% C.I. 1.02-1.69), in female patients (aHR= 1.46, 95% C.I. 1.03-2.08), and in those without allergic diseases.ConclusionsNTS is associated with an increased risk of KD in Taiwanese children

Object Goal Navigation with End-to-End Self-Supervision

A household robot should be able to navigate to target locations without requiring users to first annotate everything in their home. Current approaches to this object navigation challenge do not test on real robots and rely on expensive semantically labeled 3D meshes. In this work, our aim is an agent that builds self-supervised models of the world via exploration, the same as a child might. We propose an end-to-end self-supervised embodied agent that leverages exploration to train a semantic segmentation model of 3D objects, and uses those representations to learn an object navigation policy purely from self-labeled 3D meshes. The key insight is that embodied agents can leverage location consistency as a supervision signal - collecting images from different views/angles and applying contrastive learning to fine-tune a semantic segmentation model. In our experiments, we observe that our framework performs better than other self-supervised baselines and competitively with supervised baselines, in both simulation and when deployed in real houses

Img2Logo:Generating Golden Ratio Logos from Images

Logos are one of the most important graphic design forms that use an abstracted shape to clearly represent the spirit of a community. Among various styles of abstraction, a particular golden-ratio design is frequently employed by designers to create a concise and regular logo. In this context, designers utilize a set of circular arcs with golden ratios (i.e., all arcs are taken from circles whose radii form a geometric series based on the golden ratio) as the design elements to manually approximate a target shape. This error-prone process requires a large amount of time and effort, posing a significant challenge for design space exploration. In this work, we present a novel computational framework that can automatically generate golden ratio logo abstractions from an input image. Our framework is based on a set of carefully identified design principles and a constrained optimization formulation respecting these principles. We also propose a progressive approach that can efficiently solve the optimization problem, resulting in a sequence of abstractions that approximate the input at decreasing levels of detail. We evaluate our work by testing on images with different formats including real photos, clip arts, and line drawings. We also extensively validate the key components and compare our results with manual results by designers to demonstrate the effectiveness of our framework. Moreover, our framework can largely benefit design space exploration via easy specification of design parameters such as abstraction levels, golden circle sizes, etc

Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection

The inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for TVR detection. The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority (p < 0.01) compared to prior GLP processing. Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise.Comment: published in IEEE Journal of Translational Engineering in Health & Medicin