DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation

In previous works, only parameter weights of ASR models are optimized under fixed-topology architecture. However, the design of successful model architecture has always relied on human experience and intuition. Besides, many hyperparameters related to model architecture need to be manually tuned. Therefore in this paper, we propose an ASR approach with efficient gradient-based architecture search, DARTS-ASR. In order to examine the generalizability of DARTS-ASR, we apply our approach not only on many languages to perform monolingual ASR, but also on a multilingual ASR setting. Following previous works, we conducted experiments on a multilingual dataset, IARPA BABEL. The experiment results show that our approach outperformed the baseline fixed-topology architecture by 10.2% and 10.0% relative reduction on character error rates under monolingual and multilingual ASR settings respectively. Furthermore, we perform some analysis on the searched architectures by DARTS-ASR.Comment: Accepted at INTERSPEECH 202

Q-enhanced fold-and-bond MEMS inductors

This work presents a novel coil fabrication technology to enhance quality factor (Q factor) of microfabricated inductors for implanted medical wireless sensing and data/power transfer applications. Using parylene as a flexible thin-film device substrate, a post-microfabrication substrate folding-and-bonding method is developed to effectively increase the metal thickness of the surface-micromachined inductors, resulting in their lower self-resistance so their higher quality factor. One-fold-and-bond coils are successfully demonstrated as an example to verify the feasibility of the fabrication technology with measurement results in good agreements with device simulation. Depending on target specifications, multiple substrate folding-and-bonding can be extensively implemented to facilitate further improved electrical characteristics of the coils from single fabrication batch. Such Q-enhanced inductors can be broadly utilized with great potentials in flexible integrated wireless devices/systems for intraocular prostheses and other biomedical implants

Impacts of S1 and X2 Interfaces on eMBMS Handover Failure: Solution and Performance Analysis

In evolved Multimedia Broadcast/Multicast Service (eMBMS), service continuity enables users move from one cell to another without interrupting eMBMS service. Unlike traditional handover in unicast transmission, a UE can receive eMBMS service in either unicast or multicast mode. In this paper, we point out a new handover failure problem in eMBMS due to the miss of rekeying information. We first take a close look at the new handover scenarios. We then investigate the problem by using comprehensive mathematical models. Our models provide insights on the new handover problem and introduce theoretical guidelines for mobile operators to design and optimize their networks without wide deployment to save cost and time. Moreover, we propose a solution to combat against the handover failure. Both the simulation and analytical results demonstrate that the impacts of the eMBMS handover failure are reduced significantly. In this paper, we present a systematic way to investigate the handover failure problem in eMBMS

Design and Analysis of the Key Management Mechanism in Evolved Multimedia Broadcast/Multicast Service

3GPP introduced the key management mechanism (KMM) in evolved multimedia broadcast/multicast service (eMBMS) to provide forward security and backward security for multicast contents. In this paper, we point out that KMM may lead to frequent rekeying and re-authentication issues due to eMBMS's characteristics: 1) massive group members; 2) dynamic group topology; and 3) unexpected wireless disconnections. Such issues expose extra load for both user equipment (UE) terminals and mobile operators. It seems prolonging the rekeying interval is an intuitive solution to minimizing the impact of the issues. However, a long rekeying interval is not considered the best operational solution due to revenue loss of content providers. This paper quantifies the tradeoff between the load of the UEs and the operators as well as the revenue loss of the content providers. Moreover, we emphasize how essential this rekeying interval has impacts on the problems. Using our proposed tradeoff model, the operators can specify a suitable rekeying interval to best balance the interest between the above three parties. The tradeoff model is validated by extensive simulations and is demonstrated to be an effective approach for the tradeoff analysis and optimization on eMBMS

Body Mass Index–Mortality Relationship in Severe Hypoglycemic Patients With Type 2 Diabetes

AbstractBackgroundHypoglycemia is associated with a higher risk of death. This study analyzed various body mass index (BMI) categories and mortalities of severe hypoglycemic patients with type 2 diabetes mellitus (DM) in a hospital emergency department.MethodsThe study included 566 adults with type 2 diabetes who were admitted to 1 medical center in Taiwan between 2008 and 2009 with a diagnosis of severe hypoglycemia. Mortality data, demographics, clinical characteristics and the Charlson’s Comorbidity Index were obtained from the electronic medical records. Patients were stratified into 4 study groups as determined by the National institute of Health (NiH) and World Health organization classification for BMi, and the demographics were compared using the analysis of variance and χ2 test. Kaplan-Meier’s analysis and the Cox proportional-hazards regression model were used for mortality, and adjusted hazard ratios were adjusted for each BMi category among participants.ResultsAfter controlling for other possible confounding variables, BMI <18.5 kg/m2 was independently associated with low survival rates in the Cox regression analysis of the entire cohort of type 2 DM patients who encountered a hypoglycemic event. Compared to patients with normal BMI, the mortality risk was higher (adjusted hazard ratios = 4.9; 95% confidence interval [CI] = 2.4-9.9) in underweight patients. Infection-related causes of death were observed in 101 cases (69.2%) and were the leading cause of death.ConclusionsAn independent association was observed between BMI less than 18.5 kg/m2 and mortality among type 2 DM patient with severe hypoglycemic episode. Deaths were predominantly infection related

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training