UbiEar: Bringing location-independent sound awareness to the hard-of-hearing people with smartphones

Non-speech sound-awareness is important to improve the quality of life for the deaf and hard-of-hearing (DHH) people. DHH people, especially the young, are not always satisfied with their hearing aids. According to the interviews with 60 young hard-of-hearing students, a ubiquitous sound-awareness tool for emergency and social events that works in diverse environments is desired. In this paper, we design UbiEar, a smartphone-based acoustic event sensing and notification system. Core techniques in UbiEar are a light-weight deep convolution neural network to enable location-independent acoustic event recognition on commodity smartphons, and a set of mechanisms for prompt and energy-efficient acoustic sensing. We conducted both controlled experiments and user studies with 86 DHH students and showed that UbiEar can assist the young DHH students in awareness of important acoustic events in their daily life.</jats:p

Stereotactic radiotherapy: An alternative option for refractory ventricular tachycardia to drug and ablation therapy

Refractory ventricular tachycardia (VT) often occurs in the context of organic heart disease. It is associated with significantly high mortality and morbidity rates. Antiarrhythmic drugs and catheter ablation represent the two main treatment options for refractory VT, but their use can be associated with inadequate therapeutic responses and procedure-related complications. Stereotactic body radiotherapy (SBRT) is extensively applied in the precision treatment of solid tumors, with excellent therapeutic responses. Recently, this highly precise technology has been applied for radioablation of VT, and its early results demonstrate a favorable safety profile. This review presents the potential value of SBRT in refractory VT

An Unusual Mid-infrared Flare in a Type 2 AGN: An Obscured Turning-on AGN or Tidal Disruption Event?

We report the discovery of an exceptional MIR flare in a Type 2 AGN, SDSS J165726.81+234528.1, at z = 0.059. This object brightened by 3 mag in the Wide-field Infrared Survey Explorer (WISE) W1 and W2 bands between 2015 and 2017 (and has been fading since 2018), without significant changes (≾0.2 mag) in the optical over the same period of time. Based on the WISE light curves and near-IR imaging, the flare is more significant at longer wavelengths, suggesting an origin of hot dust emission. The estimated black hole mass (~10^(6.5) M⊙) from different methods places its peak bolometric luminosity around the Eddington limit. The high luminosity of the MIR flare and its multiyear timescale suggest that it most likely originated from reprocessed dust radiation in an extended torus surrounding the AGN, instead of from stellar explosions. The MIR color variability is consistent with known changing-look AGN and tidal disruption events (TDEs), but inconsistent with normal supernovae. We suggest that it is a turning-on Type 2 AGN or TDE, where the optical variability is obscured by the dust torus during the transition. This MIR flare event reveals a population of dramatic nuclear transients that are missed in the optical

Dimethyl [(4-fluoro­phen­yl)(6-methoxy­benzothia­zol-2-ylamino)meth­yl]phospho­nate

In the mol­ecule of title compound, C17H18FN2O4PS, both the benzene ring with its conjunction C atom and the benzothia­zole ring with its conjunction N atom are close to planar (the maximum deviations are 0.0267 and 0.0427 Å for the benzene and benzothiazole rings, respectively), the dihedral angle between the planes of the benzothia­zole and benzene rings is 119.05 (3)°. The mol­ecular packing is stabilized by inter­molecular N—H⋯O, C—H⋯N and C—H⋯F hydrogen bonding, and by C—H⋯π and π–π stacking inter­actions [centroid–centroid distances = 2.99 (2), 2.96 (3), 2.88 (2) and 3.773 (4) Å]

Polimorfizm rs10830963 w genie receptora melatoniny 1B a cukrzyca ciążowa w populacji chińskiej — metaanaliza badań

Introduction: Studies have been conducted to investigate the association between rs10830963 of MTNR1B and the risk of gestational diabetes mellitus (GDM), but with inconclusive results. We aimed to clarify these controversies, especially with regard to the association in the Chinese population. Material and methods: A systemic literature reference search inclusive to August 12, 2016 yielded 35 articles, from which 11 studies met the inclusion criteria for the final meta-analysis, including 3889 patients with GDM and 6708 controls. Results: We found statistically significant associations between rs10830963 and GDM using odds ratios (ORs) and 95% confidence intervals (CIs) [GG genotype vs. CC genotype: OR = 1.70, 95% CI: 1.38–2.10; G allele vs C allele: OR = 1.27, 95% CI: 1.20–1.36; GG+CG vs. CC (dominant model): OR = 1.31, 95% CI: 1.20–1.44; GG vs CG+CC (recessive model): OR = 1.41, 95% CI: 1.26–1.58]. In subgroup analyses stratified by ethnicity, we also observed rs10830963 to be associated with significantly increased risk of GDM in all genetic models in the Chinese population. Conclusions: Our meta-analysis indicated that the rs10830963 polymorphism might serve as a risk factor of GDM in the Chinese population.Wstęp: Wyniki dotychczas badań przeprowadzonych w celu ustalenia związku między polimorfizmem rs10830963 w genie MTNR1B a ryzykiem cukrzycy ciążowej (gestational diabetes mellitus, GDM) nie pozwoliły na sformułowanie jednoznacznych wniosków. Niniejsze badanie przeprowadzono w celu wyjaśnienia tych kontrowersji, zwłaszcza w odniesieniu do występowania tych związków w populacji chińskiej. Materiał i metody: W wyniku przeszukania w sposób systematyczny piśmiennictwa obejmującego okres do 12 sierpnia 2016 roku wytypowano 35 artykułów, spośród których 11 badań spełniało kryteria włączenia do metaanalizy. Obejmowały one 3889 chorych z GDM i 6708 osób kontrolnych. Wyniki: Autorzy stwierdzili statystycznie istotny związek między polimorfizmem rs10830963 a GDM, obliczając ilorazy szans (odds ratio, OR) i 95-procentowe przedziały ufności (confidence interval, CI) [genotyp GG vs. genotyp CC: OR = 1,70; 95% CI: 1,38–2,10; allel G vs. allel C: OR = 1,27; 95% CI: 1,20–1,36; GG+CG vs CC (model dominujący): OR = 1,31; 95% CI: 1,20–1,44; GG vs. CG+CC (model recesywny): OR = 1,41; 95% CI: 1,26–1,58]. W analizach podgrup wydzielonych na podstawie pochodzenia etnicznego również stwierdzono, że polimorfizm rs10830963 wiąże się z istotnie wyższym ryzykiem GDM we wszystkich modelach genetycznych w populacji chińskiej. Wnioski: Przeprowadzona przez autorów metaanaliza wskazuje, że polimorfizm rs10830963 może być uważany za czynnik ryzyka GDM w populacji chińskiej

TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models

Automatic Speech Recognition (ASR) models need to be optimized for specific hardware before they can be deployed on devices. This can be done by tuning the model's hyperparameters or exploring variations in its architecture. Re-training and re-validating models after making these changes can be a resource-intensive task. This paper presents TODM (Train Once Deploy Many), a new approach to efficiently train many sizes of hardware-friendly on-device ASR models with comparable GPU-hours to that of a single training job. TODM leverages insights from prior work on Supernet, where Recurrent Neural Network Transducer (RNN-T) models share weights within a Supernet. It reduces layer sizes and widths of the Supernet to obtain subnetworks, making them smaller models suitable for all hardware types. We introduce a novel combination of three techniques to improve the outcomes of the TODM Supernet: adaptive dropouts, an in-place Alpha-divergence knowledge distillation, and the use of ScaledAdam optimizer. We validate our approach by comparing Supernet-trained versus individually tuned Multi-Head State Space Model (MH-SSM) RNN-T using LibriSpeech. Results demonstrate that our TODM Supernet either matches or surpasses the performance of manually tuned models by up to a relative of 3% better in word error rate (WER), while efficiently keeping the cost of training many models at a small constant.Comment: Meta AI; Submitted to ICASSP 202