Absolute and differential measurement of water vapor supersaturation using a commercial thin-film sensor

We describe a technique for measuring the water vapor supersaturation of normal air over a temperature range of –40<~T<~0 °C. The measurements use an inexpensive commercial hygrometer which is based on a thin-film capacitive sensor. The time required for the sensor to reach equilibrium was found to increase exponentially with decreasing sensor temperature, exceeding 2 min for T = –30 °C; however, the water vapor sensitivity of the device remained high down to this temperature. After calibrating our measurement procedure, we found residual scatter in the data corresponding to an uncertainty in the absolute water vapor pressure of about ±15%. This scatter was due mainly to long-term drift, which appeared to be intrinsic to the capacitive thin-film sensor. The origin of this drift is not clear, but it effectively limits the applicability of this instrument for absolute measurements. We also found, however, that the high sensitivity of the thin-film sensor makes it rather well suited for differential measurements. By comparing supersaturated and saturated air at the same temperature we obtained a relative measurement uncertainty of about ±1.5%, an order of magnitude better than the absolute measurements

A Novel Measure of Chromosome Instability Can Account for Prognostic Difference in Multiple Myeloma

10.1371/journal.pone.0066361PLoS ONE86-POLN

HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models

Advancements in deep neural networks have allowed automatic speech recognition (ASR) systems to attain human parity on several publicly available clean speech datasets. However, even state-of-the-art ASR systems experience performance degradation when confronted with adverse conditions, as a well-trained acoustic model is sensitive to variations in the speech domain, e.g., background noise. Intuitively, humans address this issue by relying on their linguistic knowledge: the meaning of ambiguous spoken terms is usually inferred from contextual cues thereby reducing the dependency on the auditory system. Inspired by this observation, we introduce the first open-source benchmark to utilize external large language models (LLMs) for ASR error correction, where N-best decoding hypotheses provide informative elements for true transcription prediction. This approach is a paradigm shift from the traditional language model rescoring strategy that can only select one candidate hypothesis as the output transcription. The proposed benchmark contains a novel dataset, “HyPoradise” (HP), encompassing more than 334, 000 pairs of N-best hypotheses and corresponding accurate transcriptions across prevalent speech domains. Given this dataset, we examine three types of error correction techniques based on LLMs with varying amounts of labeled hypotheses-transcription pairs, which gains a significant word error rate (WER) reduction. Experimental evidence demonstrates the proposed technique achieves a breakthrough by surpassing the upper bound of traditional re-ranking based methods. More surprisingly, LLM with reasonable prompt and its generative capability can even correct those tokens that are missing in N-best list. We make our results publicly accessible for reproducible pipelines with released pre-trained models, thus providing a new evaluation paradigm for ASR error correction with LLMs

Aligning Speech to Languages to Enhance Code-switching Speech Recognition

Code-switching (CS) refers to the switching of languages within a speech signal and results in language confusion for automatic speech recognition (ASR). To address language confusion, we propose the language alignment loss that performs frame-level language identification using pseudo language labels learned from the ASR decoder. This eliminates the need for frame-level language annotations. To further tackle the complex token alternatives for language modeling in bilingual scenarios, we propose to employ large language models via a generative error correction method. A linguistic hint that incorporates language information (derived from the proposed language alignment loss and decoded hypotheses) is introduced to guide the prompting of large language models. The proposed methods are evaluated on the SEAME dataset and data from the ASRU 2019 Mandarin-English code-switching speech recognition challenge. The incorporation of the proposed language alignment loss demonstrates a higher CS-ASR performance with only a negligible increase in the number of parameters on both datasets compared to the baseline model. This work also highlights the efficacy of language alignment loss in balancing primary-language-dominant bilingual data during training, with an 8.6% relative improvement on the ASRU dataset compared to the baseline model. Performance evaluation using large language models reveals the advantage of the linguistic hint by achieving 14.1% and 5.5% relative improvement on test sets of the ASRU and SEAME datasets, respectively.Comment: Manuscript submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processin

Pine: Enabling privacy-preserving deep packet inspection on TLS with rule-hiding and fast connection establishment

National Research Foundation (NRF) Singapore; AXA Research Fund, Singapore Management Universit

Lipid profiles and outcomes of patients with prior cancer and subsequent myocardial infarction or stroke

Patients with cancer are at increased risk of myocardial infarction (MI) and stroke. Guidelines do not address lipid profile targets for these patients. Within the lipid profiles, we hypothesized that patients with cancer develop MI or stroke at lower low density lipoprotein cholesterol (LDL-C) concentrations than patients without cancer and suffer worse outcomes. We linked nationwide longitudinal MI, stroke and cancer registries from years 2007-2017. We identified 42,148 eligible patients with MI (2421 prior cancer; 39,727 no cancer) and 43,888 eligible patients with stroke (3152 prior cancer; 40,738 no cancer). Median LDL-C concentration was lower in the prior cancer group than the no cancer group at incident MI [2.43 versus 3.10 mmol/L, adjusted ratio 0.87 (95% CI 0.85-0.89)] and stroke [2.81 versus 3.22 mmol/L, adjusted ratio 0.93, 95% CI 0.91-0.95)]. Similarly, median triglyceride and total cholesterol concentrations were lower in the prior cancer group, with no difference in high density lipoprotein cholesterol. Prior cancer was associated with higher post-MI mortality [adjusted hazard ratio (HR) 1.48, 95% CI 1.37-1.59] and post-stroke mortality (adjusted HR 1.95, 95% CI 1.52-2.52). Despite lower LDL-C concentrations, patients with prior cancer had worse post-MI and stroke mortality than patients without cancer

Carfilzomib-Dexamethasone Versus Bortezomib-Dexamethasone in Relapsed or Refractory Multiple Myeloma : Updated Overall Survival, Safety, and Subgroups

Introduction: The phase III RandomizEd, OpeN Label, Phase 3 Study of Carfilzomib Plus DExamethAsone Vs Bortezomib Plus DexamethasOne in Patients With Relapsed Multiple Myeloma (ENDEAVOR) trial showed significantly improved progression-free survival and overall survival (OS) with carfilzomib (56 mg/m) and dexamethasone (Kd56) versus bortezomib and Kd56 (Vd) in patients with relapsed or refractory multiple myeloma (RRMM). We report updated OS and safety data after 6 months of additional follow-up. Patients and Methods: Patients with RRMM (1-3 previous lines of therapy) were randomized 1:1 to Kd56 or Vd. Median OS was estimated using the Kaplan-Meier method; OS was compared between treatment groups using Cox proportional hazards models. Results: As of July 19, 2017, median follow-up was 44.3 months for Kd56 and 43.7 months for Vd. Median OS was 47.8 months (Kd56) versus 38.8 months (Vd; hazard ratio, 0.76; 95% confidence interval, 0.633-0.915). OS was longer with Kd56 versus Vd within age and cytogenetic subgroups, and according to number of previous lines of therapy, previous bortezomib exposure, previous lenalidomide exposure, and lenalidomide-refractory status. Exposure-adjusted incidences per 100 patient-years of adverse events (AEs) were 1352.07 for Kd56 and 1754.86 for Vd; for Grade ≥3 AEs, these values were 162.31 and 175.90. Conclusion: With median follow-up of approximately 44 months, clinically meaningful improvements in OS were observed with Kd56 versus Vd, including in all subgroups examined. The Kd56 safety profile was consistent with previous analyses. In this updated analysis of patients with relapsed/refractory multiple myeloma (RRMM) from the RandomizEd, OpeN Label, Phase 3 Study of Carfilzomib Plus DExamethAsone Vs Bortezomib Plus DexamethasOne in Patients With Relapsed Multiple Myeloma (ENDEAVOR) trial, clinically meaningful overall survival improvements continue to be observed with carfilzomib 56 mg/m and dexamethasone (Kd56; n = 464) versus bortezomib and dexamethasone (n = 465), including in key patient subgroups. With longer-term data, the favorable benefit-risk profile of Kd56 continues to support its use as a standard-of-care in RRMM