LiteVSR: Efficient Visual Speech Recognition by Learning from Speech Representations of Unlabeled Data

This paper proposes a novel, resource-efficient approach to Visual Speech Recognition (VSR) leveraging speech representations produced by any trained Automatic Speech Recognition (ASR) model. Moving away from the resource-intensive trends prevalent in recent literature, our method distills knowledge from a trained Conformer-based ASR model, achieving competitive performance on standard VSR benchmarks with significantly less resource utilization. Using unlabeled audio-visual data only, our baseline model achieves a word error rate (WER) of 47.4% and 54.7% on the LRS2 and LRS3 test benchmarks, respectively. After fine-tuning the model with limited labeled data, the word error rate reduces to 35% (LRS2) and 45.7% (LRS3). Our model can be trained on a single consumer-grade GPU within a few days and is capable of performing real-time end-to-end VSR on dated hardware, suggesting a path towards more accessible and resource-efficient VSR methodologies.Comment: Accepted for publication at ICASSP 202

Which chronic diseases and disease combinations are specific to multimorbidity in the elderly? Results of a claims data based cross-sectional study in Germany

<p>Abstract</p> <p>Background</p> <p>Growing interest in multimorbidity is observable in industrialized countries. For Germany, the increasing attention still goes still hand in hand with a small number of studies on multimorbidity. The authors report the first results of a cross-sectional study on a large sample of policy holders (n = 123,224) of a statutory health insurance company operating nationwide. This is the first comprehensive study addressing multimorbidity on the basis of German claims data. The main research question was to find out which chronic diseases and disease combinations are specific to multimorbidity in the elderly.</p> <p>Methods</p> <p>The study is based on the claims data of all insured policy holders aged 65 and older (n = 123,224). Adjustment for age and gender was performed for the German population in 2004. A person was defined as multimorbid if she/he had at least 3 diagnoses out of a list of 46 chronic conditions in three or more quarters within the one-year observation period. Prevalences and risk-ratios were calculated for the multimorbid and non-multimorbid samples in order to identify diagnoses more specific to multimorbidity and to detect excess prevalences of multimorbidity patterns.</p> <p>Results</p> <p>62% of the sample was multimorbid. Women in general and patients receiving statutory nursing care due to disability are overrepresented in the multimorbid sample. Out of the possible 15,180 combinations of three chronic conditions, 15,024 (99%) were found in the database. Regardless of this wide variety of combinations, the most prevalent individual chronic conditions do also dominate the combinations: Triads of the six most prevalent individual chronic conditions (hypertension, lipid metabolism disorders, chronic low back pain, diabetes mellitus, osteoarthritis and chronic ischemic heart disease) span the disease spectrum of 42% of the multimorbid sample. Gender differences were minor. Observed-to-expected ratios were highest when purine/pyrimidine metabolism disorders/gout and osteoarthritis were part of the multimorbidity patterns.</p> <p>Conclusions</p> <p>The above list of dominating chronic conditions and their combinations could present a pragmatic start for the development of needed guidelines related to multimorbidity.</p

Physician and Patient Predictors of Evidence-Based Prescribing in Heart Failure: A Multilevel Study

BACKGROUND: The management of patients with heart failure (HF) needs to account for changeable and complex individual clinical characteristics. The use of renin angiotensin system inhibitors (RAAS-I) to target doses is recommended by guidelines. But physicians seemingly do not sufficiently follow this recommendation, while little is known about the physician and patient predictors of adherence. METHODS: To examine the coherence of primary care (PC) physicians' knowledge and self-perceived competencies regarding RAAS-I with their respective prescribing behavior being related to patient-associated barriers. Cross-sectional follow-up study after a randomized medical educational intervention trial with a seven month observation period. PC physicians (n = 37) and patients with systolic HF (n = 168) from practices in Baden-Wuerttemberg. Measurements were knowledge (blueprint-based multiple choice test), self-perceived competencies (questionnaire on global confidence in the therapy and on frequency of use of RAAS-I), and patient variables (age, gender, NYHA functional status, blood pressure, potassium level, renal function). Prescribing was collected from the trials' documentation. The target variable consisted of ≥50% of recommended RAAS-I dosage being investigated by two-level logistic regression models. RESULTS: Patients (69% male, mean age 68.8 years) showed symptomatic and objectified left ventricular (NYHA II vs. III/IV: 51% vs. 49% and mean LVEF 33.3%) and renal (GFR<50%: 22%) impairment. Mean percentage of RAAS-I target dose was 47%, 59% of patients receiving ≥50%. Determinants of improved prescribing of RAAS-I were patient age (OR 0.95, CI 0.92-0.99, p = 0.01), physician's global self-confidence at follow-up (OR 1.09, CI 1.02-1.05, p = 0.01) and NYHA class (II vs. III/IV) (OR 0.63, CI 0.38-1.05, p = 0.08). CONCLUSIONS: A change in physician's confidence as a predictor of RAAS-I dose increase is a new finding that might reflect an intervention effect of improved physicians' intention and that might foster novel strategies to improve safe evidence-based prescribing. These should include targeting knowledge, attitudes and skills

A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants.

This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/ng.3448Advanced age-related macular degeneration (AMD) is the leading cause of blindness in the elderly, with limited therapeutic options. Here we report on a study of >12 million variants, including 163,714 directly genotyped, mostly rare, protein-altering variants. Analyzing 16,144 patients and 17,832 controls, we identify 52 independently associated common and rare variants (P < 5 × 10(-8)) distributed across 34 loci. Although wet and dry AMD subtypes exhibit predominantly shared genetics, we identify the first genetic association signal specific to wet AMD, near MMP9 (difference P value = 4.1 × 10(-10)). Very rare coding variants (frequency <0.1%) in CFH, CFI and TIMP3 suggest causal roles for these genes, as does a splice variant in SLC16A8. Our results support the hypothesis that rare coding variants can pinpoint causal genes within known genetic loci and illustrate that applying the approach systematically to detect new loci requires extremely large sample sizes.We thank all participants of all the studies included for enabling this research by their participation in these studies. Computer resources for this project have been provided by the high-performance computing centers of the University of Michigan and the University of Regensburg. Group-specific acknowledgments can be found in the Supplementary Note. The Center for Inherited Diseases Research (CIDR) Program contract number is HHSN268201200008I. This and the main consortium work were predominantly funded by 1X01HG006934-01 to G.R.A. and R01 EY022310 to J.L.H

The Sharp Leading Edge of SHEFEX II

SHEFEXII is a faceted re-entry body with a sharp leading edge. This paper presents the design, the according aero-thermodynamic loads and the thermo-mechanical analyses of the leading edge of SHEFEXII

Two-stage visual speech recognition for intensive care patients

Abstract In this work, we propose a framework to enhance the communication abilities of speech-impaired patients in an intensive care setting via reading lips. Medical procedure, such as a tracheotomy, causes the patient to lose the ability to utter speech with little to no impact on the habitual lip movement. Consequently, we developed a framework to predict the silently spoken text by performing visual speech recognition, i.e., lip-reading. In a two-stage architecture, frames of the patient’s face are used to infer audio features as an intermediate prediction target, which are then used to predict the uttered text. To the best of our knowledge, this is the first approach to bring visual speech recognition into an intensive care setting. For this purpose, we recorded an audio-visual dataset in the University Hospital of Aachen’s intensive care unit (ICU) with a language corpus hand-picked by experienced clinicians to be representative of their day-to-day routine. With a word error rate of 6.3%, the trained system reaches a sufficient overall performance to significantly increase the quality of communication between patient and clinician or relatives

The Shefex Flight Experiment - Pathfinder Experiment for a Sky Based Test Facility

On Thursday, October 27th, 2005 the SHarp Edge Flight EXperiment SHEFEX has been successfully launched at the Andoya Rocket Range in northern Norway. The project, being performed under responsibility of the German Aerospace Center (DLR) flew on top of a two-stage solid propellant sounding rocket. One purpose of the experiment is the investigation of possible new shapes for future launcher or re-entry vehicles applying a shape with facetted surfaces and sharp edges and to enable the time accurate investi-gation of the flow effects and their structural answer during the hypersonic flight from 90 km down to an altitude of 20 km. Additionally, the SHEFEX project is a starting point for a series of experiments which enable the acquisition of important knowledge in hypersonic free flight experimentation and which are an excellent test bed for new technological concepts. The present paper gives an overview about the philosophy and the layout of ex- periment and introduces preliminary outcomes of the post-flight analysis

Online Offline Learning for Sound-Based Indoor Localization Using Low-Cost Hardware

Online Learning algorithms and Indoor Positioning Systems are complex applications in the environment of cyber-physical systems. These distributed systems are created by networking intelligent machines and autonomous robots on the Internet of Things using embedded systems that enable the exchange of information at any time. This information is processed by Machine Learning algorithms to make decisions about current developments in production or to influence logistics processes for optimization purposes. In this article, we present and categorize the further development of the prototype of a novel Indoor Positioning System, which constantly adapts its knowledge to the conditions of its environment with the help of Online Learning. Here, we apply Online Learning algorithms in the field of sound-based indoor localization with low-cost hardware and demonstrate the improvement of the system over its predecessor and its adaptability for different applications in an experimental case study