Audio is all in one: speech-driven gesture synthetics using WavLM pre-trained model

The generation of co-speech gestures for digital humans is an emerging area in the field of virtual human creation. Prior research has made progress by using acoustic and semantic information as input and adopting classify method to identify the person's ID and emotion for driving co-speech gesture generation. However, this endeavour still faces significant challenges. These challenges go beyond the intricate interplay between co-speech gestures, speech acoustic, and semantics; they also encompass the complexities associated with personality, emotion, and other obscure but important factors. This paper introduces "diffmotion-v2," a speech-conditional diffusion-based and non-autoregressive transformer-based generative model with WavLM pre-trained model. It can produce individual and stylized full-body co-speech gestures only using raw speech audio, eliminating the need for complex multimodal processing and manually annotated. Firstly, considering that speech audio not only contains acoustic and semantic features but also conveys personality traits, emotions, and more subtle information related to accompanying gestures, we pioneer the adaptation of WavLM, a large-scale pre-trained model, to extract low-level and high-level audio information. Secondly, we introduce an adaptive layer norm architecture in the transformer-based layer to learn the relationship between speech information and accompanying gestures. Extensive subjective evaluation experiments are conducted on the Trinity, ZEGGS, and BEAT datasets to confirm the WavLM and the model's ability to synthesize natural co-speech gestures with various styles.Comment: 10 pages, 5 figures, 1 tabl

Magnetic resonance imaging findings in a patient with seropositive neuromyelitis optica

We present the case of a 23-year-old female with a subacute history of complex additive neurology which consisted of progressive unilateral visual impairment and subsequent blindness of the right eye, in conjunction with distal lower motor neuron symptoms of weakness and sensory loss from T4 level down. Special investigations performed, included serology and an urgent magnetic resonance imaging (MRI) of the brain and spinal cord, which exhibited a diffuse demyelinating disease of the brain and spinal cord without the typical features of multiple sclerosis (MS) and laboratory findings, which were positive for the AQP-4 antibody, confirming the diagnosis of neuromyelitis optica (NMO). Pulsed methylprednisolone was initiated urgently with good effect and immunosuppression with cyclophosphamide was added after the exclusion of additional pathology. She experienced a complete resolution of her weakness and sensory impairment upon discharge; however, her unilateral visual loss remained. The recent advances in the identification of autoimmune biomarkers and the widening spectrum of imaging findings in NMO necessitate that the clinician and radiologist keep abreast of the current diagnostic tools and criteria that distinguish NMO from other demyelinating conditions

Effect of donor–recipient size mismatch on long-term graft survival in pediatric kidney transplantation: a multicenter cohort study

Background Donor–recipient size mismatching is commonly occurs in pediatric kidney transplantation (KT). However, its effect on graft survival remains unknown. This study aimed to determine the effect of donor–recipient size mismatch on the long-term survival rate of transplant kidneys in pediatric KT. Methods A total of 241 pediatric patients who received KT were enrolled. The medical records of all patients were retrospectively reviewed, and the correlation between donor–recipient size mismatch and graft function and long-term graft outcome was analyzed according to donor–recipient size mismatch. Results Recipients and donors’ mean body weight at the time of KT were 34.31 ± 16.85 and 56.53 ± 16.73 kg, respectively. The mean follow-up duration was 96.49 ± 52.98 months. A significant positive correlation was observed between donor–recipient body weight ratio (DRBWR) or donor–recipient body surface area ratio (DRBSR) and graft function until 1 year after KT. However, this correlation could not be confirmed at the last follow-up. The results of long-term survival analysis using Fine and Gray’s subdistribution hazard model showed no significant difference of the survival rate of the transplant kidney according to DRBWR or DRBSR. Conclusion Donor–recipient size mismatch in pediatric KT is not an important factor in determining the long-term prognosis of transplant kidneys

DECISIVE Test Methods Handbook: Test Methods for Evaluating sUAS in Subterranean and Constrained Indoor Environments, Version 1.1

This handbook outlines all test methods developed under the Development and Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle Evaluations (DECISIVE) project by the University of Massachusetts Lowell for evaluating small unmanned aerial systems (sUAS) performance in subterranean and constrained indoor environments, spanning communications, field readiness, interface, obstacle avoidance, navigation, mapping, autonomy, trust, and situation awareness. For sUAS deployment in subterranean and constrained indoor environments, this puts forth two assumptions about applicable sUAS to be evaluated using these test methods: (1) able to operate without access to GPS signal, and (2) width from prop top to prop tip does not exceed 91 cm (36 in) wide (i.e., can physically fit through a typical doorway, although successful navigation through is not guaranteed). All test methods are specified using a common format: Purpose, Summary of Test Method, Apparatus and Artifacts, Equipment, Metrics, Procedure, and Example Data. All test methods are designed to be run in real-world environments (e.g., MOUT sites) or using fabricated apparatuses (e.g., test bays built from wood, or contained inside of one or more shipping containers).Comment: Approved for public release: PAO #PR2022_4705

DECISIVE Benchmarking Data Report: sUAS Performance Results from Phase I

This report reviews all results derived from performance benchmarking conducted during Phase I of the Development and Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle Evaluations (DECISIVE) project by the University of Massachusetts Lowell, using the test methods specified in the DECISIVE Test Methods Handbook v1.1 for evaluating small unmanned aerial systems (sUAS) performance in subterranean and constrained indoor environments, spanning communications, field readiness, interface, obstacle avoidance, navigation, mapping, autonomy, trust, and situation awareness. Using those 20 test methods, over 230 tests were conducted across 8 sUAS platforms: Cleo Robotics Dronut X1P (P = prototype), FLIR Black Hornet PRS, Flyability Elios 2 GOV, Lumenier Nighthawk V3, Parrot ANAFI USA GOV, Skydio X2D, Teal Golden Eagle, and Vantage Robotics Vesper. Best in class criteria is specified for each applicable test method and the sUAS that match this criteria are named for each test method, including a high-level executive summary of their performance.Comment: Approved for public release: PAO #PR2023_74172; arXiv admin note: substantial text overlap with arXiv:2211.0180

Long-term outcome of Bartter syndrome in 54 patients: A multicenter study in Korea

IntroductionBartter syndrome (BS) is a rare salt-wasting tubulopathy caused by mutations in genes encoding sodium, potassium, or chloride transporters of the thick ascending limb of the loop of Henle and/or the distal convoluted tubule of the kidney. BS is characterized by polyuria, failure to thrive, hypokalemia, metabolic alkalosis, hyperreninemia, and hyperaldosteronism. Potassium and/or sodium supplements, potassium-sparing diuretics, and nonsteroidal anti-inflammatory drugs can be used to treat BS. While its symptoms and initial management are relatively well known, long-term outcomes and treatments are scarce.MethodsWe retrospectively reviewed 54 Korean patients who were clinically or genetically diagnosed with BS from seven centers in Korea.ResultsAll patients included in this study were clinically or genetically diagnosed with BS at a median age of 5 (range, 0–271) months, and their median follow-up was 8 (range, 0.5–27) years. Genetic diagnosis of BS was confirmed in 39 patients: 4 had SLC12A1 gene mutations, 1 had KCNJ1 gene mutations, 33 had CLCNKB gene mutations, and 1 had BSND mutation. Potassium chloride supplements and potassium-sparing diuretics were administered in 94% and 68% of patients, respectively. The mean dosage of potassium chloride supplements was 5.0 and 2.1 mEq/day/kg for patients younger and older than 18 years, respectively. Nephrocalcinosis was a common finding of BS, and it also improved with age in some patients. At the last follow-up of 8 years after the initial diagnosis, 41% had short stature (height less than 3rd percentile) and impaired kidney function was observed in six patients [chronic kidney disease (CKD) G3, n = 4; CKD G5, n = 2].ConclusionBS patients require a large amount of potassium supplementation along with potassium-sparing agents throughout their lives, but tend to improve with age. Despite management, a significant portion of this population exhibited growth impairment, while 11% developed CKD G3–G5

Real-time arrival prediction models for light rail train systems

One of the main objectives of public transport operators is to adhere to the planned timetable and to provide accurate information to passengers in order to improve actual and perceived service reliability. The aim of this thesis is to address the flowing question: how can the accuracy of a prediction system for light rail systems be measured and improved? The real-time prediction is an output of a telecommunication system, named Automatic Vehicle Location System, which computerizes the predictions. In order to improve a system, it is first important to understand how it works. The mechanism of the prediction computation will be analyzed and each part of the process will be studied in order to seek potential improvements. The first part of the prediction scheme development consists in a statistical analysis of historical data to provide the reference travel times and dwell times and their variations along a day or along a week. Then, two models (the designed-speed model and the speed/position model) will be studied to estimate the remaining time to reach the downstream stop. This estimation is mainly based on the current data (vehicle position and speed). The proposed prediction schemes were implemented and applied for a case study light rail line. Bybanen, a light rail train in Bergen was selected as case study. Real-time information displays are available at all platforms and refer to the waiting to the next two light rail trains. This study focuses on improving the accuracy of these waiting times predictions. In order to establish and analyze the performance of the current prediction scheme, a model for reproducing these computations was developed. Then, the possible improvements have been implemented in the model and the accuracy of the new predictions has been compared to the base case. The assessment and the comparison of prediction systems are not trivial tasks. Which predictions should be taken into account? How does the model identify inconsistency in the data? How could the perception of passengers be taken into account? A set of measures has been used in order to evaluate alternative prediction schemes. The comparison of the different models shows that it is possible to improve the accuracy of the short-term predictions, but it is more difficult to improve the accuracy of long-term predictions because the incertitude of small changes has more impact in long-term predictions. This thesis shows that the reference travel times and dwell times should be assimilated to the most common value instead of the average which is too dependent on high values. Moreover, the dwell time variations are related to the flow passengers. Finally, the most accurate and efficient model is the designed-speed model. The speed/position model is a bit less accurate except in the case of disturbances along the line but its modularity made easier possible improvements. Finally, this paper highlights the time-depending variations of the dwell time in the case of a light rail train system. It could be interesting to analyze the behavior of variations of two consequent dwell times and to implement a forgetting factor. Moreover, the speed-position model shows really good results and a better understanding of the drivers’ behaviors is a key to improve the model. Finally, the differences between the different models will be probably larger for a middle-distance train system, which could be an interesting application of this thesis