Miracle-dz/UniFAR-FTUs huggingface dataset:
Configuration Dynamics in a Simulated Finite-Temperature Transition Metal Glass†

The dynamics of configurations and of ‘nearest-neighbour’ relations are investigated for an amorphous metallic alloy by molecular dynamics computer simulations covering the whole range of temperatures up into the liquid state. The configuration dynamics are expressed in terms of nearest-neighbour auto-correlation functions, where at finite temperatures ‘nearest neighbours’ are defined with regard to the force-free configuration closest to the actual atomic arrangement. Results are presented for an atomistic model adapted to the recent hybridized nearly-free-electron-tight-binding-bond description of Ni0.5Zr0. 5. The results agree well the predictions of the extended mode-coupling theory for the time dependence of density fluctuations and related properties around the glass-liquid transition in ‘real’ systems. Die Dynamik der Konfigurationen und ‘nachster-Nachbar-Beziehungen’ wird fur amorphe metallische Legierungen mit Molekular-Dynamik Computer-Simulationen uber den gesamten Temperaturbereich bis in den flussigen Zustand untersucht. Beschrieben wird die Konfigurations-Dynamik mittels nachste-Nachbarn Auto-Korrelationsfunktionen, wobei ‘nachst-Nachbarn’ bei endlichen Temperaturen uber die nachstgelegene kraftefreie Konfiguration zur aktuellen Atomanordnung definiert werden. Ergebnisse werden fur ein atomistisches Modell vorgelegt, das an die kurzlich vorgestellte hybridisierte fast-freie-Elektronen-tight-binding-bond Beschreibung fur Ni0.5Zr0. 5 angepast ist. Die Ergebnisse stimmmen gut uberein mit den Vorhersagen der erweiterten Moden-Kopplungs-Theorie fur das Zeitverhalten von Dichtefluktuationen und verwandten Eigenschaften in der Umgebung des Glas-Flussigkeits-Uberganges in ‘realen’ Systemen.
The keratin network of intermediate filaments regulates keratinocyte rigidity sensing and nuclear mechanotransduction

Keratin defects associated with blistering skin diseases alter force transmission to the nucleus. The keratin network of intermediate filaments provides keratinocytes with essential mechanical strength and resilience, but the contribution to mechanosensing remains poorly understood. Here, we investigated the role of the keratin cytoskeleton in the response to altered matrix rigidity. We found that keratinocytes adapted to increasing matrix stiffness by forming a rigid, interconnected network of keratin bundles, in conjunction with F-actin stress fiber formation and increased cell stiffness. Disruption of keratin stability by overexpression of the dominant keratin 14 mutation R416P inhibited the normal mechanical response to substrate rigidity, reducing F-actin stress fibers and cell stiffness. The R416P mutation also impaired mechanotransduction to the nuclear lamina, which mediated stiffness-dependent chromatin remodeling. By contrast, depletion of the cytolinker plectin had the opposite effect and promoted increased mechanoresponsiveness and up-regulation of lamin A/C. Together, these results demonstrate that the keratin cytoskeleton plays a key role in matrix rigidity sensing and downstream signal transduction.
Efficacy and safety of sugammadex doses calculated on the basis of corrected body weight and total body weight for the reversal of deep neuromuscular blockade in morbidly obese patients

Objective We investigated the efficacy and safety of sugammadex doses calculated using corrected body weight (CBW) for reversing deep rocuronium-induced neuromuscular blockade (NMB) in morbidly obese patients undergoing laparoscopic bariatric surgery. Methods One hundred and twenty-five morbidly obese patients were randomly assigned to three groups: (1) a CBW group, n = 50; (2) a total body weight (TBW) group, n = 50; and (3) a control group, n = 25. Deep NMB was maintained using a continuous infusion of rocuronium. At the reappearance of 1 to 2 post-tetanic counts (PTCs), 4 mg/kg sugammadex, calculated using CBW or TBW, were administered. Results All the participants in the CBW and TBW groups recovered to a train-of-four (TOF) ratio of 0.9 within 5 minutes. The recovery times from the start of sugammadex administration to a TOF ratio of 0.9 were 2.2 ± 0.7 and 2.0 ± 0.7 minutes in the CBW and TBW groups, respectively. Thus, a sugammadex dose calculated using CBW was not inferior to that calculated using TBW for the reversal of rocuronium-induced deep NMB in morbidly obese patients. Conclusion A dose of 4 mg/kg of sugammadex calculated using CBW is efficient and safe for the reversal of deep NMB after a continuous infusion of rocuronium in morbidly obese patients. Clinical Trial Registration Number ChiCTR1900028652 (Chinese Clinical Trial Registry, www.chictr.org.cn)
The Measurement of a Complete Set of Transport Properties for a Concentrated Solid Polymer Electrolyte Solution

Polymer electrolytes based on alkali metal salts in poly(ethylene oxides) are important for possible use in rechargeable batteries for both electric vehicle and consumer electronics applications. The authors measure a complete set of transport properties for one particular binary salt solution: sodium trifluoromethanesulfonate in poly(ethylene oxide), over a wide range of salt concentrations (0.1 to 2.6M) at 85 C. The properties measured include the conductivity, the salt diffusion coefficient, and the Na ion transference number. The mean molar activity coefficient of the salt is also determined. The conductivity and diffusion coefficients of NaCF{sub 3}SO{sub 3} are similar in magnitude to those of LiCF{sub 3}SO{sub 3} in (polyethylene oxide). The transference number and thermodynamic factor are found by combining concentration cell data with the results of galvanostatic polarization experiments. A theoretical analysis of the experimental method based on concentrated-solution theory is given. The study verifies that the transference numbers derived from the experiments retain fundamental significance in applications involving both steady and transient processes and in systems coupling the polymer electrolyte with electrodes of all types (stoichiometries). The relevant transference numbers can be determined independently of any knowledge of speciation of the polymer electrolyte. The transference numbers found here for themore » sodium ion are much lower than those reported for the lithium ion, especially in the concentrated solutions. The transference number of the sodium ion is negative in the more concentrated solutions and levels off at its maximum value of 0.31 in the dilute concentration range. The transference number results are interpreted in terms of complexation of the sodium ion with the anionic species. « less
Identifying an Important Ingredient for Safe Discharge to Home Oxygen Therapy: The Respiratory Therapist and the RIsOTTO Study

COPD is a leading cause of death around the world and imparts considerable morbidity and health care costs. Exacerbations requiring hospitalization are common, and, at least in the United States, patients are usually discharged from the hospital after clinical improvement but typically have not returned to baseline physiologic status. Thus, patients who have not been on oxygen at home may need oxygen upon discharge at least in the short-term, and those previously on oxygen may need a change in their prescription. Oxygen can be prescribed continuously with or without ambulatory oxygen, with exertion only, or at night. Ideally, the patient’s oxygen needs should be evaluated prior to discharge and appropriate supplemental oxygen ordered. However, a recent retrospective study of subjects with COPD revealed that, while documentation of resting hypoxemia was good, evaluation for exertional hypoxemia was usually lacking. Furthermore, documentation that prescribed oxygen was adequate during both rest and with exertion was also typically lacking. Hospitalists are extremely busy and often do not have control over their time as they have to address patients’ acute needs. A more systematic approach with use of experienced nonphysicians such as respiratory therapists might lead to better results with respect to oxygen prescriptions. Usually nursing, pharmacy, and social work staff participate in discharge planning, but this help does not extend to oxygen supplementation. In the study by Tan et al in this issue of RESPIRATORY CARE, the authors surveyed hospital-based respiratory therapists who were members of the American Association for Respiratory Care to ask how often they evaluated the need for home oxygen in patients admitted with an exacerbation of COPD. There were 611 respondents, of whom 490 met entry criteria and were included in the analysis. To put this response in perspective, there are an estimated 170,000 respiratory therapists working in the United States. Nevertheless, the number who said they consistently (defined as > 75%) made such evaluations was 58% for resting hypoxemia, 43% for exertional hypoxemia, and 14% for nocturnal hypoxemia. Only 25% of respiratory therapists were consistently involved in the decisionmaking for the home oxygen equipment the patient
Implementation of the “awakening and breathing trials, choice of drugs, delirium management, and early exercise/mobility” bundle in the pediatric intensive care unit of tertiary hospitals in southwestern China: a cross-sectional survey

Objective To investigate management and implementation of the “awakening and breathing trials, choice of drugs, delirium management, and early exercise/mobility” (ABCDE) bundle in the pediatric intensive care unit (PICU) in southwestern China Methods A self-designed questionnaire for determining implementation of the ABCDE bundle was distributed to healthcare professionals in the PICU. Multiple linear regression was used to analyze results. Results A total of 270 questionnaires were collected. There was no significant difference in the awareness of the ABCDE bundle rate among Sichuan, Guizhou, and Yunnan workers. Only dynamic adjustment of drug dose accounted for more than half (55.5%) of “frequent implementation” and “general implementation”, followed by implementation of sedation assessment, pain assessment, and spontaneous breathing trials (46.4%, 39.3%, and 35.6%, respectively). A total of 80.4% of healthcare professionals never performed screening of delirium. Multivariate analysis showed that the healthcare professionals’ scores of ABCDE bundle behavior significantly differed regarding awareness of the ABCDE bundle, years of work at the hospital, the region of hospitals, and occupational category. Conclusion Implementation of the ABCDE bundle in the PICU in southwestern China is not sufficient. Existing problems need to be identified and a standardized sedation and analgesia management model needs to be established.
Runtime Software Selection for Adaptive Automotive Systems

As automotive systems become more intelligent than ever, they need to handle many functional tasks, resulting in more and more software programs running in automotive systems. However, whether a software program should be executed depends on the environmental conditions (surrounding conditions). For example, a deraining algorithm supporting object detection and image recognition should only be executed when it is raining. Supported by the advance of over-the-air (OTA) updates and plug-and-play systems, adaptive automotive systems, where the software programs are updated, activated, and deactivated before driving and during driving, can be realized. In this paper, we consider the upcoming environmental conditions of an automotive system and target the corresponding software selection problem during runtime. We formulate the problem as a set cover problem with timing constraints and then propose a heuristic approach to solve the problem. The approach is very efficient so that it can be applied during runtime, and it is a preliminary step towards the broad realization of adaptive automotive systems.
Image retrieval system for citizen services using penalized logistic regression models

This paper describes a procedure to deal with large image collections obtained by smart city services based on interaction with citizens providing pictures. The semantic gap between the low-level image features and represented concepts and situations has been addressed using image retrieval techniques. A relevance feedback procedure is proposed for Content-Based Image Retrieval (CBIR) based on the modelling of user responses. One of the novelties of the proposal is that the feedback learning procedure can use the information that citizens themselves can provide when using these services. The proposed algorithm considers the probability of an image belonging to the set of those sought by the user, by selecting a set of relevant and irrelevant images to the query, and by adjusting a penalized logistic regression model to the information provided by the user. The Wikipedia2011 image collection has been used for testing purposes. The procedure has been compared to other retrieval relevance feedback procedures in recent literature. Good results have been obtained with just one global model fitted with high dimensionality data.
A 0.57-GOPS/DSP Object Detection PIM Accelerator on FPGA

The paper presents an object detection accelerator featuring a processing-in-memory (PIM) architecture on FPGAs. PIM architectures are well known for their energy efficiency and avoidance of the memory wall. In the accelerator, a PIM unit is developed using BRAM and LUT based counters, which also helps to improve the DSP performance density. The overall architecture consists of 64 PIM units and three memory buffers to store inter-layer results. A shrunk and quantized Tiny-YOLO network is mapped to the PIM accelerator, where DRAM access is fully eliminated during inference. The design achieves a throughput of 201.6 GOPs at 100MHz clock rate and correspondingly, a performance density of 0.57 GOPS/DSP.
A specification that supports FPGA devices on the TensorFlow framework

With the rise of artificial intelligence and machine learning, many applications and services require FPGA support to speed up the training process and improve efficiency. FPGA has its unique advantages including inference; but now TensorFlow only supports CPU and GPU, TPU, does not support FPGA, cannot use FPGA to accelerate specific models, and cannot play the full role of FPGA in TensorFlow. Based on the above problems, this paper proposes a method for efficiently using FPGA in TensorFlow. This method uses TensorFlow's original device management mechanism, adds an abstract method for FPGA devices under the TensorFlow framework, and writes implementation specifications for FPGA operators. Finally, we used OpenCL to build kernels of FPGA devices, took full advantage of the parallel computing advantages of FPGA devices, and used the CNN LeNet5 model and MNIST dataset to conduct corresponding experiments. The experimental results show that the training accuracy of the two devices is basically the same. This paper provides a feasible solution for TensorFlow to use FPGA devices for neural network calculations.
An Adaptive Delay Model for Timing Yield Estimation under Wide-Voltage Range

Yield analysis for wide-voltage circuit design is a strong nonlinear integration problem. The most challenging task is how to accurately estimate the yield of long-tail distribution. This paper proposes an adaptive delay model to substitute expensive transistor-level simulation for timing yield estimation. We use the Low-Rank Tensor Approximation (LRTA) to model the delay variation from a large number of process parameters. Moreover, an adaptive nonlinear sampling algorithm is adopted to calibrate the model iteratively, which can capture the larger variability of delay distribution for different voltage regions. The proposed method is validated on benchmark circuits of TAU15 in 45nm free PDK. The experiment results show that our method achieves 20-100X speedup compared to Monte Carlo simulation at the same accuracy level.
Are the university websites accessible?: a check based on the principles of W3C

Today, the Web is considered an important resource for work, entertainment, services and communication. It is a fact that has become indispensable nowadays. Although the advancement of recent years has made the Web more popular, not all potential users have easy use of the sites available. In this group are the visually impaired. Visually impaired users still face many difficulties in using the services available on the web. In this sense, it is important to evaluate the accessibility of the websites and to verify how much is still necessary to increase the accessibility level of the websites. In this work, an automated evaluation of Universities websites was performed to verify how accessible these websites are. The assessment was based on W3C recommendations. The results point to a need to know and comply more with accessibility standards, so that visually impaired users can better understand the websites available.
Categorical Perception of Mandarin Tones Based on Acoustic Features by Japanese Learners

Based on acoustic features of four Mandarin tones, this study investigated the perceptual pattern between Tone1 (T1) and Tone4 (T4), Tone2 (T2) and Tone3 (T3) which are considered difficult for Japanese learners and Chinese native speakers to distinguish. We compared the performance of Mandarin and Japanese Listeners on the perception of Mandarin tones in a classical categorical perception experiment that employed identification and discrimination tasks. Experiments on T1 and T4 were designed using the fundamental frequency (fo) of endpoint as the acoustic cue, while experiments on T2 and T3 were designed using continual sound stimuli, which gradually changed from T2 to T3 varying in the timing of turning point (inflection point of the tone), &Dgr;fo (pitch difference between onset and turning point) or both acoustic dimensions. The results showed that when endpoint pitch was taken as the acoustic parameter, categorical perception was found between T1 and T4 by both Chinese native speakers and Japanese learners. And when the timing of turning point and &Dgr;fo were both taken as the acoustic parameters, both advanced Chinese learners and beginners demonstrated quasi-categorical perception of T2 and T3 whereas timing of turning point was used as a sole parameter, only a categorical perception tendency is observed.
Accelerate Non-unit Stride Convolutions with Winograd Algorithms

While computer vision tasks target increasingly challenging scenarios, the need for real-time processing of images rises as well, requiring more efficient methods to accelerate convolutional neural networks. For unit stride convolutions, we use FFT-based methods and Winograd algorithms to compute matrix convolutions, which effectively lower the computing complexity by reducing the number of multiplications. For non-unit stride convolutions, we usually cannot directly apply those algorithms to accelerate the computations. In this work, we propose a novel universal approach to construct the non-unit stride convolution algorithms for any given stride and filter sizes from Winograd algorithms. Specifically, we first demonstrate the steps to decompose an arbitrary convolutional kernel and apply the Winograd algorithms separately to compute non-unit stride convolutions. We then present the derivation of this method and proof by construction to confirm the validity of this approach. Finally, we discuss the minimum number of multiplications and additions necessary for the non-unit stride convolutions and evaluate the performance of the decomposed Winograd algorithms. From our analysis of the computational complexity, the new approach can benefit from 1.5x to 3x fewer multiplications. In our experiments in real DNN layers, we have acquired around 1.3x speedup (Told / Tnew) of the Winograd algorithms against the conventional convolution algorithm in various experiment settings.
Safety and Robustness for Deep Learning with Provable Guarantees

Computing systems are becoming ever more complex, with decisions increasingly often based on deep learning components. A wide variety of applications are being developed, many of them safety-critical, such as self-driving cars and medical diagnosis. Since deep learning is unstable with respect to adversarial perturbations, there is a need for rigorous software development methodologies that encompass machine learning components. This lecture will describe progress with developing automated verification and testing techniques for deep neural networks to ensure safety and robustness of their decisions with respect to bounded input perturbations. The techniques exploit Lipschitz continuity of the networks and aim to approximate, for a given set of inputs, the reachable set of network outputs in terms of lower and upper bounds, in anytime manner, with provable guarantees. We develop novel algorithms based on feature-guided search, games, global optimisation and Bayesian methods, and evaluate them on state-of-the-art networks. The lecture will conclude with an overview of the challenges in this field.
Evaluation of Channel Performance in Seizure Prediction

Since unprovoked seizures are identified as the biggest concern of epileptic patients, an effective seizure prediction device is definitely a game changer in epilepsy management. In order to apply seizure prediction in practical settings, seizure prediction algorithms are mandated to be optimized prior to installation into implantable devices which often come with low computational resources. To achieve this goal, past literature has demonstrated various machine learning approaches to reduce the number of channels or features needed in seizure prediction. However, the resultant channel or feature set generated by machine learning models can change over time, thus making the device configuration unfeasible once the device is implanted. Therefore, we evaluated the seizure prediction performance of each channel in a set of intracranial electrode-based electroencephalography (iEEG) recordings to uncover if electrode placement impacts on the predictive accuracy. The study was conducted on a dataset of three patients from the first-in-man seizure warning device trial and a state-of-the-art seizure prediction algorithm based on Extra Trees classification was applied. In three patients, the best channels achieved the area under the receiver-operating curve (AUC) scores of 0.77, 0.70, and 0.76, respectively. The result also indicated superior seizure prediction of some channels across all patients. Regarding AUC comparison between the best performing channel and all channels, in patient 1, using one channel as a single predictor surpassed the multiple channel model. While statistically significant differences were not obtained in patient 2, the one channel model offered practical benefits by preventing data overfitting and reducing computational complexity. In patient 3, the general model performed better than individual channels. These findings demonstrate the feasibility of selecting electrodes to build models in some patients and also emphasize the importance of patient-specific methods for seizure prediction.
Alzheimer's disease classification using capsule networks on structural MRI

Alzheimer's disease (AD) is the most prevalent cause of dementia among elderly people. This paper presents a deep learning model based on capsule networks for the classification of AD and Normal Control (NC) using MRI images taken from the ADNI dataset. Our framework introduces several modifications to the original capsule networks in order to significantly reduce the number of trainable parameters so that it can be implemented in computers with limited GPU while obtaining comparable results with the literature. According to the overall results of subject independent and subject dependent experiments, the proposed framework compares favorably with many existing approaches while having fewer parameters and/or using a smaller number of MRI images in AD and NC classes.