Miracle-dz/UniFAR-FTUs huggingface dataset:
AVSE Challenge: Audio-Visual Speech Enhancement Challenge

Audio-visual speech enhancement is the task of improving the quality of a speech signal when video of the speaker is available. It opens-up the opportunity of improving speech intelligibility in adverse listening scenarios that are currently too challenging for audio-only speech enhancement models. The Audio-Visual Speech Enhancement (AVSE) challenge aims to set the first benchmark in this area. We provide participants with datasets and scripts to test their audio-visual speech enhancement models under a common framework for both training and evaluation. The data is derived from real-world videos, and comprises noisy mixes, in which audio from target speaker is mixed with either a competing speaker or a noise signal. The submitted systems are evaluated by conducting AV intelligibility tests involving human participants. We expect this challenge to be a platform for advancing the field of audio-visual speech-enhancement and to provide further insight about the scope and limitations of current AV speech enhancement approaches.
Significant Tumor Regression after Neoadjuvant Chemotherapy in Gastric Cancer, but Poor Survival of the Patient? Role of MHC Class I Alterations

Simple Summary The major histocompatibility complex (MHC) class I genes, encompassing the human leukocyte antigen (HLA) class I and the beta-2 microglobulin (B2M) genes, play a key role in neoantigens presentation to the immune system. We analyzed allelic imbalance (AI) of the respective chromosomal regions by multiplex PCRs using microsatellite markers in biopsies of 158 patients with gastric/gastroesophageal adenocarcinoma before neoadjuvant platinum/fluoropyrimidine chemotherapy (CTx) for an association with clinical outcome of the patients. AI with no marker was significantly associated with response or survival. However, subgroup analysis revealed interesting differences. Of note, AI at markers of the HLA region was associated with a decreased survival only in responding but not in non-responding patients. No associations were observed for B2M markers. Our results underline the importance of intact neoantigen presentation specifically for responding patients and may help explain an unexpectedly poor survival of a patient despite significant tumor regression after neoadjuvant CTx. Abstract We aimed to determine the clinical and prognostic relevance of allelic imbalance (AI) of the major histocompatibility complex (MHC) class I genes, encompassing the human leukocyte antigen (HLA) class I and beta-2 microglobulin (B2M) genes, in the context of neoadjuvant platinum/fluoropyrimidine chemotherapy (CTx). Biopsies before CTx were studied in 158 patients with adenocarcinoma of the stomach or gastroesophageal junction. The response was histopathologically evaluated. AI was detected by multiplex PCRs analysis of four or five microsatellite markers in HLA and B2M regions, respectively. AI with no marker was significantly associated with response or survival. However, subgroup analysis revealed differences. AI at marker D6S265, close to the HLA-A gene, was associated with an obvious increased risk in responding (HR, 3.62; 95% CI, 0.96–13.68, p = 0.058) but not in non-responding patients (HR, 0.92; 95% CI, 0.51–1.65, p = 0.773). Markers D6S273 and D6S2872 showed similar results. The interaction between AI at D6S265 and response to CTx was significant in a multivariable analysis (p = 0.010). No associations were observed for B2M markers. Our results underline the importance of intact neoantigen presentation specifically for responding patients and may help explain an unexpectedly poor survival of a patient despite significant tumor regression after neoadjuvant platinum/fluoropyrimidine CTx.
Software for viewing and checking automatic detections.

Automatic detection of animal vocalizations is now used widely for handling long‐duration recording. Automatic detection methods inevitably make some errors—both false positive (wrong detection) and false negative (missed call) errors. Here a system is described for checking these errors. The MATLAB system “Osprey” allows viewing spectrograms, manipulating their parameters, and making various measurements of the displayed sounds. Another program, “checkDetections”, takes a log file that was output by an automatic detection software and systematically displays the detected sound in Osprey, allows a user to indicate whether the detection is correct, and then skips to the next detection in the log. This allows for rapid checking of detected sounds and calculation of the false‐positive (wrong‐detection) error rate. A second system, “checkMissedCalls”, displays random segments of sound in which no calls were found and allows the user to check whether there really were calls. This allows the user to estimate th.
H3 + in the Jovian ionosphere

Recent measurements and theoretical calculations suggest that H3+ ions in their ground vibrational level recombine much more slowly than in the vibrational levels υ ≥ 3. Using these results as a guide, we have investigated the impact of modified H3+ chemistry on the structure of the Jovian ionosphere for both a solar EUV ionization source and a high-altitude ionization source, associated with the electroglow. The results are sensitive to the value of the H3+ recombination coefficient, k10, adopted for υ = 0. For the solar EUV source of ionization, and a value of k10 = 2 × 10−8 cm³ s−1 equal to the published experimental upper limit, we find that H+ is the major ion at the peak of the ionosphere, similar to previous results. However, given the current uncertainty in k10, an ionosphere dominated by H3+ cannot be ruled out. A value of k10 ∼ 10−10 cm³ s−1 will produce an ionosphere that consists mainly of H3+. By including a high-altitude source of ionization we find that H3+ is the major component of the upper ionosphere, for all values of k10 less than or equal to the experimental upper limit. Thus, our calculations suggest that the topside plasma scale height measured by the radio science experiments may be indicative of electron temperatures ∼3000–5000 K or greater, consistent with current models for calculation of electron temperature. Our results may also help shed some light on the Voyager low-energy charged particle observations of H3+ ions in the magnetosphere. We also reemphasize the fact that H2 molecules in the Jovian upper atmosphere must be vibrationally excited.
Data-driven model-based flow measurement uncertainty quantification for building central cooling systems using a probabilistic approach

Uncertainties inevitably exist in measurements and may lead to biases in making management and control decisions, and thus affect the energy performance of building central cooling systems. Water flow meters are essential for the monitoring and operational control of building central cooling systems, but they often suffer from significant measurement uncertainties due to site constraints and unfavorable working environment. An effective method to quantify the flow measurement uncertainties is urgently needed. This study proposes a data-driven model-based flow measurement uncertainty quantification strategy using Bayesian inference and Markov chain Monte Carlo sampling methods. The proposed strategy is tested and validated systematically on an air-cooled chiller. Four case studies with different levels of flow measurement uncertainties are conducted. The test results show that both systematic and random uncertainties of flow measurements are quantified accurately by this strategy. The 95% Bayesian credible intervals of systematic and random uncertainties contain their pre-set (actual) values, and their posterior means (estimated values) are very close to their pre-set values. The relative errors in quantifying flow measurement uncertainties are within 10%. The performance of the proposed method is quite satisfactory. This study provides a cost-effective and promising alternative for on-site flow meter calibration in engineering practice.
Managing Severe Hypoglycaemia in Patients with Diabetes: Current Challenges and Emerging Therapies

Abstract Hypoglycaemia is common in patients with diabetes mellitus and is a limiting factor for achieving adequate glycaemic control. In the vast majority of cases, hypoglycaemia develops due to the imbalance between food intake and insulin injections. As recurrent hypoglycaemia leads to significant morbidity and mortality, the recognition and immediate treatment of hypoglycaemia in diabetic patients is thus important. In the last 20 years, the introduction of improved insulin analogues, insulin pump therapy, continuous glucose monitoring (CGM), and sensor-augmented pump therapy have all made significant improvements in helping to reduce and prevent hypoglycaemia. In terms of treatment, the American Diabetes Association recommends oral glucose as the first-line treatment option for all conscious patients with hypoglycaemia. The second line of treatment (or first line in unconscious patients) is the use of glucagon. Novel formulations of glucagon include the nasal form, the Gvoke HypoPen which is a ready-to-deliver auto-injector packaged formulation and finally a glucagon analogue, Dasiglucagon. The Dasiglucagon formulation has recently been approved for the treatment of severe hypoglycaemia. It is a ready-to-use, similar to endogenous glucagon and its potency is also the same as native glucagon. It does not require reconstitution before injection and therefore ensures better compliance. Thus, significant improvements including development of newer insulin analogues, insulin pump therapy, continuous glucose monitoring (CGM), sensor-augmented pump therapy and novel formulations of glucagon have all contributed to reducing and preventing hypoglycaemia in diabetic individuals. However, considerable challenges remain as not all patients have access to diabetes technologies and to the newer glucagon formulations to help reduce and prevent hypoglycaemia.
Field-aligned scale length of depleted structures associated with post-sunset equatorial plasma bubbles

In this study we make use of the Swarm counter-rotation constellation for estimating the typical scale length of the post-sunset equatorial plasma bubbles (EPBs) along fluxtubes. The close approaches between Swarm spacecraft near the equator occurred in September and October 2021, covering magnetic local time from 19:00 to 23:00, which is favorable for the occurrence of EPBs. It is the first time to show the quasi-simultaneously samplings by Swarm A/C and B of the same fluxtube but at different altitudes, and the observations frequently reveal plasma density depletions only at one spacecraft altitude, confirming that EPBs extend only over finite parts of the fluxtube. Based on a statistical analysis of double and single EPB detections on the same fluxtube, our results imply the typical field-aligned scale length of the depletion structures associated with EPBs of the order of 550 km. Our detections are from the lower part of the depleted fluxtubes, and they coincide well with the latitudes of the equatorial ionization anomaly. In the upper part of the fluxtube near the magnetic equator, our estimation technique does not work well because of too large field-aligned spacecraft separation of the Swarm satellites.
Effects of statins on the incidence and outcomes of acute kidney injury in critically ill patients: a systematic review and meta-analysis

Introduction In critically ill patients, acute kidney injury (AKI) is a common complication with very high mortality rates. Several studies indicated that statin therapy, primarily due to its so-called pleiotropic effects, may beneficially affect the course of the disease, otherwise leading to significant clinical complications. However, both the original research as well as available meta-analyses on these associations report equivocal results. This leaves open a question whether pre- and perioperative statins might prevent AKI and improve overall prognosis in patients undergoing surgery. Material and methods Following a systematic search of the literature, we performed a meta-analysis of selected clinical studies investigating the impact of statin treatment on the development and the clinical outcomes of AKI among subjects undergoing surgeries. The pooled odds ratios (OR) with 95% confidence intervals (CI) for the development of AKI and AKI-associated mortality, as well as the pooled mean differences (MD) and 95% CI for mean intensive care unit (ICU) stay and overall hospital length of stay were calculated for statin users compared to non-users. Results Our results showed a highly significant association between statin use and the decrease in mortality of patients with AKI (OR = 0.73, 95% CI: 0.69–0.77; p<0.001). The development of AKI (OR = 0.92, 95% CI: 0.63–1.33; p = 0.659) as well as the ICU stay (MD = –0.02, 95% CI: –0.06 – 0.02; p = 0.321) were not significantly affected, while the overall hospital length of stay (MD = –0.49, 95% CI: –0.91 –0.07; p = 0.020) was reduced. Subgroup analysis showed that both pre- and postoperative statin use were not associated with the risk of AKI. Conclusions Our analysis showed a significant association between statin therapy and overall mortality of critically ill surgical patients diagnosed with AKI, while at the same time the use of statins did not affect the length of their stay in ICU.
Arc-Fault Detection using One-Dimension Convolution Neural Network

Being one of the major safety concerns, arc-faults need to be properly detected and addressed. Arcs are commonly existed in both faulty circuits and devices under normal operation, which makes accurate arc-fault detection challenging as manually modeling arc-faults is difficult. This paper proposed to use one-dimension convolution neural network to detect arc-faults. Neural network has demonstrate its capability in other classification problems which are difficult for tradition modeling approach such as image or text recognition. By nature, arc-fault detection using current waveform is a classification task on time- series, which is suitable for one-dimension convolution network. In addition, this paper also investigate in using partial cycle data after zero crossings, instead of full cycle data as used in existing methods, to detect arc-fault, which can further reduce model complexity and allow more precise measurement. The proposed method can achieve 98.996% accuracy while using only one eighth of each cycle of current and 99.543% accuracy with a quarter cycle of current. Finally, this work also briefly studied and estimated the performance of the proposed model if to be deployed on embedded hardware for real time usage.
Advances in Experimental Aeroacoustics

A review of modern experimental methods in aeroacoustics is provided along with sample applications. Specific emphasis has been placed on four advanced experimental techniques: (i) beamforming, (ii) acoustic holography, (iii) phase averaging and simultaneous flow/acoustic measurements and (iv) higher order spectral analysis. Through a description and analysis of recent successful application scenarios of the above techniques, their immense potential in experimental aeroacoustics is highlighted.
FPGA based CNN accelerator for high-speed biomedical application

The rise in visual dataset generation has necessitated the recent advancements in the field of Deep Neural Networks (DNNs). Application domains like biomedical imaging require a high level of precision which is suitably achieved using convolutional neural networks (CNNs) at the expense of increased computation, hardware, and power resources. The implementation of such CNN architectures is constrained by currently available resource limited embedded and application-specific integrated circuit (ASIC) systems. In this work, a field-programmable gate array (FPGA) based hardware accelerator having a generalized architecture for convolution and fully connected (FC) layers has been presented that exploits a massive level of intra-layer parallelism. Compute intensive convolution layers are replaced by depthwise separable (DS) convolution layers that reduced the number of computations and memory access by 7.8x and 10x respectively for VGG8 network after detailed design space exploration. Furthermore, parallel computation of arithmetic tasks reduced the compute bound for the proposed architecture. Reduced precision data type for both input and weights resulted in overall reduction in latency and resource utilization. FPGA implementation results of the proposed CNN accelerator for classifiers trained on subsets of MedMNIST dataset depict a balance between high performance of 214.5 GOP/s for DS convolution layer and low resource utilization.
Determination of exogenous water in grape juice through the isotopic analysis of 18O/16O

Abstract The consumption of grape juice has been growing significantly, so its quality is becoming an issue of great importance, both for the consumer and for the industry. However, identifying adulteration in juice is a great challenge and requires a reliable analytical process. The isotope ratio (18O/16O) is an important tool to determine the addition of exogenous water in beverages, however, there is no official method for juice in Brazil. This study aimed to develop and validate a method for detecting exogenous water in grape juice through isotopic analysis of 18O/16O. The development and validation of the analytical method were performed using Isotope Ratio Mass Spectrometry (IRMS). The effect of temperature and evaporation of δ18O in experimental juices was evaluated, and reference values were found for juices based on the δ 18O of musts. The influence of the juice industrial production process on 18O values was verified, and commercial juices were evaluated in relation to the values of reference regarding the addition of water. The temperature and evaporation parameters did not influence the results of the 18O of the juice, as they presented differences lower than the method uncertainty. The heat exchanger system did not influence the proposed method. The reference values for juice can come from the musts, without affecting the interpretation of the final results. Of the thirty real juices analyzed, nine had exogenous water, three proved to be reconstituted juices and eighteen were considered to have no exogenous water. The method proposed and validated in this study presented values for the limit of detection (LOD) of 0.24‰, the limit of quantification (LOQ) of 0.97‰ and measurement uncertainty of 0.71‰, proving to be effective for the detection of exogenous water in grape juice, through of the analysis of the isotopic ratio of 18O/16O by IRMS.
The General Theory of Relaxation Methods Applied to Linear Systems

During the last few years Southwell and his fellow-workers have developed a new method for the numerical solution of a very general type of problem in mathematical physics and engineering. The method was originally devised for the determination of stresses in frameworks, but it has proved to be directly applicable to any problem which is reducible to the solution of a system of non-homogeneous, linear, simultaneous algebraic equations in a finite number of unknown variables. * Southwell’s “ relaxation m ethod” is one of successive approximation and, in order to complete the previous investigations of this method, it is necessary to prove that the successive approximations do actually converge towards the exact solutions. This formal proof is given in § 4. Southwell’s “ relaxation” methods are not directly applicable to continuous systems, where the number of unknown variables is infinite, but it is shown here that simple extensions and modifications of the relaxation method render it suitable for application to either discrete or continuous systems (§ 5). The general theory of relaxation methods is then developed in terms of the theory of linear operators (§7) and sufficient conditions are prescribed for the convergence of the process of approximation (§ 8). These general methods are then applied to the solution of non-homogeneous, linear integral equations (§ 10) and to the solution of nonhomogeneous, linear differential equations (§§ 11, 12).
Surface defect detection of metal castings based on Centernet

In the production process of metal castings, certain defects are easy to appear on the surface. Before the castings are put into use, it is necessary to detect whether there are defects on the surface. In this paper, the defect detection of metal casting surface is taken as the research object, and the Centernet deep learning model is used to recognize the defect of casting surface image. Centernet does not use Region Proposal Network (RPN) and Non-Maximum Suppression (NMS) in the training process. It has the advantages of simple model, good detection effect, fast running speed and easy integration and deployment. By predicting the category heat map, the length and width map of the detection frame and the center offset of the detection frame with the same image size as the input model, the defect target can be extracted. In this paper, the image dataset is expanded by cropping, rotating, changing brightness and contrast of the images. For the pit and scratch defects of metal castings, the Mean Average Precision (mAP) is higher than 0.9 for the metal casting defect image dataset, and the software integration of the model is completed.
Research on workshop manufacturing resource location method based on industrial Internet of things

The industrial internet of things has become the main solution of the dynamic scheduling method used in the workshop scheduling mechanism. Radio Frequency Identification (RFID) technology is an effective method to locate manufacturing resources in the workshop. RFID technology is mainly used for automatic identification and providing accurate location information. In RFID application, it mainly meets two different monitoring requirements: target detection and target accurate positioning. When locating workshop resources based on RFID system, it is necessary to set the location and number of RFID readers. Due to the unreliability of RFID itself, the reader will miss the tags within its detection range. In this paper, an RFID network planning method and a data joint optimization method are designed to solve the configuration problem of RFID system in practical application.
Global optimality of quadratic minimization over symmetric polytopes

In this article, we establish necessary as well as sufficient conditions for a feasible point to be a global minimizer of a quadratic function over a symmetric polytope. We also show that the necessary condition becomes necessary and sufficient for global optimality in the special case where the matrices are involved diagonal matrices. Sufficient conditions are obtained by way of simple quadratic underestimation. Examples are discussed to illustrate the optimality conditions.
AN EXTENSION OF COMPLEX MODES FOR THE RESOLUTION OF FINITE-ELEMENT POROELASTIC PROBLEMS

A new modal synthesis technique inspired from complex modes is presented in this paper. The purpose of this method is to find an alternative to the weakness of usual modal techniques in order to improve resolution of finite-element poroelastic problems. Firstly, the construction of modes and the synthesis for a general forcing is explained. Secondly, the approach is applied to a one-dimensional poroelastic problem. Finally, the approach is numerically tested and its performance is investigated by comparison with solutions obtained with a direct resolution of the system.
Numerical simulations of core-collapse supernovae imposing steady shock approximation

We carried out one-dinensional long-term numerical simulations of core-collapse supernovae imposing steady shock approximation. As a result of a computation, we found core does not explode and shock wave stalls within several hundreds milliseconds from bounce. However, we execute long-term numerical simulation until 3.5 seconds from bounce with short CPU time using Personal Computer.