Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Impact of Cross-lingual Acoustic-Phonetic Similarities on Multilingual Speech Recognition

Multilingual automatic speech recognition (ASR) systems mostly benefit low resource languages but suffer degradation in performance across several languages relative to their monolingual counterparts. Limited studies have focused on understanding the languages behaviour in the multilingual speech recognition setups. In this paper, a novel data-driven approach is proposed to investigate the cross-lingual acoustic-phonetic similarities. This technique measures the similarities between posterior distributions from various monolingual acoustic models against a target speech signal. Deep neural networks are trained as mapping networks to transform the distributions from different acoustic models into a directly comparable form. The analysis observes that the languages closeness can not be truly estimated by the volume of overlapping phonemes set. Entropy analysis of the proposed mapping networks exhibits that a language with lesser overlap can be more amenable to cross-lingual transfer, and hence more beneficial in the multilingual setup. Finally, the proposed posterior transformation approach is leveraged to fuse monolingual models for a target language. A relative improvement of ~8% over monolingual counterpart is achieved.
On regular maps and parallel lines

Let f : R m +1 → R m +2 r , where 2 r − 1 6 m + 1 < 2 r , be a continuous map. Improving a recent result of Frick and Harrison, we show that there are 4 points x 0 , x 1 , y 0 , y 1 in R m , which are distinct if m + 1 6 = 2 r − 1 , and satisfy x 0 6 = x 1 , y 0 6 = y 1 , { x 0 ,x 1 } 6 = { y 0 ,y 1 } if m + 1 = 2 r − 1 , such that the vectors f ( x 1 ) − f ( x 0 ) and f ( y 1 ) − f ( y 0 ) are parallel.
Domain knowledge driven 3D dose prediction using moment-based loss function

Objective. To propose a novel moment-based loss function for predicting 3D dose distribution for the challenging conventional lung intensity modulated radiation therapy plans. The moment-based loss function is convex and differentiable and can easily incorporate clinical dose volume histogram (DVH) domain knowledge in any deep learning (DL) framework without computational overhead. Approach. We used a large dataset of 360 (240 for training, 50 for validation and 70 for testing) conventional lung patients with 2 Gy × 30 fractions to train the DL model using clinically treated plans at our institution. We trained a UNet like convolutional neural network architecture using computed tomography, planning target volume and organ-at-risk contours as input to infer corresponding voxel-wise 3D dose distribution. We evaluated three different loss functions: (1) the popular mean absolute error (MAE) loss, (2) the recently developed MAE + DVH loss, and (3) the proposed MAE + moments loss. The quality of the predictions was compared using different DVH metrics as well as dose-score and DVH-score, recently introduced by the AAPM knowledge-based planning grand challenge. Main results. Model with (MAE + moment) loss function outperformed the model with MAE loss by significantly improving the DVH-score (11%, p < 0.01) while having similar computational cost. It also outperformed the model trained with (MAE + DVH) by significantly improving the computational cost (48%) and the DVH-score (8%, p < 0.01). Significance. DVH metrics are widely accepted evaluation criteria in the clinic. However, incorporating them into the 3D dose prediction model is challenging due to their non-convexity and non-differentiability. Moments provide a mathematically rigorous and computationally efficient way to incorporate DVH information in any DL architecture. The code, pretrained models, docker container, and Google Colab project along with a sample dataset are available on our DoseRTX GitHub (https://github.com/nadeemlab/DoseRTX)
Fractional Quantum Zeno Effect Emerging from Non-Hermitian Physics

Exploring non-Hermitian phenomenology is an exciting frontier of modern physics. However, the demonstration of a non-Hermitian phenomenon that is quantum in nature has remained elusive. Here, we predict quantum non-Hermitian phenomena: the fractional quantum Zeno (FQZ) effect and FQZ-induced photon antibunching. We consider a quantum optics platform with reservoir engineering, where nonlinear emitters are coupled to a bath of decaying bosonic modes whose own decay rates form band structures. By engineering the dissipation band, the spontaneous emission of emitters can be suppressed by strong dissipation through an algebraic scaling with fractional exponents - the FQZ effect. This fractional scaling originates uniquely from the divergent dissipative density of states near the dissipation band edge, different from the traditional closed-bath context. We find FQZ-induced strong photon antibunching in the steady state of a driven emitter even for weak nonlinearities. Remarkably, we identify that the sub-Poissonian quantum statistics of photons, which has no classical analogs, stems here from the key role of non-Hermiticity. Our setup is experimentally feasible with the techniques used to design lattice models with dissipative couplings.
Variance in Classifying Affective State via Electrocardiogram and Photoplethysmography

—Advances in wearable technology have signiﬁcantly increased the sensitivity and accuracy of devices for recording physiological signals. Commercial off-the-shelf wearable devices can gather large quantities of physiological data un-obtrusively. This enables momentary assessments of human physiology, which provide valuable insights into an individual’s health and psychological state. Leveraging these insights provides signiﬁcant bene-ﬁts for human-to-computer interaction and personalised health-care. This work contributes an analysis of variance occurring in features representative of affective states extracted from electro-cardiograms and photoplethysmography; subsequently identiﬁes the cardiac measures most descriptive of affective states from both signals and provides insights into signal and emotion-speciﬁc cardiac measures; ﬁnally baseline performance for automated affective state detection from physiological signals is established.
Neural Stein Critics With Staged L 2-Regularization

Learning to differentiate model distributions from observed data is a fundamental problem in statistics and machine learning, and high-dimensional data remains a challenging setting for such problems. Metrics that quantify the disparity in probability distributions, such as the Stein discrepancy, play an important role in high-dimensional statistical testing. In this paper, we investigate the role of <inline-formula> <tex-math notation="LaTeX">$L^{2}$ </tex-math></inline-formula> regularization in training a neural network Stein critic so as to distinguish between data sampled from an unknown probability distribution and a nominal model distribution. Making a connection to the Neural Tangent Kernel (NTK) theory, we develop a novel staging procedure for the weight of regularization over training time, which leverages the advantages of highly-regularized training at early times. Theoretically, we prove the approximation of the training dynamic by the kernel optimization, namely the “lazy training”, when the <inline-formula> <tex-math notation="LaTeX">$L^{2}$ </tex-math></inline-formula> regularization weight is large, and training on <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> samples converge at a rate of <inline-formula> <tex-math notation="LaTeX">${O}(n^{-1/2})$ </tex-math></inline-formula> up to a log factor. The result guarantees learning the optimal critic assuming sufficient alignment with the leading eigen-modes of the zero-time NTK. The benefit of the staged <inline-formula> <tex-math notation="LaTeX">$L^{2}$ </tex-math></inline-formula> regularization is demonstrated on simulated high dimensional data and an application to evaluating generative models of image data.
Nonparametric Regression for 3D Point Cloud Learning

Over the past two decades, we have seen an exponentially increased amount of point clouds collected with irregular shapes in various areas. Motivated by the importance of solid modeling for point clouds, we develop a novel and efficient smoothing tool based on multivariate splines over the tetrahedral partitions to extract the underlying signal and build up a 3D solid model from the point cloud. The proposed smoothing method can denoise or deblur the point cloud effectively and provide a multi-resolution reconstruction of the actual signal. In addition, it can handle sparse and irregularly distributed point clouds and recover the underlying trajectory. The proposed smoothing and interpolation method also provides a natural way of numerosity data reduction. Furthermore, we establish the theoretical guarantees of the proposed method. Specifically, we derive the convergence rate and asymptotic normality of the proposed estimator and illustrate that the convergence rate achieves the optimal nonparametric convergence rate. Through extensive simulation studies and a real data example, we demonstrate the superiority of the proposed method over traditional smoothing methods in terms of estimation accuracy and efficiency of data reduction.
Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure

Novel test selectors used in simulation-based verification have been shown to significantly accelerate coverage closure regardless of the number of coverage holes. This paper presents a configurable and highly-automated framework for novel test selection based on neural networks. Three configurations of this framework are tested with a commercial signal processing unit. All three convincingly outperform random test selection with the largest saving of simulation being 49.37% to reach 99.5% coverage. The computational expense of the configurations is negligible compared to the simulation reduction. We compare the experimental results and discuss important characteristics related to the performance of the configurations.
Pre-trained Gaussian processes for Bayesian optimization

Bayesian optimization (BO) has become a popular strategy for global optimization of expensive real-world functions. Contrary to a common expectation that BO is suited to optimizing black-box functions, it actually requires domain knowledge about those functions to deploy BO successfully. Such domain knowledge often manifests in Gaussian process (GP) priors that specify initial beliefs on functions. However, even with expert knowledge, it is non-trivial to quantitatively define a prior. This is especially true for hyperparameter tuning problems on complex machine learning models, where landscapes of tuning objectives are often difficult to comprehend. We seek an alternative practice for setting these functional priors. In particular, we consider the scenario where we have data from similar functions that allow us to pre-train a tighter distribution a priori. In this work, we detail what pre-training entails for GPs using a KL divergence based loss function, and propose a new pre-training based BO framework named HyperBO. Theoretically, we show bounded posterior predictions and near-zero regrets for HyperBO without assuming the"ground truth"GP prior is known. To verify our approach in realistic model training setups, we collect a large multi-task hyperparameter tuning dataset by training tens of thousands of configurations of near-state-of-the-art deep learning models on popular image and text datasets, as well as a protein sequence dataset. Our results show that on average, HyperBO is able to locate good hyperparameters at least 3 times more efficiently than the best competing methods on both our new tuning dataset and classic multi-task BO benchmarks.
Pharmacological Mechanism of Ganlu Powder in the Treatment of NASH Based on Network Pharmacology and Molecular Docking

Nonalcoholic steatohepatitis (NASH), a progression of nonalcoholic fatty liver disease (NAFLD), is a clinical syndrome characterized by liver steatosis, inflammation, and hepatocellular damage. Ganlu powder (GLP) is a classic traditional Chinese medicine prescription that has shown favorable treatment effects on NASH. However, the underlying therapeutic mechanisms are still poorly understood. This study is aimed at exploring the potential mechanism of GLP in the treatment of NASH via network pharmacology and molecular docking. PubMed and CNKI databases were used to identify the components of GLP. Swiss and STITCH databases were employed to obtain corresponding drug targets. NASH targets were adopted from the Therapeutic Target Database (TTD), DisGeNET, DrugBank, GeneCards, and MalaCards databases. Cytoscape software was utilized to construct “drug-ingredient-target-disease” networks and the protein-protein interaction (PPI) network of GLP in NASH. AKT1 was identified as the key target. The GO functional enrichment analysis revealed that GLP might treat NASH by modulating the inflammatory response and regulating phosphatidylinositol 3-kinase signaling. The KEGG analysis showed that GLP might treat NASH by regulating the tumor necrosis factor (TNF) signal pathway by affecting the role of AKT1. According to the network pharmacology results, a virtual docking of active compounds with AKT1 was carried out, and the results indicated that the 7 components, berberine, epiberberine, jatrorrhizine, coptisine, palmatine, evodiamine, and rutecarpine, can bind stably with AKT1 and have higher binding energy than AKT1 inhibitors. The overall study findings suggest that GLP may treat NASH by regulating AKT1.
Combination of compressed sensing-based iterative reconstruction and offset acquisition for I-123 FP-CIT SPECT: a simulation study

Objective(s): The purpose of this study was to validate undersampled single-photon emission computed tomography (SPECT) imaging using a combination of compressed sensing (CS) iterative reconstruction (CS-IR) and offset acquisition. Methods: Three types of numerical phantoms were used to evaluate image quality and quantification derived from CS with offset acquisition. SPECT images were reconstructed using filtered back-projection (FBP), maximum likelihood-expectation maximization (ML-EM), CS-IR, and CS-IR with offset acquisition. The efficacy of CS-IR with offset acquisition was examined in terms of spatial resolution, aspect ratio (ASR), activity concentration linearity, contrast, percent coefficient of variation (%CV), and specific binding ratio (SBR). Results: The full widths at half maximum remained unchanged as the number of projections decreased in CS-IR with offset acquisition. Changes in ASRs and linearities of count density were observed for ML-EM and CS-IR from undersampled projections. The %CV obtained by CS-IR with offset acquisition was substantially lower than that obtained by ML-EM and CS-IR. There were no significant differences between the %CVs obtained from 60 projections by CS-IR with offset acquisition and from 120 projections by FBP. Although the SBRs for CS-IR with offset acquisition tended to be slightly lower than for FBP, the SBRs for CS-IR with offset acquisition did not change with the number of projections. Conclusions: CS-IR with offset acquisition can provide good image quality and quantification compared with a commonly used SPECT reconstruction method, especially from undersampled projection data. Our proposed method could shorten overall SPECT acquisition times, which would benefit patients and enable quantification with dynamic SPECT acquisitions.
Saam Acupuncture for Treating Functional Dyspepsia: A Feasibility Randomized Controlled Trial

Objective Functional dyspepsia (FD) is a common gastrointestinal disorder that significantly affects sufferers' quality of life and increases the economic burden on society. Saam acupuncture, a form of traditional Korean acupuncture, is frequently used to treat FD in Korean medicine clinical settings. This study aimed to evaluate the feasibility and preliminary effectiveness and safety of Saam acupuncture for treating FD. Methods We conducted a pilot, pragmatic, assessor-blinded randomized controlled trial. Patients with FD according to the ROME III criteria were randomly allocated to an acupuncture plus usual care group or a usual care group. Saam acupuncture based on individualized FD and systemic symptoms was conducted in the acupuncture group three times per week for 4 weeks. Study feasibility outcomes, including recruitment, completion, and acupuncture adherence rates, were calculated. In addition, preliminary evaluation of participant responses to the intervention was tested using the gastrointestinal symptom (GIS), FD-related quality of life (FD-QoL), visual analog scale (VAS), patient global assessment (PGA), and EuroQol-5 Dimensions (EQ-5D) scores. Results Twenty-four participants who met the eligibility criteria were included. The recruitment and completion rates of the clinical trials were 60% and 79.2%, and the acupuncture adherence rate was 83.3%. Although there was no significant difference between the two groups in the dyspepsia symptoms as measured by GIS, VAS, and PGA at Week 4, significant differences were found between the two groups at the follow-up assessments (Weeks 8 and 12). In particular, the early satiety subscore of GIS was significantly improved in the Saam acupuncture group compared with the usual care group at Week 4. The quality of life measured by FD-QoL and EQ-5D improved only in the Saam acupuncture group, although there were no significant differences between the two groups. No adverse events related to Saam acupuncture were reported. Conclusions Saam acupuncture can be a feasible, preliminarily effective, and safe treatment for FD. Further confirmatory trials with a larger sample size are needed to confirm its effectiveness and safety. The trail is registered with CRIS-KCT0000164, URL: https://cris.nih. go.kr/cris/search/detailSearch.do/2098.
The Differences of Metabolites in Different Parts of the Brain Induced by Shuxuetong Injection against Cerebral Ischemia-Reperfusion and Its Corresponding Mechanism

Ischemic stroke is often associated with a large disease burden. The existence of ischemia-reperfusion injury brings great challenges to the treatment of ischemic stroke. The purpose of this study was to explore the differences of metabolites in different parts of the brain induced by Shuxuetong injection against cerebral ischemia-reperfusion and to extend the corresponding mechanism. The rats were modeled by transient middle cerebral artery occlusion (t-MCAO) operation, and the success of modeling was determined by neurological function score and TTC staining. UPLC-Q/TOF-MS metabolomics technique and multivariate statistical analysis were used to analyze the changes and differences of metabolites in the cortex and hippocampus of cerebral ischemia-reperfusion rats. Compared with the model group, the neurological function score and cerebral infarction volume of the Shuxuetong treatment group were significantly different. There were differences and changes in the metabolic distribution of the cortex and hippocampus in each group, the distribution within the group was relatively concentrated. The separation trend between the groups was obvious, and the distribution of the Shuxuetong treatment group was similar to that of the sham operation group. We identified 13 metabolites that were differentially expressed in the cortex, including glutamine, dihydroorotic acid, and glyceric acid. We also found five differentially expressed metabolites in the hippocampus, including glutamic acid and fumaric acid. The common metabolic pathways of Shuxuetong on the cortex and hippocampus were D-glutamine and D-glutamate metabolism and nitrogen metabolism, which showed inhibition of cortical glutamine and promotion of hippocampal glutamic acid. Specific pathways of Shuxuetong enriched in the cortex included glyoxylate and dicarboxylate metabolism and pyrimidine metabolism, which showed inhibition of glyceric acid and dihydroorotic acid. Specific pathways of Shuxuetong enriched in the hippocampus include arginine biosynthesis and citrate cycle (TCA cycle), which promotes fumaric acid. Shuxuetong injection can restore and adjust the metabolic disorder of the cortex and hippocampus in cerebral ischemia-reperfusion rats. The expression of Shuxuetong in different parts of the brain is different and correlated.
Numerical and Experimental Investigations on the Stress Distribution of Bolted Glass Connections under In-Plane Loads

To calculate the loadbearing of bolted connections in glass panels under the application of in-plane loads exact knowledge of the contact mechanisms and the parameters which influence the stress distribution around the hole is necessary. At first analytical and numerical solutions of contact between a bolt and a conforming hole in a panel are formulated to determine the stress distribution. An approach for verifying the numerical model with the analytical solutions is developed. To determine relevant results for design the verified FEM-model is used to examine the influences of different parameters on the stress distribution around the hole. In addition experimental investigations are carried out in order to check the results of the FEM-calculations and the analytical solutions.
Sound Radiation of Aerodynamically Excited Flat Plates into Cavities

Flow-induced vibrations and the sound radiation of flexible plate structures of different thickness mounted in a rigid plate are experimentally investigated. Therefore, flow properties and turbulent boundary layer parameters are determined through measurements with a hot-wire anemometer in an aeroacoustic wind tunnel. Furthermore, the excitation of the vibrating plate is examined by laser scanning vibrometry. To describe the sound radiation and the sound transmission of the flexible aluminium plates into cavities, a cuboid-shaped room with adjustable volume and 34 flush-mounted microphones is installed at the non flow-excited side of the aluminium plates. Results showed that the sound field inside the cavity is on the one hand dependent on the flow parameters and the plate thickness and on the other hand on the cavity volume which indirectly influences the level and the distribution of the sound pressure behind the flexible plate through different excited modes.
A1C to Detect Diabetes in Healthy Adults

OBJECTIVE To evaluate the optimal interval for rechecking A1C levels below the diagnostic threshold of 6.5% for healthy adults. RESEARCH DESIGN AND METHODS This was a retrospective cohort study. Participants were 16,313 apparently healthy Japanese adults not taking glucose-lowering medications at baseline. Annual A1C measures from 2005 to 2008 at the Center for Preventive Medicine, a community teaching hospital in Japan, estimated cumulative incidence of diabetes. RESULTS Mean age (±SD) of participants was 49.7 ± 12.3 years, and 53% were male. Mean A1C at baseline was 5.4 ± 0.5%. At 3 years, for those with A1C at baseline of <5.0%, 5.0–5.4%, 5.5–5.9%, and 6.0–6.4%, cumulative incidence (95% CI) was 0.05% (0.001–0.3), 0.05% (0.01–0.11), 1.2% (0.9–1.6), and 20% (18–23), respectively. CONCLUSIONS In those with an A1C <6.0%, rescreening at intervals shorter than 3 years identifies few individuals (∼≤1%) with an A1C ≥6.5%.