Miracle-dz/UniFAR-FTUs huggingface dataset:
ScaleFormer: Transformer-based speech enhancement in the multi-scale time domain

Processing speech at multiple temporal scales greatly improves the performance of automatic speech recognition, but its effect has not been fully exploited in speech enhancement tasks. In this study, we propose a novel Transformer-based neural network termed ScaleFormer, which analyzes speech at multiple temporal resolutions. In ScaleFormer, we utilize an encoder that employs multi-scale convolution to extract different temporal scale features. Then, an intra-scale transformer is used to extract the representation within each scale. After obtaining the output of the intra-scale transformer, an inter-scale transformer is used to model the relationship between multiple scales. All transformer block in ScaleFormer is designed with a dual-path framework to learn short and long-term dependencies. We conduct the experiments on the WSJ0 SI-84 corpus. Experimental results show that our approach outperforms previous representative systems in terms of objective metrics.
Study of Gastropod Community Structure After Development of Tourism Activities on the Senjoyo River

It is thought that the development of the Senjoyo River as a tourist attraction in Semarang Regency will cause changes to the natural ecosystem in the area, one of which is changing the structure of the existing gastropod community. This research is a quantitative analysis research using a survey method that aims to examine the structure of the gastropod community after the development of tourism activities on the Senjoyo River. Gastropod samples were taken from 10 different observation stations along the Senjoyo River, which are located at 7˚22'23"S and 110˚31'37"E. Observation station locations were determined by the purposive random sampling method, based on tourism activity density. The result showed types of gastropods that were found in the Senjoyo River namely, Sulcospira testudinaria, Tarebia granifera, Brotia costula, Thiara scraba, and Melanoides tuberculata with clustered distribution patterns (Id>1). Statistical analysis showed that location affected gastropod abundance (Asymp. Sig of 0.043), with the highest abundance found at station seven (45,11 individuals/m2). So station seven, with sandy and muddy substrate conditions, moderate current speed (0.257 m/s), and rarely found human activity, is the most suitable location to support the survival of gastropods in the Senjoyo River. Furthermore, the gastropod community structure indicates environmental changes at observation stations nine and ten, the locations where the most activities and construction of tourism facilities were found. That was indicated by the low diversity (H’= 0.00-0.01) and evenness (E = 0.00) index of gastropods, and there is dominance by Sulcospira testudinaria (C= 0.98-1.00).
Ready-to-Eat Foods: A Potential Vehicle for the Spread of Coagulase-Positive Staphylococci and Antimicrobial-Resistant Staphylococcus aureus in Buea Municipality, South West Cameroon

The consumption of ready-to-eat (RTE) foods contaminated with coagulase-positive staphylococci (CoPS) and especially Staphylococcus aureus puts consumers at a potential risk of food-borne disease or colonization and subsequent infection. This cross-sectional study determined the levels of CoPS and the presence of S. aureus in RTE foods sold in Buea municipality. A total of 420 RTE food samples, comprising 70 each of cake, bread, fruit salad, meat hot-pot, suya, and boiled rice were randomly purchased from February to August 2020. The CoPS counts were determined by culturing on Baird-Parker agar, and S. aureus was identified by amplification of the nuc gene using the polymerase chain reaction. All S. aureus isolates were screened for the presence of classical staphylococcal enterotoxin genes. To determine antimicrobial resistance profiles, each isolate was tested against 11 antimicrobials. Oxacillin-resistant S. aureus strains were analyzed for the presence of the mecA gene. Overall, 161 (38.3%) samples had detectable levels of CoPS ranging from 2.0 to 5.81 log10 CFU/g. Based on CoPS levels, 37 (8.81%) of the 420 RTE food samples–only fruit salad and meat hot-pot, had unsatisfactory microbiological quality. A total of 72 S. aureus isolates, comprising 52.78% from fruit salad, 16.67% from meat hot-pot, 12.5% from boiled rice, 9.72% from suya, 5.56% from bread, and 4.17% from cake, were recovered. None of the S. aureus isolates possessed any of the classical enterotoxin genes. All the isolates were susceptible to vancomycin and ofloxacin, while 68 (94.44%) and 66 (91.67%) were susceptible to oxacillin and ciprofloxacin, respectively. Resistance to penicillin (93.06%) was highest, followed by amoxicillin (91.67%) and erythromycin (79.17%). Four isolates were identified as methicillin-resistant S. aureus, all of which carried the mecA gene. A total of 24 antibiotypes were identified. Our findings showed that RTE foods sold in the Buea municipality are likely vehicles for the transmission of CoPS and antimicrobial-resistant S. aureus.
ChatGPT-4 and Human Researchers Are Equal in Writing Scientific Introduction Sections: A Blinded, Randomized, Non-inferiority Controlled Study

Background Natural language processing models are increasingly used in scientific research, and their ability to perform various tasks in the research process is rapidly advancing. This study aims to investigate whether Generative Pre-trained Transformer 4 (GPT-4) is equal to humans in writing introduction sections for scientific articles. Methods This randomized non-inferiority study was reported according to the Consolidated Standards of Reporting Trials for non-inferiority trials and artificial intelligence (AI) guidelines. GPT-4 was instructed to synthesize 18 introduction sections based on the aim of previously published studies, and these sections were compared to the human-written introductions already published in a medical journal. Eight blinded assessors randomly evaluated the introduction sections using 1-10 Likert scales. Results There was no significant difference between GPT-4 and human introductions regarding publishability and content quality. GPT-4 had one point significantly better scores in readability, which was considered a non-relevant difference. The majority of assessors (59%) preferred GPT-4, while 33% preferred human-written introductions. Based on Lix and Flesch-Kincaid scores, GPT-4 introductions were 10 and two points higher, respectively, indicating that the sentences were longer and had longer words. Conclusion GPT-4 was found to be equal to humans in writing introductions regarding publishability, readability, and content quality. The majority of assessors preferred GPT-4 introductions and less than half could determine which were written by GPT-4 or humans. These findings suggest that GPT-4 can be a useful tool for writing introduction sections, and further studies should evaluate its ability to write other parts of scientific articles.
Performance Comparison of ZDAC vs MTPA for Permanent Magnet Synchronous Motor (PMSM) Control

The permanent magnet synchronous motor (PMSM) consists of two types, namely surface mounted PMSM (SPMSM) and interior PMSM (IPMSM). IPMSM has the potential to generate a greater torque than SPMSM due to its torque reluctance. To exploit this potential, a comparison of two control methods is conducted, namely ZDAC (zero d-axis current control) vs MTPA (maximum torque per ampere) control. Testing between these two control methods was carried out with variations in speed and load, and it was found that ZDAC could not maximize reluctance torque, because of that limiting the torque capability. MTPA control is applied to optimize efficiency. Compared to ZDAC or $Id=\mathbf{0}$ A, MTPA control can produce maximum electromagnetic torque with smaller stator current values. Therefore, in the constant torque region or below the base speed, the IPMSM is controlled with MTPA to obtain better efficiency in motor operation.
Adaptive Beamforming Based on Interference-Plus-Noise Covariance Matrix Reconstruction for Speech Separation

Estimating the interference-plus-noise covariance matrix (INCM) is critical for the robustness of the minimum variance distortionless response (MVDR) beamformer. Existing INCM reconstruction methods are computationally intensive and not suitable for real-time speech separation. We propose a singular value decomposition (SVD) based INCM reconstruction method for speech separation. The spatial covariance matrix (SCM) for each source is obtained by rank-1 approximation using the nominal steering vector (SV) or the pre-measured relative transfer function (RTF). The INCM used to separate each source is reconstructed as the sum of the covariance matrices of interference and spherical isotropic noise. The proposed method is evaluated using the mixed signal received by a circular array with six microphones placed in a simulated reverberation chamber. The results show that the proposed method has comparable sound quality performance to the reference method, but requires much less computation.
Understanding barriers, recommended solutions, and future prospects for the diagnosis and management of Pythium insidiosum keratitis

Pythium insidiosum keratitis (PIK) is a devastating corneal infection resulting in blindness in a large number of cases. Clinically and morphologically, it closely mimics fungal keratitis, and hence is also labeled as “parafungus. ” Although many clinical studies have documented evidence regarding the virulence of microorganism, and anatomical and functional outcomes, it remains a clinical challenge and diagnostic dilemma for most clinicians. Till today, PIK is being diagnosed and treated with certainty at only limited centers across the globe. But the question is why this is so? Taking this as the research question, this section on current ophthalmology aims to highlight the understanding of barriers to diagnosing and treating PIK, the suggestions to improve diagnosis and treatment, and the future prospects.
A Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model

Computer vision technologies have greatly improved in the last few years. Many problems have been solved using deep learning merged with more computational power. Action recognition is one of society's problems that must be addressed. Human Action Recognition (HAR) may be adopted for intelligent video surveillance systems, and the government may use the same for monitoring crimes and security purposes. This paper proposes a deep learning-based HAR model, i.e., a 3-dimensional Convolutional Network with multiplicative LSTM. The suggested model makes it easier to comprehend the tasks that an individual or team of individuals completes. The four-phase proposed model consists of a 3D Convolutional neural network (3DCNN) combined with an LSTM multiplicative recurrent network and Yolov6 for real-time object detection. The four stages of the proposed model are data fusion, feature extraction, object identification, and skeleton articulation approaches. The NTU-RGB-D, KITTI, NTU-RGB-D 120, UCF 101, and Fused datasets are some used to train the model. The suggested model surpasses other cutting-edge models by reaching an accuracy of 98.23%, 97.65%, 98.76%, 95.45%, and 97.65% on the abovementioned datasets. Other state-of-the-art (SOTA) methods compared in this study are traditional CNN, Yolov6, and CNN with BiLSTM. The results verify that actions are classified more accurately by the proposed model that combines all these techniques compared to existing ones.
The implementation of the nowcasting system INCA for Belgium: current status

In the beginning of 2010, the Royal Meteorological Institute of Belgium (RMI) started the implementation of the INCA system in Belgium: INCA-BE. INCA is a nowcasting system for the analysis and nowcasts of several meteorological fields, like temperature, humidity, wind, cloudiness, precipitation and precipitation type, and some derived fields like icing potential, wind chill, wind gusts and visibility. Several of these fields are analysed in a three-dimensional grid. For the calculation of these fields, INCA ingests a wide range of observational and model data: radar composites, data from automatic weather stations, satellite data and NWP output. The nowcasts of the fields gradually converge towards the NWP forecast for longer lead times. INCA has been developed at the national meteorological service of Austria (ZAMG), and is currently used in some European countries as the operational nowcasting system. We report on the current status of the implementation of INCA at the RMI, and discuss some first results. Especially the performance of INCA in a domain with a coastal region and with a relatively flat topography compared to Austria, will be an important point of interest in the evaluation of the system.
Geometrically Constrained Blind Moving Source Extraction based on Constant Separation Vector and Auxiliary Function Technique

This paper focuses on the permutation problem in Blind Source Extraction (BSE) by employing beamforming-based Geometrical Constraints (GC). Specifically, we focus on the recently proposed auxiliary function-based Independent Vector Extraction (Aux-IVE) with Constant Separation Vector (CSV) mixing model. Building upon this mixing model, we provide the source signals’ spatial information, utilizing GC based on beamforming. We facilitate the extraction of moving target source and improve the extraction performance. Furthermore, we discuss the effect of the number of constraints on the extraction performance. Experimental results confirmed that our proposed method, GC-CSV-Aux-IVE, exhibits superior extraction performance and achieves nearly 100% accuracy in extracting the moving target source.
A revised surface age for the North Polar Layered Deposits of Mars

The North Polar Layered Deposits (NPLD) of Mars contain a complex stratigraphy that has been suggested to retain a record of past eccentricity‐ and obliquity‐forced climate changes. The surface accumulation rate in the current climate can be constrained by the crater retention age. We scale NPLD crater diameters to account for icy target strength and compare surface age using a new production function for recent small impacts on Mars to the previously used model of Hartmann (2005). Our results indicate that ice is accumulating in these craters several times faster than previously thought, with a 100 m diameter crater being completely infilled within centuries. Craters appear to have a diameter‐dependent lifetime, but the data also permit a complete resurfacing of the NPLD at ~1.5 ka.
Interaction Mechanism Characterized by Bond Performance and Diffusion Performance between TiO2@LDO and Asphalt Based on Molecular Dynamics Simulation

In order to study the interaction between composite photocatalytic material TiO2@LDO and matrix asphalt, the four-component 12 molecular structure model of 70# matrix asphalt was optimized by using software Materials Studio 2020, and its heterostructure with TiO2@LDO composite was modeled. The bonding performance between asphalt and composite photocatalytic material was analyzed by interface energization, and the diffusion performance between asphalt and composite photocatalytic material was analyzed from the perspectives of particle movement and Z-direction density. By changing the temperature and other parameters in the simulation process, the change in bonding strength between TiO2@LDO and asphalt was investigated. Through the calculation and analysis of interaction energy, it was found that the adsorption and bonding strength between asphalt and TiO2@LDO were the strongest at 40 °C. At the same time, the diffusion performance was studied, and it was found that the molecular diffusion distribution of TiO2@LDO was more extensive at 60 °C, which laid the foundation for further blending of asphalt and TiO2@LDO. The simulation results show that TiO2@LDO molecules have a certain attraction to asphalt molecules and can modify the matrix asphalt to some extent.
Jiangtang Decoction Ameliorates Diabetic Kidney Disease Through the Modulation of the Gut Microbiota

Purpose This study aimed to elucidate the impact of Jiangtang decoction (JTD) on diabetic kidney disease (DKD) and its association with alterations in the gut microbiota. Methods Using a diabetic mouse model (KK-Ay mice), daily administration of JTD for eight weeks was undertaken. Weekly measurements of body weight and blood glucose were performed, while kidney function, uremic toxins, inflammation factors, and fecal microbiota composition were assessed upon sacrifice. Ultra-structural analysis of kidney tissue was conducted to observe the pathological changes. Results The study findings demonstrated that JTD improve metabolism, kidney function, uremic toxins and inflammation, while also exerting a modulatory effect on the gut microbiota. Specifically, the genera Rikenella, Lachnoclostridium, and unclassified_c_Bacilli exhibited significantly increased abundance following JTD treatment, accompanied by reduced abundance of norank_f_Lachnospiraceae compared to the model group. Importantly, Rikenella and unclassified_c_Bacilli demonstrated negative correlations with urine protein levels. Lachnoclostridium and norank_f_Lachnospiraceae were positively associated with creatinine (Cr), indoxyl sulfate (IS) and interleukin (IL)-6. Moreover, norank_f_Lachnospiraceae exhibited positive associations with various indicators of DKD severity, including weight, blood glucose, urea nitrogen (UN), kidney injury molecule-1 (KIM-1) levels, trimethylamine-N-oxide (TMAO), p-cresyl sulfate (pCS), nucleotide-binding oligomerization domain (Nod)-like receptor family pyrin domain-containing 3 (NLRP3) and IL-17A production. Conclusion These findings suggested that JTD possess the ability to modulate the abundance of Rikenella, Lachnoclostridium, unclassified_c_Bacilli and norank_f_Lachnospiraceae within the gut microbiota. This modulation, in turn, influenced metabolic processes, kidney function, uremic toxin accumulation, and inflammation, ultimately contributing to the amelioration of DKD.
A reflection on the long-term water balance of the Upper Indus Basin

Rapid glacier retreats due to rising temperatures have been predicted in the Hindukush–Karakoram–Himalaya (HKKH). Recent findings indicate shrinking glaciers in parts of the Himalayas, affecting ice storage and ultimately water availability. Insights on ice storage of the HKKH remain controversial, where glaciers retreat in some parts, while surging in others. In high-altitude areas only few in-situ observations are available, leading to ambiguous closure of the hydrological balance. Objective of this paper is to analyze the closure for the Upper Indus Basin (UIB). A first-order analysis using long-term flow and precipitation records, estimates of evaporation and ice storage is performed. Satellite information, atmospheric reanalyses, in-situ observations and related uncertainty are independently investigated. Trend analysis of 50-year stream flow indicates a statistically insignificant decrease of basin outflow. Analysis of 100-year precipitation data at valley stations shows no significant long-term trend, whereas temperature has increased moderately. Estimates of evaporation and sublimation in the HKKH system are notably few. Findings suggest that a substantial loss of ice in the UIB during the 1999–2009 decade is unlikely. Ice storage is probably at equilibrium or under slight accumulation, as indicated by recent altimetry studies in the Karakoram. In the UIB there is no evidence for intermediate-term risk to water supply as suggested in recent literature.
Potential Tumor Suppressor Role of Polo-like Kinase 5 in Cancer

Simple Summary Cancer is a complex disease and the underlying molecular mechanisms driving cancer initiation and progression is crucial for the development of effective approaches for cancer management. Exploring the molecular landscape of cancer can shed light on novel biomarkers, therapeutic targets, and strategies for personalized medicine. In this study, we determined the potential involvement of PLK5 in multiple cancers employing cancer tissue microarrays (TMAs). We found a downregulation of PLK5 in these cancers. Further, using publicly available GTEx and TCGA databases, we validated our findings and extended our investigations to additional cancer types. Overall, our data suggested a potential tumor suppressor role for PLK5. Abstract The polo-like kinase (PLK) family of serine/threonine kinases contains five members (PLK1–5). Most PLKs are involved in cell cycle regulation and DNA damage response. However, PLK5 is different as it lacks a functional kinase domain and is not involved in cell cycle control. PLK5 remains the least-studied family member, and its role in oncogenesis remains enigmatic. Here, we identified tissues with high PLK5 expression by leveraging the Protein Atlas and GTEx databases with relevant literature and selected ovarian, lung, testis, endometrium, cervix, and fallopian tube tissues as candidates for further investigation. Subsequently, we performed immunohistochemical staining for PLK5 on multiple tissue microarrays followed by Vectra scanning and quantitative inForm analysis. This revealed consistently downregulated PLK5 expression in these cancers compared to normal tissues. To validate and extend our findings, we performed pan-cancer analysis of PLK5 expression using public RNAseq databases (TCGA and GTEx). We found PLK5 is downregulated in 18 cancer types, including our selected candidates. Interestingly, we also observed PLK5 expression remains consistently low in later stages of cancer, suggesting PLK5 may have a greater role in tumor initiation than cancer progression. Overall, our study demonstrates PLK5 downregulation in multiple cancers, highlighting its role as a tumor suppressor.
Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Probabilistic Programs

We describe AOS, the first general-purpose system for model-based control of autonomous robots using AI planning that fully supports partial observability and noisy sensing. The AOS provides a code-based language for specifying a generative model of the system, making model specification easier and model sampling efficient. It provides a language for specifying the relation between the model and the code, using which it auto-generates all required integration code. This allows Plug'n Play behavior, which facilitates incremental and modular system design. Extensive experiments on real and simulated robotic platforms demonstrate these advantages.
Instance Implant-Aided Non-uniformly Cropping for Person Detection in Aerial Images

Human inspection of aerial-based scenarios is a time-consuming, labor-intensive, and error-prone task. However, current aerial-vision-based object detection techniques have not adequately improved the detection performance of persons with sparse and weak features. In this article, a novel approach is proposed to automatically detect persons in aerial images. Specifically, numerous and multiscale instances are first implanted on fresh background images with a copy-paste mechanism to collect rich training data. Then, cropping high-resolution images into irregular sizes with density maps in the training stage. Lastly, hyper-inferencing on the raw and sliced blocks of the image by merging these overlap results to find persons. Experiments were run on the original Heridal datasets against eight approaches of popular object detectors. The metrics of precision, APtest@0.5, and APtest@0.5:0.95 achieved 0.9277 (+2.77%), 0.8012, and 0.547, respectively. Meanwhile, our proposed method is being evaluated on the modified Heridal and a number of publicly available datasets to make a more robust person detector in aerial images.