Miracle-dz/UniFAR-FTUs huggingface dataset:
Communication patterns in engineering enterprise social networks: an exploratory analysis using short text topic modelling

Abstract Enterprise social network messaging sites are becoming increasingly popular for team communication in engineering and product design. These digital communication platforms capture detailed messages between members of the design team and are an appealing data set for researchers who seek to better understand communication in design. This exploratory study investigates whether we can use enterprise social network messages to model communication patterns throughout the product design process. We apply short text topic modelling (STTM) to a data set comprising 250,000 messages sent by 32 teams enrolled in a 3-month intensive product design course. Many researchers describe the engineering design process as a series of convergent and divergent thinking stages, such as the popular double diamond model, and we use this theory as a case study in this work. Quantitative and qualitative analysis of STTM results reveals several trends, such as it is indeed possible to see evidence of cyclical convergence and divergence of topics in team communication; within the convergence–divergence pattern, strong teams have fewer topics in their topic models than weaker teams; and teams show characteristics of product, project, course, and other themes within each topic. We provide evidence that the analysis of enterprise social networking messages, with advanced topic modelling techniques, can uncover insights into design processes and can identify the communication patterns of successful teams.
The benthic foraminiferal response to the mid-Maastrichtian event in the NW-European chalk sea of the Maastrichtian type area

Abstract The mid-Maastrichtian carbon isotope event (MME), dated at ∼69 Ma, reflects a perturbation of the global carbon cycle that, in part, correlates with the enigmatic global extinction of ‘true’ (i.e., non-tegulated) inoceramid bivalves. The mechanisms of this extinction event are still debated. While both the inoceramid extirpation and MME have been recorded in a variety of deep-sea sites, little is known about their expression in epicontinental chalk seas. In order to study the shallow-marine signature of the MME in this epicontinental shelf sea, we have generated quantitative foraminiferal assemblage data for two quarries (Hallembaye, NE Belgium; ENCI, SE Netherlands) in the Maastrichtian type area, complemented by a species-specific benthic δ13C record. In contrast to deep-sea records, no significant changes in benthic foraminiferal assemblages and benthic foraminiferal accumulation rates are observed across the MME in the type-Maastrichtian area. At the Hallembaye quarry, the otherwise rare endobenthic species Cuneus trigona reaches a transient peak abundance of 33.3% at the onset of the MME, likely caused by a local transient change in organic matter flux to the seafloor. Nevertheless, high and near-constant species evenness shows that neither oxygen nor organic matter flux was limited across the extinction level or during the MME. Benthic foraminiferal data from the uppermost part of the studied section, above the MME, indicate a significant increase in food supply to the seafloor. Decreased amounts of terrigenous elements across this interval document a lesser riverine or aeolian influx, which means that the increased benthic productivity is linked to a different origin. Potentially, the continuous precipitation of chalk under nutrient-poor conditions in the Late Cretaceous chalk sea was enabled by efficient nutrient recycling in the water column. In shallower depositional settings, nutrient recycling took place closer to the seafloor, which allowed more organic matter to reach the bottom. These results provide insights in the importance of nutrient cycling for biological productivity in the NW-European chalk sea.
First Report of the Presence of Enterotoxin Gene in Coagulase-negative Staphylococci Recovered from Meat of Snails (Achatina Achatina)

Abstract Introduction It has been suggested that coagulase-negative staphylococci can serve as reservoirs of virulence genes for other bacteria. This study assessed the presence of such genes in selected isolates recovered from meat of the giant African snail (Achatina achatina). Material and Methods Virulence genes were detected using a polymerase chain reaction targeting specific primers. Two representative isolates were identified using 16S rRNA gene sequencing. Results The results showed that the staphylococcal enterotoxin A gene (sea) was present in five out of the eight isolates studied. The isolates expressed resistance mainly to three antibiotics: chloramphenicol, norfloxacin and cloxacillin in descending order of incidence. Most importantly, the Staphylococcus sciuri isolate NEDU 181, in addition to being resistant to the three aforementioned antibiotics, also harboured the sea gene. Conclusion Our findings demonstrate, for the first time, the presence of toxigenic and antibiotic-resistant coagulase-negative Staphylococcus spp. in commercially-available fresh snail meat. With staphylococcal enterotoxin A known to survive cooking temperature, this presents a food safety concern.
An LSTM-based Online Fault Diagnosis Method for Capacitor Voltage Transformer

Due to the popularization and importance of power system, the security and stability of power supply are paid more attention to than before. However, as a key equipment of monitoring the voltage in power system, capacitor voltage transformer (CVT) is vulnerable to ambient environments, leading to various kinds of faults, and it is often difficult to determine the specific fault in real situations, which could bring potential safety hazards to the power grid. Therefore, this paper proposes an LSTM-based CVT online fault diagnosis method. Specifically, according to the collected fault types and related circuit parameters of CVT, the fault parameter model is established. Then, the relevant parameters can be determined based on the collected CVT detection signal, and the normalization of original data is performed to generate the sample set for the LSTM neural network. Finally, experimental simulations show that this method can achieve up to 95% fault diagnosis accuracy, and has the better performance than BP and RNN models, indicating this method could effectively diagnose CVT faults online.
High Reliability Vehicle Motion Planning Algorithm Based on Optimal Control Principle

This paper presents a dynamic obstacle avoidance motion planning algorithm based on optimal control principle. Firstly, the condition model of non-intersect between the vehicle pose and the predicted obstacle trajectory and the condition model of the vehicle body in the highway are established through the relationship between the position of the vehicle pose in the connected coordinate system and the position of the four vertexes of the vehicle body, in addition, the kinematics model of the vehicle and the kinematics constraints of the vehicle constitute the constraints in the optimal control of the vehicle . aiming at the measurement of the termination state, the measurement of the energy consumption of vehicle movement and the riding comfort, constructed an aggregative performance index function. Finally, according to the constraint conditions of the constructed motion planning and the aggregative performance index function of the motion planning, establish the corresponding dynamic solving algorithm.
Sex‐related differences in cardiogenic shock: Can we do better in women?

Sex disparities in care and outcomes in patients with acute myocardial infarction (AMI), heart failure, and cardiogenic shock (CS) have been well documented. In the current report from a large representative national database, Osman and colleagues further expanded our knowledge in the field of healthcare disparities by examining sex differences in the incidence, management, and outcomes of patients with AMI ‐ and non ‐ AMI ‐ related CS, stratified by different age groups. 1 The investigators performed retrospective analyses from the National Inpatient Sample (NIS) database from 2004 to 2018 inclusive of 1,506,281 hospitalizations, of which nearly 40% were categorized as AMI ‐ CS and 60% as non ‐ AMI ‐ CS hospitalizations. The investigators found that the overall incidence of CS increased over time in both men and women, although women had lower incidence of AMI ‐ CS and non ‐ AMI ‐ CS across all age groups. A favorable trend of reduction in in ‐ hospital mortality was seen among all age groups and regardless of sex in CS patients, except in young AMI ‐ CS patients (20 – 44 years) in whom a notable uptrend was observed. After adjustment using propensity score matching, middle ‐ aged women (45 – 64 years) with AMI ‐ CS had higher in ‐ hospital mortality compared to their male counterparts, while women with non ‐ AMI ‐ CS experienced a higher mortality across all age categories (except among elderly patients ≥ 85 years). Sex disparities in the rates of revascularization and use of mechanical circulatory support (MCS) devices were also noted. The investigators are be congratulated on their well ‐ conducted analyses. The
Doa Estimation Via Coarray Tensor Completion with Missing Slices

In this paper, a coarray tensor completion-based direction-of-arrival (DOA) estimation method is proposed for coprime planar array. To perform Nyquist-matched coarray signal processing, the completion of the coarray tensor corresponding to an augmented discontinuous virtual array is pursued. However, it is difficult to impose a low-rank regularization on the incomplete coarray tensor with slices of missing elements for its completion. To solve this problem, a structural tensorization approach is designed to reshape the incomplete coarray tensor into one with distributed missing elements. As such, a coarray tensor completion problem based on tensor nuclear norm minimization is formulated to complete these missing elements. By exploiting the filled virtual array obtained from the completed coarray tensor, a super-resolution DOA estimation can be achieved in closed-form.
Experts Versus All-Rounders: Target Language Extraction for Multiple Target Languages

Target language extraction (TLE) is a novel task in the field of selective auditory attention, which seeks to extract all speech signals that are spoken in a target language from other sources in a multilingual cocktail party. In our prior studies, a TLE model was trained to extract a predefined, single target language, referred to as Single-TLE. In this paper, we extend the Single-TLE framework to Multi-TLE. Multi-TLE models can also extract all speech signals of one specific target language, but they are optimized on a set of multiple target languages during training. As such, they learn the characteristics of several target languages and can replace multiple Single-TLE models without retraining. We perform experiments on the GlobalPhoneMCP database and incorporate a dynamic language mixing scheme for training. The Multi-TLE model does not only outperform Single-TLE models, but when given a language ID as additional input, it is also able to extract the speech of a specific target language from a mixture which contains multiple learned target languages.
Exploiting Annotators’ Typed Description of Emotion Perception to Maximize Utilization of Ratings for Speech Emotion Recognition

The decision of ground truth for speech emotion recognition (SER) is still a critical issue in affective computing tasks. Previous studies on emotion recognition often rely on consensus labels after aggregating the classes selected by multiple annotators. It is common for a perceptual evaluation conducted to annotate emotional corpora to include the class “other,” allowing the annotators the opportunity to describe the emotion with their own words. This practice provides valuable emotional information, which, however, is ignored in most emotion recognition studies. This paper utilizes easy-accessed natural language processing toolkits to mine the sentiment of these typed descriptions, enriching and maximizing the information obtained from the annotators. The polarity information is combined with primary and secondary annotations provided by individual evaluators under a label distribution framework, creating a complete representation of the emotional content of the spoken sentences. Finally, we train multitask learning SER models with existing learning methods (soft-label, multi-label, and distribution-label) to show the performance of the novel ground truth in the MSP-Podcast corpus.
Regularization Using Denoising: Exact and Robust Signal Recovery

We consider the problem of signal reconstruction from linearly corrupted data using plug-and-play (PnP) regularization. As opposed to traditional sparsity-promoting regularizers, PnP uses an off-the-shelf denoiser within a proximal algorithm such as ISTA or ADMM for image reconstruction. Although PnP has become popular in the imaging community, its regularization capacity is not fully understood. For example, it is not known if PnP can in theory recover a signal from few noiseless measurements as in classical compressed sensing and if the recovery is robust. We explore these questions in this work and present some theoretical and experimental results. In particular, we prove that if the denoiser in question has low rank and if the ground- truth lies in the range of the denoiser, then it can be recovered exactly from noiseless measurements. To the best of knowledge, this is first such result. Furthermore, we show using numerical simulations that even if the aforementioned conditions are violated, PnP recovery is robust in practice. We formulate a theorem regarding the recovery error based on these observations.
Convolutional Weighted Minimum Mean Square Error Filter for Joint Source Separation and Dereverberation

Practical scenarios with multiple simultaneously active speakers recorded using one or more microphones in reverberant rooms pose a challenging problem when the extraction of the desired speaker signal is sought for. The majority of techniques found in the literature facilitate either source separation or dereverberation, which can at best be performed as subsequent, cascade processing. Recently, a solution to the joint task has been proposed, which is known as the weighted power minimization distortionless response (WPD) beamformer. In this paper, we derive a convolutional multichannel filter which performs jointly optimum dereverberation and desired source signal extraction. We formulate a single optimization criterion which minimizes the convolutional source-variance weighted mean square error (CW-MMSE), thereby effectively unifying the weighted prediction error (WPE) based dereverberation and MMSE filtering for the desired source extraction from reverberant mixtures of speakers. Experimental results show a significant performance improvement over the compared state-of-the-art methods such as WPD for datasets with simulated and recorded impulse responses.
Automatic Depression Level Assessment from Speech By Long-Term Global Information Embedding

Depression is a serious mood disorder which brings negative effects on people's social activities. Therefore, growing attention has been paid to automatic depression assessment, especially from speech. However, most of the previous work uses hand-crafted features or deep neural network-based feature extractors to obtain deep features and then feed them into a classifier or a regression, which ignores the temporal relation of these features. To address this issue, this paper proposes a global information embedding (GIE) to make use of the long-term global information of depression and re-weight the LSTM output sequence. The short-term features are then pooled into long-term features by LASSO optimization to further improve the accuracy of depression recognition. Experiments on AVEC 2013 and AVEC 2014 verified the proposed method, and the RMSEs are 9.63 and 9.40, respectively.
Transformer-Based Domain Adaptation for Event Data Classification

Event cameras encode the change of brightness into events, differing from conventional frame cameras. The novel working principle makes them to have stronger potential in high-speed applications. However, the lack of labeled event annotations limits the applications of such cameras in deep learning frameworks, making it appealing to study more efficient deep learning algorithms and architectures. This paper devises the Convolutional Transformer Network (CTN) for processing event data. The CTN enjoys the advantages of convolution networks and transformers, presenting stronger capability in event-based classification tasks compared with existing models. To address the insufficiency issue of annotated event data, we propose to train the CTN via the source-free Unsupervised Domain Adaptation (UDA) algorithm leveraging large-scale labeled image data. Extensive experiments verify the effectiveness of the UDA algorithm. And our CTN outperforms recent state-of-the-art methods on event-based classification tasks, suggesting that it is an effective model for this task. To our best acknowledge, it is an early attempt of employing vision transformers with the source-free UDA algorithm to process event data.
Modulo Event-Driven Sampling: System Identification and Hardware Experiments

In event-driven sampling (EDS) the signal is represented in terms of a series of spikes at non-uniform time locations. Owing to the limited dynamic range (DR), just like how conventional analog-to-digital converters (ADC) suffer from signal saturation, in EDS a similar manifestation is observed. Namely, when the input exceeds a threshold, no output spikes are generated. Recently, the Unlimited Sensing Framework (USF) was presented to overcome the DR limitation. In USF, the key idea is to fold the signal using a modulo non-linearity so that its DR is fixed. Subsequently, we combined EDS with USF leading to a new architecture called Modulo Event-Driven Sampling (MEDS), where a modulo signal is input to the EDS model. The goal of this work is to bridge the gap between theory and practice for a MEDS model. Our hardware experiments suggest that for the MEDS approach to work, there are system parameters that must be identified beforehand so that accurate reconstruction of the input is possible. To this end, we introduce a system identification methodology for MEDS that is backed by theoretical guarantees. Using synthetic and experimental data, we validate the performance of our approach, thus demonstrating the utility of system identification.
Proximal-Based Adaptive Simulated Annealing for Global Optimization

Simulated annealing (SA) is a widely used approach to solve global optimization problems in signal processing. The initial non-convex problem is recast as the exploration of a sequence of Boltzmann probability distributions, which are increasingly harder to sample from. They are parametrized by a temperature that is iteratively decreased, following the so-called cooling schedule. Convergence results of SA methods usually require the cooling schedule to be set a priori with slow decay. In this work, we introduce a new SA approach that selects the cooling schedule on the fly. To do so, each Boltzmann distribution is approximated by a proposal density, which is also sequentially adapted. Starting from a variational formulation of the problem of joint temperature and proposal adaptation, we derive an alternating Bregman proximal algorithm to minimize the resulting cost, obtaining the sequence of Boltzmann distributions and proposals. Numerical experiments in an idealized setting illustrate the potential of our method compared with state-of-the-art SA algorithms.
On The Convergence of ADAM-Type Algorithms for Solving Structured Single Node and Decentralized Min-Max Saddle Point Games

Many modern machine learning problems require solving min-max saddle point games, whose computational complexity is NP-hard in general. To overcome this issue, most available algorithms aim for finding a first-order Nash equilibrium solution that always exists under mild assumptions. However, the proposed algorithms for obtaining such solutions are non-adaptive and also exhibit slow convergence rates in real settings. Further, most algorithms are centralized in nature and cannot be adapted to a decentralized architecture in a straightforward manner. This study aims to address these issues by introducing general two-step adaptive algorithms for obtaining first-order Nash equilibrium solutions of min-max games in both single-node and decentralized architectures. We also obtain the non-asymptotic convergence rates of the algorithms assuming the objective functions satisfy the weak Minty variational inequality condition which is standard in recent literature. Finally, we illustrate the performance of the proposed algorithms by using them to train neural networks which are more robust against adversarial attacks compared to neural networks trained using existing algorithms.
An Audio-Saliency Masking Transformer for Audio Emotion Classification in Movies

The process of perception to affective response of humans is gated by a bottom-up saliency mechanism at the sensory level. In specifics, auditory saliency emphasizes audio segments that need to be attended to cognitively appraise and experience emotion. In this work, inspired by this mechanism, we propose an end-to-end feature masking network for audio emotion recognition in movies. Our proposed Audio-Saliency Masking Transformer (ASTM) adjusts feature embedding using two learnable masks; one of them cross-refers to an auditory saliency map, and the other one is through self-reference. By joint training for front-end mask gating and the transformer as the back-end emotion classifier, we achieve three-class UARs improvement of 1.74%, 1.27%, 0.95%, 0.82% when comparing to the best of the other models on experienced arousal, experienced valence, intended arousal, and intended valence, respectively. We further analyze which acoustic feature categories that our saliency mask attends to the most.
Multiscale Attention Aggregation Network for 2D Vessel Segmentation

Vessel segmentation is essential for clinical diagnosis and surgical planning. However, it is quite challenging for automatic blood vessel segmentation due to low contrast, complex structure, and variable scale, especially when the annotated data is scarce. In this paper, we propose a novel multiscale attention aggregation network (MAA-Net) for vessel segmentation. In MAA-Net, based on a U-shaped encoder-decoder architecture, the dual attention module with scale factors is employed behind the decoder at each stage to generate multi-resolution feature maps adaptively weighted by channel and location attention. In this way, multiscale contextual information with long-range dependencies can be captured to tackle scale variations of vessels. Meanwhile, these attention feature maps are gradually integrated into multi-level aggregate supervision to assemble multiscale context information for refining segmentation results. The proposed method was evaluated on the retinal vessel and coronary angiography dataset (DRIVE and DCA1). Results demonstrate that MAA-Net achieves state-of-the-art performance for vessel segmentation. The code will be available at: https://github.com/lseventeen/MAA-Net-Vessel-Segmentation.