Miracle-dz/UniFAR-FTUs huggingface dataset:
Multilinear Jointly Sparse Robust Discriminant Regression

Tensor data, such as image, video, etc. is drawing more and more attention from researchers. Therefore, in this paper, we will focus on the tensor data, proposing a novel tensor-based feature extraction model. Previously, Lai et al. proposed Robust Discriminant Regression (RDR) by using L2, 1 -norm as basic metric to improve the robustness of model. However, since RDR is based on vectors, it requires high computation cost for optimization. In this paper, we propose an improved method called Multilinear Jointly Sparse RDR (MJSRDR), whose inputs are tensors. Therefore, the computation complexity of algorithm can be reduced. Besides, to obtain jointly sparse projections, we introduce L2, 1 -norm as regularization term. Experiments on various datasets verify the effectiveness of the model.
Speaker Diarization Based on Improved Loss Functions

The speaker embeddings need to have the characteristics of compactness within the class and large degree of separation between classes, while the traditional cross-entropy with softmax loss function only guarantees the class separability, resulting in the dispersion of learned features and poor generalization of the measurement space. Therefore, from the perspective of enhancing the discrimination of speaker embeddings, we improve the loss function of the speaker model in the system, which effectively improves the segmentation and clustering performance of system. On the one hand, we introduce the AM-Softmax loss function to the time delay neural network of baseline system. The AM-Softmax redefines the classification boundary through the additive angular margin, which effectively increases the inter-class distance and decreases intra-class distances of speaker embeddings. On the other hand, in order to better aggregate the features within the class, the center loss function is introduced to assist the cross-entropy with softmax loss function to jointly optimize the network. Applying the two improved speaker embeddings to the system, the results show that the segmentation clustering performance of the proposed methods on the DIHARD II development set is better than the baseline system.
The 2020 Climate Informatics Hackathon: Generating Nighttime Satellite Imagery from Infrared Observations

The 2020 Climate Informatics hackathon virtually gathered 40 participants to work on techniques for the generation of nighttime satellite imagery from infrared observations. In this paper, we discuss the data preparation process, the methods used by the participants, and the results of the privately held competition. A public version of the competition provides freely the data and is open on the Codalab platform to foster further research on this direction, it can be found here: https://competitions.codalab. org/competitions/26644.
N-DPC: Dense 3D Point Cloud Completion Based on Improved Multi-Stage Network

Point cloud shape completion aims to reconstruct complete point clouds from partial point clouds. The denser point clouds are, the richer information they contain. Different from existing methods that pay more attention to sparse completion of point clouds and global feature information of partial point clouds, this paper proposes a novel dense point cloud completion network called N-DPC, which combines self attention unit with the fusion of local feature and global feature information. First, we apply self attention unit to point clouds' global feature extraction to make it lay emphasis on the dependency between different points. Second, we adopt the method of multi-stage completion where we obtain coarse point clouds at first stage and combine local and global information to achieve the completion of dense point clouds. Quantitative and qualitative evaluations of experiments demonstrate that the proposed method has achieved better performance on ShapeNet dataset compared with existing state-of-the-art point cloud completion works and shows a good robustness for different missing ratios of point clouds. Additionally, the proposed N-DPC is valid for real point clouds on KITTI dataset.
Non-invasive Glucose Monitoring with Combinations of Near Infrared Spectroscopy and Metabolic Heat Conformation Technology Using Multivariate Analysis Approach

Diabetes Mellitus is among the top global health concern. It was reported by World Health Organization that millions of death was caused by diabetes and has been predicted that will increase in the next coming years. Hence monitoring of blood glucose level should be done since diabetes has not yet been proclaimed to be a curable disease. Current existing methods used to measure blood glucose concentration are still predominantly and minimally invasive which involves pricking of finger or in another way to take blood samples. In this research, multi-technology -based non-invasive glucose monitoring system is being studied to measure blood glucose concentration level. The use of Near infrared spectroscopy and Metabolic Heat Conformation study is being conducted. The data gathered in Near Infrared Spectroscopy and conformation from different temperatures has been weighted using multivariate analysis. Fifteen participants were tested and data were calculated through Clarke Error Grid Analysis and percent error for validation. With CEGA, the results fall to region A which is estimated glucose value deviates for ±20% from the actual value with a percent error of 1.29%.
An instance segmentation framework for in-situ plankton taxa assessment

In this paper, we propose a deep learning instance segmentation framework for particle extraction of microscopic images that aims at calculating planktonic species distribution and concentration in-situ. The framework comprises three essential functional tasks on in-situ time-series images collected from an autonomous underwater vehicle: 1) manual labeling of the captured images, 2) object localization, segmentation, and identification, and 3) class distribution and planktonic organisms concentration calculation. Our proposed framework is based on the mask R-CNN architecture provided by the Detectron2 library developed by Facebook Artificial Intelligence Research (FAIR) for instance segmentation. Due to its modular design, we compare the performance of different networks by alternating the backbone sub-network in order to choose the most suitable architecture for the task of instance and semantic segmentation. We compile a custom annotated dataset from planktonic time-series images and train the different models over this dataset to perform the instance semantic segmentation. Evaluation results of the proposed framework, utilizing the best performing deep learning architecture along with the new annotated dataset, show better performance in terms of speed and accuracy of both in-situ segmentation and classification compared to traditional segmentation methods. In addition, we observe a significant improvement in the object classification quality when we train the model over our newly annotated dataset instead of training it over the dataset generated from the traditional methods. The inferred data from our novel instance segmentation framework, which provides the particle class distribution and concentration, can then be used to assist in constructing a dynamic probability density map of planktonic communities dispersion and abundance.
Fatigue detection based on non-contact respiratory detection

Most of current methods for fatigue detection are based on fatigue features extracted from face or head. This paper proposes a novel fatigue detection mode based on non-contact respiratory detection. This paper proposes to use the respiratory rate feature for fatigue detection. Since the appearance of the respiratory rate fatigue feature will be earlier than the facial fatigue feature, fatigue can be predicted early by the respiratory rate. The difficulty of respiratory rate fatigue detection lies in the extraction of respiratory rate features and detection accuracy, so this paper adopts the non-contact respiratory detection based on dense optical flow method to solve the above problems. In this paper, a large number of experimental comparisons between respiratory rate fatigue detection and facial fatigue detection have confirmed that fatigue can be detected faster through respiratory rate fatigue characteristics, but there will still be a small deviation in the detection of respiratory rate, so this paper proposes to compare facial fatigue characteristics with breathing The fatigue detection method combined with the rate of fatigue characteristics, this method greatly improves the accuracy and speed of fatigue detection.
Radar cross section analysis for meander line frequency selective surfaces

In this paper, we propose a radar cross section analysis for frequency selective surfaces using a meander line antenna as a unit cell. The periodic structure consists of a passive copper antenna backed by a dielectric substrate. Two configurations of constant aperture composed of three and five elements are presented. The radar cross section is evaluated by using a plane wave excitation with linear polarization in order to assess the ratio of the backscattered power.
An approach to road scene text recognition with per-frame accumulation and dynamic stopping decision

Camera-based road scene analysis is an important task for building driving assistance systems and autonomous vehicles. An crucial component of road scene analysis is detection, tracking, and recognition of text object. In this paper, we consider the recognition of road scene text objects in sequences of video frames, and propose an approach to per-frame recognition results accumulation with a dynamic stopping decision. Experimental evaluation on an open dataset RoadText-1K showed that the proposed approach allows to achieve mean lower recognition error for the same mean number of processed frames, and significantly reduce the number of text objects which have to be recognized in each frame, thus relieving the load on the computational unit.
Realistic models of cultured cells for electroporation simulations starting from phase images

Electroporation-based techniques are known for their potential to temporarily increase cells membrane permeability by controlled electric fields for transfer of non-permeant molecules; these techniques evolved in many useful biomedical applications. Current research in this domain addresses both experimental and computational analysis in a complementary manner. Numerical simulations, considering realistic cell shapes and field exposure conditions can complete the experimental investigations by opening insights and providing quantitative data. Our approach here provides cell models for EP simulations, based on experimental acquisition of images in a holographic microscopy setup and digital reconstruction of phase images of living attached B16F10 murine melanoma cells. A procedure to process and import phase images in dedicated finite element software COMSOL Multiphysics is described in detail. Based on such realistically shaped computational domains, the electric field problem is successively defined and solved under time-harmonic electric excitation, uniformly applied; the frequency dependent dielectric properties are set accordingly. Induced transmembrane voltage distribution is the representative numerical output of the analysis shown here for different exposure conditions (membrane regions under stress, dielectric properties, field frequency), aiming to evaluate their potential efficiency on electroporation.
Steel pipe counting system based on image recognition

Traditional steel pipe counting can only be performed manually, which has the problems of large workload, error prone and low efficiency. This paper proposes a steel pipe counting system based on image recognition. First of all, the steel pipe image is collected from the camera, and then the digital image processing technology is used to perform image enhancement, edge detection and morphological operation and other preprocessing, then the steel pipe is identified and counted by the Hough circle transformation, and finally the steel pipe automatic is developed using VC++ software Technical software. Experiments show that the automatic counting accuracy and efficiency of steel pipes in this system are high.
How Do People With Intellectual Disability Engage With and Understand Gambling? A Qualitative Study of Adults in Victoria, Australia

Objective: This study aimed to understand the factors that may influence how and why people with intellectual disability may engage in gambling. Method: Nineteen people with intellectual disability were recruited from a disability advocacy organization and participated in face to face, semi-structured qualitative interviews. Open ended questions were used to explore participants' gambling participation, recall of, and attitudes toward, different gambling products, understanding of gambling harm, and awareness of responsible gambling messages. Results: All participants could remember gambling in their lifetime and some participants had recently engaged in gambling. Many participants were aware of different gambling products, and a few participants could describe in detail the technical aspects of electronic gambling machines. Most participants did not specifically recall seeing gambling harm minimization messages, however some described engaging in individual responsibility measures, such as limits and control, as they perceived this reduced the risks of experiencing harm. Conclusions: People with intellectual disability are engaging with gambling products in a similar way to the general community. Therefore, it is important to understand the different pathways that may lead people with intellectual disability to initiate and continue gambling and to ensure that they are aware of and protected from the potential risk. Implications for Public Health: Policy makers and practitioners should seek to understand and implement a range of strategies to reduce and prevent the harms associated with particular gambling products and environments for this population sub-group.
Real-Time Bibliometrics: Dimensions as a Resource for Analyzing Aspects of COVID-19

Dimensions was built as a platform to allow stakeholders in the research community, including academic bibliometricians, to more easily create and understand the context of different types of research object through the linkages between these objects. Links between objects are created via persistent identifiers and machine learning techniques, while additional context is introduced via data enhancements such as per-object categorisations and person and institution disambiguation. While these features make analytical use cases accessible for end users, the COVID-19 crisis has highlighted a different set of needs to analyze trends in scholarship as they occur: Real-time bibliometrics. The combination of full-text search, daily data updates, a broad set of scholarly objects including pre-prints and a wider set of data fields for analysis, broadens opportunities for a different style of analysis. A subset of these emerging capabilities is discussed and three basic analyses are presented as illustrations of the potential for real-time bibliometrics.
Genomic Instability and Cellular Senescence: Lessons From the Budding Yeast

Aging is a complex biological process that occurs in all living organisms. Aging is initiated by the gradual accumulation of biomolecular damage in cells leading to the loss of cellular function and ultimately death. Cellular senescence is one such pathway that leads to aging. The accumulation of nucleic acid damage and genetic alterations that activate permanent cell-cycle arrest triggers the process of senescence. Cellular senescence can result from telomere erosion and ribosomal DNA instability. In this review, we summarize the molecular mechanisms of telomere length homeostasis and ribosomal DNA stability, and describe how these mechanisms are linked to cellular senescence and longevity through lessons learned from budding yeast.
A Novel Test of the Duchenne Marker: Smiles After Botulinum Toxin Treatment for Crow’s Feet Wrinkles

Smiles that vary in muscular configuration also vary in how they are perceived. Previous research suggests that “Duchenne smiles,” indicated by the combined actions of the orbicularis oculi (cheek raiser) and the zygomaticus major muscles (lip corner puller), signal enjoyment. This research has compared perceptions of Duchenne smiles with non-Duchenne smiles among individuals voluntarily innervating or inhibiting the orbicularis oculi muscle. Here we used a novel set of highly controlled stimuli: photographs of patients taken before and after receiving botulinum toxin treatment for crow’s feet lines that selectively paralyzed the lateral orbicularis oculi muscle and removed visible lateral eye wrinkles, to test perception of smiles. Smiles in which the orbicularis muscle was active (prior to treatment) were rated as more felt, spontaneous, intense, and happier. Post treatment patients looked younger, although not more attractive. We discuss the potential implications of these findings within the context of emotion science and clinical research on botulinum toxin.
Saying “I Don’t Know”: A Video-Based Study on Physicians’ Claims of No-Knowledge in Assisted Reproductive Technology Consultations

Introduction The assisted reproductive technology (ART) field deals with consistent and predictable gaps in knowledge. Expressing lack of knowledge with a sentence like “I don’t know” can be challenging for doctors. This study examined physicians’ negative epistemic disclaimer “non lo so” in Italian ART doctor-couple interactions. In particular, it aimed to reveal specific features of “non lo so”: function, topic, temporality, responsibility, and interactional aspects. Methods This was a video-based observational study. We used microanalysis of face-to-face dialogue to analyze 20 purposively selected triadic consultations from a corpus of 85. This inductive analysis focused on the function, the content (topic and temporality) and some selected interactional aspects of the “non lo so”, quantifying and capturing the interaction between these qualitative features. Results We found 82 doctors’ “non lo so” in the corpus (mean = 4.4; range = 0–15). We discovered three main functions of this expression: propositional (n = 73/82), relational (n = 6/82), discursive (n = 3/82). The most frequent topics raising doctors’ “non lo so” were costs (n = 11/82), treatment-related aspects (n = 10/82), and timing issues (n = 9/82). In more than half of the cases (n = 44/82), present issues emerged. The majority (n = 70/82) of “non lo so” was framed using the “I,” with doctors’ taking personal responsibility. Patients played a role in these expressions from doctors: Patients initiated more than one third of them, and in one fourth of the cases, patients followed up immediately. Conclusion Our findings may be related to characteristics of the specific field of ART. Doctors in this setting must frequently express a direct lack of knowledge to their patients, and when they do, they mean it literally. Patients contribute to such disclosures, and their responses suggest that they find them acceptable, showing that they may expect limitations in their potential to conceive.
Extracellular Vesicles From Liver Progenitor Cells Downregulates Fibroblast Metabolic Activity and Increase the Expression of Immune-Response Related Molecules

Extracellular vesicles (EVs) mediate cell-to-cell crosstalk whose content can induce changes in acceptor cells and their microenvironment. MLP29 cells are mouse liver progenitor cells that release EVs loaded with signaling cues that could affect cell fate. In the current work, we incubated 3T3-L1 mouse fibroblasts with MLP29-derived EVs, and then analyzed changes by proteomics and transcriptomics. Results showed a general downregulation of protein and transcript expression related to proliferative and metabolic routes dependent on TGF-beta. We also observed an increase in the ERBB2 interacting protein (ERBIN) and Cxcl2, together with an induction of ribosome biogenesis and interferon-related response molecules, suggesting the activation of immune system signaling.
The Association Between Handgrip Strength and Predialysis Serum Sodium Level in Patients With Chronic Kidney Disease Stage 5D

Purpose: Handgrip strength (HGS) is a useful tool for the systematic assessment of muscle function related to nutritional status. Reduced HGS has been associated with adverse clinical outcomes in chronic kidney disease (CKD) stage 5D patients. In the same patients, predialysis low serum sodium (sNa) has been associated with malnutrition and mortality. Here, we investigated the role of predialysis sNa on muscle function in CKD-5D patients. Methods: We evaluated 45 patients on hemodialysis (HD) and 28 patients on peritoneal dialysis (PD) with HGS measurement, bioimpedance analysis, anthropometric measures, and malnutrition inflammation score (MIS). According to established diagnostic criteria, reduced HGS was defined as strength below 30 and 20 Kg in men and women, respectively. Predialysis sNa values were defined as the mean of all predialysis measurements during the preceding 6 months. Data analysis was performed separately for each of the HD and PD groups. Results: The proportions of reduced HGS did not differ between the HD (66%) and PD (54%) groups, respectively. Patients in the HD group as compared to those in the PD group had higher serum albumin and potassium and mid-arm muscle circumference and lower residual renal function (RRF) and residual urine volume. Multivariate logistic analysis, after controlling for muscle mass, nutritional biomarkers, MIS, fluid overload and RRF, showed that for every 1 mmol/l increase of sNa the odds of reduced HGS was decreased by 60% (OR = 0.40, 95% CI: 0.16–0.99) and 42% (OR = 0.58, 95% CI: 0.36–0.93) in HD and PD patients, respectively. However, stratified analysis indicated that lower sNa levels predicted reduced HGS in individuals with a background of malnutrition, inflammation, overhydration and less preserved RRF, representing unfavorable conditions strongly related to muscle wasting in the dialysis setting. Conclusions: Predialysis sNa is a strong and independent determinant of HGS, a reliable nutritional marker in CKD-5D stage patients. However, according to our findings, lower sNa levels appear to be a marker of underlying unfavorable conditions that are heavily associated with reduced HGS, rather than a causal determinant of reduced HGS. Whether optimizing sNa levels improves patient muscle performance requires further investigations.