Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized Minimum Error Entropy for Adaptive Filtering

Error entropy is a important nonlinear similarity measure, and it has received increasing attention in many practical applications. The default kernel function of error entropy criterion is Gaussian kernel function, however, which is not always the best choice. In our study, a novel concept, called generalized error entropy, utilizing the generalized Gaussian density (GGD) function as the kernel function is proposed. We further derivate the generalized minimum error entropy (GMEE) criterion, and a novel adaptive filtering called GMEE algorithm is derived by utilizing GMEE criterion. The stability, steady-state performance, and computational complexity of the proposed algorithm are investigated. Some simulation indicate that the GMEE algorithm performs well in Gaussian, sub-Gaussian, and super-Gaussian noises environment, respectively. Finally, the GMEE algorithm is applied to acoustic echo cancelation and performs well.
Shuffled Patch-Wise Supervision for Presentation Attack Detection

Face anti-spoofing is essential to prevent false facial verification by using a photo, video, mask, or a different substitute for an authorized person’s face. Most of the state-of-the-art presentation attack detection (PAD) systems suffer from overfitting, where they achieve near-perfect scores on a single dataset but fail on a different dataset with more realistic data. This problem drives researchers to develop models that perform well under real-world conditions. This is an especially challenging problem for frame-based presentation attack detection systems that use convolutional neural networks (CNN). To this end, we propose a new PAD approach, which combines pixel-wise binary supervision with patch-based CNN. We believe that training a CNN with face patches allows the model to distinguish spoofs without learning background or dataset-specific traces. We tested the proposed method both on the standard benchmark datasets —Replay-Mobile, OULU-NPU—and on a real-world dataset. The proposed approach shows its superiority on challenging experimental setups. Namely, it achieves higher performance on OULU-NPU protocol 3, 4 and on inter-dataset real-world experiments
NSP-BERT: A Prompt-based Few-Shot Learner through an Original Pre-training Task —— Next Sentence Prediction

Using prompts to utilize language models to perform various downstream tasks, also known as prompt-based learning or prompt-learning, has lately gained significant success in comparison to the pre-train and fine-tune paradigm. Nonetheless, virtually most prompt-based methods are token-level such as PET based on mask language model (MLM). In this paper, we attempt to accomplish several NLP tasks in the zero-shot and few-shot scenarios using a BERT original pre-training task abandoned by RoBERTa and other models——Next Sentence Prediction (NSP). Unlike token-level techniques, our sentence-level prompt-based method NSP-BERT does not need to fix the length of the prompt or the position to be predicted, allowing it to handle tasks such as entity linking with ease. NSP-BERT can be applied to a variety of tasks based on its properties. We present an NSP-tuning approach with binary cross-entropy loss for single-sentence classification tasks that is competitive compared to PET and EFL. By continuing to train BERT on RoBERTa’s corpus, the model’s performance improved significantly, which indicates that the pre-training corpus is another important determinant of few-shot besides model size and prompt method.
AWGAN: Empowering High-Dimensional Discriminator Output for Generative Adversarial Networks

Empirically multidimensional discriminator (critic) output can be advantageous, while a solid explanation for it has not been discussed. In this paper, (i) we rigorously prove that high-dimensional critic output has advantage on distinguishing real and fake distributions; (ii) we also introduce an square-root velocity transformation (SRVT) block which further magniﬁes this advantage. The proof is based on our proposed maximal p -centrality discrepancy which is bounded above by p -Wasserstein distance and perfectly ﬁts the Wasser-stein GAN framework with high-dimensional critic output n . We have also showed when n = 1 , the proposed discrepancy is equivalent to 1 -Wasserstein distance. The SRVT block is applied to break the symmetric structure of high-dimensional critic output and improve the generalization capability of the discriminator network. In terms of implementation, the proposed framework does not require additional hyper-parameter tuning, which largely facilitates its usage. Experiments on image generation tasks show performance improvement on benchmark datasets.
Ten years in the making: application of CrossMab technology for the development of therapeutic bispecific antibodies and antibody fusion proteins

ABSTRACT Bispecific antibodies have recently attracted intense interest. CrossMab technology was described in 2011 as novel approach enabling correct antibody light-chain association with their respective heavy chain in bispecific antibodies, together with methods enabling correct heavy-chain association using existing pairs of antibodies. Since the original description, CrossMab technology has evolved in the past decade into one of the most mature, versatile, and broadly applied technologies in the field, and nearly 20 bispecific antibodies based on CrossMab technology developed by Roche and others have entered clinical trials. The most advanced of these are the Ang-2/VEGF bispecific antibody faricimab, currently undergoing regulatory review, and the CD20/CD3 T cell bispecific antibody glofitamab, currently in pivotal Phase 3 trials. In this review, we introduce the principles of CrossMab technology, including its application for the generation of bi-/multispecific antibodies with different geometries and mechanisms of action, and provide an overview of CrossMab-based therapeutics in clinical trials.
Mitochondrial dysfunction–induced H3K27 hyperacetylation perturbs enhancers in Parkinson’s disease

Mitochondrial dysfunction is a major pathophysiological contributor to the progression of Parkinson’s disease (PD); however, whether it contributes to epigenetic dysregulation remains unknown. Here, we show that both chemically and genetically driven mitochondrial dysfunctions share a common mechanism of epigenetic dysregulation. Under both scenarios, lysine 27 acetylation of likely variant H3.3 (H3.3K27ac) increased in dopaminergic neuronal models of PD, thereby opening that region to active enhancer activity via H3K27ac. These vulnerable epigenomic loci represent potential transcription factor motifs for PD pathogenesis. We further confirmed that mitochondrial dysfunction induces H3K27ac in ex vivo and in vivo (MitoPark) neurodegenerative models of PD. Notably, the significantly increased H3K27ac in postmortem PD brains highlights the clinical relevance to the human PD population. Our results reveal an exciting mitochondrial dysfunction-metabolism-H3K27ac-transcriptome axis for PD pathogenesis. Collectively, the mechanistic insights link mitochondrial dysfunction to epigenetic dysregulation in dopaminergic degeneration and offer potential new epigenetic intervention strategies for PD.
Hypertension as a Risk Factor for Contrast-Associated Acute Kidney Injury: A Meta-Analysis Including 2,830,338 Patients

Objective: Previous studies have shown that the relationship between hypertension (HT) and contrast-associated acute kidney injury (CA-AKI) is not clear. We apply a systematic review and meta-analysis to assess the association between HT and CA-AKI. Methods: We searched for articles on the study of risk factors for CA-AKI in the Embase, Medline, and Cochrane Database of Systematic Reviews (by March 25, 2021). Two authors independently performed quality assessment and extracted data such as the studies’ clinical setting, the definition of CA-AKI, and the number of patients. The CA-AKI was defined as a serum creatinine (SCr) increase ≥25% or ≥0.5 mg/dL from baseline within 72 h. We used fixed or random models to pool adjusted OR (aOR) by STATA. Results: A total of 45 studies (2,830,338 patients) were identified, and the average incidence of CA-AKI was 6.48%. There was an increased risk of CA-AKI associated with HT (aOR: 1.378, 95% CI: 1.211–1.567, I2 = 67.9%). In CA-AKI with a SCr increase ≥50% or ≥0.3 mg/dL from baseline within 72 h, an increased risk of CA-AKI was associated with HT (aOR: 1.414, 95% CI: 1.152–1.736, I2 = 0%). In CA-AKI with a Scr increase ≥50% or ≥0.3 mg/dL from baseline within 7 days, HT increases the risk of CA-AKI (aOR: 1.317, 95% CI: 1.049–1.654, I2 = 51.5%). Conclusion: Our meta-analysis confirmed that HT is an independent risk factor for CA-AKI and can be used to identify risk stratification. Physicians should pay more attention toward prevention and treatment of patients with HT in clinical practice.
Space-Dividing-Based Cluster Synchronization of Reaction–Diffusion Genetic Regulatory Networks via Intermittent Control

In this paper, we focus on the cluster synchronization of reaction-diffusion genetic regulatory networks (RDGRNs) with time-varying delays, where the state of the system is not only time-dependent but also spatially-dependent due to the presence of the reaction-diffusion terms. First, we construct an intermittent space-dividing controller that effectively combines the two control strategies, making it more cost-effective. Furthermore, based on the activation function division approach, we propose a regulation function division method that can improve the delay upper bound of RDGRNs; meanwhile, the cluster synchronization criteria of RDGRNs under the proposed controller are derived based on the Lyapunov theory and Halanay’s et al. inequality techniques. Finally, the criteria’s effectiveness is demonstrated by numerical examples of the system in one- and two-dimensional space.
On the differences between quality increasing and other changes in open source Java projects

Many software metrics are designed to measure aspects that are believed to be related to software quality. Static software metrics, e.g., size, complexity and coupling are used in defect prediction research as well as software quality models to evaluate software quality. Static analysis tools also include boundary values for complexity and size that generate warnings for developers. While this indicates a relationship between quality and software metrics the extend of it is unknown. Moreover, recent studies found that complexity metrics may be unreliable indicators for understandability of the source code. To explore this relationship, we leverage the intent of developers about what constitutes a quality improvement in their own code base. We manually classify a randomized sample of 2,533 commits from 54 Java open source projects as quality improving depending on the intent of the developer by in-specting the commit message. We distinguish between perfective and corrective maintenance via predeﬁned guidelines and use this data as ground truth for ﬁne-tuning a state-of-the art deep learning model created for natural language processing. The benchmark we provide with our ground truth indicates that the deep learning model can be conﬁdently used for commit intent classiﬁcation. We use the model to increase our data set to 125,482 commits. Based on
Highly Scalable and Provably Accurate Classification in Poincaré Balls

Many high-dimensional and large-volume data sets of practical relevance have hierarchical structures induced by trees, graphs or time series. Such data sets are hard to process in Euclidean spaces and one often seeks low-dimensional embeddings in other space forms to perform required learning tasks. For hierarchical data, the space of choice is hyperbolic since it guarantees low-distortion embeddings for tree-like structures. Unfortunately, the geometry of hyperbolic spaces has properties not encountered in Euclidean spaces that pose challenges when trying to rigorously analyze algorithmic solutions. Here, for the first time, we establish a unified framework for learning scalable and simple hyperbolic linear classifiers with provable performance guarantees. The gist of our approach is to focus on Poincaré ball models and formulate the classification problems using tangent space formalisms. Our results include a new hyperbolic and second-order perceptron algorithm as well as an efficient and highly accurate convex optimization setup for hyperbolic support vector machine classifiers. All algorithms provably converge and are highly scalable as they have complexities comparable to those of their Euclidean counterparts. Their performance accuracies on synthetic data sets comprising millions of points, as well as on complex real-world data sets such as single-cell RNA-seq expression measurements, CIFAR10, 1 Fashion-MNIST and mini-ImageNet.
Hybridized Methods for Quantum Simulation in the Interaction Picture

Conventional methods of quantum simulation involve trade-offs that limit their applicability to specific contexts where their use is optimal. In particular, the interaction picture simulation has been found to provide substantial asymptotic advantages for some Hamiltonians, but incurs prohibitive constant factors and is incompatible with methods like qubitization. We provide a framework that allows different simulation methods to be hybridized and thereby improve performance for interaction picture simulations over known algorithms. These approaches show asymptotic improvements over the individual methods that comprise them and further make interaction picture simulation methods practical in the near term. Physical applications of these hybridized methods yield a gate complexity scaling as log2⁡Λ in the electric cutoff Λ for the Schwinger Model and independent of the electron density for collective neutrino oscillations, outperforming the scaling for all current algorithms with these parameters. For the general problem of Hamiltonian simulation subject to dynamical constraints, these methods yield a query complexity independent of the penalty parameter λ used to impose an energy cost on time-evolution into an unphysical subspace.
Antecedents of Perceived Organisational Politics and Psychological Withdrawal

Quick Response Code Abstract: The study is on the Antecedents of Perceived Organisational Politics and Psychological Withdrawal. The objectives of the study are to examine the antecedents of perceived organisational politics as well as to assess the relationship between POP and psychological withdrawal. Although many studies on the antecedents and consequences of POP have been reported, the antecedents and Psychological withdrawal mentioned herein was not examined previously by any study in the North East of Nigeria. To collect primary data questionnaires were developed and distributed. The collected data were analyzed using SEM-AMOS. The Findings from this study indicates that workforce diversity have positive impacts and significant relationship with POP while the need for power have negative influence on POP. Further findings show that employees who perceive politics in their work place, develop psychological withdrawal in terms of job anxiety and low organisational commitment. Conclusively understanding POP and it consequence will go a long way in reducing its negative effects on employee’s performance. This study recommended that, to control the unfavorable consequences of POP, organizations must design effective policies to manage workforce diversities and relationship conflicts.
The Indication of Poor Prognosis by High Expression of ENO1 in Squamous Cell Carcinoma of the Lung

The purpose of this study is to investigate the significance of alpha-enolase (ENO1) expression in squamous cell carcinoma of the lung (LUSC), its prognostic value, and prospective molecular mechanism. Using multiplatforms data, including in-house immunohistochemistry, in-house real-time fluorescence quantitative polymerase chain reaction (RT-qPCR), in-house microarray, and public high-throughput data, the expression significance and prognostic role of ENO1 in LUSC tissues were analyzed comprehensively. With the combination of all eligible cases, compared with 941 non-LUSC lung tissues, ENO1 was significantly overexpressed in 1163 cases of LUSC (standardized mean difference (SMD) = 1.23, 95% confidence interval (CI) = 0.76–1.70, P < 0.001). ENO1 also displayed a great ability to differentiate LUSC tissues from non-LUSC lung tissues (AUC = 0.8705) with the comprehensive sensitivity being 0.88 [0.83–0.92], and comprehensive specificity being 0.89 [0.84–0.94]). Moreover, in 1860 cases of LUSC with survival information, patients with higher expression of ENO1 had poorer prognosis (hazard ratio (HR) = 1.20, 95% CI = 1.01–1.43, P = 0.043). ENO1 and its related genes mainly participated in the pathways of cell division and proliferation. In conclusion, the upregulation of ENO1 could affect the carcinogenesis and unfavorable outcome of LUSC.
Evaluation of the health level of the elderly: patient care team considerations.

OBJECTIVE To understand the differences of physicians, nurses, and social workers in the evaluation of the health status of the elderly. METHODS A cross-sectional quantitative study, using descriptive statistics. Non-probabilistic sample, consisting of 291 participants from three professional categories: 71 (24.4%) physicians, 192 (66%) nurses, and 28 (9.6%) social workers. We used a questionnaire including the variables: sociodemographic characteristics and instruments used for evaluation. RESULTS Instruments with greater utility for the evaluation of the elderly: for physicians, Mini Mental State Examination; for nurses, Braden scale; and for social workers, genogram. In the physical examination, the data most collected by physicians and nurses are the vital signs; and by social workers, the condition for performing the Activities of Daily Living. CONCLUSIONS The evaluation of the elderly is based on a diversity of instruments and is an area in which health and social professionals need to share information.
Gut microbiota associations with metabolic syndrome and relevance of its study in pediatric subjects

ABSTRACT Childhood obesity and T2DM have shown a recent alarming increase due to important changes in global lifestyle and dietary habits, highlighting the need for urgent and novel solutions to improve global public health. Gut microbiota has been shown to be relevant in human health and its dysbiosis has been associated with MetS, a health condition linked to the onset of relevant diseases including T2DM. Even though there have been recent improvements in the understanding of gut microbiota–host interactions, pediatric gut microbiota has been poorly studied compared to adults. This review provides an overview of MetS and its relevance in school-age children, discusses gut microbiota and its possible association with this metabolic condition including relevant emerging gut microbiome-based interventions for its prevention and treatment, and outlines future challenges and perspectives in preventing microbiota dysbiosis from the early stages of life.
The effect of the social aspect, media dependency, and uncertainty against the formation of Trust toward information in social network sites: A case study of COVID‐19 information in Indonesia

This study developed a comprehensive theoretical model to analyze individual Trust's formation concerning information related to COVID‐19 circulated in Social Network Sites (SNS) in Indonesia. The developed model used Social Impact, Uncertainty, and SNS Dependency theories. This study narrows the concept of Trust, which refers to how much individuals judge and consider the COVID‐19 information received in SNS as reliable and trustworthy. Data collected from 390 Indonesian respondents were used to analyze the proposed model via SEM analysis techniques. The findings indicated that SNS dependency had the most substantial immediate impact on Trust. Meanwhile, the result from a social impact perspective explicitly showed the various forms through which other people influence individuals' Trust in COVID‐19 information circulated in SNS. Uncertainty was also found to indirectly affect and moderate the direct impact of SNS dependency on Trust. However, this comprehensive observation has not been discussed thoroughly from a social perspective by prior studies and can contribute to future research. The indirect and moderating effects were additional contributions of this study that had not been profoundly utilized by previous related research.
Impact of sociodemographic factors on early mortality in acute promyelocytic leukemia in the United States: A time‐trend analysis

The survival of patients with acute promyelocytic leukemia (APL) has dramatically improved with the use of all‐trans retinoic acid (ATRA) and arsenic trioxide (ATO). However, because of the complexity of the initial management, early mortality (EM) remains a major contributor to treatment failure. It is less known whether advances in treatment, urgent access to specialized care, and broad availability of ATRA/ATO have reduced EM in the last 2 decades. Furthermore, the influence of sociodemographic factors on the risk of EM also remains unclear.
Evaluation of Complications of Intra-Prostatic Local Anaesthesia and Periprostatic Local Anaesthesia for Trus Guided Prostate Biopsy

Objectives : To evaluate complications in Intra-prostatic local Anaesthesia and Periprostatic local Anaesthesia for TRUS Guided Prostate Biopsy. Methods: This study was carried out in the Department of Urology, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka Medical College (DMCH) and Comfort Nursing Home, Dhaka during the period of January 2009 to October 2009, evaluate complications between periprostatic with intraprostatic local anaesthesia for transrectal prostate biopsy. For this purpose, a total number of 60 consecutive patients having increased prostate specific antigen (PSA >4.0ng/ml), abnormal DRE/transrectal ultrasound were admitted in the above mentioned hospitals were enrolled in this study for surgical management. Result : The age ranged from 50 to 90 years and the maximum number was found in the age group of 61-70 years in both groups. The mean(±SD) age was 68.5±7.5 years and 70.3±8.2 years in group I and group II respectively. No systemic lidocaine toxicity was observed in group II. But only dizziness were found in 2 cases (6.7%) and visual disturbance were found in 1 (3.3%) case respectively in group I. Urinary tract infection was found 2(6.7%) in group I and 1(3.3%) in group II patients. Hematuria was found 23(76.7%) in group I and 19(63.3%) in group II. Rectal bleeding was found 14(46.7%) in group I and 11(36.7%) in group II. The statistically not significant (p>0.05) between two groups in chi square and fisher exact test respectively. Conclusion : It is a simple and safe method that is less painful and it should be considered in all patients undergoing transrectal ultrasound guided prostate biopsy. Complications wer4e less in intra-prostatic local anaesthesia than periprostatic local anaesthesia for TRUS guided prostate Biopsy.
Current Status of Contrast Enhanced Mammography: A Comprehensive Review

Objectives: The purpose of this article is to provide a detailed and updated review of the physics, techniques, indications, limitations, reporting, implementation and management of contrast enhanced mammography. Background: Contrast enhanced mammography (CEM), is an emerging iodine-based modified dual energy mammography technique. In addition to having the same advantages as standard full-field digital mammography (FFDM), CEM provides information regarding tumor enhancement, relying on tumor angiogenesis, similar to dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). This article reviews current literature on CEM and highlights considerations that are critical to the successful use of this modality. Conclusion: Multiple studies point to the advantage of using CEM in the diagnostic setting of breast imaging, which approaches that of DCE-MRI.