Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-objective Optimization of Clustering-based Scheduling for Multi-workflow On Clouds Considering Fairness

Distributed computing, such as cloud computing, provides promising platforms to execute multiple workflows. Workflow scheduling plays an important role in multi-workflow execution with multi-objective requirements. Although there exist many multi-objective scheduling algorithms, they focus mainly on optimizing makespan and cost for a single workflow. There is a limited research on multi-objective optimization for multi-workflow scheduling. Considering multi-workflow scheduling, there is an additional key objective to maintain the fairness of workflows using the resources. To address such issues, this paper first defines a new multi-objective optimization model based on makespan, cost, and fairness, and then proposes a global clustering-based multi-workflow scheduling strategy for resource allocation. Experimental results show that the proposed approach performs better than the compared algorithms without significant compromise of the overall makespan and cost as well as individual fairness, which can guide the simulation workflow scheduling on clouds.
A Pilot Study of Relating MYCN-Gene Amplification with Neuroblastoma-Patient CT Scans

Neuroblastoma is one of the most common cancers in infants, and the initial diagnosis of this disease is difficult. At present, the MYCN gene amplification (MNA) status is detected by invasive pathological examination of tumor samples. This is time-consuming and may have a hidden impact on children. To handle this problem, we adopt multiple machine learning (ML) algorithms to predict the presence or absence of MYCN gene amplification. The dataset is composed of retrospective CT images of 23 neuroblastoma patients. Different from previous work, we develop the algorithm without manually-segmented primary tumors which is time-consuming and not practical. Instead, we only need the coordinate of the center point and the number of tumor slices given by a subspecialty-trained pediatric radiologist. Specifically, CNN-based method uses pre-trained convolutional neural network, and radiomics-based method extracts radiomics features. Our results show that CNN-based method outperforms the radiomics-based method.
DistilCamemBERT: a distillation of the French model CamemBERT

Modern Natural Language Processing (NLP) models based on Transformer structures represent the state of the art in terms of performance on very diverse tasks. However, these models are complex and represent several hundred million parameters for the smallest of them. This may hinder their adoption at the industrial level, making it difficult to scale up to a reasonable infrastructure and/or to comply with societal and environmental responsibilities. To this end, we present in this paper a model that drastically reduces the computational cost of a well-known French model (CamemBERT), while preserving good performance.
An intrinsically disordered protein region encoded by the human disease gene CLEC16A regulates mitophagy

ABSTRACT CLEC16A regulates mitochondrial health through mitophagy and is associated with over 20 human diseases. However, the key structural and functional regions of CLEC16A, and their relevance for human disease, remain unknown. Here, we report that a disease-associated CLEC16A variant lacks a C-terminal intrinsically disordered protein region (IDPR) that is critical for mitochondrial quality control. IDPRs comprise nearly half of the human proteome, yet their mechanistic roles in human disease are poorly understood. Using carbon detect NMR, we find that the CLEC16A C terminus lacks secondary structure, validating the presence of an IDPR. Loss of the CLEC16A C-terminal IDPR in vivo impairs mitophagy, mitochondrial function, and glucose-stimulated insulin secretion, ultimately causing glucose intolerance. Deletion of the CLEC16A C-terminal IDPR increases CLEC16A ubiquitination and degradation, thus impairing assembly of the mitophagy regulatory machinery. Importantly, CLEC16A stability is dependent on proline bias within the C-terminal IDPR, but not amino acid sequence order or charge. Together, we elucidate how an IDPR in CLEC16A regulates mitophagy and implicate pathogenic human gene variants that disrupt IDPRs as novel contributors to diabetes and other CLEC16A-associated diseases. Abbreviations : CAS: carbon-detect amino-acid specific; IDPR: intrinsically disordered protein region; MEFs: mouse embryonic fibroblasts; NMR: nuclear magnetic resonance.
Towards automatic detection of wildlife trade using machine vision models

Unsustainable trade in wildlife is one of the major threats affecting the global biodiversity crisis. An important part of the trade now occurs on the internet, especially on digital marketplaces and social media. Automated methods to identify trade posts are needed as resources for conservation are limited. Here, we developed machine vision models based on Deep Neural Networks with the aim to automatically identify images of exotic pet animals for sale. A new training dataset representing exotic pet animals advertised for sale on the web was generated for this purpose. We trained 24 neural-net models spanning a combination of five different architectures, three methods of training and two types of datasets. Specifically, model generalisation improved after setting a portion of the training images to represent negative features. Models were evaluated on both within and out of distribution data to test wider model applicability. The top performing models achieved an f-score of over 0.95 on within distribution evaluation and between 0.75 to 0.87 on the two out of distribution datasets. Notably, feature visualisation indicated that models performed well in detecting the surrounding context (e.g. a cage) in which an animal was located, therefore helping to automatically detect images of animals in non-natural environments. The proposed methods can help investigate the online wildlife trade, but can also be adapted to study other types of people-nature interactions from digital platforms. Future studies can use these findings to build robust machine learning models and new data collection pipelines for more taxonomic groups.
Document Intelligence Metrics for Visually Rich Document Evaluation

The processing of Visually-Rich Documents (VRDs) is highly important in information extraction tasks associated with Document Intelligence. We introduce DI-Metrics, a Python library devoted to VRD model evaluation comprising text-based, geometric-based and hierarchical metrics for information extraction tasks. We apply DI-Metrics to evaluate information extraction performance using publicly available CORD dataset, comparing performance of three SOTA models and one industry model. The open-source library is available on GitHub.
PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stageRankingtrack: DL

This paper describes the PASH participation in TREC 2021 Deep Learning Track. In the recall stage, we adopt a scheme combining sparse and dense retrieval method. In the multi-stage ranking phase, point-wise and pair-wise ranking strategies are used one after another based on model continual pre-trained on general knowledge and document-level data. Compared to TREC 2020 Deep Learning Track, we have additionally introduced the generative model T5 to further enhance the performance.
Exploring Apache Incubator Project Trajectories with APEX

Open Source Software (OSS) is a major component of our digital infrastructure, yet more than 80% of such projects fail. Seeking less uncertainty, many OSS projects join established software communities, e.g., the Apache Software Foundation (ASF), with established rules and community support to guide projects toward sustainability. In their nascent stage, ASF projects are incubated in the ASF incubator (ASFI), which provides systematic mentorship toward long-term sustainability. Projects in ASFI eventually conclude their incubation by either graduating, if successful, or retiring, if not. Time-stamped traces of developer activities are publicly available from ASF, and can be used for monitoring project trajectories toward sustainability. Here we present a web app dashboard tool, APEX, that allows internal and external stakeholders to monitor and explore ASFI project sustainability trajectories, including social and technical networks.
CHEX-MATE: Morphological analysis of the sample

A classiﬁcation of the galaxy cluster’s dynamical state is crucial when dealing with large samples. The identiﬁcation of the most relaxed and most disturbed objects is necessary for both cosmological analysis, focused on spherical and virialised systems, and astrophysical studies, centred around all those micro-physical processes that take place in disturbed clusters (such as particle accel- eration or turbulence). Among the most powerful tools for the identiﬁcation of the dynamical state of clusters is the analysis of their intracluster medium (ICM) distribution. In this work, we performed an analysis of the X-ray morphology of the 118 (Cluster HEritage project with XMM-Newton – Mass Assembly and Thermodynamics at the Endpoint of structure formation) CHEX-MATE clusters, with the aim of providing a classiﬁcation of their dynamical state. To investigate the link between the X-ray appearance and the dy- namical state, we considered four morphological parameters: the surface brightness concentration, the centroid shift, and the second-and third-order power ratios. These indicators result to be strongly correlated with each other, powerful in identifying the disturbed and relaxed population, characterised by a unimodal distribution, and not strongly inﬂuenced by systematic uncertainties. In order to obtain a continuous classiﬁcation of the CHEX-MATE objects, we combined these four parameters in a single quantity, M , which represents the grade of relaxation of a system. On the basis of the M value, we identiﬁed the most extreme systems of the sample, ﬁnding 15 very relaxed and 27 very disturbed galaxy clusters. From a comparison with previous analysis on X-ray selected samples, we conﬁrmed that the Sunyaev-Zeldovich (SZ) clusters tend to be more disturbed. Finally, by applying our analysis to a simulated sample, we found a general agreement between the observed and simulated results, with the only exception being the concentration. This latter behaviour is partially related to the presence of particles with a high smoothed-particle-hydrodynamics density in the central regions of the simulated clusters due to the action of the idealised isotropic thermal active galactic nucleus (AGN) feedback.
High-precision temperature monitoring system for room-temperature equipment in astrophysical observations

Abstract. We present a precise thermometry system to monitor room-temperature components of a telescope for radio-astronomy such as cosmic microwave background (CMB) observation. The system realizes precision of 1 mKs on a timescale of 20 s at 300 K. We achieved this high precision by tracking only relative fluctuation and combining thermistors with a low-noise measurement device. We show the required precision of temperature monitors for CMB observation and introduce the performance of our thermometry system. This precise room-temperature monitoring system enables us to reduce the low-frequency noise in a wide range of radio-astronomical detector signals observation and to operate a large detector array perform at its designed high sensitivity.
Probing the topological surface states in superconducting Sn4Au single crystal: a magneto transport study

Materials exhibiting bulk superconductivity along with magnetoresistance (MR) in their normal state have emerged as suitable candidates for topological superconductivity. In this article, we report a flux free method to synthesize single crystal of topological superconductor candidate Sn4Au. The phase purity and single crystalline nature are confirmed through various characterizations viz. x-ray diffraction, field emission scanning electron microscopy, selected area electron diffraction, and transmission electron microscopy. Chemical states of the constituent element viz. Sn and Au are analysed through x-ray photoelectron spectroscopy. Superconductivity in synthesized Sn4Au single crystal is evident form ρ-T plot, for which the critical field (H c) is determined through ρ-H plot at 2 K i.e. just below critical temperature T c. A positive MR is observed in ρ-H measurements at different temperatures above T c, viz. at 3 K, 5 K, 10 K and 20 K. Further, the magnetoconductivity (MC) is analysed by using Hikami–Larkin–Nagaoka formalism, which signifies the presence of weak antilocalization (WAL) effect in Sn4Au. Angle dependent magneto-transport measurement has been performed to detect the origin of observed WAL effect in Sn4Au single crystal. Normalized MC vs Hcosθ plot shows presence of topological surface states in the studied system. It is evident that Sn4Au is a 2.6 K topological superconductor.
6G Radio Requirements to Support Integrated Communication, Localization, and Sensing

6G will be characterized by extreme use cases, not only for communication, but also for localization, and sensing. The use cases can be directly mapped to requirements in terms of standard key performance indicators (KPIs), such as data rate, latency, or localization accuracy. The goal of this paper is to go one step further and map these standard KPIs to requirements on signals, on hardware architectures, and on deployments. Based on this, system solutions can be identified that can support several use cases simultaneously. Since there are several ways to meet the KPIs, there is no unique solution and preferable configurations will be discussed.
Using machine learning on new feature sets extracted from 3D models of broken animal bones to classify fragments according to break agent

Distinguishing agents of bone modification at paleoanthropological sites is at the root of much of the research directed at understanding early hominin exploitation of large animal resources and the effects those subsistence behaviors had on early hominin evolution. However, current methods, particularly in the area of fracture pattern analysis as a signal of marrow exploitation, have failed to overcome equifinality. Furthermore, researchers debate the replicability and validity of current and emerging methods for analyzing bone modifications. Here we present a new approach to fracture pattern analysis aimed at distinguishing bone fragments resulting from hominin bone breakage and those produced by carnivores. This new method uses 3D models of fragmentary bone to extract a much richer dataset that is more transparent and replicable than feature sets previously used in fracture pattern analysis. Supervised machine learning algorithms are properly used to classify bone fragments according to agent of breakage with average mean accuracy of 77% across tests.
Impact of delayed sternal closure on wound infections following neonatal and infant cardiac surgery

Objectives Delayed sternal closure is a routine procedure to reduce hemodynamic and respiratory instability in pediatric patients following cardiac surgery, particularly in neonates and infants. In this setting, the possible links between sternal wound infection and delayed sternal closure are still a matter of debate. As a part of our routine, there was a low threshold for delayed sternal closure, so we reviewed our experience with sternal wound infections with a focus on potentially related perioperative risk factors, particularly delayed sternal closure. Methods We retrospectively identified 358 operated neonates (37%) and infants (mean age 3.6 months) in our local congenital heart disease database between January 2013 and June 2017. Potential risk factors for sternal wound infections, such as age, gender, complexity (based on Aristotle- and STS-EACTS mortality category), reoperation, use of cardiopulmonary bypass, extracorporeal membrane oxygenation, mortality and delayed sternal closure (163/358, 46%), were subjected to uni- and multivariate analysis. Results A total of 26/358 patients (7.3%) developed a superficial sternal wound infection. There were no deep sternal wound infections, no mediastinitis or sepsis. Applying univariate analysis, the prevalence of sternal wound infections was related to younger age, more complex surgery and delayed sternal closure. However, in multivariate analysis, sternal wound infection was only associated with delayed sternal closure (p = 0.013, odds ratio 8.6). Logistic regression revealed the prevalence of delayed sternal closure to be related to younger age, complexity, and the use of extracorporeal membrane oxygenation. Conclusion In patients younger than one year, sternal wound infections are clearly related to delayed sternal closure. However, in our cohort, all sternal wound infections were superficial and acceptable, considering the improved postoperative hemodynamic stability.
Pion damping width and pion spectral function in hot pion gas

The temperature behaviour of the pion width in the hadronic phase is investigated in the framework of the NJL model. The contribution to the width from the pion-pion collision is considered with a scalar sigma-meson as an intermediate state. It is shown that the pion width signiﬁcantly broadens at T > 0 . 1 GeV. Using the two-step iteration method, suggested by Kadanoﬀ and Baym, the pion spectral function in a hot pion gas is calculated at diﬀerent temperatures.
NPGA: A Unified Algorithmic Framework for Decentralized Constraint-Coupled Optimization

This work focuses on a class of general decentralized constraint-coupled optimization problems. We propose a novel nested primal-dual gradient algorithm (NPGA), which can achieve linear convergence under the weakest known condition, and its theoretical convergence rate surpasses all known results. More importantly, NPGA serves not only as an algorithm but also as a unified algorithmic framework, encompassing various existing algorithms as special cases. By designing different network matrices, we can derive numerous versions of NPGA and analyze their convergences by leveraging the convergence results of NPGA conveniently, thereby enabling the design of more efficient algorithms. Finally, we conduct numerical experiments to compare the convergence rates of NPGA and existing algorithms, providing empirical evidence for the superior performance of NPGA.
Quantum Simulations of Interacting Systems with Broken Time-Reversal Symmetry

Many-body systems of quantum interacting particles in which time-reversal symmetry is broken give rise to a variety of rich collective behaviors, and are therefore a major target of research in modern physics. Quantum simulators can potentially be used to explore and understand such systems, which are often beyond the computational reach of classical simulation. Of these, platforms with universal quantum control can experimentally access a wide range of physical properties. However, simultaneously achieving strong programmable interactions, strong time-reversal symmetry breaking, and high fidelity quantum control in a scalable manner is challenging. Here we realized quantum simulations of interacting, time-reversal broken quantum systems in a universal trapped-ion quantum processor. Using a scalable scheme that was recently proposed we implemented time-reversal breaking synthetic gauge fields, shown for the first time in a trapped ion chain, along with unique coupling geometries, potentially extendable to simulation of multi dimensional systems. Our high fidelity single-site resolution in control and measurement, along with highly programmable interactions, allow us to perform full state tomography of a ground state showcasing persistent current, and to observe dynamics of a time-reversal broken system with nontrivial interactions. Our results open a path towards simulation of time-reversal broken many-body systems with a wide range of features and coupling geometries.
Infective Endocarditis After Transcatheter Aortic Valve Implantation: A Systematic Review

Infective endocarditis (IE) after transcatheter aortic valve implantation (TAVI) is a rare but potentially fatal event. In this systematic review, we searched PubMed and Embase for large TAVI studies and registries to identify the incidence, presentation, microbiology, risk factors, and outcomes of IE in this population. After application of the selection criteria and quality assessment, 8 studies representing 255,310 TAVR cases and 4218 cases of IE qualified for this review. IE following TAVI is uncommon with an incidence of 0.87 to 1.7 events per 100 person-years. Most events occur in the first year following valve implantation. Staphylococcus, Enterococcus, and Streptococcus species are the most common pathogens. Risk factors include age, sex, concomitant comorbidities, and procedural factors. Outcomes are dismal, and surgical intervention is rare in this population.
Learning Dense Reward with Temporal Variant Self-Supervision

Rewards play an essential role in reinforcement learning. In contrast to rule-based game environments with well-defined reward functions, complex real-world robotic applications, such as contact-rich manipulation, lack explicit and informative descriptions that can directly be used as a reward. Previous effort has shown that it is possible to algorithmically extract dense rewards directly from multimodal observations. In this paper, we aim to extend this effort by proposing a more efficient and robust way of sampling and learning. In particular, our sampling approach utilizes temporal variance to simulate the fluctuating state and action distribution of a manipulation task. We then proposed a network architecture for self-supervised learning to better incorporate temporal information in latent representations. We tested our approach in two experimental setups, namely joint-assembly and door-opening. Preliminary results show that our approach is effective and efficient in learning dense rewards, and the learned rewards lead to faster convergence than baselines.
Memristive, Spintronic, and 2D‐Materials‐Based Devices to Improve and Complement Computing Hardware

In a data‐driven economy, virtually all industries benefit from advances in information technology—powerful computing systems are critically important for rapid technological progress. However, this progress might be at risk of slowing down if the discrepancy between the current computing power demands and what the existing technologies can offer is not addressed. Key limitations to improving energy efficiency are the excessive growth of data transfer costs associated with the von Neumann architecture and the fundamental limits of complementary metal–oxide–semiconductor (CMOS) technologies, such as transistors. Herein, three approaches that will likely play an essential role in future computing systems are discussed: memristive electronics, spintronics, and electronics based on 2D materials. The authors present how these technologies may transform conventional digital computers and contribute to the adoption of new paradigms, like neuromorphic computing.
Deep Learning for Visual Speech Analysis: A Survey

Visual speech, referring to the visual domain of speech, has attracted increasing attention due to its wide applications, such as public security, medical treatment, military defense, and film entertainment. As a powerful AI strategy, deep learning techniques have extensively promoted the development of visual speech learning. Over the past five years, numerous deep learning based methods have been proposed to address various problems in this area, especially automatic visual speech recognition and generation. To push forward future research on visual speech, this paper will present a comprehensive review of recent progress in deep learning methods on visual speech analysis. We cover different aspects of visual speech, including fundamental problems, challenges, benchmark datasets, a taxonomy of existing methods, and state-of-the-art performance. Besides, we also identify gaps in current research and discuss inspiring future research directions.