Miracle-dz/UniFAR-FTUs huggingface dataset:
An independent external validation of the QRISK3 cardiovascular risk prediction model applied to UK Biobank participants

BackgroundThe QRISK3 cardiovascular disease (CVD) risk prediction model was derived using primary care data; however, it is frequently used outside of clinical settings. The use of QRISK3 in epidemiological studies without external validation may lead to inaccurate results, however it has been used multiple times on data from UK Biobank. We aimed to externally evaluate the performance of QRISK3 for predicting 10-year risk of cardiovascular events in the UK Biobank cohort. MethodsWe used data from the UK Biobank, a large-scale prospective cohort study of 403,370 participants aged 40-69 years recruited between 2006 and 2010 in the United Kingdom (UK). We included participants with no previous history of CVD or statin treatment and the outcome was the first occurrence of coronary heart disease, ischaemic stroke or transient ischaemic attack, derived from linked hospital episode statistics (HES) and death registration data (DRD). ResultsOur study population included 233,233 females and 170,137 males, with 9295 and 13,028 incident cardiovascular events, respectively. The overall median follow-up time after recruitment was 11.7 years. The discrimination measure of QRISK3 in the overall population was reasonable (Harrells C-Index 0.722 in females and 0.697 in males), this was poorer in older participants (<0.62 in all participants aged 65 or older). QRISK3 had systematic over-prediction of CVD risk in UK Biobank, particularly in older participants, by as much as 20%. ConclusionsQRISK3 had reasonable overall discrimination for the whole study population, which was best in younger participants. The observed CVD risk in UK Biobank participants was lower than that predicted by QRISK3, particularly for older participants. The UK Biobank cohort is known to be healthier than the general population and therefore it is necessary to recalibrate QRISK3 before using it to predict absolute CVD risk in the UK Biobank cohort.
The WWOX gene : investigation of its function and its role in ovarian tumourigenesis

WWOX is a putative tumour suppressor gene. Although this statement is supported by the identification of homozygous deletions and high frequency allelic loss in a variety of human tumours, the role of the gene in human tumourigenesis is still unknown. In particular, no in vitro phenotype has been identified for WWOX. The reconstitution of WWOX in the PEOl ovarian cancer cell line (homozygously deleted for most of the coding region) resulted in the abolition of tumourigenicity in nude mice. In vitro studies revealed that exogenous WWOX expression decreased the ability ofPEOl cells to migrate towards fibronectin. Preliminary data also suggested that lEIEOA-transfected PEO1 cells had a decreased ability to attach to matrigel and fibronectin. In a panel of 71 human ovarian tumours, 2 tumours expressing no fulllength WWOX mRNA (isoform 1) were identified. The level of WWOX isoform 1 expression in the ovarian tumour panel was significantly lower than that of 13 normal human ovaries (pO.OOOl). In addition, the expression of the WWOX A6-8 transcript (isoform 4) was associated with high grade (p=0.006), advanced stage (p=0.012) ovarian cancer. However, the expression of WWOX isoform 4 was also identified in 9 out of 13 normal ovaries. The data presented support the role of WWOX as a tumour suppressor in ovarian cancer and suggest that it plays a role in the prevention of cell migration and attachment to extracellular matrix components.
Positioning the decoupling point along a supply chain: a case study

In today's business world where the supply chains compete with one another, companies need to match their products with different supply chain (SC) strategies in order to increase their market shares, deliver the mass customised products that their customers demand at the right time and adapt to the rapid changes in the market. This paper contributes to the literature by demonstrating how products and supply chain strategies matching can be matched and how the decoupling point can be positioned along a leagile supply chain. To this end, a case study was conducted in a furniture components manufacturing company. First the products of the company were classified and matched with lean, agile or leagile supply chain strategies. With a view to determining the decoupling point of a leagile strategy, fuzzy Dematel method was applied to identify the relationships between the criteria, Dematel-based ANP (DANP) was applied to determine the weight of the criteria, fuzzy TOPSIS was applied to identify the best decoupling point (DP) and 0-1 goal programming (ZOGP) method was applied to identify the optimum alternative solution set under the constraints. This study aims to present a road map for the decision makers by using a case study as an example.
Deriving a Maximum Contaminant Level Goal

ANSES undertakes independent and pluralistic scientific expert assessments. ANSES primarily ensures environmental, occupational and food safety as well as assessing the potential health risks they may entail. It also contributes to the protection of the health and welfare of animals, the protection of plant health and the evaluation of the nutritional characteristics of food. It provides the competent authorities with all necessary information concerning these risks as well as the requisite expertise and scientific and technical support for drafting legislative and statutory provisions and implementing risk management strategies (Article L.1313-1 of the French Public Health Code). Its opinions are published on its website. This opinion is a translation of the original French version. In the event of any discrepancy or ambiguity the French language text dated 3 February 2022 shall prevail.
Exciton tuning in monolayer WSe2via substrate induced electron doping

We report large exciton tuning in WSe2 monolayers via substrate induced non-degenerate doping. We observe a redshift of ∼62 meV for the A exciton together with a 1–2 orders of magnitude photoluminescence (PL) quenching when the monolayer WSe2 is brought in contact with highly oriented pyrolytic graphite (HOPG) compared to dielectric substrates such as hBN and SiO2. As the evidence of doping from HOPG to WSe2, a drastic increase of the intensity ratio of trions to neutral excitons was observed. Using a systematic PL and Kelvin probe force microscopy (KPFM) investigation on WSe2/HOPG, WSe2/hBN, and WSe2/graphene, we conclude that this unique excitonic behavior is induced by electron doping from the substrate. Our results propose a simple yet efficient way for exciton tuning in monolayer WSe2, which plays a central role in the fundamental understanding and further device development.
Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proven to be of vital significance in applications since the unreliable distant–distance similarity estimation would degrade clustering performance. Although existing localized MKC algorithms exhibit improved performance compared with globally designed competitors, most of them widely adopt the KNN mechanism to localize kernel matrix by accounting for $\tau $ -nearest neighbors. However, such a coarse manner follows an unreasonable strategy that the ranking importance of different neighbors is equal, which is impractical in applications. To alleviate such problems, this article proposes a novel local sample-weighted MKC (LSWMKC) model. We first construct a consensus discriminative affinity graph in kernel space, revealing the latent local structures. Furthermore, an optimal neighborhood kernel for the learned affinity graph is output with naturally sparse property and clear block diagonal structure. Moreover, LSWMKC implicitly optimizes adaptive weights on different neighbors with corresponding samples. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algorithms. The source code of LSWMKC can be publicly accessed from https://github.com/liliangnudt/LSWMKC.
Partial Shape Similarity via Alignment of Multi-Metric Hamiltonian Spectra

Evaluating the similarity of non-rigid shapes with significant partiality is a fundamental task in numerous computer vision applications. Here, we propose a novel axiomatic method to match similar regions across shapes. Matching similar regions is formulated as the alignment of the spectra of operators closely related to the Laplace-Beltrami operator (LBO). The main novelty of the proposed approach is the consideration of differential operators defined on a manifold with multiple metrics. The choice of a metric relates to fundamental shape properties while considering the same manifold under different metrics can thus be viewed as analyzing the underlying manifold from different perspectives. Specifically, we examine the scale-invariant metric and the corresponding scale-invariant Laplace-Beltrami operator (SI-LBO) along with the regular metric and the regular LBO. We demonstrate that the scale-invariant metric emphasizes the locations of important semantic features in articulated shapes. A truncated spectrum of the SI-LBO consequently better captures locally curved regions and complements the global information encapsulated in the truncated spectrum of the regular LBO. We show that matching these dual spectra outperforms competing axiomatic frameworks when tested on standard benchmarks. We introduced a new dataset and compare the proposed method with the state-of-the-art learning based approach in a cross-database configuration. Specifically, we show that, when trained on one data set and tested on another, the proposed axiomatic approach which does not involve training, outperforms the deep learning alternative.
CoQAR: Question Rewriting on CoQA

Questions asked by humans during a conversation often contain contextual dependencies, i.e., explicit or implicit references to previous dialogue turns. These dependencies take the form of coreferences (e.g., via pronoun use) or ellipses, and can make the understanding difficult for automated systems. One way to facilitate the understanding and subsequent treatments of a question is to rewrite it into an out-of-context form, i.e., a form that can be understood without the conversational context. We propose CoQAR, a corpus containing 4.5K conversations from the Conversational Question-Answering dataset CoQA, for a total of 53K follow-up question-answer pairs. Each original question was manually annotated with at least 2 at most 3 out-of-context rewritings. CoQA originally contains 8k conversations, which sum up to 127k question-answer pairs. CoQAR can be used in the supervised learning of three tasks: question paraphrasing, question rewriting and conversational question answering. In order to assess the quality of CoQAR’s rewritings, we conduct several experiments consisting in training and evaluating models for these three tasks. Our results support the idea that question rewriting can be used as a preprocessing step for (conversational and non-conversational) question answering models, thereby increasing their performances.
Roadside IRS-Aided Vehicular Communication: Efficient Channel Estimation and Low-Complexity Beamforming Design

Intelligent reflecting surface (IRS) has emerged as a promising technique to control wireless propagation environment for enhancing the communication performance cost-effectively. However, the rapidly time-varying channel in high-mobility communication scenarios such as vehicular communication renders it challenging to obtain the instantaneous channel state information (CSI) efficiently for IRS with a large number of reflecting elements. In this paper, we propose a new roadside IRS-aided vehicular communication system to tackle this challenge. Specifically, by exploiting the symmetrical deployment of IRSs with inter-laced equal intervals on both sides of the road and the cooperation among nearby IRS controllers, we propose a new two-stage channel estimation scheme with off-line and online training, respectively, to obtain the static/time-varying CSI required by the proposed low-complexity passive beamforming scheme efficiently. The proposed IRS beamforming and online channel estimation designs leverage the existing uplink pilots in wireless networks and do not require any change of the existing transmission protocol. Moreover, they can be implemented by each of IRS controllers independently, without the need of any real-time feedback from the user’s serving BS. Simulation results show that the proposed designs can efficiently achieve the high IRS passive beamforming gain and thus significantly enhance the achievable communication throughput for high-speed vehicular communications.
State Prediction of Human-in-the-Loop Multi-rotor System with Stochastic Human Behavior Model

Reachability analysis is a widely used method to analyze the safety of a Human-in-the-Loop Cyber Physical System (HiLCPS). This strategy allows the HiLCPS to respond against an imminent threat in advance by predicting reachable states of the system. However, it could lead to an unnecessarily conservative reachable set if the prediction only relies on the system dynamics without explicitly considering human behavior, and thus the risk might be overestimated. To reduce the conservativeness of the reachability analysis, we present a state prediction method which takes into account a stochastic human behavior model represented as a Gaussian Mixture Model (GMM). In this paper, we focus on the multi-rotor in a near-collision situation. The stochastic human behavior model is trained using experimental data to represent human operators' evasive maneuver. Then, we can retrieve a human control input probability distribution from the trained stochastic human behavior model using the Gaussian Mixture Regression (GMR). The proposed algorithm predicts the probability distribution of the multi-rotor's future state based on the given dynamics and the retrieved human control input probability distribution. Besides, the proposed state prediction method considers the uncertainty of the initial state modeled as a GMM, which yields more robust performance. Human subject experiment results are provided to demonstrate the effectiveness of the proposed algorithm.
Egocentric Visual Self-Modeling for Autonomous Robot Dynamics Prediction and Adaptation

The ability of robots to model their own dynamics is key to autonomous planning and learning, as well as for autonomous damage detection and recovery. Traditionally, dynamic models are pre-programmed or learned from external observations. Here, we demonstrate for the first time how a task-agnostic dynamic self-model can be learned using only a single first-person-view camera in a self-supervised manner, without any prior knowledge of robot morphology, kinematics, or task. Through experiments on a 12-DoF robot, we demonstrate the capabilities of the model in basic locomotion tasks using visual input. Notably, the robot can autonomously detect anomalies, such as damaged components, and adapt its behavior, showcasing resilience in dynamic environments. Furthermore, the model's generalizability was validated across robots with different configurations, emphasizing its potential as a universal tool for diverse robotic systems. The egocentric visual self-model proposed in our work paves the way for more autonomous, adaptable, and resilient robotic systems.
Bayesian Inference of the Symmetry Energy and the Neutron Skin in $^{48}$Ca and $^{208}$Pb from CREX and PREX-2

Using the recent model-independent determination of the charge-weak form factor difference $\Delta F_{\rm CW}$ in $^{48}$Ca and $^{208}$Pb by the CREX and PREX-2 collaborations together with some well-determined properties of doubly magic nuclei, we perform Bayesian inference of the symmetry energy $E_{\rm sym}(\rho)$ and the neutron skin thickness $\Delta r_{\rm np}$ of $^{48}$Ca and $^{208}$Pb within the Skyrme energy density functional (EDF). We find the inferred $E_{\rm sym}(\rho)$ and $\Delta r_{\rm np}$ separately from CREX and PREX-2 are compatible with each other at $90\%$ C.L. , although they are inconsistent at $68.3\%$ C.L. with CREX (PREX-2) favoring a very soft (stiff) $E_{\rm sym}(\rho)$ and rather small (large) $\Delta r_{\rm np}$. By combining the CREX and PREX-2 data, we obtain a soft symmetry energy around saturation density $\rho_0$ and thinner $\Delta r_{\rm np}$ of $^{48}$Ca and $^{208}$Pb, which are found to be closer to the corresponding results from CREX alone, implying the PREX-2 is less effective to constrain the $E_{\rm sym}(\rho)$ and $\Delta r_{\rm np}$ due to its lower precision of $\Delta F_{\rm CW}$. Furthermore, we find the Skyrme EDF results inferred by combining the CREX and PREX-2 data nicely agree with the measured dipole polarizabilities $\alpha_D$ in $^{48}$Ca and $^{208}$Pb as well as the neutron matter equation of state from microscopic calculations. The implications of the inferred soft $E_{\rm sym}(\rho)$ around $\rho_0$ are discussed.
Attention Round for Post-Training Quantization

At present, the quantification methods of neural network models are mainly divided into post-training quantization (PTQ) and quantization aware training (QAT). Post-training quantization only need a small part of the data to complete the quantification process, but the performance of its quantitative model is not as good as the quantization aware training. This paper presents a novel quantification method called Attention Round. This method gives parameters w the opportunity to be mapped to all possible quantized values, rather than just the two quantized values nearby w in the process of quantization. The probability of being mapped to different quantified values is negatively correlated with the distance between the quantified values and w, and decay with a Gaussian function. In addition, this paper uses the lossy coding length as a measure to assign bit widths to the different layers of the model to solve the problem of mixed precision quantization, which effectively avoids to solve combinatorial optimization problem. This paper also performs quantitative experiments on different models, the results confirm the effectiveness of the proposed method. For ResNet18 and MobileNetV2, the post-training quantization proposed in this paper only require 1,024 training data and 10 minutes to complete the quantization process, which can achieve quantization performance on par with quantization aware training.
Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks

Measuring the structural parameters (size, total brightness, light concentration, etc.) of galaxies is a significant first step towards a quantitative description of different galaxy populations. In this work, we demonstrate that a Bayesian Neural Network (BNN) can be used for the inference, with uncertainty quantification, of such morphological parameters from simulated low-surface-brightness galaxy images. Compared to traditional profile-fitting methods, we show that the uncertainties obtained using BNNs are comparable in magnitude, well-calibrated, and the point estimates of the parameters are closer to the true values. Our method is also significantly faster, which is very important with the advent of the era of large galaxy surveys and big data in astrophysics.
Machine Learning based Interference Whitening in 5G NR MIMO Receiver

We address the problem of computing the interference-plus-noise covariance matrix from a sparsely 10-cated demodulation reference signal (DMRS) for spatial domain interference whitening (IW). The IW procedure is critical at the user equipment (UE) to mitigate the co-channel interference in 5G new radio (NR) systems. A supervised learning based algorithm is proposed to compute the covariance matrix with goals of minimizing both the block-error rate (BLER) and the whitening complexity. A single neural network is trained to select an IW option for covariance computation in various interference scenarios consisting of different interference occupancy, signal-to-interference ratio, signal-to-noise ratio, modulation order, coding rate, etc. In interference-dominant scenarios, the proposed algorithm computes the covariance matrix using DMRS in one resource block (RB) due to the frequency selectivity of the interference channel. On the other hand, in noise-dominant scenarios, the covariance matrix is computed from DMRS in entire signal bandwidth. Further, the proposed algorithm approximates the covariance matrix into a diagonal matrix when the spatial correlation of interference-plus-noise is low. This approximation reduces the complexity of whitening from $\mathcal{O}(N^{3})$ to $\mathcal{O}(N)$ where N is the number of receiver antennas. Results show that the selection algorithm can minimize the BLER under both trained as well as untrained interference scenarios.
Sensitivity Analysis on Transferred Neural Architectures of BERT and GPT-2 for Financial Sentiment Analysis

The explosion in novel NLP word embedding and deep learning techniques has induced significant endeavors into potential applications. One of these directions is in the financial sector. Although there is a lot of work done in state-of-the-art models like GPT and BERT, there are relatively few works on how well these methods perform through fine-tuning after being pre-trained, as well as info on how sensitive their parameters are. We investigate the performance and sensitivity of transferred neural architectures from pre-trained GPT-2 and BERT models. We test the fine-tuning performance based on freezing transformer layers, batch size, and learning rate. We find that BERT’s parameters are hypersensitive to stochasticity in fine-tuning and that GPT-2 is more stable in such practice. It is also clear that the earlier layers of GPT-2 and BERT contain essential word pattern information that should be maintained.
An Additive Instance-Wise Approach to Multi-class Model Interpretation

Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. A large number of interpreting methods focus on identifying explanatory input features, which generally fall into two main categories: attribution and selection. A popular attribution-based approach is to exploit local neighborhoods for learning instance-specific explainers in an additive manner. The process is thus inefficient and susceptible to poorly-conditioned samples. Meanwhile, many selection-based methods directly optimize local feature distributions in an instance-wise training framework, thereby being capable of leveraging global information from other inputs. However, they can only interpret single-class predictions and many suffer from inconsistency across different settings, due to a strict reliance on a pre-defined number of features selected. This work exploits the strengths of both methods and proposes a framework for learning local explanations simultaneously for multiple target classes. Our model explainer significantly outperforms additive and instance-wise counterparts on faithfulness with more compact and comprehensible explanations. We also demonstrate the capacity to select stable and important features through extensive experiments on various data sets and black-box model architectures.