Miracle-dz/UniFAR-FTUs huggingface dataset:
tmVar 3.0: an improved variant concept recognition and normalization tool

Abstract Motivation Previous studies have shown that automated text-mining tools are becoming increasingly important for successfully unlocking variant information in scientific literature at large scale. Despite multiple attempts in the past, existing tools are still of limited recognition scope and precision. Result We propose tmVar 3.0: an improved variant recognition and normalization system. Compared to its predecessors, tmVar 3.0 recognizes a wider spectrum of variant-related entities (e.g. allele and copy number variants), and groups together different variant mentions belonging to the same genomic sequence position in an article for improved accuracy. Moreover, tmVar 3.0 provides advanced variant normalization options such as allele-specific identifiers from the ClinGen Allele Registry. tmVar 3.0 exhibits state-of-the-art performance with over 90% in F-measure for variant recognition and normalization, when evaluated on three independent benchmarking datasets. tmVar 3.0 as well as annotations for the entire PubMed and PMC datasets are freely available for download. Availability and implementation https://github.com/ncbi/tmVar3
AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles

Due to the rapid growth of data transmissions in internet of vehicles (IoV), finding schemes that can effectively alleviate access congestion has become an important issue. Recently, many traffic control schemes have been studied. Nevertheless, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it. Firstly, IoV devices are divided into various classes based on delay characteristics. The target of maximizing the successful transmission of packets with the success rate constraint is established. Then, the optimization objective is transformed into a markov decision process (MDP) model. Finally, the access class barring (ACB) factors are obtained based on the PPO method to maximize the number of successful access devices. The performance of the proposal algorithm in respect of successful events and delay compared to existing schemes is verified by simulations.
Understanding vibration transmitted to the human finger

Prolonged exposure of the hand to tool-induced vibrations is associated with the occurrence of debilitating conditions such as vibration white finger. The primary aim of this work is to gain a better understanding of the effects of different aspects of exposure to finger transmitted vibration (FTV) related to operators using hand-held vibrating tools. To achieve this, firstly, a new method for measuring finger transmitted vibration was developed and assessed, including a tool vibration test rig and measurement protocol. The effect on FTV measurement of using a small accelerometer attached to the back of the finger was investigated using 2D finite element modelling. Comparisons were also made using a laser vibrometer. Analysis showed that the new test rig is capable of measuring FTV at frequencies ranging from 10 to 400 Hz, under different grip force levels, and that adding a small accelerometer mass (0.3 grams) did not significantly affect measurements. A human participant study then carried out using the new rig. Various characteristic measurements were collected in tandem, including anthropometry, skin characterisation and behaviour under loading to investigate the effect of different factors on FTV. The results showed that FTV varied among individuals and the key finding was that exposure to vibration has a significant effect on finger temperature even for a short period of testing. Anti-vibration (AV) glove materials were investigated using dynamic mechanical analysis (DMA) and tested using human participants. The results showed that the mechanical properties of AV materials change under real world industrial conditions such as excitation frequencies and temperature. Finally, a new artificial test-bed was developed to replicate the transmitted vibration of the index finger. Studies were conducted on a range of 5 test-beds, to allow comparison with the human measurements, including indentation, vibration transmissibility and FE modelling. FE modelling showed that the distribution of dynamic strain was found to be highest in the vasculature region of the finger, indicating that this could be one of the contributing factors of VWF. One of the finger test-bed was selected as best replicating the mechanical properties of the real finger. The artificial test-bed provided better consistency than human participants, for testing parameters, such as grip force, and can be used in future for testing AV gloves with no need for human subjects.ii Further investigations are suggested to be made to enhance the limitations of this project, including material analysis, testing protocol and finite element modelling. Keywords:, hand-arm vibration syndromes, vibration white finger, FTV, transmissibility, resonance frequency, grip force, AV glove, finger mechanical properties, artificial finger, finite element modelling
Boosting Self-Supervised Embeddings for Speech Enhancement

Self-supervised learning (SSL) representation for speech has achieved state-of-the-art (SOTA) performance on several downstream tasks. However, there remains room for improvement in speech enhancement (SE) tasks. In this study, we used a cross-domain feature to solve the problem that SSL embeddings may lack fine-grained information to regenerate speech signals. By integrating the SSL representation and spectrogram, the result can be significantly boosted. We further study the relationship between the noise robustness of SSL representation via clean-noisy distance (CN distance) and the layer importance for SE. Consequently, we found that SSL representations with lower noise robustness are more important. Furthermore, our experiments on the VCTK-DEMAND dataset demonstrated that fine-tuning an SSL representation with an SE model can outperform the SOTA SSL-based SE methods in PESQ, CSIG and COVL without invoking complicated network architectures. In later experiments, the CN distance in SSL embeddings was observed to increase after fine-tuning. These results verify our expectations and may help design SE-related SSL training in the future.
AutoRF: Learning 3D Object Radiance Fields from Single View Observations

We introduce AutoRF - a new approach for learning neural 3D object representations where each object in the training set is observed by only a single view. This setting is in stark contrast to the majority of existing works that leverage multiple views of the same object, employ explicit priors during training, or require pixel-perfect annotations. To address this challenging setting, we propose to learn a normalized, object-centric representation whose embedding describes and disentangles shape, appearance, and pose. Each encoding provides well-generalizable, compact information about the object of interest, which is decoded in a single-shot into a new target view, thus enabling novel view synthesis. We further improve the reconstruction quality by optimizing shape and appearance codes at test time by fitting the representation tightly to the input image. In a series of experiments, we show that our method generalizes well to unseen objects, even across different datasets of challenging real-world street scenes such as nuScenes, KITTI, and Mapillary Metropolis. Additional results can be found on our project page https://sirwyver.github. io/AutoRF/.
High‐speed rail and local development in different institutional contexts: towards a spatialization of services innovation analyses

Expectations in cities served by high-speed rail in terms of local economic development are numerous. While nearly everyone would agree that the structuring effects of transportation infrastructures don't exist, the factors that influence the link between high-speed rail and local development need to be analyzed. This is all the more important because high-speed lines are currently built in emerging or developing countries, in other words in very different institutional and social contexts. Based on the literature which focuses on service innovations, this article intends to go beyond an analysis concerning the infrastructure to focus on the services that it enables. It highlights that the services relationship varies in time and in space. Consequently, the analysis of service innovation must be spatialized. Indeed, technologies, services characteristics, clients and provider resources, as well as the rules guiding their action are embedded in given societies.
Maximizing Sums of Non-monotone Submodular and Linear Functions: Understanding the Unconstrained Case

Motivated by practical applications, recent works have considered maximization of sums of a submodular function $g$ and a linear function $\ell$. Almost all such works, to date, studied only the special case of this problem in which $g$ is also guaranteed to be monotone. Therefore, in this paper we systematically study the simplest version of this problem in which $g$ is allowed to be non-monotone, namely the unconstrained variant, which we term Regularized Unconstrained Submodular Maximization (RegularizedUSM). Our main algorithmic result is the first non-trivial guarantee for general RegularizedUSM. For the special case of RegularizedUSM in which the linear function $\ell$ is non-positive, we prove two inapproximability results, showing that the algorithmic result implied for this case by previous works is not far from optimal. Finally, we reanalyze the known Double Greedy algorithm to obtain improved guarantees for the special case of RegularizedUSM in which the linear function $\ell$ is non-negative; and we complement these guarantees by showing that it is not possible to obtain (1/2, 1)-approximation for this case (despite intuitive arguments suggesting that this approximation guarantee is natural).
Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification

A pretrain-finetune strategy is widely used to reduce the overfitting that can occur when data is insufficient for CNN training. First few layers of a CNN pretrained on a large-scale RGB dataset are capable of acquiring general image characteristics which are remarkably effective in tasks targeted for different RGB datasets. However, when it comes down to hyperspectral domain where each domain has its unique spectral properties, the pretrain-finetune strategy no longer can be deployed in a conventional way while presenting three major issues: 1) inconsistent spectral characteristics among the domains (e.g., frequency range), 2) inconsistent number of data channels among the domains, and 3) absence of large-scale hyperspectral dataset. We seek to train a universal cross-domain model which can later be deployed for various spectral domains. To achieve, we physically furnish multiple inlets to the model while having a universal portion which is designed to handle the inconsistent spectral characteristics among different domains. Note that only the universal portion is used in the finetune process. This approach naturally enables the learning of our model on multiple domains simultaneously which acts as an effective workaround for the issue of the absence of large-scale dataset. We have carried out a study to extensively compare models that were trained using cross-domain approach with ones trained from scratch. Our approach was found to be superior both in accuracy and in training efficiency. In addition, we have verified that our approach effectively reduces the overfitting issue, enabling us to deepen the model up to 13 layers (from 9) without compromising the accuracy.
Superconductivity, Charge Density Wave, and Supersolidity in Flat Bands with a Tunable Quantum Metric.

Predicting the fate of an interacting system in the limit where the electronic bandwidth is quenched is often highly nontrivial. The complex interplay between interactions and quantum fluctuations driven by the band geometry can drive competition between various ground states, such as charge density wave order and superconductivity. In this work, we study an electronic model of topologically trivial flat bands with a continuously tunable Fubini-Study metric in the presence of on-site attraction and nearest-neighbor repulsion, using numerically exact quantum Monte Carlo simulations. By varying the electron filling and the minimal spatial extent of the localized flat-band Wannier wave functions, we obtain a number of intertwined orders. These include a phase with coexisting charge density wave order and superconductivity, i.e., a supersolid. In spite of the nonperturbative nature of the problem, we identify an analytically tractable limit associated with a "small" spatial extent of the Wannier functions and derive a low-energy effective Hamiltonian that can well describe our numerical results. We also provide unambiguous evidence for the violation of any putative lower bound on the zero-temperature superfluid stiffness in geometrically nontrivial flat bands.
A tutorial for using propensity score weighting for moderation analysis: an application to smoking disparities among sexual minority adults

Objective. To provide step-by-step guidance and STATA and R code for using propensity score (PS) weighting to estimate moderation effects. Research Design. Tutorial illustrating the key steps for estimating and testing moderation using observational data. Steps include (1) examining covariate overlap across treatment groups within levels of the moderator, (2) estimating the PS weights, (3) evaluating whether PS weights improved covariate balance, (4) estimating moderated treatment effects, and (5) assessing sensitivity of findings to unobserved confounding. Our illustrative case study uses data from 41,832 adults from the 2019 National Survey on Drug Use and Health to examine if gender moderates the association between sexual minority status (e.g., lesbian, gay, or bisexual [LGB] identity) and adult smoking prevalence. Results. For our case study, there were no noted concerns about covariate overlap and we were able to successfully estimate the PS weights within each level of the moderator. Moreover, balance criteria indicated that PS weights successfully achieved covariate balance for both moderator groups. PS weighted results indicated there was significant evidence of moderation for the case study and sensitivity analyses demonstrated that results were highly robust for one level of the moderator but not the other. Conclusions. When conducting moderation analyses, covariate imbalances across levels of the moderator can cause biased estimates. As demonstrated in this tutorial, PS weighting within each level of the moderator can improve the estimated moderation effects by minimizing bias from imbalance within the moderator subgroups.
Pressure‐Induced Isostructural Phase Transition in Biskyrmion Host Hexagonal MnNiGa

Magnetic skyrmions are vortex‐like spin textures, which can be manipulated by external stress or pressure via magnetoelastic effects. Herein, the observation of isostructural phase transition in a biskyrmion host hexagonal MnNiGa at pressure P ≈4 GPa using pressure‐dependent synchrotron X‐Ray powder diffraction (XRD) data analysis is presented. The XRD data reveals anisotropic compression behavior with pressure with different compression rates of the a‐axis in the basal plane and the c‐axis in the prismatic plane. However, the hexagonal symmetry remains unchanged for pressure up to 14 GPa. Fitting of unit cell volume with pressure using a second‐order Birch–Murnaghan equation of state reveals that the data fall into two distinct curves for those above and below 4 GPa. Herein, the understanding of crystal structure with the application of hydrostatic pressure in the biskyrmion host MnNiGa is contributed to, wherein the skyrmion textures can be manipulated by pressure due to their magnetoelastic character.
Efficient Community Detection in Large-Scale Dynamic Networks Using Topological Data Analysis

In this paper, we propose a method that extends the persistence-based topological data analysis (TDA) that is typically used for characterizing shapes to general networks. We introduce the concept of the community tree, a tree structure established based on clique communities from the clique percolation method, to summarize the topological structures in a network from a persistence perspective. Furthermore, we develop efﬁcient algorithms to construct and update community trees by maintaining a series of clique graphs in the form of spanning forests, in which each spanning tree is built on an underlying Euler Tour tree. With the information revealed by community trees and the corresponding persistence diagrams, our proposed approach is able to detect clique communities and keep track of the major structural changes during their evolution given a stability threshold. The results demonstrate its effectiveness in extracting useful structural insights for time-varying social networks.
Quasi-static approximation error of electric field analysis for transcranial current stimulation

Objective. Numerical modeling of electric fields induced by transcranial alternating current stimulation (tACS) is currently a part of the standard procedure to predict and understand neural response. Quasi-static approximation (QSA) for electric field calculations is generally applied to reduce the computational cost. Here, we aimed to analyze and quantify the validity of the approximation over a broad frequency range. Approach. We performed electromagnetic modeling studies using an anatomical head model and considered approximations assuming either a purely ohmic medium (i.e. static formulation) or a lossy dielectric medium (QS formulation). The results were compared with the solution of Maxwell’s equations in the cases of harmonic and pulsed signals. Finally, we analyzed the effect of electrode positioning on these errors. Main results. Our findings demonstrate that the QSA is valid and produces a relative error below 1% up to 1.43 MHz. The largest error is introduced in the static case, where the error is over 1% across the entire considered spectrum and as high as 20% in the brain at 10 Hz. We also highlight the special importance of considering the capacitive effect of tissues for pulsed waveforms, which prevents signal distortion induced by the purely ohmic approximation. At the neuron level, the results point a difference of sense electric field as high as 22% at focusing point, impacting pyramidal cells firing times. Significance. QSA remains valid in the frequency range currently used for tACS. However, neglecting permittivity (static formulation) introduces significant error for both harmonic and non-harmonic signals. It points out that reliable low frequency dielectric data are needed for accurate transcranial current stimulation numerical modeling.
Reconstructing Bayesian Networks on a Quantum Annealer

Bayesian networks are widely used probabilistic graphical models, whose structure is hard to learn starting from the generated data. O'Gorman et al. have proposed an algorithm to encode this task, i.e., the Bayesian network structure learning (BNSL), into a form that can be solved through quantum annealing, but they have not provided an experimental evaluation of it. In this paper, we present (i) an implementation in Python of O'Gorman's algorithm, (ii) a divide et impera approach that allows addressing BNSL problems of larger sizes in order to overcome the limitations imposed by the current architectures, and (iii) their empirical evaluation. Specifically, several problems with an increasing number of variables have been used in the experiments. The results have shown the effectiveness of O'Gorman's formulation for BNSL instances of small sizes, and the superiority of the divide et impera approach on the direct execution of O'Gorman's algorithm.
Spin dynamics and exchange interaction in orthoferrite 
TbFeO3
 with non-Kramers rare-earth ion

The low-temperature spin dynamics of the orthorhombic TbFeO 3 perovskite has been studied. It has been found that the inelastic neutron scattering (INS) spectrum contains two modes corresponding to diﬀerent sublattices in the compound. The iron subsystem orders antiferromagnetically at T N = 632 K and exhibits the high-energy magnon dispersion. Magnetic dynamics of this subsystem has been described using the linear spin wave theory and our solution yields sizable anisotropy between in-plane and out-of-plane exchange interactions. This approach was previously used to describe the magnon dispersion in the TmFeO 3 compound. Three non-dispersive crystal electric ﬁeld levels corresponding to Tb 3+ ions have been established in the region below 40 meV at about 17, 26, and 35 meV. Study of diﬀuse scattering at diﬀerent temperatures has elucidated the behavior of the magnetic correlation length. The behavior of the Tb 3+ ion subsystem has been numerically described in the framework of the point charge model. The numerical data agree satisfactorily with the experiment and with the general concept of the single-ion approximation applied to the rare-earth subsystem of orthorhombic perovskites. without was measured of after the was decreased from and the temperature dependence of the was measured. Of particular interest is the range to
Robust Model-based Inference for Non-Probability Samples

With the ubiquitous availability of unstructured data, growing attention is paid as how to adjust for selection bias in such non-probability samples. The majority of the robust estimators proposed by prior literature are either fully or partially design-based, which may lead to inefficient estimates if outlying (pseudo-)weights are present. In addition, correctly reflecting the uncertainty of the adjusted estimator remains a challenge when the available reference survey is complex in the sample design. This article proposes a fully model-based method for inference using non-probability samples where the goal is to predict the outcome variable for the entire population units. We employ a Bayesian bootstrap method with Rubin's combing rules to derive the adjusted point and interval estimates. Using Gaussian process regression, our method allows for kernel matching between the non-probability sample units and population units based on the estimated selection propensities when the outcome model is misspecified. The repeated sampling properties of our method are evaluated through two Monte Carlo simulation studies. Finally, we examine it on a real-world non-probability sample with the aim to estimate crash-attributed injury rates in different body regions in the United States.