Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraining U(1) L μ – L τ charged dark matter model for muon g – 2 anomaly with AMS-02 electron and positron data

Very recently, the Fermi-Lab reported the new experimental combined results on the magnetic momentum of muon with a 4. 2σ discrepancy compared with the expectation of the Standard Model [1]. A new light gauge boson X in the Lμ – Lτ model provides a good explanation for the g – 2 anomaly. A Dirac fermion dark matter with a large Lμ – Lτ charge can explain both the g – 2 anomaly and the dark matter relic density [2]. In this work, we focus on the case that the mass of the dark matter is larger than the mass of muon (i.e. m Ψ > m μ) for which the channel ΨΨ ⟶ μ - μ + opens. Although the cross section (σ v) μ - μ + is smaller by a factor of 1/q Ψ 2 (q Ψ represents the Lμ – Lτ charge of the dark matter) compared with the channel ΨΨ ⟶ X X ⟶ ννν̅ν̅, the resulting secondary electrons and positrons could imprint on their spectra above GeV energies due to the reacceleration effect of cosmic ray propagation. We use the AMS-02 measurements of electrons and positrons to constrain the annihilation cross section of the channel ΨΨ ⟶ μ - μ +, which rules out part of the parameter space of the large Lμ – Lτ charged dark matter model to account for the muon g – 2 anomaly.
Gas Turbine Model Parameter Classification During Abnormal Operation Using Support Vector Machine For Maintenance

When there is a contingency in a major transmission system, it is crucial to locate and detect abnormal parameters using an accurately modeled gas turbine (GT) generator. In this paper, a new method was proposed to model, a GT generator system. Firstly, MATLAB/Simulink was used to rebuild GT-based model which was validated using a model in PSS/E. Secondly, an artificial intelligence (AI) based approach, namely Support Vector Machine (SVM) was used for problem recognition in GT power plant. The results showed that, under steady-state, the electrical power of the rebuilt GT model was the same as the model in PSS/E. The advantage of having the MATLAB / Simulink GT model was the ability to visually observe the output from each block which was not possible in PSS/E. In addition, the average training accuracy was over 90% for the detection of GT governor parameter during abnormal behavior. As an implication, the developed model should be considered to apply in real-world grids for operation engineers to detect abnormal governor parameters in GT in the next stage. In turn, it assists in the restoration of GT to its operating condition and minimizes troubleshooting time.
Fault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Review

Fault diagnosis is increasingly important given the worldwide demand on wind energy as one of the promising renewable energy sources. This systematic review aimed to summarize the fault diagnosis using Extreme Learning Machine (ELM) on wind energy. Firstly, two databases (i.e. Engineering Village (EV) and IEEE Explore were searched to identify relevant articles, using three important keywords, including Extreme Learning Machine/ELM, fault and wind. Of the 14 included studies, only eight studies mentioned the use of sensor to collect vibration signals as the fault data. Sensors were commonly installed at four places (gearbox, generator, bearing, or rotor) in the included studies. Only nine studies used either single or fusion feature extractions for the fault data. Two types of ELM (i.e. single/multi-layered or hybrid-ELM) were identified to diagnose fault. In general, studies showed the superiority of the application of ELM in producing accuracy results in fault diagnosis of WT, compared to other algorithms. Future studies should incorporate the use of real-world data, and improve on the reporting on the methodological components of the study, to better inform on the usefulness of ELM for fault diagnosis in real-world wind energy settings.
Self Reported Behavioral Health Habits and Other Health Issues Influencing Capabilities and Mission Readiness of Combat Search and Rescue Personnel

Abstract : U.S. Air Force (USAF) combat search and rescue (CSAR) fixed- and rotary-wing aircrew special duty rescue personnel are highly trained airmen operating across the globe in a wide range of Department of Defense and Joint Military Coalition combat rescue and recovery missions. The health and wellness of such aircrew and special duty operators are critical to sustaining warfigther performance and readiness capabilities. As a result, the USAF School of Aerospace Medicine was requested by USAF line operator and medical leadership to conduct a field survey to assess for general areas of health-related behaviors (i.e., sleep and exercise; alcohol, tobacco, and caffeine use; common reasons for seeking medical care and mental health support services; and reasons for increased prescription and over-the-counter medication usage) relevant to understanding the needs and issues specific to this community.
Scaling Nanowire-Supported GaN Quantum Dots to the Sub-10 nm Limit, Yielding Complete Suppression of the Giant Built-in Potential

The nanowire-supported quantum dot (NWQD) of GaN is an unconventional nanostructure, which is extremely promising for realization of UV photonics in general, and room-temperature single photon generation, in particular. While GaN-NWQDs have several promising attributes, the crucial challenge in exploiting their full potential, is to reduce the lateral dimensions of the QDs, to the order of the exciton Bohr-radius in GaN. Also critical is to suppress the built-in electric field due to spontaneous and piezoelectric polarization, which adversely affects the radiative recombination lifetime. We report here the innovation of a simple yet powerful single-step epitaxial growth technique, to achieve both of these targets. By combining controlled and on-demand thermal decomposition of GaN nanowires, with our previously-developed strategy of inhibiting the same via AlN-capping, we demonstrate that the NWQD-diameter can indeed be reduced to the truly strong-quantum-confinement limit. In these ultra-scaled GaN QDs, we show that the built-in electric fields are almost completely suppressed. The NWQD fabrication-strategy developed in this work may pave the way for fabrication of highly efficient classical and quantum UV-emitters based on GaN.
Quantitative Rigidity of Differential Inclusions in Two Dimensions

For any compact connected one-dimensional submanifold $K\subset \mathbb R^{2\times 2}$ without boundary that has no rank-one connection and is elliptic, we prove the quantitative rigidity estimate $$\begin{align*} \inf_{M\in K}\int_{B_{1/2}}| Du -M |^2\, \textrm{d}x \leq C \int_{B_1} \operatorname{dist}^2(Du, K)\, \textrm{d}x, \qquad\forall u\in H^1(B_1;\mathbb R^2). \end{align*}$$This is an optimal generalization, for compact connected submanifolds of $\mathbb R^{2\times 2}$ without boundary, of the celebrated quantitative rigidity estimate of Friesecke, James, and Müller for the approximate differential inclusion into $SO(n)$. The proof relies on the special properties of elliptic subsets $K\subset{{\mathbb{R}}}^{2\times 2}$ with respect to conformal–anticonformal decomposition, which provide a quasilinear elliptic partial differential equation satisfied by solutions of the exact differential inclusion $Du\in K$. We also give an example showing that no analogous result can hold true in $\mathbb R^{n\times n}$ for $n\geq 3$.
Contrastive Semi-Supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for medical image segmentation, yet need plenty of manual annotations for training. Semi-Supervised Learning (SSL) methods are promising to reduce the requirement of annotations, but their performance is still limited when the dataset size and the number of annotated images are small. Leveraging existing annotated datasets with similar anatomical structures to assist training has a potential for improving the model’s performance. However, it is further challenged by the cross-anatomy domain shift due to the image modalities and even different organs in the target domain. To solve this problem, we propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation (CS-CADA) that adapts a model to segment similar structures in a target domain, which requires only limited annotations in the target domain by leveraging a set of existing annotated images of similar structures in a source domain. We use Domain-Specific Batch Normalization (DSBN) to individually normalize feature maps for the two anatomical domains, and propose a cross-domain contrastive learning strategy to encourage extracting domain invariant features. They are integrated into a Self-Ensembling Mean-Teacher (SE-MT) framework to exploit unlabeled target domain images with a prediction consistency constraint. Extensive experiments show that our CS-CADA is able to solve the challenging cross-anatomy domain shift problem, achieving accurate segmentation of coronary arteries in X-ray images with the help of retinal vessel images and cardiac MR images with the help of fundus images, respectively, given only a small number of annotations in the target domain. Our code is available at https://github.com/HiLab-git/DAG4MIA.
Meta Sparse Principle Component Analysis

We study the meta-learning for support (i.e. the set of non-zero entries) recovery in high-dimensional Principal Component Analysis. We reduce the suﬃcient sample complexity in a novel task with the information that is learned from auxiliary tasks. We assume each task to be a diﬀerent random Principal Component (PC) matrix with a possibly diﬀerent support and that the support union of the PC matrices is small. We then pool the data from all the tasks to execute an improper estimation of a single PC matrix by maximising the l 1 -regularised predictive covariance to establish that with high probability the true support union can be recovered provided a suﬃcient number of tasks m and a suﬃcient number of samples O (cid:16) log( p ) m (cid:17) for each task, for p -dimensional vectors. Then, for a novel task, we prove that the maximisation of the l 1 -regularised predictive covariance with the additional constraint that the support is a subset of the estimated support union could reduce the suﬃcient sample complexity of successful support recovery to O (log | J | ) , where J is the support union recovered from the auxiliary tasks. Typically, | J | would be much less than p for sparse matrices. Finally, we demonstrate the validity of our experiments through numerical simulations.
Domain Camera Adaptation and Collaborative Multiple Feature Clustering for Unsupervised Person Re-ID

Recently unsupervised person re-identification (re-ID) has drawn much attention due to its open-world scenario settings where limited annotated data is available. Existing supervised methods often fail to generalize well on unseen domains, while the unsupervised methods, mostly lack multi-granularity information and are prone to suffer from confirmation bias. In this paper, we aim at finding better feature representations on the unseen target domain from two aspects, 1) performing unsupervised domain adaptation on the labeled source domain and 2) mining potential similarities on the unlabeled target domain. Besides, a collaborative pseudo re-labeling strategy is proposed to alleviate the influence of confirmation bias. Firstly, a generative adversarial network is utilized to transfer images from the source domain to the target domain. Moreover, person identity preserving and identity mapping losses are introduced to improve the quality of generated images. Secondly, we propose a novel collaborative multiple feature clustering framework (CMFC) to learn the internal data structure of target domain, including global feature and partial feature branches. The global feature branch (GB) employs unsupervised clustering on the global feature of person images while the Partial feature branch (PB) mines similarities within different body regions. Finally, extensive experiments on two benchmark datasets show the competitive performance of our method under unsupervised person re-ID settings.
See Finer, See More: Implicit Modality Alignment for Text-based Person Retrieval

Text-based person retrieval aims to find the query person based on a textual description. The key is to learn a common latent space mapping between visual-textual modalities. To achieve this goal, existing works employ segmentation to obtain explicitly cross-modal alignments or utilize attention to explore salient alignments. These methods have two shortcomings: 1) Labeling cross-modal alignments are time-consuming. 2) Attention methods can explore salient cross-modal alignments but may ignore some subtle and valuable pairs. To relieve these issues, we introduce an Implicit Visual-Textual (IVT) framework for text-based person retrieval. Different from previous models, IVT utilizes a single network to learn representation for both modalities, which contributes to the visual-textual interaction. To explore the fine-grained alignment, we further propose two implicit semantic alignment paradigms: multi-level alignment (MLA) and bidirectional mask modeling (BMM). The MLA module explores finer matching at sentence, phrase, and word levels, while the BMM module aims to mine \textbf{more} semantic alignments between visual and textual modalities. Extensive experiments are carried out to evaluate the proposed IVT on public datasets, i.e., CUHK-PEDES, RSTPReID, and ICFG-PEDES. Even without explicit body part alignment, our approach still achieves state-of-the-art performance. Code is available at: https://github.com/TencentYoutuResearch/PersonRetrieval-IVT.
Finite-time blowup for the inviscid vortex stretching equation

In this paper, we will introduce the inviscid vortex stretching equation, which is a model equation for the 3D Euler equation where the advection of vorticity is neglected. We will show that there are smooth solutions of this equation which blowup in finite-time, even when restricting to axisymmetric, swirl-free solutions. This provides further evidence of the role of advection in depleting nonlinear vortex stretching for solutions of the 3D Euler equation.
A Decorrelating and Debiasing Approach to Simultaneous Inference for High-Dimensional Confounded Models

Motivated by the simultaneous association analysis with the presence of latent confounders, this paper studies the large-scale hypothesis testing problem for the high-dimensional confounded linear models with both non-asymptotic and asymptotic false discovery control. Such model covers a wide range of practical settings where both the response and the predictors may be confounded. In the presence of the high-dimensional predictors and the unobservable confounders, the simultaneous inference with provable guarantees becomes highly challenging, and the unknown strong dependence among the confounded covariates makes the challenge even more pronounced. This paper first introduces a decorrelating procedure that shrinks the confounding effect and weakens the correlations among the predictors, then performs debiasing under the decorrelated design based on some biased initial estimator. Following that, an asymptotic normality result for the debiased estimator is established and standardized test statistics are then constructed. Furthermore, a simultaneous inference procedure is proposed to identify significant associations, and both the finite-sample and asymptotic false discovery bounds are provided. The non-asymptotic result is general and model-free, and is of independent interest. We also prove that, under minimal signal strength condition, all associations can be successfully detected with probability tending to one. Simulation and real data studies are carried out to evaluate the performance of the proposed approach and compare it with other competing methods.
Neural Capture of Animatable 3D Human from Monocular Video

We present a novel paradigm of building an animatable 3D human representation from a monocular video input, such that it can be rendered in any unseen poses and views. Our method is based on a dynamic Neural Radiance Field (NeRF) rigged by a mesh-based parametric 3D human model serving as a geometry proxy. Previous methods usually rely on multi-view videos or accurate 3D geometry information as additional inputs; besides, most methods suffer from degraded quality when generalized to unseen poses. We identify that the key to generalization is a good input embedding for querying dynamic NeRF: A good input embedding should define an injective mapping in the full volumetric space, guided by surface mesh deformation under pose variation. Based on this observation, we propose to embed the input query with its relationship to local surface regions spanned by a set of geodesic nearest neighbors on mesh vertices. By including both position and relative distance information, our embedding defines a distance-preserved deformation mapping and generalizes well to unseen poses. To reduce the dependency on additional inputs, we first initialize per-frame 3D meshes using off-the-shelf tools and then propose a pipeline to jointly optimize NeRF and refine the initial mesh. Extensive experiments show our method can synthesize plausible human rendering results under unseen poses and views.
An Alternative Derivation of the Gaussian Noise model

By extending the results in Bononi 2012 , we provide here a complete alternative derivation of Turin’s Gaussian Noise (GN) model for dual-polarization dispersion uncompensated coherent optical links. This paper contains the lecture notes used by the authors ﬁrst on July 19, 2012, and then again on june 6, 2016 at the University of Parma.
A Tree-structured Transformer for Program Representation Learning

When using deep learning techniques to model program languages, neural networks with tree or graph structures are widely adopted to capture the rich structural information within program abstract syntax trees (AST). However, long-term/global dependencies widely exist in programs, and most of these neural architectures fail to capture these dependencies. In this paper, we propose Tree-Transformer, a novel recursive tree-structured neural network which aims to overcome the above limitations. Tree-Transformer leverages two multi-head attention units to model the dependency between siblings and parent-children node pairs. Moreover, we propose a bi-directional propagation strategy to allow node information passing in two directions: bottom-up and top-down along trees. By combining bottom-up and top-down propagation, Tree-Transformer can learn both global contexts and meaningful node features. The extensive experimental results show that our Tree-Transformer outperforms existing tree-based or graph-based neural networks in program-related tasks with tree-level and node-level prediction tasks, indicating that Tree-Transformer performs well on learning both tree-level and node-level representations.
Application of Traditional Vaccine Development Strategies to SARS-CoV-2

Over the past 150 years, vaccines have revolutionized the relationship between people and disease. During the COVID-19 pandemic, technologies such as mRNA vaccines have received attention due to their novelty and successes. However, more traditional vaccine development platforms have also yielded important tools in the worldwide fight against the SARS-CoV-2 virus. A variety of approaches have been used to develop COVID-19 vaccines that are now authorized for use in countries around the world. In this review, we highlight strategies that focus on the viral capsid and outwards, rather than on the nucleic acids inside. These approaches fall into two broad categories: whole-virus vaccines and subunit vaccines. Whole-virus vaccines use the virus itself, either in an inactivated or attenuated state. Subunit vaccines contain instead an isolated, immunogenic component of the virus. Here, we highlight vaccine candidates that apply these approaches against SARS-CoV-2 in different ways. In a companion manuscript, we review the more recent and novel development of nucleic-acid based vaccine technologies. We further consider the role that these COVID-19 vaccine development programs have played in prophylaxis at the global scale. Well-established vaccine technologies have proved especially important to making vaccines accessible in low- and middle-income countries. Vaccine development programs that use established platforms have been undertaken in a much wider range of countries than those using nucleic-acid-based technologies, which have been led by wealthy Western countries. Therefore, these vaccine platforms, though less novel from a biotechnological standpoint, have proven to be extremely important to the management of SARS-CoV-2.
Conviformers: Convolutionally guided Vision Transformer

Vision transformers are nowadays the de-facto choice for image classification tasks. There are two broad categories of classification tasks, fine-grained and coarse-grained. In fine-grained classification, the necessity is to discover subtle differences due to the high level of similarity between sub-classes. Such distinctions are often lost as we downscale the image to save the memory and computational cost associated with vision transformers (ViT). In this work, we present an in-depth analysis and describe the critical components for developing a system for the fine-grained categorization of plants from herbarium sheets. Our extensive experimental analysis indicated the need for a better augmentation technique and the ability of modern-day neural networks to handle higher dimensional images. We also introduce a convolutional transformer architecture called Conviformer which, unlike the popular Vision Transformer (ConViT), can handle higher resolution images without exploding memory and computational cost. We also introduce a novel, improved pre-processing technique called PreSizer to resize images better while preserving their original aspect ratios, which proved essential for classifying natural plants. With our simple yet effective approach, we achieved SoTA on Herbarium 202x and iNaturalist 2019 dataset.
Global Convergence of Two-timescale Actor-Critic for Solving Linear Quadratic Regulator

The actor-critic (AC) reinforcement learning algorithms have been the powerhouse behind many challenging applications. Nevertheless, its convergence is fragile in general. To study its instability, existing works mostly consider the uncommon double-loop variant or basic models with finite state and action space. We investigate the more practical single-sample two-timescale AC for solving the canonical linear quadratic regulator (LQR) problem, where the actor and the critic update only once with a single sample in each iteration on an unbounded continuous state and action space. Existing analysis cannot conclude the convergence for such a challenging case. We develop a new analysis framework that allows establishing the global convergence to an epsilon-optimal solution with at most an order of epsilon to -2.5 sample complexity. To our knowledge, this is the first finite-time convergence analysis for the single sample two-timescale AC for solving LQR with global optimality. The sample complexity improves those of other variants by orders, which sheds light on the practical wisdom of single sample algorithms. We also further validate our theoretical findings via comprehensive simulation comparisons.