Miracle-dz/UniFAR-FTUs huggingface dataset:
Mass spectrum of heavy Higgs bosons in two-Higgs-doublet model in the light of the CDF 
W
-mass anomaly

We analyze the mass spectrum of the charged and neutral Higgs bosons in the framework of two Higgs doublet model (2HDM) in the light of the precision measurement of the $W$ boson mass by the CDF collaboration. We have considered the most general 2HDM potential with explicit CP violation in the Higgs basis which contains the three CP-mixed neutral mass eigenstates $H_1$, $H_2$, and $H_3$ with $M_{H_1}\leq M_{H_2}\leq M_{H_3}$. The high-precision CDF measurement of the $W$ boson mass is characterized by the large positive value of the $T$ parameter. By identifying the lightest neutral Higgs boson $H_1$ as the SM-like one discovered at the LHC, we find that it is necessary to have the mass splitting between the charged Higgs boson $H^\pm$ and the second heaviest neutral one $H_2$ to accommodate the sizable positive deviation of the $T$ parameter from its SM value of $0$. By combining the mass splitting between $H^\pm$ and $H_2$ with the theoretical constraints from the perturbative unitarity and for the Higgs potential to be bounded from below, we implement comprehensive analysis of the mass spectrum of the heavy Higgs bosons taking account of the effects of deviation from the alignment limit and also the mass splitting between $H_3$ and $H_2$. We further analyze the behavior of the heavy-Higgs mass spectrum according to the variation of the $T$ parameter. Finally, concentrating on the heavy mass region $M\equiv (M_{H_2}+M_{H^\pm})/2\gsim 500$ GeV in which we find a mass hierarchy of $M\gg M_{H_1}\sim |\Delta| \gsim \delta$ with $\Delta \equiv M_{H_2}-M_{H^\pm}$ and $\delta \equiv M_{H_3}-M_{H_2}$, we discuss some benchmarking scenarios for the searches of heavy Higgs bosons at future colliders such as the high luminosity option of the LHC and a 100 TeV hadron collider.
Relightable Neural Human Assets from Multi-view Gradient Illuminations

Human modeling and relighting are two fundamental problems in computer vision and graphics, where high-quality datasets can largely facilitate related research. However, most existing human datasets only provide multi-view human images captured under the same illumination. Although valuable for modeling tasks, they are not read-ily used in relighting problems. To promote research in both fields, in this paper, we present UltraStage, a new 3D human dataset that contains more than 2, 000 high-quality human assets captured under both multi-view and multi-illumination settings. Specifically, for each example, we provide 32 surrounding views illuminated with one white light and two gradient illuminations. In addition to regular multi-view images, gradient illuminations help recover de-tailed surface normal and spatially-varying material maps, enabling various relighting applications. Inspired by recent advances in neural representation, we further interpret each example into a neural human asset which allows novel view synthesis under arbitrary lighting conditions. We show our neural human assets can achieve extremely high capture performance and are capable of representing fine details such as facial wrinkles and cloth folds. We also validate UltraStage in single image relighting tasks, training neural networks with virtual relighted data from neural assets and demonstrating realistic rendering improvements over prior arts. UltraStage will be publicly available to the community to stimulate significant future developments in various human modeling and rendering tasks. The dataset is available at https://miaoing.github. io/RNHA.
Intensional First Order Logic for Strong-AI Generation of Robots

Neuro-symbolic AI attempts to integrate neural and symbolic architectures in a manner that addresses strengths and weaknesses of each, in a complementary fashion, in order to support robust strong AI capable of reasoning, learning, and cognitive modeling. In this paper we consider the intensional First Order Logic (IFOL) [1] as a symbolic architecture of modern robots, able to use natural languages to communicate with humans and to reason about their own knowledge with self-reference and abstraction language property. We intend to obtain the grounding of robot’s language by experience of how it uses its neuronal architectures and hence by associating this experience with the mining (sense) of non-deﬁned language concepts (particulars/individuals and uni-versals) in PRP (Properties/Relations/propositions) theory of IFOL. We consider three natural language levels: The syntax of particular natural language (Italian, French, etc.), and two universal language properties: its semantic logic structure (based on virtual predicates of FOL and logic connectives), and its corresponding conceptual PRP structure which universally represents the composite mining of FOL formulae grounded on the robot’s neuro system.
Causes and Cures for Interference in Multilingual Translation

Multilingual machine translation models can benefit from synergy between different language pairs, but also suffer from interference. While there is a growing number of sophisticated methods that aim to eliminate interference, our understanding of interference as a phenomenon is still limited. This work identifies the main factors that contribute to interference in multilingual machine translation. Through systematic experimentation, we find that interference (or synergy) are primarily determined by model size, data size, and the proportion of each language pair within the total dataset. We observe that substantial interference occurs mainly when the model is very small with respect to the available training data, and that using standard transformer configurations with less than one billion parameters largely alleviates interference and promotes synergy. Moreover, we show that tuning the sampling temperature to control the proportion of each language pair in the data is key to balancing the amount of interference between low and high resource language pairs effectively, and can lead to superior performance overall.
Robust Policy Optimization in Deep Reinforcement Learning

The policy gradient method enjoys the simplicity of the objective where the agent optimizes the cumulative reward directly. Moreover, in the continuous action domain, parameterized distribution of action distribution allows easy control of exploration, resulting from the variance of the representing distribution. Entropy can play an essential role in policy optimization by selecting the stochastic policy, which eventually helps better explore the environment in reinforcement learning (RL). However, the stochasticity often reduces as the training progresses; thus, the policy becomes less exploratory. Additionally, certain parametric distributions might only work for some environments and require extensive hyperparameter tuning. This paper aims to mitigate these issues. In particular, we propose an algorithm called Robust Policy Optimization (RPO), which leverages a perturbed distribution. We hypothesize that our method encourages high-entropy actions and provides a way to represent the action space better. We further provide empirical evidence to verify our hypothesis. We evaluated our methods on various continuous control tasks from DeepMind Control, OpenAI Gym, Pybullet, and IsaacGym. We observed that in many settings, RPO increases the policy entropy early in training and then maintains a certain level of entropy throughout the training period. Eventually, our agent RPO shows consistently improved performance compared to PPO and other techniques: entropy regularization, different distributions, and data augmentation. Furthermore, in several settings, our method stays robust in performance, while other baseline mechanisms fail to improve and even worsen the performance.
An efficient symmetric primal-dual algorithmic framework for saddle point problems

In this paper, we propose a new primal-dual algorithmic framework for a class of convex-concave saddle point problems frequently arising from image processing and machine learning. Our algorithmic framework updates the primal variable between the twice calculations of the dual variable, thereby appearing a symmetric iterative scheme, which is accordingly called the {\bf s}ymmetric {\bf p}r{\bf i}mal-{\bf d}ual {\bf a}lgorithm (SPIDA). It is noteworthy that the subproblems of our SPIDA are equipped with Bregman proximal regularization terms, which make SPIDA versatile in the sense that it enjoys an algorithmic framework covering some existing algorithms such as the classical augmented Lagrangian method (ALM), linearized ALM, and Jacobian splitting algorithms for linearly constrained optimization problems. Besides, our algorithmic framework allows us to derive some customized versions so that SPIDA works as efficiently as possible for structured optimization problems. Theoretically, under some mild conditions, we prove the global convergence of SPIDA and estimate the linear convergence rate under a generalized error bound condition defined by Bregman distance. Finally, a series of numerical experiments on the matrix game, basis pursuit, robust principal component analysis, and image restoration demonstrate that our SPIDA works well on synthetic and real-world datasets.
Hubble Space Telescope survey of Magellanic Cloud star clusters. Photometry and astrometry of 113 clusters and early results

In the past years, we have undertaken an extensive investigation of LMC and SMC star clusters based on HST data. We present photometry and astrometry of stars in 101 fields observed with the WFC/ACS, UVIS/WFC3 and NIR/WFC3 cameras. These fields comprise 113 star clusters. We provide differential-reddening maps and illustrate various scientific outcomes that arise from the early inspection of the photometric catalogs. In particular, we provide new insights on the extended main-sequence turn-off (eMSTO) phenomenon: i) We detected eMSTOs in two clusters, KMHK361 and NGC265, which had no previous evidence of multiple populations. This finding corroborates the conclusion that the eMSTO is a widespread phenomenon among clusters younger than ~2 Gyr. ii) The homogeneous color-magnitude diagrams (CMDs) of 19 LMC clusters reveal that the distribution of stars along the eMSTO depends on cluster age. iii) We discovered a new feature along the eMSTO of NGC1783, which consists of a distinct group of stars going on the red side of the eMSTO in CMDs composed of ultraviolet filters. Furthermore, we derived the proper motions of stars in the fields of view of clusters with multi-epoch images. Proper motions allowed us to separate the bulk of bright field stars from cluster members and investigate the internal kinematics of stellar populations in various LMC and SMC fields. As an example, we analyze the field around NGC346 to disentangle the motions of its stellar populations, including NGC364 and BS90, young and pre-MS stars in the star-forming region associated with NGC346, and young and old field stellar populations of the SMC. Based on these results and the fields around five additional clusters, we find that young SMC stars exhibit elongated proper-motion distributions that point toward the LMC, thus bringing new evidence for a kinematic connection between the LMC and SMC.
Joint Channel Estimation and Equalization in Massive MIMO Using a Single Pilot Subcarrier

The focus of this letter is on the reduction of the large pilot overhead in orthogonal frequency division multiplexing (OFDM) based massive multiple-input multiple-output (MIMO) systems. We propose a novel joint channel estimation and equalization technique that requires only one pilot subcarrier, reducing the pilot overhead by orders of magnitude. We take advantage of the coherence bandwidth spanning over multiple subcarrier bands. This allows for a band of subcarriers to be equalized with the channel frequency response (CFR) at a single subcarrier. Subsequently, the detected data symbols are considered as virtual pilots, and their CFRs are updated without additional pilot overhead. Thereafter, the remaining channel estimation and equalization can be performed in a sliding manner. With this approach, we use multiple channel estimates to equalize the data at each subcarrier. This allows us to take advantage of frequency diversity and improve the detection performance. Finally, we corroborate the above claims through extensive numerical analysis, showing the superior performance of our proposed technique compared to conventional methods.
Interpretable ML for Imbalanced Data

Deep learning models are being increasingly applied to imbalanced data in high stakes fields such as medicine, autonomous driving, and intelligence analysis. Imbalanced data compounds the black-box nature of deep networks because the relationships between classes may be highly skewed and unclear. This can reduce trust by model users and hamper the progress of developers of imbalanced learning algorithms. Existing methods that investigate imbalanced data complexity are geared toward binary classification, shallow learning models and low dimensional data. In addition, current eXplainable Artificial Intelligence (XAI) techniques mainly focus on converting opaque deep learning models into simpler models (e.g., decision trees) or mapping predictions for specific instances to inputs, instead of examining global data properties and complexities. Therefore, there is a need for a framework that is tailored to modern deep networks, that incorporates large, high dimensional, multi-class datasets, and uncovers data complexities commonly found in imbalanced data (e.g., class overlap, sub-concepts, and outlier instances). We propose a set of techniques that can be used by both deep learning model users to identify, visualize and understand class prototypes, sub-concepts and outlier instances; and by imbalanced learning algorithm developers to detect features and class exemplars that are key to model performance. Our framework also identifies instances that reside on the border of class decision boundaries, which can carry highly discriminative information. Unlike many existing XAI techniques which map model decisions to gray-scale pixel locations, we use saliency through back-propagation to identify and aggregate image color bands across entire classes. Our framework is publicly available at \url{https://github.com/dd1github/XAI_for_Imbalanced_Learning}
AirVO: An Illumination-Robust Point-Line Visual Odometry

This paper proposes an illumination-robust visual odometry (VO) system that incorporates both accelerated learning-based corner point algorithms and an extended line feature algorithm. To be robust to dynamic illumination, the proposed system employs the convolutional neural network (CNN) and graph neural network (GNN) to detect and match reliable and informative corner points. Then point feature matching results and the distribution of point and line features are utilized to match and triangulate lines. By accelerating CNN and GNN parts and optimizing the pipeline, the proposed system is able to run in real-time on low-power embedded platforms. The proposed VO was evaluated on several datasets with varying illumination conditions, and the results show that it outperforms other state-of-the-art VO systems in terms of accuracy and robustness. The open-source nature of the proposed system allows for easy implementation and customization by the research community, enabling further development and improvement of VO for various applications.
Topological linear magnetoresistivity and thermoconductivity induced by noncentrosymmetric Berry curvature

The Berry curvature plays a key role in the magnetic transport of topological materials. Yet, it is not clear whether the Berry curvature by itself can give rise to universal transport phenomena with specific scaling behaviors. In this work, based on the semiclassical Boltzmann formalism and the symmetry analysis, we show that the noncentrosymmetric distribution of the Berry curvature generally results in linear magnetoresistivity and thermoconductivity both exhibiting the B-scaling behavior. We then study such kind of topological linear magnetoresistivity in the 2D MnBi2Te4 flakes and the 3D spin-orbit-coupled electron gas, the former showing good agreement with the experimental observations. The difference between our mechanism and the conventional anisotropic magnetoresistance is elucidated. Our theory proposes a universal scenario for the topological linear magnetoresistivity and thermoconductivity and predicts such effects to occur in various materials, which also provides a reasonable explanation for the recent observations of linear magnetoresistivity.
A procedure for approximation of the error in the EFG method

In this paper, we present a procedure to estimate the error in elliptic equations using the element‐free Galerkin (EFG) method, whose evaluation is computationally simple and can be readily implemented in existing EFG codes. The estimation of the error works very well in all numerical examples for 2‐D potential problems that are presented here, for regular and irregular clouds of points. Moreover, it was demonstrated that this method is very simple in terms of economy and gives a good performance.
Zero-Shot Learning for Joint Intent and Slot Labeling

It is expensive and difficult to obtain the large number of sentence-level intent and token-level slot label annotations required to train neural network (NN)-based Natural Language Understanding (NLU) components of task-oriented dialog systems, especially for the many real world tasks that have a large and growing number of intents and slot types. While zero shot learning approaches that require no labeled examples -- only features and auxiliary information -- have been proposed only for slot labeling, we show that one can profitably perform joint zero-shot intent classification and slot labeling. We demonstrate the value of capturing dependencies between intents and slots, and between different slots in an utterance in the zero shot setting. We describe NN architectures that translate between word and sentence embedding spaces, and demonstrate that these modifications are required to enable zero shot learning for this task. We show a substantial improvement over strong baselines and explain the intuition behind each architectural modification through visualizations and ablation studies.
Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classification

The challenges of collecting medical data on neurological disorder diagnosis problems paved the way for learning methods with scarce number of samples. Due to this reason, one-shot learning still remains one of the most challenging and trending concepts of deep learning as it proposes to simulate the human-like learning approach in classification problems. Previous studies have focused on generating more accurate fingerprints of the population using graph neural networks (GNNs) with connectomic brain graph data. Thereby, generated population fingerprints named connectional brain template (CBTs) enabled detecting discriminative bio-markers of the population on classification tasks. However, the reverse problem of data augmentation from single graph data representing brain connectivity has never been tackled before. In this paper, we propose an augmentation pipeline in order to provide improved metrics on our binary classification problem. Divergently from the previous studies, we examine augmentation from a single population template by utilizing graph-based generative adversarial network (gGAN) architecture for a classification problem. We benchmarked our proposed solution on AD/LMCI dataset consisting of brain connectomes with Alzheimer's Disease (AD) and Late Mild Cognitive Impairment (LMCI). In order to evaluate our model's generalizability, we used cross-validation strategy and randomly sampled the folds multiple times. Our results on classification not only provided better accuracy when augmented data generated from one sample is introduced, but yields more balanced results on other metrics as well.
SAIF: Sparse Adversarial and Imperceptible Attack Framework

Adversarial attacks hamper the decision-making ability of neural networks by perturbing the input signal. The addition of calculated small distortion to images, for instance, can deceive a well-trained image classification network. In this work, we propose a novel attack technique called Sparse Adversarial and Interpretable Attack Framework (SAIF). Specifically, we design imperceptible attacks that contain low-magnitude perturbations at a small number of pixels and leverage these sparse attacks to reveal the vulnerability of classifiers. We use the Frank-Wolfe (conditional gradient) algorithm to simultaneously optimize the attack perturbations for bounded magnitude and sparsity with $O(1/\sqrt{T})$ convergence. Empirical results show that SAIF computes highly imperceptible and interpretable adversarial examples, and outperforms state-of-the-art sparse attack methods on the ImageNet dataset.
Die Architektur der Mathematik II

Einen Uberblick uber den ganzen gegenwartigen Bereich der mathematischen Wissenschaft zu geben, ist ein Unternehmen, das auf den ersten Blick wegen des Umfangs und der Vielfalt des Gegenstandes fast unuberwindliche Schwierigkeiten bietet. Wie in allen anderen Wissenschaften hat auch die Zahl der Mathematiker und ihrer Arbeiten seit dem Ende des 19. Jahrhunderts stark zugenommen. So wird die mathematische Wissenschaft jedes Jahr durch eine Unmenge neuer Resultate bereichert, sie breitet sich unaufhaltsam aus und verzweigt sich in immer neuen Theorien. Kein Mathematiker konnte alle Einzelheiten dieser Entwicklung verfolgen. Viele Mathematiker lassen sich daher in einer Ecke des mathematischen Reiches nieder, die sie niemals verlassen; sie ignorieren fast vollig, was nichts mit ihrem Spezialgebiet zu tun hat, und konnen die Sprache und Ausdrucksweise von Kollegen, die in einer anderen Ecke arbeiten, nicht verstehen. Sogar unter den mathematisch Hochstgebildeten gibt es keinen, der sich in gewissen Gegenden der unermeslichen Welt der Mathematik nicht verloren fuhlte. Manner wie Poincare oder Hilbert, die fast jedem Gebiet der Mathematik das Zeichen ihres Genius aufpragten, bilden eine ganz seltene Ausnahme.
Faster Maximum Inner Product Search in High Dimensions

Maximum Inner Product Search (MIPS) is a ubiquitous task in machine learning applications such as recommendation systems. Given a query vector and $n$ atom vectors in $d$-dimensional space, the goal of MIPS is to find the atom that has the highest inner product with the query vector. Existing MIPS algorithms scale at least as $O(\sqrt{d})$, which becomes computationally prohibitive in high-dimensional settings. In this work, we present BanditMIPS, a novel randomized MIPS algorithm whose complexity is independent of $d$. BanditMIPS estimates the inner product for each atom by subsampling coordinates and adaptively evaluates more coordinates for more promising atoms. The specific adaptive sampling strategy is motivated by multi-armed bandits. We provide theoretical guarantees that BanditMIPS returns the correct answer with high probability, while improving the complexity in $d$ from $O(\sqrt{d})$ to $O(1)$. We also perform experiments on four synthetic and real-world datasets and demonstrate that BanditMIPS outperforms prior state-of-the-art algorithms. For example, in the Movie Lens dataset ($n$=4,000, $d$=6,000), BanditMIPS is 20$\times$ faster than the next best algorithm while returning the same answer. BanditMIPS requires no preprocessing of the data and includes a hyperparameter that practitioners may use to trade off accuracy and runtime. We also propose a variant of our algorithm, named BanditMIPS-$\alpha$, which achieves further speedups by employing non-uniform sampling across coordinates. Finally, we demonstrate how known preprocessing techniques can be used to further accelerate BanditMIPS, and discuss applications to Matching Pursuit and Fourier analysis.