Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhomogeneous closure and statistical mechanics for Rossby wave turbulence over topography

The quasi-diagonal direct interaction approximation (QDIA) closure theory is formulated for the interaction of mean fields, Rossby waves and inhomogeneous turbulence over topography on a generalized $\beta$-plane. An additional small term, corresponding to the solid-body rotation vorticity on the sphere, is included in the barotropic equation and it is shown that this results in a one-to-one correspondence between the dynamical equations, Rossby wave dispersion relations, nonlinear stability criteria and canonical equilibrium theory on the generalized $\beta$-plane and on the sphere. The dynamics, kinetic energy spectra, mean field structures and mean streamfunction tendencies contributed by transient eddies are compared with the ensemble-averaged results from direct numerical simulations (DNS) at moderate resolution. A series of numerical experiments is performed to examine the generation of Rossby waves when eastward large-scale flows impinge on a conical mountain in the presence of moderate to strong two-dimensional turbulence. The ensemble predictability of northern hemisphere flows in 10-day forecasts is also examined on a generalized $\beta$-plane. In all cases, the QDIA closure is found to be in very good agreement with the statistics of DNS except in situations of strong turbulence and weak mean fields where ensemble-averaged DNS fails to predict mean field amplitudes correctly owing to sampling problems even with as many as 1800 ensemble members.
Heuristics for Vehicle Routing Problem: A Survey and Recent Advances

Vehicle routing is a well-known optimization research topic with significant practical importance. Among different approaches to solving vehicle routing, heuristics can produce a satisfactory solution at a reasonable computational cost. Consequently, much effort has been made in the past decades to develop vehicle routing heuristics. In this article, we systematically survey the existing vehicle routing heuristics, particularly on works carried out in recent years. A classification of vehicle routing heuristics is presented, followed by a review of their methodologies, recent developments, and applications. Moreover, we present a general framework of state-of-the-art methods and provide insights into their success. Finally, three emerging research topics with notable works and future directions are discussed.
Online Control Barrier Functions for Decentralized Multi-Agent Navigation

Control barrier functions (CBFs) enable guaranteed safe multi-agent navigation in the continuous domain. The resulting navigation performance, however, is highly sensitive to the underlying hyperparameters. Traditional approaches consider fixed CBFs (where parameters are tuned apriori), and hence, typically do not perform well in cluttered and highly dynamic environments: conservative parameter values can lead to inefficient agent trajectories, or even failure to reach goal positions, whereas aggressive parameter values can lead to infeasible controls. To overcome these issues, in this paper, we propose online CBFs, whereby hyperparameters are tuned in real-time, as a function of what agents perceive in their immediate neighborhood. Since the explicit relationship between CBFs and navigation performance is hard to model, we leverage reinforcement learning to learn CBF-tuning policies in a model-free manner. Because we parameterize the policies with graph neural networks (GNNs), we are able to synthesize decentralized agent controllers that adjust parameter values locally, varying the degree of conservative and aggressive behaviors across agents. Simulations as well as real-world experiments show that (i) online CBFs are capable of solving navigation scenarios that are infeasible for fixed CBFs, and (ii), that they improve navigation performance by adapting to other agents and changes in the environment.
Patch of Invisibility: Naturalistic Black-Box Adversarial Attacks on Object Detectors

Adversarial attacks on deep-learning models have been receiving increased attention in recent years. Work in this area has mostly focused on gradient-based techniques, so-called “white-box” attacks, wherein the attacker has access to the targeted model’s internal parameters; such an as-sumption is usually unrealistic in the real world. Some attacks additionally use the entire pixel space to fool a given model, which is neither practical nor physical (i.e., real-world). On the contrary, we propose herein a gradient-free method that uses the learned image manifold of a pretrained generative adversarial network (GAN) to generate naturalistic physical adversarial patches for object detectors. We show that our proposed method works both digitally and physically.
Feeling Optimistic? Ambiguity Attitudes for Online Decision Making

Due to the complexity of many decision making problems, tree search algorithms often have inadequate information to produce accurate transition models. Robust methods, designed to make safe decisions when faced with these uncertainties, often overlook the impact expressions of uncertainty have on how the decision is made. This work introduces the Ambiguity Attitude Graph Search (AAGS), advocating for more precise representation of ambiguities (uncertainty from a set of plausible models) in decision making. Additionally, AAGS allows users to adjust their ambiguity attitude (or preference), promoting exploration and improving users' ability to control how an agent should respond when faced with a set of valid alternatives. Simulation in a dynamic sailing environment shows how highly stochastic environments can lead robust methods to fail. Results further demonstrate how adjusting ambiguity attitudes better fulfills objectives while mitigating this failure mode of robust approaches. Because this approach is a generalization of the robust framework, these results further demonstrate how algorithms focused on ambiguity have applicability beyond safety-critical systems.
A Survey for High-redshift Gravitationally Lensed Quasars and Close Quasar Pairs. I. The Discoveries of an Intermediately Lensed Quasar and a Kiloparsec-scale Quasar Pair at z ∼ 5

We present the first results from a new survey for high-redshift (z ≳ 5) gravitationally lensed quasars and close quasar pairs. We carry out candidate selection based on the colors and shapes of objects in public imaging surveys, then conduct follow-up observations to confirm the nature of high-priority candidates. In this paper, we report the discoveries of J0025–0145 (z = 5.07), which we identify as an intermediately lensed quasar, and J2329–0522 (z = 4.85), which is a kiloparsec-scale close quasar pair. The Hubble Space Telescope (HST) image of J0025–0145 shows a foreground lensing galaxy located 0. ″6 away from the quasar. However, J0025–0145 does not exhibit multiple lensed images of the quasar, and we identify J0025–0145 as an intermediate lensing system (a lensing system that is not multiply imaged but has a significant magnification). The spectrum of J0025–0145 implies an extreme Eddington ratio if the quasar luminosity is intrinsic, which could be explained by a large lensing magnification. The HST image of J0025–0145 also indicates a tentative detection of the quasar host galaxy in the rest-frame UV, illustrating the power of lensing magnification and distortion in studies of high-redshift quasar host galaxies. Object J2329–0522 consists of two resolved components with significantly different spectral properties and a lack of lensing galaxy detection under subarcsecond seeing. We identify it as a close quasar pair, which is the highest confirmed kiloparsec-scale quasar pair to date. We also report four lensed quasars and quasar pairs at 2 < z < 4 and discuss possible improvements to our survey strategy.
Densely Connected G-invariant Deep Neural Networks with Signed Permutation Representations

We introduce and investigate, for finite groups $G$, $G$-invariant deep neural network ($G$-DNN) architectures with ReLU activation that are densely connected-- i.e., include all possible skip connections. In contrast to other $G$-invariant architectures in the literature, the preactivations of the$G$-DNNs presented here are able to transform by \emph{signed} permutation representations (signed perm-reps) of $G$. Moreover, the individual layers of the $G$-DNNs are not required to be $G$-equivariant; instead, the preactivations are constrained to be $G$-equivariant functions of the network input in a way that couples weights across all layers. The result is a richer family of $G$-invariant architectures never seen previously. We derive an efficient implementation of $G$-DNNs after a reparameterization of weights, as well as necessary and sufficient conditions for an architecture to be ``admissible''-- i.e., nondegenerate and inequivalent to smaller architectures. We include code that allows a user to build a $G$-DNN interactively layer-by-layer, with the final architecture guaranteed to be admissible. We show that there are far more admissible $G$-DNN architectures than those accessible with the ``concatenated ReLU'' activation function from the literature. Finally, we apply $G$-DNNs to two example problems -- (1) multiplication in $\{-1, 1\}$ (with theoretical guarantees) and (2) 3D object classification -- % finding that the inclusion of signed perm-reps significantly boosts predictive performance compared to baselines with only ordinary (i.e., unsigned) perm-reps.
Toward a Geometric Theory of Manifold Untangling

It has been hypothesized that the ventral stream processing for object recognition is based on a mechanism called cortically local subspace untangling. A mathematical abstraction of object recognition by the visual cortex is how to untangle the manifolds associated with different object category. Such a manifold untangling problem is closely related to the celebrated kernel trick in metric space. In this paper, we conjecture that there is a more general solution to manifold untangling in the topological space without artificially defining any distance metric. Geometrically, we can either $embed$ a manifold in a higher dimensional space to promote selectivity or $flatten$ a manifold to promote tolerance. General strategies of both global manifold embedding and local manifold flattening are presented and connected with existing work on the untangling of image, audio, and language data. We also discuss the implications of untangling the manifold into motor control and internal representations.
A Privacy Preserving System for Movie Recommendations Using Federated Learning

Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are utilized by many online businesses to drive engagement and sales. Besides other criticisms, like creating filter bubbles within social networks, recommender systems are often reproved for collecting considerable amounts of personal data. However, to personalize recommendations, personal information is fundamentally required. A recent distributed learning scheme called federated learning has made it possible to learn from personal user data without its central collection. Consequently, we present a recommender system for movie recommendations, which provides privacy and thus trustworthiness on multiple levels: First and foremost, it is trained using federated learning and thus, by its very nature, privacy-preserving, while still enabling users to benefit from global insights. Furthermore, a novel federated learning scheme, called FedQ, is employed, which not only addresses the problem of non-i.i. d. -ness and small local datasets, but also prevents input data reconstruction attacks by aggregating client updates early. Finally, to reduce the communication overhead, compression is applied, which significantly compresses the exchanged neural network parametrizations to a fraction of their original size. We conjecture that this may also improve data privacy through its lossy quantization stage.
Proximity effect and inverse proximity effect in a topological-insulator/iron-based-superconductor heterostructure

We theoretically study the proximity effect and the inverse proximity effect in a topological insulator/iron-based superconductor heterostructure based on the microscopic model. The superconducting order parameter is self-consistently calculated. Its magnitude decreases when the coupling of these two systems increases. The induced pairing order parameter exhibits negative and positive values, while the negative pairing near the $\Gamma=(0,0)$ point is dominant. This parameter has twofold symmetry and includes an $s$-wave component and a $d$-wave component. The magnitude of the induced pairing order parameter has a maximal value at the coupling strength $t_p=0.3\approx 0.06$ eV. The spectral function and the local density of states are calculated and may be used to probe the proximity effect. We also discuss the feedback of the topological insulator to the iron-based superconductor layer. The normal-state Fermi surface is distorted by the coupling, and additional Fermi pockets are induced. An effective spin-orbit interaction term is induced. In the superconducting state, the previous fourfold symmetry of the order parameter is broken, and a $d$-wave component pairing term is also induced. Our main results can be well understood by analyzing the Fermi surfaces of the original systems.
Study on the global minimum and 
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 in the Dirac scotogenic model

We have analyzed the vacuum structure of the Dirac scotogenic model, whose scalar sector consists of two complex Higgs doublets and a real singlet field. In this model, the standard model like Higgs doublet acquires non-zero vacuum expectation value (VEV), whereas, the other two fields acquire zero VEVs. This pattern of VEVs constitute a minimum, which is the desired vacuum of the model. After analyzing the scalar potential of this model, we have found that other vacua are also possible in this model. We have shown that plenty of parameter space exist where the desired vacuum of this model is the global minimum. We have studied the implications of scalar sector of this model on the observable quantity of signal strength of Higgs to diphoton decay. After evaluating this quantity, we have found that the current experimental values of this quantity can be fitted in this model. Lastly, we have studied on the possibility of making any of the additional scalar fields of this model as a candidate for dark matter.
Characteristics of optical phase conjugation of picosecond pulses in semiconductor optical amplifiers

Nondegenerate four-wave mixing (FWM) of picosecond optical pulses in semiconductor optical amplifiers is investigated theoretically, taking into account various carrier processes, probe depletion, and cross-gain modulation effects. Analytical solutions for greatly simplified coupled-wave mixing equations are obtained from which conditions where probe depletion and cross-gain modulation effects cannot be neglected are found. On the other hand, the frequency detuning dependence of optimum input pump pulse energy and shaping of output conjugate pulses are observed from numerical simulations. In particular, calculations show that the frequency symmetry of conjugate and probe pulses with respect to the pump pulse does not hold for optical pulse FWM. Two-photon absorption and ultrafast nonlinear refraction effects are shown to decrease the FWM conversion efficiency.
Viable vector coherent oscillation dark matter

We construct a viable model of the vector coherent oscillation dark matter. The vector boson is coupled to the inflaton through the kinetic function so that the effective Hubble mass term is cancelled out. In order to avoid strong constraints from isocurvature perturbation and statistically anisotropic curvature perturbation, the inflaton is arranged so that it does not contribute to the observed large scale curvature perturbation and we introduce a curvaton. We found viable vector coherent oscillation dark matter scenario for the wide vector mass range from 10-21 eV to 1 eV.
ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System

Thanks to the rapid growth in wearable technologies and recent advancement in machine learning and signal processing, monitoring complex human contexts becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, a central challenge in designing many of these IoT systems arises from the requirement to infer the human mental state, such as intention, stress, cognition load, or learning ability. While different human contexts can be inferred from the fusion of different sensor modalities that can correlate to a particular mental state, the human brain provides a richer sensor modality that gives us more insights into the required human context. This paper proposes ERUDITE, a human-in-the-loop IoT system for the learning environment that exploits recent wearable neurotechnology to decode brain signals. Through insights from concept learning theory, ERUDITE can infer the human state of learning and understand when human learning increases or declines. By quantifying human learning as an input sensory signal, ERUDITE can provide adequate personalized feedback to humans in a learning environment to enhance their learning experience. ERUDITE is evaluated across $15$ participants and showed that by using the brain signals as a sensor modality to infer the human learning state and providing personalized adaptation to the learning environment, the participants' learning performance increased on average by $26\%$. Furthermore, we showed that ERUDITE can be deployed on an edge-based prototype to evaluate its practicality and scalability.
Dimensional deformation of sine-Gordon breathers into oscillons

Oscillons are localized field configurations oscillating in time with lifetimes orders of magnitude longer than their oscillation period. In this paper, we simulate non-travelling oscillons produced by deforming the breather solutions of the sine-Gordon model. Such a deformation treats the dimensionality of the model as a real parameter to produce spherically symmetric oscillons. After considering the post-transient oscillation frequency as a control parameter, we probe the initial parameter space to continuously connect breathers and oscillons at various dimensionalities. For sufficiently small dimensional deformations, we find that oscillons can be treated as perturbatively deformed breathers. In $D\gtrsim 2$ spatial dimensions, we observe solutions undergoing intermittent phases of contraction and expansion in their cores. Knowing that stable and unstable configurations can be mapped to disjoint regions of the breather parameter space, we find that amplitude modulated solutions are located in the middle of both stability regimes. These solutions display the dynamics of critical behavior around the stability limit.
A Computer Vision Enabled damage detection model with improved YOLOv5 based on Transformer Prediction Head

Objective:Computer vision-based up-to-date accurate damage classification and localization are of decisive importance for infrastructure monitoring, safety, and the serviceability of civil infrastructure. Current state-of-the-art deep learning (DL)-based damage detection models, however, often lack superior feature extraction capability in complex and noisy environments, limiting the development of accurate and reliable object distinction. Method: To this end, we present DenseSPH-YOLOv5, a real-time DL-based high-performance damage detection model where DenseNet blocks have been integrated with the backbone to improve in preserving and reusing critical feature information. Additionally, convolutional block attention modules (CBAM) have been implemented to improve attention performance mechanisms for strong and discriminating deep spatial feature extraction that results in superior detection under various challenging environments. Moreover, additional feature fusion layers and a Swin-Transformer Prediction Head (SPH) have been added leveraging advanced self-attention mechanism for more efficient detection of multiscale object sizes and simultaneously reducing the computational complexity. Results: Evaluating the model performance in large-scale Road Damage Dataset (RDD-2018), at a detection rate of 62.4 FPS, DenseSPH-YOLOv5 obtains a mean average precision (mAP) value of 85.25 %, F1-score of 81.18 %, and precision (P) value of 89.51 % outperforming current state-of-the-art models. Significance: The present research provides an effective and efficient damage localization model addressing the shortcoming of existing DL-based damage detection models by providing highly accurate localized bounding box prediction. Current work constitutes a step towards an accurate and robust automated damage detection system in real-time in-field applications.
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning

While preserving the privacy of federated learning (FL), differential privacy (DP) inevitably degrades the utility (i.e., accuracy) of FL due to model perturbations caused by DP noise added to model updates. Existing studies have considered exclusively noise with persistent root-mean-square amplitude and overlooked an opportunity of adjusting the amplitudes to alleviate the adverse effects of the noise. This paper presents a new DP perturbation mechanism with a time-varying noise amplitude to protect the privacy of FL and retain the capability of adjusting the learning performance. Specifically, we propose a geometric series form for the noise amplitude and reveal analytically the dependence of the series on the number of global aggregations and the $(\epsilon,\delta)$ -DP requirement. We derive an online refinement of the series to prevent FL from premature convergence resulting from excessive perturbation noise. Another important aspect is an upper bound developed for the loss function of a multi-layer perceptron (MLP) trained by FL running the new DP mechanism. Accordingly, the optimal number of global aggregations is obtained, balancing the learning and privacy. Extensive experiments are conducted using MLP, supporting vector machine, and convolutional neural network models on four public datasets. The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.
ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression

Federated sampling algorithms have recently gained great popularity in the community of machine learning and statistics. This paper studies variants of such algorithms called Error Feedback Langevin algorithms (ELF). In particular, we analyze the combinations of EF21 and EF21-P with the federated Langevin Monte-Carlo. We propose three algorithms: P-ELF, D-ELF, and B-ELF that use, respectively, primal, dual, and bidirectional compressors. We analyze the proposed methods under Log-Sobolev inequality and provide non-asymptotic convergence guarantees.
On the Implicit Bias of Linear Equivariant Steerable Networks: Margin, Generalization, and Their Equivalence to Data Augmentation

We study the implicit bias of gradient flow on linear equivariant steerable networks in group-invariant binary classification. Our findings reveal that the parameterized predictor converges in direction to the unique group-invariant classifier with a maximum margin defined by the input group action. Under a unitary assumption on the input representation, we establish the equivalence between steerable networks and data augmentation. Furthermore, we demonstrate the improved margin and generalization bound of steerable networks over their non-invariant counterparts.