Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Neural Networks to Differentiate Newly Discovered BL Lacertae Objects and FSRQs among the 4FGL Unassociated Sources Employing Gamma-Ray, X-Ray, UV/Optical, and IR Data

Among the ∼2157 unassociated sources in the third data release (DR3) of the fourth Fermi catalog, ∼1200 were observed with the Neil Gehrels Swift Observatory pointed instruments. These observations yielded 238 high signal-to-noise ratio X-ray sources within the 95% Fermi uncertainty regions. Recently, Kerby et al. employed neural networks to find blazar candidates among these 238 X-ray counterparts to the 4FGL unassociated sources and found 112 likely blazar counterpart sources. A complete sample of blazars, along with their subclassification, is a necessary step to help understand the puzzle of the blazar sequence and for the overall completeness of the gamma-ray emitting blazar class in the Fermi catalog. We employed a multi-perceptron neural network classifier to identify flat spectrum radio quasars (FSRQs) and BL Lac objects among these 112 blazar candidates using the gamma-ray, X-ray, UV/optical, and IR properties. This classifier provided probability estimates for each source to be associated with one or the other category, such that P fsrq represents the probability for a source to be associated with the FSRQ subclass. Using this approach, four FSRQs and 50 BL Lac objects are classified as such with >99% confidence, while the remaining 58 blazars could not be unambiguously classified as either BL Lac objects or FSRQs.
Efficient numerical method for reliable upper and lower bounds on homogenized parameters

A numerical procedure providing guaranteed two-sided bounds on the effective coefficients of elliptic partial differential operators is presented. The upper bounds are obtained in a standard manner through the variational formulation of the problem and by applying the finite element method. To obtain the lower bounds we formulate the dual variational problem and introduce appropriate approximation spaces employing the finite element method as well. We deal with the 3D setting, which has been rarely considered in the literature so far. The theoretical justification of the procedure is presented and supported with illustrative examples.
On the Sombor index of graphs with given connectivity and number of bridges

Recently in 2021, Gutman introduced the Sombor index of a graph, a novel degree-based topological index. It has been shown that the Sombor index efficiently models the thermodynamic properties of chemical compounds. Assume $\mathbb{B}_n^k$ (resp. $\mathbb{V}_n^k$) comprises all graphs with order $n$ having number of bridges (resp. vertex-connectivity) $k$. Horoldagva&Xu (2021) characterized graphs achieving the maximum Sombor index of graphs in $\mathbb{B}_n^k$. This paper characterizes graphs achieving the minimum Sombor index in $\mathbb{B}_n^k$. Certain auxiliary operation on graphs in $\mathbb{B}_n^k$ are introduced and employed for the characterization. Moreover, we characterize graphs achieving maximum Sombor index in $\mathbb{V}_n^k$. ome open problems, which naturally arise from this work, have been proposed at the end.
Latent Neural Stochastic Differential Equations for Change Point Detection

The purpose of change point detection algorithms is to locate an abrupt change in the time evolution of a process. In this paper, we introduce an application of latent neural stochastic differential equations for change point detection problem. We demonstrate the detection capabilities and performance of our model on a range of synthetic and real-world datasets and benchmarks. Most of the studied scenarios show that the proposed algorithm outperforms the state-of-the-art algorithms. We also discuss the strengths and limitations of this approach and indicate directions for further improvements.
Green Joint Communications and Sensing Employing Analog Multi-Beam Antenna Arrays

Joint communications and sensing (JCAS) is potentially a hallmark technology for the sixth generation mobile network (6G). Most existing JCAS designs are based on digital arrays, analog arrays with tunable phase shifters, or hybrid arrays, which are effective but are generally complicated to design and power inefficient. This article introduces the energy-efficient and easy-to-design multi-beam antenna arrays (MBAAs) for JCAS. Using pre-designed and fixed analog devices, such as lens or Butler matrix, MBAA can simultaneously steer multiple beams yet with negligible power consumption compared with other techniques. Moreover, MBAAs enable flexible beam synthesis, accurate angle-of-arrival estimation, and easy handling/utilization of the beam squint effect. All these features have not been well captured by the JACS community yet. To promote the awareness of them, we intuitively illustrate them and also exploit them for constructing a multi-beam JCAS framework. Finally, the challenges and opportunities are discussed to foster the development of green JCAS systems.
Trigger-free Event Detection via Derangement Reading Comprehension

Event detection (ED), aiming to detect events from texts and categorize them, is vital to understanding actual happenings in real life. However, mainstream event detection models require high-quality expert human annotations of triggers, which are often costly and thus deter the application of ED to new domains. Therefore, in this paper, we focus on low-resource ED without triggers and aim to tackle the following formidable challenges: multi-label classiﬁcation, insufﬁcient clues, and imbalanced events distribution. We propose a novel trigger-free ED method via Derangement mechanism on a machine Reading Comprehension (DRC) framework. More speciﬁ-cally, we treat the input text as Context and concatenate it with all event type tokens that are deemed as Answers with an omitted default question. So we can leverage the self-attention in pre-trained language models to absorb semantic relations between input text and the event types. Moreover, we design a simple yet effective event derangement module (EDM) to prevent major events from being excessively learned so as to yield a more balanced training process. The experiment results show that our proposed trigger-free ED model is remarkably competitive to mainstream trigger-based models, showing its strong performance on low-source event detection.
A Novel Hybrid Sampling Framework for Imbalanced Learning

Class imbalance is a frequently occurring scenario in classification tasks. Learning from imbalanced data poses a major challenge, which has instigated a lot of research in this area. Data preprocessing using sampling techniques is a standard approach to deal with the imbalance present in the data. Since standard classification algorithms do not perform well on imbalanced data, the dataset needs to be adequately balanced before training. This can be accomplished by oversampling the minority class or undersampling the majority class. In this study, a novel hybrid sampling algorithm has been proposed. To overcome the limitations of the sampling techniques while ensuring the quality of the retained sampled dataset, a sophisticated framework has been developed to properly combine three different sampling techniques. Neighborhood Cleaning rule is first applied to reduce the imbalance. Random undersampling is then strategically coupled with the SMOTE algorithm to obtain an optimal balance in the dataset. This proposed hybrid methodology, termed "SMOTE-RUS-NC" , has been compared with other state-of-the-art sampling techniques. The strategy is further incorporated into the ensemble learning framework to obtain a more robust classification algorithm, termed "SRN-BRF" . Rigorous experimentation has been conducted on 26 imbalanced datasets with varying degrees of imbalance. In virtually all datasets, the proposed two algorithms outperformed existing sampling strategies, in many cases by a substantial margin. Especially in highly imbalanced datasets where popular sampling techniques failed utterly, they achieved unparalleled performance. The superior results obtained demonstrate the efficacy of the proposed models and their potential to be powerful sampling algorithms in imbalanced domain. datasets, g-mean score was completely 0, indicating a complete bias towards the majority class.
Distributed Optimal Secondary Frequency Control in Power Networks with Delay Independent Stability

Distributed secondary frequency control for power systems, is a problem that has been extensively studied in the literature, and one of its key features is that an additional communication network is required to achieve optimal power allocation. Therefore, being able to provide stability guarantees in the presence of communication delays is an important requirement. Primal-dual and distributed averaging proportional-integral (DAPI) protocols, respectively, are two main control schemes that have been proposed in the literature. Each has its own relative merits, with the former allowing to incorporate general cost functions and additional operational constraints, and the latter being more straightforward in its implementation. Although delays have been addressed in DAPI schemes, there are currently no theoretical guarantees for the stability of primal-dual schemes for frequency control, when these are subject to communication delays. In fact, simulations illustrate that even small delays can destabilize such schemes. In this paper, we show how a novel formulation of prima-dual schemes allows to construct a distributed algorithm with delay independent stability guarantees. We also show that this algorithm can incorporate many of the key features of these schemes such as tie-line power flow requirements, generation constraints, and the relaxation of demand measurements with an observer layer. Finally, we illustrate our results through simulations on a 5-bus example and on the IEEE-39 test system.
Group selection and shrinkage: Structured sparsity for semiparametric additive models

Sparse regression and classification estimators that respect group structures have application to an assortment of statistical and machine learning problems, from multitask learning to sparse additive modeling to hierarchical selection. This work introduces structured sparse estimators that combine group subset selection with shrinkage. To accommodate sophisticated structures, our estimators allow for arbitrary overlap between groups. We develop an optimization framework for fitting the nonconvex regularization surface and present finite-sample error bounds for estimation of the regression function. As an application requiring structure, we study sparse semiparametric additive modeling, a procedure that allows the effect of each predictor to be zero, linear, or nonlinear. For this task, the new estimators improve across several metrics on synthetic data compared to alternatives. Finally, we demonstrate their efficacy in modeling supermarket foot traffic and economic recessions using many predictors. These demonstrations suggest sparse semiparametric additive models, fit using the new estimators, are an excellent compromise between fully linear and fully nonparametric alternatives. All of our algorithms are made available in the scalable implementation grpsel.
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data

Probabilistic modeling of multidimensional spatiotemporal data is critical to many real-world applications. As real-world spatiotemporal data often exhibits complex dependencies that are nonstationary and nonseparable, developing effective and computationally efficient statistical models to accommodate nonstationary/nonseparable processes containing both long-range and short-scale variations becomes a challenging task, in particular for large-scale datasets with various corruption/missing structures. In this paper, we propose a new statistical framework -- Bayesian Complementary Kernelized Learning (BCKL) -- to achieve scalable probabilistic modeling for multidimensional spatiotemporal data. To effectively characterize complex dependencies, BCKL integrates two complementary approaches -- kernelized low-rank tensor factorization and short-range spatiotemporal Gaussian Processes. Specifically, we use a multi-linear low-rank factorization component to capture the global/long-range correlations in the data and introduce an additive short-scale GP based on compactly supported kernel functions to characterize the remaining local variabilities. We develop an efficient Markov chain Monte Carlo (MCMC) algorithm for model inference and evaluate the proposed BCKL framework on both synthetic and real-world spatiotemporal datasets. Our experiment results show that BCKL offers superior performance in providing accurate posterior mean and high-quality uncertainty estimates, confirming the importance of both global and local components in modeling spatiotemporal data.
Non--regular McKean--Vlasov equations and calibration problem in local stochastic volatility models

In order to deal with the question of the existence of a calibrated local stochastic volatility model in finance, we investigate a class of McKean--Vlasov equations where a minimal continuity assumption is imposed on the coefficients. Namely, the drift coefficient and, in particular, the volatility coefficient are not necessarily continuous in the measure variable for the Wasserstein topology. In this paper, we provide an existence result and show an approximation by $N$--particle system or propagation of chaos for this type of McKean--Vlasov equations. As a direct result, we are able to deduce the existence of a calibrated local stochastic volatility model for an appropriate choice of stochastic volatility parameters. The associated propagation of chaos result is also proved.
Radiation emitted from axion dark matter in a homogeneous magnetic field and possibilities for detection

We study the direct radiation excited by oscillating axion (or axion-like particle) dark matter in a homogenous magnetic ﬁeld and its detection scheme. We concretely derive the analytical expression of the axion-induced radiated power for a cylindrical uniform magnetic ﬁeld. In the long wave limit, the radiation power is proportional to the square of the B-ﬁeld volume and the axion mass m a , whereas it oscillate as approaching the short wave limit and the peak powers are proportional to the side area of the cylindrical magnetic ﬁeld and m − 2 a . The maximum power locates at mass m a ∼ 3 π 4 R for ﬁxed radius R . Based on this characteristic of the power, we discuss a scheme to detect the axions in the mass range 1 − 10 4 neV, where four detectors of diﬀerent bandwidths surround the B-ﬁeld. The expected sensitivity for m a (cid:46) 1 µ eV under typical-parameter values can far exceed the existing constraints.
High-performance transmission mechanism design of multi-stream carrier aggregation for 5G non-standalone network

Multi-stream carrier aggregation is a key technology to expand bandwidth and improve the throughput of the fifth-generation wireless communication systems. However, due to the diversified propagation properties of different frequency bands, the traffic migration task is much more challenging, especially in hybrid sub-6 GHz and millimeter wave bands scenario. Existing schemes either neglected to consider the transmission rate difference between multi-stream carrier, or only consider simple low mobility scenario. In this paper, we propose a low-complexity traffic splitting algorithm based on fuzzy proportional integral derivative control mechanism. The proposed algorithm only relies on the local radio link control buffer information of sub-6 GHz and mmWave bands, while frequent feedback from user equipment (UE) side is minimized. As shown in the numerical examples, the proposed traffic splitting mechanism can achieve more than 90% link resource utilization ratio for different UE transmission requirements with different mobilities, which corresponds to 10% improvement if compared with conventional baselines.
Dual Space Coupling Model Guided Overlap-Free Scatterplot

The overdraw problem of scatterplots seriously interferes with the visual tasks. Existing methods, such as data sampling, node dispersion, subspace mapping, and visual abstraction, cannot guarantee the correspondence and consistency between the data points that reflect the intrinsic original data distribution and the corresponding visual units that reveal the presented data distribution, thus failing to obtain an overlap-free scatterplot with unbiased and lossless data distribution. A dual space coupling model is proposed in this paper to represent the complex bilateral relationship between data space and visual space theoretically and analytically. Under the guidance of the model, an overlap-free scatterplot method is developed through integration of the following: a geometry-based data transformation algorithm, namely DistributionTranscriptor; an efficient spatial mutual exclusion guided view transformation algorithm, namely PolarPacking; an overlap-free oriented visual encoding configuration model and a radius adjustment tool, namely <inline-formula><tex-math notation="LaTeX">$f_{r_{draw}}$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="tvcg-li-3209459-eqinline-1-small.tif"/></alternatives></inline-formula>. Our method can ensure complete and accurate information transfer between the two spaces, maintaining consistency between the newly created scatterplot and the original data distribution on global and local features. Quantitative evaluation proves our remarkable progress on computational efficiency compared with the state-of-the-art methods. Three applications involving pattern enhancement, interaction improvement, and overdraw mitigation of trajectory visualization demonstrate the broad prospects of our method.
ArNet-ECG: Deep Learning for the Detection of Atrial Fibrillation from the Raw Electrocardiogram

Introduction: Atrial fibrillation (AF) is the most prevalent heart arrhythmia. AF manifests on the electrocar-diogram (ECG) though irregular beat-to-beat time interval variation, the absence of P-wave and the presence of fibrillatory waves (f-wave). We hypothesize that a deep learning (DL) approach trained on the raw ECG will enable robust detection of AF events and the estimation of the AF burden (AFB). We further hypothesize that the performance reached leveraging the raw ECG will be superior to previously developed methods using the beat-to-beat interval variation time series. Consequently, we develop a new DL algorithm, denoted ArNet-ECG, to robustly detect AF events and estimate the AFB from the raw ECG and bench-mark this algorithms against previous work. Methods: A dataset including 2,247 adult patients and totaling over 53,753 hours of continuous ECG from the University of Virginia (UVAF) was used. Results: ArNet-ECG obtained an F1 of 0.96 and ArNet2 obtained an F1 0.94. Discussion and conclusion: ArNet-ECG outperformed ArNet2 thus demonstrating that using the raw ECG provides added performance over the beat-to-beat interval time series. The main reason found for explaining the higher performance of ArNet-ECG was its high performance on atrial flutter examples versus poor performance on these recordings for ArNet2.
Maniplexes with automorphism group $\textrm{PSL}_2(q)$

A maniplex of rank n is a combinatorial object that generalises the notion of a rank n abstract polytope. A maniplex with the highest possible degree of symmetry is called reﬂexible. In this paper we prove that there is a rank 4 reﬂexible maniplex with automorphism group PSL 2 ( q ) for inﬁnitely many prime powers q , and that no reﬂexible maniplex of rank n > 4 exists that has PSL 2 ( q ) as its full automorphism group.
Energy Decay in $1+1$ Rindler Spacetime

We consider solutions of the massless scalar wave equation $\Box_g\psi=0$ on a fixed Rindler background and show polynomial decay of the energy flux related to the Rindler observers near null infinity and to local observers near the Rindler horizon. The main estimates are obtained via the vector field method using suitable vector fields multipliers which are analogous to the ones used in Schwarzschild spacetime and Minkowski spacetime.
Complex Variables: Introduction and Applications

Part I. 1. Complex numbers and elementary functions 2. Analytic functions and integration 3. Sequences, series and singularities of complex functions 4. Residue calculus and applications of contour integration Part II. 5. Conformal mapping and applications 6. Asymptotic evaluation of integrals 7. Riemann-Hilbert problems Index.
Technical Report: Asymmetric Mutual Exclusion for RDMA

“New instances of the mutual exclusion problem can still arise that make currently impractical algorithms useful, or that require new algorithms. One source of those instances is new hardware designs. ” Lamport Abstract Coordinating concurrent access to a shared resource using mutual exclusion is a fundamental problem in computation. In this paper, we present a novel approach to mutual exclusion designed speciﬁcally for distributed systems leveraging a popular network communication technology, remote direct memory access (RDMA). Our approach enables local processes to avoid using RDMA operations entirely, limits the number of RDMA operations required by remote processes, and guarantees both starvation-freedom and fairness.
CenDerNet: Center and Curvature Representations for Render-and-Compare 6D Pose Estimation

We introduce CenDerNet, a framework for 6D pose estimation from multi-view images based on center and curvature representations. Finding precise poses for reflective, textureless objects is a key challenge for industrial robotics. Our approach consists of three stages: First, a fully convolutional neural network predicts center and curvature heatmaps for each view; Second, center heatmaps are used to detect object instances and find their 3D centers; Third, 6D object poses are estimated using 3D centers and curvature heatmaps. By jointly optimizing poses across views using a render-and-compare approach, our method naturally handles occlusions and object symmetries. We show that CenDerNet outperforms previous methods on two industry-relevant datasets: DIMO and T-LESS.
Finding Emotions in Faces: A Meta-Classifier

—Machine learning has been used to recognize emotions in faces, typically by looking for 8 different emotional states (neutral, happy, sad, surprise, fear, disgust, anger and contempt). We consider two approaches: feature recognition based on facial landmarks and deep learning on all pixels; each produced 58% overall accuracy. However, they produced different results on different images and thus we propose a new meta-classifier combining these approaches. It produces far better results with 77% accuracy.
Modeling, Quantifying, and Predicting Subjectivity of Image Aesthetics

Assessing image aesthetics is a challenging computer vision task. One reason is that aesthetic preference is highly subjective and may vary significantly among people for certain images. Thus, it is important to properly model and quantify such \textit{subjectivity}, but there has not been much effort to resolve this issue. In this paper, we propose a novel unified probabilistic framework that can model and quantify subjective aesthetic preference based on the subjective logic. In this framework, the rating distribution is modeled as a beta distribution, from which the probabilities of being definitely pleasing, being definitely unpleasing, and being uncertain can be obtained. We use the probability of being uncertain to define an intuitive metric of subjectivity. Furthermore, we present a method to learn deep neural networks for prediction of image aesthetics, which is shown to be effective in improving the performance of subjectivity prediction via experiments. We also present an application scenario where the framework is beneficial for aesthetics-based image recommendation.