May 2022 arXiv papers — page 14

H. De Raedt, S. Miyashita, K. Michielsen, H. Vezin

Recent electron spin resonance experiments on CaWO$_4$:Gd$^{3+}$ and on other magnetic impurities have demonstrated that sustained Rabi oscillations can be created by driving a magnetic moment with a microwave field frequency slightly larger than the Larmor frequency and tuned to the Floquet resonance together with another microwave field (image drive). Thes

Charles H. Conley, Jon Erickson

Consider a "simplicial molecule": $n$ equal point masses placed at the vertices of a regular $(n-1)$-simplex, connected by ${n \choose 2}$ identical springs. We apply the representation theory of the symmetric group $S_n$ to compute its vibrational modes.

Yimeng Wang, Siddhant Pandey, Chris H. Greene, Niranjan Shivaram

We propose the generation of attosecond entangled bi-photons in the extreme-ultraviolet regime by two-photon decay of a metastable atomic state as a source similar to spontaneous parametric down-conversion photons. The 1s2s $^1S_0$ metastable state in helium decays to the ground state by emission of two energy-time entangled photons with a photon bandwidth e

M. -L. Cai, Y. -K. Wu, Q. -X. Mei, W. -D. Zhao

Supersymmetry (SUSY) helps solve the hierarchy problem in high-energy physics and provides a natural groundwork for unifying gravity with other fundamental interactions. While being one of the most promising frameworks for theories beyond the Standard Model, its direct experimental evidence in nature still remains to be discovered. Here we report experimenta

Jin Chen, Defu Lian, Yucheng Li, Baoyun Wang

Recommender retrievers aim to rapidly retrieve a fraction of items from the entire item corpus when a user query requests, with the representative two-tower model trained with the log softmax loss. For efficiently training recommender retrievers on modern hardwares, inbatch sampling, where the items in the mini-batch are shared as negatives to estimate the s

Lixia Yuan, Ji Yang, Fujun Du, Yang Su

After the morphological classification of the 18,190 $^{12}$CO molecular clouds, we further investigate the properties of their internal molecular gas structures traced by the $^{13}$CO($J=$ 1$-$0) line emissions. Using three different methods to extract the $^{13}$CO gas structures within each $^{12}$CO cloud, we find that $\sim$ 15$\%$ of $^{12}$CO clouds

Mingda Li, Jin Wang, Guorui Xiao, Youfu Li

With the ever-increasing volume of data, there is an urgent need to provide expressive and efficient tools to support Big Data analytics. The declarative logical language Datalog has proven very effective at expressing concisely graph, machine learning, and knowledge discovery applications via recursive queries. In this demonstration, we develop Logic Librar

Oluwaseyi Onasami, Ming Feng, Hao Xu, Mulugeta Haile

Underwater acoustic (UWA) communications have been widely used but greatly impaired due to the complicated nature of the underwater environment. In order to improve UWA communications, modeling and understanding the UWA channel is indispensable. However, there exist many challenges due to the high uncertainties of the underwater environment and the lack of r

Aparup Khatua, Wolfgang Nejdl

Social media deliberations allow to explore refugee-related is-sues. AI-based studies have investigated refugee issues mostly around a specific event and considered unimodal approaches. Contrarily, we have employed a multimodal architecture for probing the refugee journeys from their home to host nations. We draw insights from Arnold van Gennep's anthropolog

Anderson Y. Zhang, Harrison H. Zhou

The singular subspaces perturbation theory is of fundamental importance in probability and statistics. It has various applications across different fields. We consider two arbitrary matrices where one is a leave-one-column-out submatrix of the other one and establish a novel perturbation upper bound for the distance between the two corresponding singular sub

Glenda Tan Hui En, Koay Tze Erhn, Shen Bingquan

More infectious virus variants can arise from rapid mutations in their proteins, creating new infection waves. These variants can evade one's immune system and infect vaccinated individuals, lowering vaccine efficacy. Hence, to improve vaccine design, this project proposes Optimus PPIme - a deep learning approach to predict future, more infectious variants f

Jiunn-Kai Huang, Yingwen Tan, Dongmyeong Lee, Vishnu R. Desaraju

Multi-objective or multi-destination path planning is crucial for mobile robotics applications such as mobility as a service, robotics inspection, and electric vehicle charging for long trips. This work proposes an anytime iterative system to concurrently solve the multi-objective path planning problem and determine the visiting order of destinations. The sy

Ye Zheng, Xiang Wang, Yu Qi, Wei Li

Unsupervised anomaly detection and localization, as of one the most practical and challenging problems in computer vision, has received great attention in recent years. From the time the MVTec AD dataset was proposed to the present, new research methods that are constantly being proposed push its precision to saturation. It is the time to conduct a comprehen

Songlin Yang, Wei Wang, Chenye Xu, Ziwen He

Face anti-spoofing aims to discriminate the spoofing face images (e.g., printed photos) from live ones. However, adversarial examples greatly challenge its credibility, where adding some perturbation noise can easily change the predictions. Previous works conducted adversarial attack methods to evaluate the face anti-spoofing performance without any fine-gra

Maximilian Du, Olivia Y. Lee, Suraj Nair, Chelsea Finn

Humans are capable of completing a range of challenging manipulation tasks that require reasoning jointly over modalities such as vision, touch, and sound. Moreover, many such tasks are partially-observed; for example, taking a notebook out of a backpack will lead to visual occlusion and require reasoning over the history of audio or tactile information. Whi

Francisco Lai Liang, Dvira Segal

We study the interplay of intrinsic-electronic and environmental factors on long-range charge transport across molecular chains with up to $N\sim 80$ monomers. We describe the molecular electronic structure of the chain with a tight-binding Hamiltonian. Thermal effects in the form of electron decoherence and inelastic scatterings are incorporated with the La

Gonzalo Contreras, Fernando Oliveira

We prove that the geodesic flow of a Kupka-Smale riemannian metric on a closed surface has homoclinic orbits for all of its hyperbolic closed geodesics.

Qi Zeng, Qiusi Zhan, Heng Ji

Events are inter-related in documents. Motivated by the one-sense-per-discourse theory, we hypothesize that a participant tends to play consistent roles across multiple events in the same document. However recent work on document-level event argument extraction models each individual event in isolation and therefore causes inconsistency among extracted argum

Lechao Xiao, Hong Hu, Theodor Misiakiewicz, Yue M. Lu

As modern machine learning models continue to advance the computational frontier, it has become increasingly important to develop precise estimates for expected performance improvements under different model and data scaling regimes. Currently, theoretical understanding of the learning curves that characterize how the prediction error depends on the number o

Hoa T. Nguyen, Muhammad Usman, Rajkumar Buyya

Recent breakthroughs in quantum hardware are creating opportunities for its use in many applications. However, quantum software engineering is still in its infancy with many challenges, especially dealing with the diversity of quantum programming languages and hardware platforms. To alleviate these challenges, we propose QFaaS, a novel Quantum Function-as-a-

Yeongtae Jang, Eunho Kim, Jinkyu Yang, Junsuk Rho

We propose an efficient method to build a simple discrete element model (DEM) that accurately simulates the oscillation of a continuum beam. The DEM is based on the Timoshenko beam theory of slender cylindrical members and their corresponding wave dynamics in assembly. This physics-informed DEM accounts for multiple vibration modes of the constituting beam e

Jiarui Zhang, Jian Huang, Ji Guang, Jialong Gao

The current driver nodes search methods are difficult to cope with large networks, and the solution process does not consider the node cost. In order to solve the practical control problem of networks with different node costs in finite states, this paper proposes a pruning and motif isomorph search method for driver node set. Firstly, we prove the sufficien

Yinglun Xu, Qi Zeng, Gagandeep Singh

We study reward poisoning attacks on online deep reinforcement learning (DRL), where the attacker is oblivious to the learning algorithm used by the agent and the dynamics of the environment. We demonstrate the intrinsic vulnerability of state-of-the-art DRL algorithms by designing a general, black-box reward poisoning framework called adversarial MDP attack

Pan-Yu Hou, Jenny J. Wu, Stephen D. Erickson, Daniel C. Cole

Precise quantum control and measurement of several harmonic oscillators, such as the modes of the electromagnetic field in a cavity or of mechanical motion, are key for their use as quantum platforms. The motional modes of trapped ions can be individually controlled and have good coherence properties. However, achieving high-fidelity two-mode operations and

Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith

Federated learning typically considers collaboratively training a global model using local data at edge clients. Clients may have their own individual requirements, such as having a minimal training loss threshold, which they expect to be met by the global model. However, due to client heterogeneity, the global model may not meet each client's requirements,

Jung-hun Kim, Se-Young Yun

We study the adversarial bandit problem against arbitrary strategies, where the difficulty is captured by an unknown parameter $S$, which is the number of switches in the best arm in hindsight. To handle this problem, we adopt the master-base framework using the online mirror descent method (OMD). We first provide a master-base algorithm with simple OMD, ach

Lirong Xia

In social choice theory, anonymity (all agents being treated equally) and neutrality (all alternatives being treated equally) are widely regarded as ``minimal demands'' and ``uncontroversial'' axioms of equity and fairness. However, the ANR impossibility -- there is no voting rule that satisfies anonymity, neutrality, and resolvability (always choosing one w

Yixin Zhang, Yong Liu, Yonghui Xu, Hao Xiong

The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequence and learn model parameters solely based on the item prediction loss. Thus, they usually fail to learn appro

Kartick Ramakrishnan, Sai Krishna Kishore Nori, Seung-Cheol Lee, Gour P Das

We present an efficient and scalable computational approach for conducting projected population analysis from real-space finite-element (FE) based Kohn-Sham density functional theory calculations (DFT-FE). This work provides an important direction towards extracting chemical bonding information from large-scale DFT calculations on materials systems involving

Alex Abreu, Antonio Nigro

To any element of a connected, simply connected, semisimple complex algebraic group G and a choice of an element of the corresponding Weyl group there is an associated Lusztig variety. When the element of G is regular semisimple, the corresponding variety carries an action of the Weyl group on its (equivariant) intersection cohomology. From this action, we r

Sai Munikoti, Balasubramaniam Natarajan, Mahantesh Halappanavar

Influence maximization (IM) is a combinatorial problem of identifying a subset of nodes called the seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and

Chengfei Lv, Chaoyue Niu, Renjie Gu, Xiaotang Jiang

To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system, called Walle, as the foundation. Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input

Junyan He, Shashank Kushwaha, Diab Abueidda, Iwona Jasiuk

This paper introduces a heuristic topology optimization framework for thin-walled, 2D extruded lattice structures subject to complex high-speed loading. The proposed framework optimizes the wall thickness distribution in the lattice cross section through different thickness update schemes, inspired by the idea of equalization of absorbed energy density acros

Truong Son Hy, Viet Bach Nguyen, Long Tran-Thanh, Risi Kondor

In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph structures and incorporates the time-series signals to capture the temporal changes of the dynamic graphs. We have applied our proposed model to the task of predicting future spreadi

Aditya Bhosale, Xiaoliang Zhang

High impedance RF coils, such as self-decoupled coils, reduce the electromagnetic coupling between the coil elements and eliminates the use of complex decoupling technologies. Although the high impedance design promises excellent decoupling between the coil elements, it also results in high electric fields across the RF coil, leading to potential safety prob

Ziping Xu, Eunjae Shim, Ambuj Tewari, Paul Zimmerman

In this paper, we study a sequential decision-making problem, called Adaptive Sampling for Discovery (ASD). Starting with a large unlabeled dataset, algorithms for ASD adaptively label the points with the goal to maximize the sum of responses. This problem has wide applications to real-world discovery problems, for example drug discovery with the help of mac

Reymond Mesuga

The discovery of neutrino oscillation, proving that neutrinos do have masses, reveals the misfits of particles in the current Standard Model (SM) theory. In theory, neutrinos having masses could result in lepton flavour not being a symmetry called Lepton Flavour Violation (LFV). While SM theory extensions allowed LFV processes, their branching fractions are

Thomas Goller, Yinbang Lin

Over the projective plane and at most two-step blowups of Hirzebruch surfaces, where there are strong full exceptional sequences of line bundles, we obtain foundational results about Gaeta resolutions of coherent sheaves by these line bundles. Under appropriate conditions, we show the locus of semistable sheaves not admitting Gaeta resolutions has codimensio

Chaojian Yu, Bo Han, Mingming Gong, Li Shen

Overfitting widely exists in adversarial robust training of deep networks. An effective remedy is adversarial weight perturbation, which injects the worst-case weight perturbation during network training by maximizing the classification loss on adversarial examples. Adversarial weight perturbation helps reduce the robust generalization gap; however, it also

Jun Lu

In this paper, we introduce a probabilistic model for learning interpolative decomposition (ID), which is commonly used for feature selection, low-rank approximation, and identifying hidden patterns in data, where the matrix factors are latent variables associated with each data dimension. Prior densities with support on the specified subspace are used to ad

He Gao, Haoran Xue, Zhongming Gu, Linhu Li

We present a one-dimensional coupled ring resonator lattice exhibiting a variant of the non- Hermitian skin effect (NHSE) that we call the anomalous Floquet NHSE. Unlike existing approaches to achieving the NHSE by engineering gain and loss on different ring segments, our design uses fixed on-site gain or loss in each ring. The anomalous Floquet NHSE is mark

Tong Wu, Yong Wang, Xue Wang

In this paper, we define the $W_2$-curvature tensor on super Riemannian manifolds. And we compute the curvature tensor, the Ricci tensor and the $W_2$-curvature tensor on super twisted product spaces. Furthermore, we investigate the $W_2$-curvature flat super twisted product manifolds. And, we get a result that a mixed Ricci-flat super twisted product semi-R

Bo Jiang, Meijun Gu, Chengjiu Yin

The purpose of this study is to explore students' backtracking patterns in using a digital textbook and reveal the relationship between backtracking behaviors and academic performance as well as learning styles. The study was carried out for two semesters on 102 university students and they are required to use a digital textbook system called DITeL to review

Yao Li, Chao-Jian Wu, Yong-Qiang Yao, Wei Zhang

The spectral observations and analysis for the W80 Region are presented by using the data of Medium-Resolution Spectroscopic Survey of Nebulae (MRS-N) with the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST). A total of 2982 high-quality nebular spectra have been obtained in the 20 square degree field of view (FoV) which covers the W80 comp

Sho Sonoda, Isao Ishikawa, Masahiro Ikeda

We show the universality of depth-2 group convolutional neural networks (GCNNs) in a unified and constructive manner based on the ridgelet theory. Despite widespread use in applications, the approximation property of (G)CNNs has not been well investigated. The universality of (G)CNNs has been shown since the late 2010s. Yet, our understanding on how (G)CNNs

Shunta Akiyama, Taiji Suzuki

While deep learning has outperformed other methods for various tasks, theoretical frameworks that explain its reason have not been fully established. To address this issue, we investigate the excess risk of two-layer ReLU neural networks in a teacher-student regression model, in which a student network learns an unknown teacher network through its outputs. E

Beomsu Kim, Jong Chul Ye

Deep energy-based models (EBMs), which use deep neural networks (DNNs) as energy functions, are receiving increasing attention due to their ability to learn complex distributions. To train deep EBMs, the maximum likelihood estimation (MLE) with short-run Langevin Monte Carlo (LMC) is often used. While the MLE with short-run LMC is computationally efficient c

Yichuan Deng, Zhao Song, Omri Weinstein, Ruizhe Zhang

In the Distance Oracle problem, the goal is to preprocess $n$ vectors $x_1, x_2, \cdots, x_n$ in a $d$-dimensional metric space $(\mathbb{X}^d, \| \cdot \|_l)$ into a cheap data structure, so that given a query vector $q \in \mathbb{X}^d$ and a subset $S\subseteq [n]$ of the input data points, all distances $\| q - x_i \|_l$ for $x_i\in S$ can be quickly app

Wenbo Wu, Gang Zhao, Xiang-Xiang Xue, Wenxiang Pei

We present a study of the influence of the Gaia-Sausage-Enceladus (GSE) on the density shape of the Galactic stellar halo using 11624 K giants from the LAMOST survey. Every star is assigned a probability of being a member of the GSE based on its spherical velocities and metallicity by a Gaussian Mixture Model. We divide the stellar halo into two parts by the

Tianyang Hu, Zhili Liu, Fengwei Zhou, Wenjia Wang

Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contribute to the theoretical understanding of SSCL and uncover its connection to the classic data visualization method, stochastic neighbor embedding (SNE), whose goal is to preserve p

Dhong Yeon Cheong, Kazunori Kohri, Seong Chan Park

The running of the Higgs self coupling may lead to numerous phenomena in early universe cosmology. In this paper we introduce a scenario where the Higgs running induces turns in the trajectory passing a region with tachyonic mass, leading to a temporal tachyonic growth in the curvature power spectrum. This effect induced by the Higgs leaves phenomena in the

Daniel Pfrommer, Thomas T. C. K. Zhang, Stephen Tu, Nikolai Matni

We propose Taylor Series Imitation Learning (TaSIL), a simple augmentation to standard behavior cloning losses in the context of continuous control. TaSIL penalizes deviations in the higher-order Taylor series terms between the learned and expert policies. We show that experts satisfying a notion of $\textit{incremental input-to-state stability}$ are easy to

Dongpo Xu, Jinlan Liu, Yinghua Lu, Jun Kong

The stochastic momentum method is a commonly used acceleration technique for solving large-scale stochastic optimization problems in artificial neural networks. Current convergence results of stochastic momentum methods under non-convex stochastic settings mostly discuss convergence in terms of the random output and minimum output. To this end, we address th

Qing-Wen Wang, Mahmoud Saad Mehany

This study establishes consistency conditions and a general solution for a coupled system that consists of five two-sided Sylvester-like tensor equations in ten quaternion variables throughout the Einstein tensor product. Certain specific cases are thus established. In a direct application, we investigate certain necessary and sufficient conditions for the e

Xinyan Li, Han Wang, Chunyang Chen, John Grundy

The ongoing COVID-19 pandemic highlights the importance of dashboards for providing critical real-time information. In order to enable people to obtain information in time and to understand complex statistical data, many developers have designed and implemented public-oriented COVID-19 "information dashboards" during the pandemic. However, development often

Anupam Shaw

For more than a century the Abraham-Lorentz equation has generally been regarded as the correct description of the dynamics of a charged particle. However, there are pathological solutions of the Abraham-Lorentz equation in which a particle accelerates in advance of the application of a force, the so-called preacceleration solutions, and solutions in which t

Elena Giorgi, Sergiu Klainerman, Jeremie Szeftel

This is the last part of our proof of the nonlinear stability of the Kerr family for small angular momentum, i.e $|a|/m\ll 1$, in which we deal with the nonlinear wave type estimates needed to complete the project. More precisely we provide complete proofs for Theorems M1 and M2 as well the curvature estimates of Theorem M8, which were stated without proof i

Yichong Leng, Zehua Chen, Junliang Guo, Haohe Liu

Binaural audio plays a significant role in constructing immersive augmented and virtual realities. As it is expensive to record binaural audio from the real world, synthesizing them from mono audio has attracted increasing attention. This synthesis process involves not only the basic physical warping of the mono audio, but also room reverberations and head/e

Mayra Samaniego

People IoT surroundings have become valuable information sources that can positively impact individuals and society. A user IoT data can be used for different purposes. For instance, research and improvement of public services. However, individuals lack the governance power to share their IoT data. Data trust is a concept that brings opportunities to address

Dae San Kim, Hye Kyung Kim, Taekyun Kim

Recently, the $\lambda$-analogues of $r$-Stirling numbers of the first kind were studied by Kim-Kim. The aim of this paper is to introduce the $\lambda$-analogues of $r$-Stirling numbers of the second kind and to investigate some properties, recurrence relations and certain identities on those numbers. We also introduce the $\lambda$-analogues of Whitney-typ

Shamik Ghosh, Yang Liu, Le Zhang, Siyu Li

AliCPT is the first Chinese cosmic microwave background (CMB) experiment which will make the most precise measurements of the CMB polarization in the northern hemisphere. The key science goal for AliCPT is the detection of primordial gravitational waves (PGWs). It is well known that an epoch of cosmic inflation, in the very early universe, can produce PGWs,

Yan Liu, Hong-Da Lyu

We study the interior structure of five dimensional neutral helical black holes in Einstein gravity and charged helical black holes in Einstein-Maxwell gravity. Inside the neutral helical black holes, the systems evolve to a stable spacelike Kasner singularity. The metric field related to the helical deformation strength exhibits oscillation behavior close t

Elliott Rosenberg, Jonathan M. DeStefano, Yucheng Guo, Ji Seop Oh

The synthesis and characterization of the vanadium-based kagome metal TbV${_6}$Sn${_6}$ is presented. X-ray measurements confirm this material forms with the same crystal structure type as the recently investigated kagome metals GdV$_6$Sn$_6$ and YV$_6$Sn$_6$, with space group symmetry P6/mmm. A signature of a phase transition at 4.1K is observed in heat cap

Osamu Fujino

We discuss vanishing theorems for projective morphisms between complex analytics spaces and some related results. They will play a crucial role in the minimal model theory for projective morphisms of complex analytic spaces. Roughly speaking, we establish an ultimate generalization of Koll\'ar's package from the minimal model theoretic viewpoint.

Phuc D. Nguyen, Kristy L. Hansen, Peter Catcheside, Colin Hansen

The large-scale expansion of wind farms has prompted community debate regarding adverse impacts of wind farm noise (WFN). One of the most annoying and potentially sleep disturbing components of WFN is amplitude modulation (AM). Here we quantified and characterised AM over one year using acoustical and meteorological data measured at three locations near thre

Yuki Nagai, Hiroshi Shinaoka

We propose the sparse modeling approach for quasiclassical theory of superconductivity, which reduces the computational cost of solving the gap equations. The recently proposed sparse modeling approach is based on the fact that the Green's function has less information than its spectral function and hence is compressible without loss of relevant information.

Masahiko Ueda

Zero-determinant strategies are a class of memory-one strategies in repeated games which unilaterally enforce linear relationships between payoffs. It has long been unclear for what stage games zero-determinant strategies exist. We provide a necessary and sufficient condition for the existence of zero-determinant strategies. This condition can be interpreted

Haris Aziz, Alexander Lam, Mashbat Suzuki, Toby Walsh

Proportionality is an attractive fairness concept that has been applied to a range of problems including the facility location problem, a classic problem in social choice. In our work, we propose a concept called Strong Proportionality, which ensures that when there are two groups of agents at different locations, both groups incur the same total cost. We sh

Vignesh Manoharan, Vijaya Ramachandran

We present several results in the CONGEST model on round complexity for Replacement Paths (RPaths), Minimum Weight Cycle (MWC), and All Nodes Shortest Cycles (ANSC). We study these fundamental problems in both directed and undirected graphs, both weighted and unweighted. Many of our results are optimal to within a polylog factor: For an $n$-node graph $G$ we

Safwan Alfattani, Animesh Yadav, Halim Yanikomeroglu, Abbas Yongacoglu

The ever-increasing number of users and new services in urban regions can lead terrestrial base stations (BSs) to become overloaded and, consequently, some users to go unserved. Compounding this, users in urban areas can face severe shadowing and blockages, which means that some users do not receive a desired quality of service (QoS). Motivated by the energy

Bo-Nan Jiang

The measurement precision of the static atomic gravimetry is limited by white Gaussian noise in short term, which costs previous works an inevitable integration to reach the precision demanded. Here, we propose a statistical model based on the quantum projection noise and apply the Kalman filter to the waveform estimation in static atomic gravimetry. With th

Hidefumi Takahashi, Tomoki Akiba, Alex Hiro Mayo, Kazuto Akiba

Two-dimensional magnetic materials have recently attracted great interest due to their unique functions as the electric field control of a magnetic phase and the anomalous spin Hall effect. For such remarkable functions, a spin-orbit coupling (SOC) serves as an essential ingredient. Here we report a giant positive magnetoresistance in a layered magnetic semi

Aniket Didolkar, Kshitij Gupta, Anirudh Goyal, Nitesh B. Gundavarapu

Recurrent neural networks have a strong inductive bias towards learning temporally compressed representations, as the entire history of a sequence is represented by a single vector. By contrast, Transformers have little inductive bias towards learning temporally compressed representations, as they allow for attention over all previously computed elements in

F. Anzuini, A. Melatos, C. Dehman, D. Viganò

The dissipation of intense crustal electric currents produces high Joule heating rates in cooling neutron stars. Here it is shown that Joule heating can counterbalance fast cooling, making it difficult to infer the presence of hyperons (which accelerate cooling) from measurements of the observed thermal luminosity $L_\gamma$. Models with and without hyperon

Yongli Zhu, Chanan Singh

Conventional power system reliability suffers from the long run time of Monte Carlo simulation and the dimension-curse of analytic enumeration methods. This paper proposes a preliminary investigation on end-to-end machine learning for directly predicting the reliability index, e.g., the Loss of Load Probability (LOLP). By encoding the system admittance matri

Michael Bulois, Lucy Moser-Jauslin, Ronan Terpereau

We determine the equivariant real structures on nilpotent orbits and the normalizations of their closures for the adjoint action of a complex semisimple algebraic group on its Lie algebra.

Wenjie Li, Sunseng Pyon, Ataru Ichinose, Satoru Okayasu

Effects of columnar defects created by 320 MeV Au irradiation on 2H-NbSe2 single crystals with a dose equivalent matching field up to 16 T were studied. Critical temperature is found to be suppressed almost linearly at a rate of 0.07 K/T. At the same time, the lattice parameters a and c are found to be expanded at rates of 0.016%/T and 0.030%/T, respectively

Orestis Papadigenopoulos, Constantine Caramanis, Sanjay Shakkottai

The stochastic multi-armed bandit setting has been recently studied in the non-stationary regime, where the mean payoff of each action is a non-decreasing function of the number of rounds passed since it was last played. This model captures natural behavioral aspects of the users which crucially determine the performance of recommendation platforms, ad place

Huagui Duan, Hui Liu, Wenyan Ren

In this paper, we prove that if all closed characteristics on a compact non-degenerate star-shaped hypersurface $\Sigma$ in $\mathbf{R}^{2n}$ are elliptic, then either there exist exactly $n$ geometrically distinct closed characteristics, or there exist infinitely many geometrically distinct closed characteristics.

Arvind Balasubramanian, Alessandra Corsi, Kunal P. Mooley, Kenta Hotokezaka

GW170817 is the first binary neutron star (NS) merger detected in gravitational waves (GWs) and photons, and so far remains the only GW event of its class with a definitive electromagnetic (EM) counterpart. Radio emission from the structured jet associated with GW170817 has faded below the sensitivity achievable via deep radio observations with the most sens

Andy L. Zhang, Sean A. Ridout, Celia Parts, Aarushi Sachdeva

The role of fixed degrees of freedom in soft/granular matter systems has broad applicability and theoretical interest. Here we address questions of the geometrical role that a scaffolding of fixed particles plays in tuning the threshold volume fraction and force network in the vicinity of jamming. Our 2d simulated system consists of soft particles and fixed

J. P. Halpern

I obtained time-series photometry of the compact binary candidate for the Fermi source 4FGL J0935.3+0901. Superposed on the 2.44 hr orbital modulation are day-to-day variations and frequent flaring as seen in several redback and black widow millisecond pulsars (MSPs). The short orbital period favors a black widow. While the modulation of $\leq 1$ mag is smal

Aditya Bhosale, Leslie Ying, Xiaoliang Zhang

Microstrip lines are being used in MR applications due to their unique properties, such as reduced radiation loss, high-frequency capability, and reduced perturbation of sample loading to the RF coil compared to conventional coils. Here, we present the design of the 13-channel hybrid array consisting of 12 Microstrips, 1 volume half birdcage coil placed on t

Yu Xing, Karl H. Johansson

We study transient behavior of gossip opinion dynamics, in which agents randomly interact pairwise over a weighted graph with two communities. Edges within a community have identical weights different from edge weights between communities. We first derive an upper bound for the second moment of agent opinions. Using this bound, we obtain upper bounds for pro

John A. Scott, James Bishop, Milos Toth

Ion beams are used routinely for processing of semiconductors, particularly sputtering, ion implantation and direct-write fabrication of nanostructures. However, the utility of ion beam techniques is limited by crystal damage and surface roughening. Damage can be reduced or eliminated by performing irradiation at elevated temperatures. However, at these cond

Md. Ataur Rahman, Nazifa Tabassum, Mitu Paul, Riya Pal

We introduce a new dataset for offline Handwritten Text Recognition (HTR) from images of Bangla scripts comprising words, lines, and document-level annotations. The BN-HTRd dataset is based on the BBC Bangla News corpus, meant to act as ground truth texts. These texts were subsequently used to generate the annotations that were filled out by people with thei

Sanku Satya Uday, Satti Thanuja Pavani, T. Jaya Lakshmi, Rohit Chivukula

The novel coronavirus disease (COVID-19) began in Wuhan, China, in late 2019 and to date has infected over 148M people worldwide, resulting in 3.12M deaths. On March 10, 2020, the World Health Organisation (WHO) declared it as a global pandemic. Many academicians and researchers started to publish papers describing the latest discoveries on covid-19. The lar

Eyad Shtaiwi, Ahmed El Ouadrhiri, Majid Moradikia, Salma Sultana

Automatic modulation classification (AMC) using the Deep Neural Network (DNN) approach outperforms the traditional classification techniques, even in the presence of challenging wireless channel environments. However, the adversarial attacks cause the loss of accuracy for the DNN-based AMC by injecting a well-designed perturbation to the wireless channels. I

Tomoyuki Oka, Ryota Misawa, Takayuki Yamada

This paper discusses level set-based structural optimization. Level set-based structural optimization is a method used to determine an optimal configuration for minimizing an objective functional by updating level set functions characterized as solutions to partial differential equations (PDEs) (e.g., Hamilton-Jacobi and reaction-diffusion equations). In thi

Patrick Hosein, Kevin Baboolal

The Naive-Bayes classifier is widely used due to its simplicity, speed and accuracy. However this approach fails when, for at least one attribute value in a test sample, there are no corresponding training samples with that attribute value. This is known as the zero frequency problem and is typically addressed using Laplace Smoothing. However, Laplace Smooth

Elham Ghasemi, Hossein Ghaffarnejad

In this paper, the Joule-Thomson (JT) adiabatic expansion is investigated for generalized Ayon-Beato-Garcia (ABG) regular black hole. It has a magnetic charge which makes central region of the black hole metric to be AdS spacetime and so become non singular. Thus we not need to use an additional cosmological parameter coming from ADS/CFT correspondence for p

Pengmiao Zhang, Ajitesh Srivastava, Anant V. Nori, Rajgopal Kannan

Data Prefetching is a technique that can hide memory latency by fetching data before it is needed by a program. Prefetching relies on accurate memory access prediction, to which task machine learning based methods are increasingly applied. Unlike previous approaches that learn from deltas or offsets and perform one access prediction, we develop TransforMAP,

Fraser A. Evans, Tommaso Marchetti, Elena Maria Rossi

A dynamical encounter between a stellar binary and Sgr A* in the Galactic Centre (GC) can tidally separate the binary and eject one member with a velocity beyond the escape speed of the Milky Way. These hypervelocity stars (HVSs) can offer insight into the stellar populations in the GC environment. In a previous work, our simulations showed that the lack of

Dmitry Ponomarev

Inverse magnetisation problem consists in inferring information about a magnetic source from measurements of its magnetic field. Unlike a general magnetisation distribution, the total magnetisation (net moment) of the source is a quantity that theoretically can be uniquely determined from the field. At the same time, it is often the most useful quantity for

Kihyuk Hong, Yuhang Li, Ambuj Tewari

We propose an algorithm for non-stationary kernel bandits that does not require prior knowledge of the degree of non-stationarity. The algorithm follows randomized strategies obtained by solving optimization problems that balance exploration and exploitation. It adapts to non-stationarity by restarting when a change in the reward function is detected. Our al

Luca M. Ghiringhelli, Carsten Baldauf, Tristan Bereau, Sandor Brockhauser

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider ma

Andrei G. Lebed

We calculate the parallel upper critical magnetic field $H_{\parallel}(0)$ for an in-plane isotropic quasi-two-dimensional (Q2D) chiral triplet superconductor at zero temperature, $T=0$. In particular, the ratio $H_{\parallel}(0)/(|dH^{GL}_{\parallel}/dT|_{T=T_c}T_c) = 0.815$ is defined, where $|dH^{GL}_{\parallel}/dT|_{T=T_c}$ is the so-called Ginzburg-Land

Zachariah Carmichael, Walter J Scheirer

Monumental advancements in artificial intelligence (AI) have lured the interest of doctors, lenders, judges, and other professionals. While these high-stakes decision-makers are optimistic about the technology, those familiar with AI systems are wary about the lack of transparency of its decision-making processes. Perturbation-based post hoc explainers offer

Igor Uljarevic

We prove a contact non-squeezing phenomenon on homotopy spheres that are fillable by Liouville domains with infinite dimensional symplectic homology: there exists a smoothly embedded ball in such a sphere that cannot be made arbitrarily small by a contact isotopy. These homotopy spheres include examples that are diffeomorphic to standard spheres and whose co

M. Katsanikas, M. Agaoglou, S. Wiggins, A. M. Mancho

We apply the method of Lagrangian Descriptors (LDs) to a symmetric Caldera-type potential energy surface which has three index-1 saddles surrounding a relatively flat region that contains no minimum. Using this method we show the phase space transport mechanism that is responsible for the existence and non-existence of the phenomenon of dynamical matching fo