March 2023 arXiv papers — page 141

Yi Ren, Hongyan Tang, Siwen Zhu

It is a well-known challenge to learn an unbiased ranker with biased feedback. Unbiased learning-to-rank(LTR) algorithms, which are verified to model the relative relevance accurately based on noisy feedback, are appealing candidates and have already been applied in many applications with single categorical labels, such as user click signals. Nevertheless, t

Kalyan Varma Nadimpalli, Amit Chattopadhyay, Bastian Rieck

The computer vision task of reconstructing 3D images, i.e., shapes, from their single 2D image slices is extremely challenging, more so in the regime of limited data. Deep learning models typically optimize geometric loss functions, which may lead to poor reconstructions as they ignore the structural properties of the shape. To tackle this, we propose a nove

Huaqing Wang, Junfeng Jing, Ning Li, Weichuan Zhang

Gabor wavelet is an essential tool for image analysis and computer vision tasks. Local structure tensors with multiple scales are widely used in local feature extraction. Our research indicates that the current corner detection method based on Gabor wavelets can not effectively apply to complex scenes. In this work, the capability of the Gabor function to di

Qian Shao, Shih-Fen Cheng

Optimizing delivery routes for last-mile logistics service is challenging and has attracted the attention of many researchers. These problems are usually modeled and solved as variants of vehicle routing problems (VRPs) with challenging real-world constraints (e.g., time windows, precedence). However, despite many decades of solid research on solving these V

Haotian Zhang, Jintu Zhang, Huifeng Zhao, Dejun Jiang

Conformation Generation is a fundamental problem in drug discovery and cheminformatics. And organic molecule conformation generation, particularly in vacuum and protein pocket environments, is most relevant to drug design. Recently, with the development of geometric neural networks, the data-driven schemes have been successfully applied in this field, both f

Arian Eamaz, Farhang Yeganegi, Kumar Vijay Mishra, Mojtaba Soltanalian

Conventional sensing applications rely on electromagnetic far-field channel models with plane wave propagation. However, recent ultra-short-range automotive radar applications at upper millimeter-wave or low terahertz (THz) frequencies envisage operation in the near-field region, where the wavefront is spherical. Unlike far-field, the near-field beampattern

Anurag K. Singh, Kei-ichi Watanabe

We study the behavior of various properties of commutative Noetherian rings under Segre products, with a special focus on properties in positive prime characteristic defined using the Frobenius endomorphism. Specifically, we construct normal graded rings of finite Frobenius representation type that are not Cohen-Macaulay.

Max Varverakis, Robert Holtzapple, Hiroki Fujii, Spencer Gessner

Positron targets are a critical component of future Linear Colliders. Traditional targets are composed of high-Z metals that become brittle over time due to constant bombardment by high-power electron beams. We explore the possibility of a liquid xenon target which is continuosly refreshed and therefore not susceptible to the damage mechanisms of traditional

Joshua A. Gill, Dipan Sengupta, Anthony G. Williams

A recent letter Cai et al. [2107.14548] within a phenomenological dark matter framework with a massive graviton in the external state indicated a divergence with increasing centre-of-momentum energy arising from the longitudinal polarizations of the graviton. In this letter we point out that in processes such as graviton-photon production from matter annihil

Jiaxu Liu, Song Chen, Shengze Cai, Chao Xu

The vanilla fractional order gradient descent may oscillatively converge to a region around the global minimum instead of converging to the exact minimum point, or even diverge, in the case where the objective function is strongly convex. To address this problem, a novel adaptive fractional order gradient descent (AFOGD) method and a novel adaptive fractiona

Song Bian, Xiating Ouyang, Zhiwei Fan, Paraschos Koutris

We study the certifiable robustness of ML classifiers on dirty datasets that could contain missing values. A test point is certifiably robust for an ML classifier if the classifier returns the same prediction for that test point, regardless of which cleaned version (among exponentially many) of the dirty dataset the classifier is trained on. In this paper, w

Mrinal Verghese, Chris Atkeson

Tasks where the set of possible actions depend discontinuously on the state pose a significant challenge for current reinforcement learning algorithms. For example, a locked door must be first unlocked, and then the handle turned before the door can be opened. The sequential nature of these tasks makes obtaining final rewards difficult, and transferring info

Kentaro Hayashi, Yasuhiko Tsue

A possibility of a pseudovector-type quark-antiquark condensed phase, which leads to a quark spin polarized phase, in the quark matter is investigated taking account of the vacuum effects leading to the chiral symmetry breaking by using the Nambu-Jona-Lasinio model. Also, possible Nambu-Goldstone modes on the pseudovector-type quark-antiquark condensate and

Wanmei Zhang, Wenhan Zhang, Gan Ren

The two functional forms, D~1/tau and D~T/tau, are usually adopted as the variants of the Stokes-Einstein relation; where D is the diffusion constant, tau the relaxation time and T the temperature. The self-consistent generalized Langevin equation (SCGLE) theory is presented as an analytical tool to predict the long time dynamics of colloids and molecular li

Zhenlin Ran

Let $q$ be an odd number and $q>5$, and $\mathbb{F}_q$ be a finite field of $q$ elements. We prove that at most finitely many singular moduli of rank 2 $\mathbb{F}_q[t]$-Drinfeld modules are algebraic units. In particular, we develop some techniques of heights of Drinfeld modules to approach it.

Shuaifeng Li, Yasuhiro Miyazawa, Koshiro Yamaguchi, Panayotis G. Kevrekidis

Origami structures often serve as the building block of mechanical systems due to their rich static and dynamic behaviors. Experimental observation and theoretical modeling of origami dynamics have been reported extensively, whereas the data-driven modeling of origami dynamics is still challenging due to the intrinsic nonlinearity of the system. In this stud

Dipam Patel, Phu Pham, Aniket Bera

We present a novel optimization algorithm called DroNeRF for the autonomous positioning of monocular camera drones around an object for real-time 3D reconstruction using only a few images. Neural Radiance Fields or NeRF, is a novel view synthesis technique used to generate new views of an object or scene from a set of input images. Using drones in conjunctio

Yanyan Wang, Wanchun Liu, Xiangyun Zhou

Recently proposed splitting receivers, utilizing both coherently and non-coherently processed signals for detection, have demonstrated remarkable performance gain compared to conventional receivers in the single-antenna scenario. In this paper, we propose a multi-antenna splitting receiver, where the received signal at each antenna is split into an envelope

Rashmi Bhaskara, Maurice Chiu, Aniket Bera

With the increasing availability and affordability of personal robots, they will no longer be confined to large corporate warehouses or factories but will instead be expected to operate in less controlled environments alongside larger groups of people. In addition to ensuring safety and efficiency, it is crucial to minimize any negative psychological impact

Sang-Yoon Kim, Woochang Lim

We are concerned about the main encoding granule cells (GCs) in the hippocampal dentate gyrus (DG). Young immature GCs (imGCs) appear through adult neurogenesis. In comparison to the mature GCs (mGCs) (born during development), the imGCs show high activation due to lower firing threshold. On the other hand, they receive low excitatory drive from the entorhin

Adela Habib, Nicholas Lubbers, Sergei Tretiak, Benjamin Nebgen

Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay in metallic nanostructures promise sustainable pathways for energy-harvesting devices. However, efficient collection before thermalization remains an obstacle for realization of their full energy generating potential. Addressing this challenge requires detailed understanding of phy

Koichi Saka

In this papae we introduce and investigate new 2-microlocal spaces associated with Besov type and Triebel-LIzorkin type spaces. We establish characterizations of these function spaces via the phi transform, the atom and molecular decomposition and wavelet decomposition. As applications we consider the boundedness of the Calderon-Zygmund operator and the pseu

Joyce A. Casimiro, Ricardo M. Martins, Douglas D. Novaes

The Poincar\'e-Hopf Theorem relates the Euler characteristic of a 2-dimensional compact manifold to the local behavior of smooth vector fields defined on it. However, despite the importance of Filippov vector fields, concerning both their theoretical and applied aspects, until now, it was not known whether this theorem extends to Filippov vector fields. In t

Felipe Manfio Barbosa, Fernando Santos Osório

Semantic image and video segmentation stand among the most important tasks in computer vision nowadays, since they provide a complete and meaningful representation of the environment by means of a dense classification of the pixels in a given scene. Recently, Deep Learning, and more precisely Convolutional Neural Networks, have boosted semantic segmentation

Alessandro Bacchetta, Marco Radici, Lorenzo Rossi

We describe the formal analogies in the description of the inclusive production in hard processes of hadron pairs (based on dihadron fragmentation functions) and of a single hadron inside a jet (based on hadron-in-jet fragmentation functions). Since several observables involving dihadron fragmentation functions have been proposed in the past, we are able to

Zhan Gao, Guang Yang, Amanda Prorok

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

Andrea L. Gallo, Denis E. Videla

In this work, given $(R,\frak m)$ a finite commutative local ring with identity and $k \in \mathbb{N}$ with $(k,|R|)=1$, we study the number of cliques of any size in the Cayley graph $G_R(k)=Cay(R,U_R(k))$ %and $W_R(k)=Cay(R,S_R(k))$ with $U_R(k)=\{x^k : x\in R^*\}$. Using the known fact that the graph $G_R(k)$ can be obtained by blowing-up the vertices of

Megan J. McCarthy, Jacob Startt, Rémi Dingreville, Aidan P. Thompson

The exceptional properties observed in complex concentrated alloys (CCAs) arise from the interplay between crystalline order and chemical disorder at the atomic scale, complicating a unique determination of properties. In contrast to conventional alloys, CCA properties emerge as distributions due to varying local chemical environments and the specific scale

Zedong Bi

A pervasive research protocol of cognitive neuroscience is to train subjects to perform deliberately designed experiments and record brain activity simultaneously, aiming to understand the brain mechanism underlying cognition. However, how the results of this protocol can be applied in technology is seldom discussed. Here, I review the studies on time proces

Kostas Vilkelis, Ady Stern, Anton Akhmerov

Orbital diamagnetism requires closed orbits according to the Liftshiftz-Kosevich theory. Therefore, one might expect that open Fermi surfaces do not have a diamagnetic response. Contrary to this expectation, we show that open orbits in finite systems do contribute a magnetic response which oscillates between diamagnetism and paramagnetism. The oscillations a

Nadav Gropper

Following the philosophy of arithmetic topology, we describe a point of view which helps look at surfaces and $p$-adic fields in a "uniform way", and show that results on mapping class groups can be extended to this point of view, and thus be applied to $G_{K}$, the absolute Galois groups of the $p$-adic field $K$. By moving both groups to the world of pro-$

Nevena Francetić, Mateja Šajna

We examine the necessary and sufficient conditions for a complete symmetric equipartite digraph $K_{n[m]}^\ast$ with $n$ parts of size $m$ to admit a resolvable decomposition into directed cycles of length $t$. We show that the obvious necessary conditions are sufficient for $m,n,t \ge 2$ in each of the following four cases: (i) $m(n-1)$ is even; (ii) $\gcd(

Uksang Yoo, Hanwen Zhao, Alvaro Altamirano, Wenzhen Yuan

Pneumatic soft robots present many advantages in manipulation tasks. Notably, their inherent compliance makes them safe and reliable in unstructured and fragile environments. However, full-body shape sensing for pneumatic soft robots is challenging because of their high degrees of freedom and complex deformation behaviors. Vision-based proprioception sensing

Yu-Wei Huang

A generalization of incidence relations in abstract polytope has been explored, and parameterized surfaces are used as primers. The abstract orientable incidence structure is defined as an algebraic model of incidence relations, in which some algebraic properties in abtract polytope theory are generalized. The geometric interpretation of abstract orientable

Karl Kreder, Shreekara Shastry

Nakamoto consensus has been incredibly influential in enabling robust blockchain systems, and one of its components is the so-called heaviest chain rule (HCR). Within this rule, the calculation of the weight of the chain tip is performed by adding the difficulty threshold value to the previous total difficulty. Current difficulty based weighting systems do n

Chiang-Mei Chen, Yi Chen, Akihiro Ishibashi, Nobuyoshi Ohta

We study the phase structure of quantum improved Schwarzschild-(A)dS black holes in asymptotically safe gravity. Our results confirm some of the well-known properties of quantum black holes. For example, the quantum effect provides a repulsive force in the core region near singularity which stabilizes the thermodynamically unstable small black holes, and als

Richard de Grijs, Doru Costache

We discuss David Bohm's dual contributions as a physicist and thinker. First, de Grijs introduces Bohm's universe, with an emphasis on the physical quest that led Bohm to the elaboration of an original cosmology at the nexus of science and philosophy. Next, Costache takes his cue from de Grijs' explorations by highlighting the affinity between Bohm's scienti

Felipe Manfio Barbosa, Fernando Santos Osório

One of the main paths towards the reduction of traffic accidents is the increase in vehicle safety through driver assistance systems or even systems with a complete level of autonomy. In these types of systems, tasks such as obstacle detection and segmentation, especially the Deep Learning-based ones, play a fundamental role in scene understanding for correc

Kiarash Banihashem, MohammadTaghi Hajiaghayi, Max Springer

Decision trees are widely used for their low computational cost, good predictive performance, and ability to assess the importance of features. Though often used in practice for feature selection, the theoretical guarantees of these methods are not well understood. We here obtain a tight finite sample bound for the feature selection problem in linear regress

Robert I McLachlan, David I McLaren, G R W Quispel

The main result of this paper is the discretization of Hamiltonian systems of the form $\ddot x = -K \nabla W(x)$, where $K$ is a constant symmetric matrix and $W\colon\mathbb{R}^n\to \mathbb{R}$ is a polynomial of degree $d\le 4$ in any number of variables $n$. The discretization uses the method of polarization and preserves both the energy and the invarian

T. Bhowmick, J. Seesing, K. Gustavsson, J. Guettler

The orientation of non-spherical particles in the atmosphere, such as volcanic ash and ice crystals, influences their residence times, and the radiative properties of the atmosphere. Here, we demonstrate experimentally that the orientation of heavy submillimeter spheroids settling in still air exhibits decaying oscillations, whereas it relaxes monotonically

Xingjian Li, Qipeng Liu, Angelos Pelecanos, Takashi Yamakawa

It is a long-standing open question to construct a classical oracle relative to which BQP/qpoly $\neq$ BQP/poly or QMA $\neq$ QCMA. In this paper, we construct classically-accessible classical oracles relative to which BQP/qpoly $\neq$ BQP/poly and QMA $\neq$ QCMA. Here, classically-accessible classical oracles are oracles that can be accessed only classical

Aranya Goswami, Sanchayeta R. Mudi, Connor Dempsey, Po Zhang

Superconductor-semiconductor nanowire hybrid structures are useful in fabricating devices for quantum information processing. While selective area growth (SAG) offers the flexibility to grow semiconductor nanowires in arbitrary geometries, in-situ evaporation of superconductors ensures pristine superconductor-semiconductor interfaces, resulting in strong ind

Ze-Hao Wu, Feiqi Deng, Pengyu Zeng, Hua-Cheng Zhou

In this paper, event-triggered active disturbance rejection control (ADRC) is first addressed for a class of uncertain random nonlinear systems driven by bounded noise and colored noise. The event-triggered extended state observer (ESO) and ADRC controller are designed, where two respective event-triggering mechanisms with a fixed positive lower bound for th

Akihiro Hashimoto, Yuta Murakami, Akihisa Koga

We study the Kitaev model with direction dependent interactions to investigate how the flux configuration and/or the anisotropy in the exchanges affect the Majorana excitations. Systematic numerical calculations demonstrate how the anisotropy of the exchange couplings and flux configuration make the Majorana excitation gapped. The induced gapped quantum spin

Andrew Warren

We investigate the stability of the Wasserstein distance, a metric structure on the space of probability measures arising from the theory of optimal transport, under metric ultralimits. We first show that if $(X_{i},d_{i})_{i\in\mathbb{N}}$ is a sequence of metric spaces with metric ultralimit $(\hat{X},\hat{d})$, then the p-Wasserstein space $(\mathcal{P}_{

Andrés G. Jirón Vicente, Luis B. Castro, Angel E. Obispo

We obtain the correct expressions for the energy and normalized eigenfunctions for a spin-zero relativistic quantum oscillator model under the violation of Lorentz symmetry defined by an arbitrary constant vector field $v^μ$.

J. S. C. Prentice

We determine the pointwise error in Hermite interpolation by numerically solving an appropriate differential equation, derived from the error term itself. We use this knowledge to approximate the error term by means of a polynomial, which is then added to the original Hermite polynomial to form a more accurate approximation. An example demonstrates that impr

Camila C. Soares, Angel E. Obispo, Andrés G. Jirón Vicente, Luis B. Castro

In the present work, the relativistic quantum motion of massless fermions in a helicoidal graphene nanoribbon under the influence of a uniform magnetic field is investigated. Considering a uniform magnetic field ($B$) aligned along the axis of helicoid, this problem is explored in the context of Dirac equation in a curved space-time. As this system does not

Gunnar Kudrjavets, Nachiappan Nagappan, Ayushi Rastogi

This paper investigates how the duration of various code review periods changes over a projects' lifetime. We study four open-source software (OSS) projects: Blender, FreeBSD, LLVM, and Mozilla. We mine and analyze the characteristics of 283,235 code reviews that cover, on average, seven years' worth of development. Our main conclusion is that neither the pa

Mojtaba Taherisadr, Mohammad Abdullah Al Faruque, Salma Elmalaki

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

Cindy M. Nguyen, Eric R. Chan, Alexander W. Bergman, Gordon Wetzstein

Capturing images is a key part of automation for high-level tasks such as scene text recognition. Low-light conditions pose a challenge for high-level perception stacks, which are often optimized on well-lit, artifact-free images. Reconstruction methods for low-light images can produce well-lit counterparts, but typically at the cost of high-frequency detail

Yanqing Wang, Otto Chkhetiani

In this paper, by exploiting the feature of the Hall term, we establish some local version four-thirds laws for the dissipation rates of energy and magnetic helicity in both electron and Hall magnetohydrodynamic equations in the sense of Duchon-Robert type. New 4/3 laws for the dissipation rates of magnetic helicity in these systems are first observed and fo

Atli Thor Sigurgeirsson, Simon King

Some recent models for Text-to-Speech synthesis aim to transfer the prosody of a reference utterance to the generated target synthetic speech. This is done by using a learned embedding of the reference utterance, which is used to condition speech generation. During training, the reference utterance is identical to the target utterance. Yet, during synthesis,

Stephen R. Piccolo, Paul Denny, Andrew Luxton-Reilly, Samuel Payne

Computer programming is a fundamental tool for life scientists, allowing them to carry out many essential research tasks. However, despite a variety of educational efforts, learning to write code can be a challenging endeavor for both researchers and students in life science disciplines. Recent advances in artificial intelligence have made it possible to tra

Jamil Arbas, Hassan Ashtiani, Christopher Liaw

We study the problem of privately estimating the parameters of $d$-dimensional Gaussian Mixture Models (GMMs) with $k$ components. For this, we develop a technique to reduce the problem to its non-private counterpart. This allows us to privatize existing non-private algorithms in a blackbox manner, while incurring only a small overhead in the sample complexi

Naoya Kitajima, Kazunori Nakayama

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

Chanwoo Lee

We address the problem of sufficient dimension reduction for feature matrices, which arises often in sensor network localization, brain neuroimaging, and electroencephalography analysis. In general, feature matrices have both row- and column-wise interpretations and contain structural information that can be lost with naive vectorization approaches. To addre

Sergey Shuvaev, Evgeny Amelchenko, Dmitry Smagin, Natalia Kudryavtseva

Social hierarchy in animal groups carries a crucial adaptive function by reducing conflict and injury while protecting valuable group resources. Social hierarchy is dynamic and can be altered by social conflict, agonistic interactions, and aggression. Understanding social conflict and aggressive behavior is of profound importance to our society and welfare.

Shyam Sundar Kannan, Vishnunandan L. N. Venkatesh, Revanth Krishna Senthilkumaran, Byung-Cheol Min

In this paper, a new demonstration-based path-planning framework for the visual inspection of large structures using UAVs is proposed. We introduce UPPLIED: UAV Path PLanning for InspEction through Demonstration, which utilizes a demonstrated trajectory to generate a new trajectory to inspect other structures of the same kind. The demonstrated trajectory can

Francesco Fabiano, Vishal Pallagani, Marianna Bergamaschi Ganapini, Lior Horesh

The concept of Artificial Intelligence has gained a lot of attention over the last decade. In particular, AI-based tools have been employed in several scenarios and are, by now, pervading our everyday life. Nonetheless, most of these systems lack many capabilities that we would naturally consider to be included in a notion of "intelligence". In this work, we

Ricardo Carrizo Vergara

In this work we study the self-integral of a function-measure kernel and its importance on stochastic integration. A continuous-function measure kernel $K$ over $D \subset \mathbb{R}^{d}$ is a function of two variables which acts as a continuous function in the first variable and as a real Radon measure in the second. Some analytical properties of such kerne

Hao Huang, Katherine R. Davis, H. Vincent Poor

The long-term resilient property of ecosystems has been quantified as ecological robustness (RECO) in terms of the energy transfer over food webs. The RECO of resilient ecosystems favors a balance of food webs' network efficiency and redundancy. By integrating RECO with power system constraints, the authors are able to optimize power systems' inherent resili

Subramanian Ramasamy, Md Safwan Mondal, Pranav A. Bhounsule

Fast moving but power hungry unmanned aerial vehicles (UAVs) can recharge on slow-moving unmanned ground vehicles (UGVs) to survey large areas in an effective and efficient manner. In order to solve this computationally challenging problem in a reasonable time, we created a two-level optimization heuristics. At the outer level, the UGV route is parameterized

Alphonsus Adu-Bredu, Grant Gibson, Jessy W. Grizzle

For bipedal humanoid robots to successfully operate in the real world, they must be competent at simultaneously executing multiple motion tasks while reacting to unforeseen external disturbances in real-time. We propose Kinodynamic Fabrics as an approach for the specification, solution and simultaneous execution of multiple motion tasks in real-time while be

Vinu Sankar Sadasivan, Mahdi Soltanolkotabi, Soheil Feizi

Large-scale training of modern deep learning models heavily relies on publicly available data on the web. This potentially unauthorized usage of online data leads to concerns regarding data privacy. Recent works aim to make unlearnable data for deep learning models by adding small, specially designed noises to tackle this issue. However, these methods are vu

T. M. Kamsma, W. Q. Boon, C. Spitoni, R. van Roij

Conical channels filled with an aqueous electrolyte have been proposed as promising candidates for iontronic neuromorphic circuits. This is facilitated by a novel analytical model for the internal channel dynamics [Kamsma et al., arXiv:2301.06158, 2023], the relative ease of fabrication of conical channels, and the wide range of achievable memory retention t

Philippe Di Francesco, Rinat Kedem

This note summarizes certain properties common to Macdonald, Koornwinder and Arthamonov-Shakirov $q$-difference operators, relating to the duality or bi-spectrality properties of their eigenfunctions. This results in Pieri operators which, in the $q$-Whittaker limit, are relativistic difference Toda type Hamiltonians which have a related quantum cluster alge

Gaith Rjoub, Jamal Bentahar, Omar Abdel Wahab, Rabeb Mizouni

The black-box nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field o

Arunabha M. Roy, Jayabrata Bhaduri

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

Xin Yuan, Wei Ni, Ming Ding, Kang Wei

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

Grigorios Papigkiotis, George Pappas

As some of the most compact stellar objects in the universe, neutron stars are unique cosmic laboratories. The study of neutron stars provides an ideal theoretical testbed for investigating both physics at supra-nuclear densities as well as fundamental physics. Their global astrophysical properties however depend strongly on the star's internal structure, wh

Somayeh Aghashahi, Zolfa Zeinalpour-Yazdi, Aliakbar Tadaion, Mahdi Boloursaz Mashhadi

In this letter, we investigate the signal-to-interference-plus-noise-ratio (SINR) maximization problem in a multi-user massive multiple-input-multiple-output (massive MIMO) system enabled with multiple reconfigurable intelligent surfaces (RISs). We examine two zero-forcing (ZF) beamforming approaches for interference management namely BS-UE-ZF and BS-RIS-ZF

Yupeng Yang, Yiwei Lyu, Wenhao Luo

In this paper, we consider a team of mobile robots executing simultaneously multiple behaviors by different subgroups, while maintaining global and subgroup line-of-sight (LOS) network connectivity that minimally constrains the original multi-robot behaviors. The LOS connectivity between pairwise robots is preserved when two robots stay within the limited co

Gabriel T. Landi, Michael J. Kewming, Mark T. Mitchison, Patrick P. Potts

Continuously measured quantum systems are characterized by an output current, in the form of a stochastic and correlated time series which conveys crucial information about the underlying quantum system. The many tools used to describe current fluctuations are scattered across different communities: quantum opticians often use stochastic master equations, wh

Javad Manashti, Pouyan Pirnia, Alireza Manashty, Sahar Ujan

This project aimed to determine the grain size distribution of granular materials from images using convolutional neural networks. The application of ConvNet and pretrained ConvNet models, including AlexNet, SqueezeNet, GoogLeNet, InceptionV3, DenseNet201, MobileNetV2, ResNet18, ResNet50, ResNet101, Xception, InceptionResNetV2, ShuffleNet, and NASNetMobile w

Mustafa O. Karabag, Ufuk Topcu

Offline reinforcement learning (offline RL) considers problems where learning is performed using only previously collected samples and is helpful for the settings in which collecting new data is costly or risky. In model-based offline RL, the learner performs estimation (or optimization) using a model constructed according to the empirical transition frequen

Harrison Jesse Smith, Qingyuan Zheng, Yifei Li, Somya Jain

Children's drawings have a wonderful inventiveness, creativity, and variety to them. We present a system that automatically animates children's drawings of the human figure, is robust to the variance inherent in these depictions, and is simple and straightforward enough for anyone to use. We demonstrate the value and broad appeal of our approach by building

Dale R. Worley

Birkhoff's representation theorem for finite distributive lattices states that any finite distributive lattice is isomorphic to the lattice of order ideals (lower sets) of the partial order of the join-irreducible elements of the lattice. This theorem can be extended as follows: A non-finite distributive lattice that is locally finite and has a $\hat{0}$ is

Xiaoyu Ma, Negar Mehr

It is known that autonomous cars can increase road capacities by maintaining a smaller headway through vehicle platooning. Recent works have shown that these capacity increases can influence vehicles' route choices in unexpected ways similar to the well-known Braess's paradox, such that the network congestion might increase. In this paper, we propose that in

Javad Manashti, François Duhaime, Matthew F. Toews, Pouyan Pirnia

This study aims to evaluate PSDNet, a series of convolutional neural networks (ConvNets) trained with photographs to predict the particle size distribution of granular materials. Nine traditional feature extraction methods and 15 pretrained ConvNets were also evaluated and compared. A dataset including 9600 photographs of 15 different granular materials was

Xuemin Shen, Jie Gao, Mushu Li, Conghao Zhou

The sixth generation (6G) networks are expected to enable immersive communications and bridge the physical and the virtual worlds. Integrating extended reality, holography, and haptics, immersive communications will revolutionize how people work, entertain, and communicate by enabling lifelike interactions. However, the unprecedented demand for data transmis

Elijah Bodish, Daniel Tubbenhauer

We prove a nonsemisimple quantum version of Howe's duality with the rank 2n symplectic and the rank 2 special linear group acting on the exterior algebra of type C. We also discuss the first steps towards the symplectic analog of harmonic analysis on quantum spheres, give character formulas for various fundamental modules, and construct canonical bases of th

Miloslav Znojil

The classical Coriolis force finds its quantum analogue in the difference $\Sigma(t)=H(t)-G(t)$ where the ``true'', observable Hamiltonian $H(t)$ represents the instantaneous energy. The other, ``false'' Hamiltonian $G(t)$ generates the time-evolution of wave functions. Whenever $\Sigma(t)\neq 0$, quantum mechanics acquires an interaction-picture form. Then,

Elias Andrade, Florentino López-Urías, Gerardo G. Naumis

Flat-bands play a central role in the presence of correlated phases in Moir\'e and other modulated two dimensional systems. In this work, flat-bands are shown to exist in uniaxially periodic strained graphene. Such strain should be produced for example by a substrate. The model is thus mapped into a one-dimensional effective Hamiltonian and this allows to fi

Yujin Cho, Kristin M. Beck, Alessandro R. Castelli, Kyle A. Wendt

Advanced simulations and calculations on quantum computers require high-fidelity implementations of quantum operations. The universal gateset approach builds complex unitaries from a small set of primitive gates, often resulting in a long gate sequence which is typically a leading factor in the total accumulated error. Compiling a complex unitary for process

Krzysztof Szczygielski

We propose and explore a notion of decomposably divisible (D-divisible) differentiable quantum evolution families on matrix algebras. This is achieved by replacing the complete positivity requirement, imposed on the propagator, by more general condition of decomposability. It is shown that such D-divisible dynamical maps satisfy a generalized version of Mast

Stephanie Long, Tibor Schuster, Alexandre Piché

Building causal graphs can be a laborious process. To ensure all relevant causal pathways have been captured, researchers often have to discuss with clinicians and experts while also reviewing extensive relevant medical literature. By encoding common and medical knowledge, large language models (LLMs) represent an opportunity to ease this process by automati

Sashikanta Mohapatra, Ajit C. Balram

Experimental observation of coherent oscillations in a Rydberg atom chain [Bernien et al., Nature 551, 579 (2017)] has led to the discovery of quantum many-body scars (QMBS) which is a new paradigm for ergodicity-breaking. The experimental findings in the Rydberg chain can be well captured by a kinetically constrained model called the "PXP" model, which has

Johannes Lederer, Marco Oesting

Extreme value theory for univariate and low-dimensional observations has been explored in considerable detail, but the field is still in an early stage regarding high-dimensional settings. This paper focuses on H\"usler-Reiss models, a popular class of models for multivariate extremes similar to multivariate Gaussian distributions, and their domain of attrac

Mojtaba Taherisadr, Stelios Andrew Stavroulakis, Salma Elmalaki

Reinforcement learning (RL) presents numerous benefits compared to rule-based approaches in various applications. Privacy concerns have grown with the widespread use of RL trained with privacy-sensitive data in IoT devices, especially for human-in-the-loop systems. On the one hand, RL methods enhance the user experience by trying to adapt to the highly dynam

Z. Sukurma, M. Schlipf, M. Humer, A. Taheridehkordi

We report a scalable Fortran implementation of the phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) and demonstrate its excellent performance and beneficial scaling with respect to system size. Furthermore, we investigate modifications of the phaseless approximation that can help to reduce the overcorrelation problems common to the ph-AFQMC. We apply

Chenyang Gao, Yue Gu, Francesco Caliva, Yuzong Liu

Self-supervised speech representation learning (S3RL) is revolutionizing the way we leverage the ever-growing availability of data. While S3RL related studies typically use large models, we employ light-weight networks to comply with tight memory of compute-constrained devices. We demonstrate the effectiveness of S3RL on a keyword-spotting (KS) problem by us

É. J. Harvey, E. Aydi, L. Izzo, C. Morisset

V906 Carinae was one of the best observed novae of recent times. It was a prolific dust producer and harboured shocks in the early evolving ejecta outflow. Here, we take a close look at the consequences of these early interactions through study of high-resolution UVES spectroscopy of the nebular stage and extrapolate backwards to investigate how the final st

Lijing Zhu, Qizhen Lan, Alvaro Velasquez, Houbing Song

Detecting human-object interactions (HOIs) is an intricate challenge in the field of computer vision. Existing methods for HOI detection heavily rely on appearance-based features, but these may not fully capture all the essential characteristics necessary for accurate detection. To overcome these challenges, we propose an innovative graph-based approach call

Shuhei Ohyama, Yuji Terashima, Ken Shiozaki

A $1$-parameter family of invertible states gives a topological transport phenomenon, similar to the Thouless pumping. As a natural generalization of this, we can consider a family of invertible states parametrized by some topological space $X$. This is called a higher pump. It is conjectured that $(1+1)$-dimensional bosonic invertible state parametrized by

Xiangmin Jiao

Linear systems in applications are typically well-posed, and yet the coefficient matrices may be nearly singular in that the condition number $\kappa(\boldsymbol{A})$ may be close to $1/\varepsilon_{w}$, where $\varepsilon_{w}$ denotes the unit roundoff of the working precision. It is well known that iterative refinement (IR) can make the forward error indep

Paul M. Donaldson, Greg M. Greetham, Chris T. Middleton, Brad M. Luther

Ultrafast spectroscopy and imaging have become tools utilized by a broad range of scientists involved in materials, energy, biological, and chemical sciences. Commercialization of ultrafast spectrometers including transient absorption spectrometers, vibrational sum frequency generation spectrometers, and even multidimensional spectrometers have put these adv

Brandon Clark, Alec Kerrigan, Parth Parag Kulkarni, Vicente Vivanco Cepeda

Determining the exact latitude and longitude that a photo was taken is a useful and widely applicable task, yet it remains exceptionally difficult despite the accelerated progress of other computer vision tasks. Most previous approaches have opted to learn a single representation of query images, which are then classified at different levels of geographic gr

David Berthelot, Arnaud Autef, Jierui Lin, Dian Ang Yap

Denoising Diffusion models have demonstrated their proficiency for generative sampling. However, generating good samples often requires many iterations. Consequently, techniques such as binary time-distillation (BTD) have been proposed to reduce the number of network calls for a fixed architecture. In this paper, we introduce TRAnsitive Closure Time-distilla