May 2023 arXiv papers — page 165

Hasan Al Maruf, Mosharaf Chowdhury

Compute and memory are tightly coupled within each server in traditional datacenters. Large-scale datacenter operators have identified this coupling as a root cause behind fleet-wide resource underutilization and increasing Total Cost of Ownership (TCO). With the advent of ultra-fast networks and cache-coherent interfaces, memory disaggregation has emerged a

Wenxuan Zhou, Bowen Jiang, Fan Yang, Chris Paxton

Manipulating objects without grasping them is an essential component of human dexterity, referred to as non-prehensile manipulation. Non-prehensile manipulation may enable more complex interactions with the objects, but also presents challenges in reasoning about gripper-object interactions. In this work, we introduce Hybrid Actor-Critic Maps for Manipulatio

Hashini Gunatilake, John Grundy, Ingo Mueller, Rashina Hoda

Empathy is widely used in many disciplines such as philosophy, sociology, psychology, health care. Ability to empathise with software end-users seems to be a vital skill software developers should possess. This is because engineering successful software systems involves not only interacting effectively with users but also understanding their true needs. Empa

Kaan Akyuz, Boris Skoric

Six-state Quantum Key Distribution (QKD) achieves the highest key rate in the class of qubit-based QKD schemes. The standard security proof, which has been developed since 2005, invokes complicated theorems involving smooth Renyi entropies. In this paper we present a simpler security proof for 6-state QKD that entirely avoids Renyi entropies. This is achieve

Guanjie Wang, Smita Sahu, Qifeng Liao

It is known that standard stochastic Galerkin methods encounter challenges when solving partial differential equations with high-dimensional random inputs, which are typically caused by the large number of stochastic basis functions required. It becomes crucial to properly choose effective basis functions, such that the dimension of the stochastic approximat

Nachuan Xiao, Xiaoyin Hu, Xin Liu, Kim-Chuan Toh

In this paper, we present a comprehensive study on the convergence properties of Adam-family methods for nonsmooth optimization, especially in the training of nonsmooth neural networks. We introduce a novel two-timescale framework that adopts a two-timescale updating scheme, and prove its convergence properties under mild assumptions. Our proposed framework

Anastasia Razdaibiedina, Yuning Mao, Rui Hou, Madian Khabsa

Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-trained language models. Despite being arguably the most parameter-efficient (tuned soft prompts constitute <0.1% of total parameters), it typically performs worse than other efficient tuning methods and is quite sensitive to hyper-parameters. In this work, we introduce R

Haoran Zhang, Jianlong Yang, Ce Zheng, Shiqing Zhao

Deep learning has been successfully applied to OCT segmentation. However, for data from different manufacturers and imaging protocols, and for different regions of interest (ROIs), it requires laborious and time-consuming data annotation and training, which is undesirable in many scenarios, such as surgical navigation and multi-center clinical trials. Here w

Kaiwen Zheng, Cheng Lu, Jianfei Chen, Jun Zhu

Diffusion models have exhibited excellent performance in various domains. The probability flow ordinary differential equation (ODE) of diffusion models (i.e., diffusion ODEs) is a particular case of continuous normalizing flows (CNFs), which enables deterministic inference and exact likelihood evaluation. However, the likelihood estimation results by diffusi

Jason Kim, Daniel Genkin, Kevin Leach

A binary's behavior is greatly influenced by how the compiler builds its source code. Although most compiler configuration details are abstracted away during compilation, recovering them is useful for reverse engineering and program comprehension tasks on unknown binaries, such as code similarity detection. We observe that previous work has thoroughly explor

Zhen Wang, Sen Zhu

Let $(X,\mathcal{B},\mu)$ be a measure space and $A$ be a norm closed subalgebra of $\mathcal{B}(L^p(X,\mu))$, where $p\in [1,\infty)$. Let $(G,A,\alpha)$ be an $L^p$-operator algebra dynamical system, where $G$ is a countable discrete amenable group. We prove that the full $L^p$-operator crossed product $F^p(G,A,\alpha)$ is $p$-nuclear if and only if $A$ is

Isaac Harris, Thu Le, Dinh-Liem Nguyen

This short paper is concerned with the numerical reconstruction of small sources from boundary Cauchy data for a single frequency. We study a sampling method to determine the location of small sources in a very fast and robust way. Furthermore, the method can also compute the intensity of point sources provided that the sources are well separated. A simple j

I. S. Krasil'shchik, O. I. Morozov

We find two one-parametric families of recursion operators and use them to construct higher symmetries for the Calogero--Bogoyavlenskii--Schiff breaking soliton equation. Then we prove that the recursion operators from the first family pair-wise commute with respect to the Nijenhuis bracket (are compatible).

Byeong-Gil Choe, Hyeong-Kwan Ju

In this paper we obtained several properties that the characteristic polynomials of the unit-primitive matrix satisfy. In addition, using these properties we have shown that the recurrence relation given as in the formula (1) is true. In fact, Xin and Zhong([4]) showed it earlier. However, we provide simpler method here.

Jaemin Lee, Jeeseop Kim, Aaron D. Ames

The safety-critical control of robotic systems often must account for multiple, potentially conflicting, safety constraints. This paper proposes novel relaxation techniques to address safety-critical control problems in the presence of conflicting safety conditions. In particular, Control Barrier Function (CBFs) provide a means to encode safety as constraint

Zongwei Li, Dechao Kong, Yuanzheng Niu, Hongli Peng

With the widespread attention and application of artificial intelligence (AI) and blockchain technologies, privacy protection techniques arising from their integration are of notable significance. In addition to protecting privacy of individuals, these techniques also guarantee security and dependability of data. This paper initially presents an overview of

Filippo Calderoni, Adam Clay

We develop new tools to analyze the complexity of the conjugacy equivalence relation $E_\mathsf{lo}(G)$, whenever $G$ is a left-orderable group. Our methods are used to demonstrate non-smoothness of $E_\mathsf{lo}(G)$ for certain groups $G$ of dynamical origin, such as certain amalgams constructed from Thompson's group $F$. We also initiate a systematic anal

Arindam Fadikar, Mickael Binois, Nicholson Collier, Abby Stevens

Epidemiological models must be calibrated to ground truth for downstream tasks such as producing forward projections or running what-if scenarios. The meaning of calibration changes in case of a stochastic model since output from such a model is generally described via an ensemble or a distribution. Each member of the ensemble is usually mapped to a random n

Hua Zheng, Wei Xie, Paul Whitford, Ailun Wang

RNA structure and functional dynamics play fundamental roles in controlling biological systems. Molecular dynamics simulation, which can characterize interactions at an atomistic level, can advance the understanding on new drug discovery, manufacturing, and delivery mechanisms. However, it is computationally unattainable to support the development of a digit

Ying Zhu, Boris Semisalov, Giorgio Krstulovic, Sergey Nazarenko

We study the universal non-stationary evolution of wave turbulence (WT) in Bose-Einstein condensates (BECs). Their temporal evolution can exhibit different kinds of self-similar behavior corresponding to a large-time asymptotic of the system or to a finite-time blowup. We identify self-similar regimes in BECs by numerically simulating the forced and unforced

Thuy-Trang Vu, Shahram Khadivi, Mahsa Ghorbanali, Dinh Phung

Acquiring new knowledge without forgetting what has been learned in a sequence of tasks is the central focus of continual learning (CL). While tasks arrive sequentially, the training data are often prepared and annotated independently, leading to the CL of incoming supervised learning tasks. This paper considers the under-explored problem of active continual

Jie Liu, Xiaoqing Ou, Jiawei Chen

In this paper, we propose a penalty dual-primal augmented lagrangian method for solving convex minimization problems under linear equality or inequality constraints. The proposed method combines a novel penalty technique with updates the new iterates in a dual-primal order, and then be extended to solve multiple-block separable convex programming problems wi

Yufeng Huang, Jiji Tang, Zhuo Chen, Rongsheng Zhang

Large-scale vision-language pre-training has achieved significant performance in multi-modal understanding and generation tasks. However, existing methods often perform poorly on image-text matching tasks that require structured representations, i.e., representations of objects, attributes, and relations. As illustrated in Fig.~reffig:case (a), the models ca

Tingyin Ning, Yingying Ren, Yanyan Huo, Yangjian Cai

Photonic moir\'e superlattice as an emerging platform of flatbands can tightly confine the light inside the cavity and has important applications not only in linear optics but also in nonlinear optics. In this paper, we numerically investigate the third- and fifth-order harmonic generation (THG and FHG) in photonic moir\'e superlattices fabricated by the non

Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang

Among various region embedding methods, graph-based region relation learning models stand out, owing to their strong structure representation ability for encoding spatial correlations with graph neural networks. Despite their effectiveness, several key challenges have not been well addressed in existing methods: i) Data noise and missing are ubiquitous in ma

Yuwen Heng, Srinandan Dasmahapatra, Hansung Kim

The objective of dense material segmentation is to identify the material categories for every image pixel. Recent studies adopt image patches to extract material features. Although the trained networks can improve the segmentation performance, their methods choose a fixed patch resolution which fails to take into account the variation in pixel area covered b

Edmond Jonckheere, Sophie G. Schirmer, Frank C. Langbein, Carrie A. Weidner

A method to quantify robust performance for situations where structured parameter variations and initial state errors rather than extraneous disturbances are the main performance limiting factors is presented. The approach is based on the error dynamics, the difference between nominal and perturbed dynamics, driven by either the unperturbed or perturbed stat

Alejandro R. Urzúa, Héctor M. Moya-Cessa

We study the decaying dynamics in the mirror-field interaction by means of the intrinsic decoherence scheme. Factorization of the mirror-field Hamiltonian with the use of displacement operators, allows us to calculate the explicit solution to Milburn's equation for arbitrary initial conditions. We show expectation values, correlations, and Husimi functions f

Jyoti Prakash, Abhishek Tiwari, Christian Hammer

Modular analysis of polyglot applications is challenging because heap object flows across language boundaries must be resolved. The state-of-the-art analyses for polyglot applications have two fundamental limitations. First, they assume explicit boundaries between the host and the guest language to determine inter-language dataflows. Second, they rely on spe

Mithun Das, Rohit Raj, Punyajoy Saha, Binny Mathew

Hate speech has become one of the most significant issues in modern society, having implications in both the online and the offline world. Due to this, hate speech research has recently gained a lot of traction. However, most of the work has primarily focused on text media with relatively little work on images and even lesser on videos. Thus, early stage aut

Kaushik Moudgalya, Ankit Ramakrishnan, Vamsikrishna Chemudupati, Xing Han Lu

Code based Language Models (LMs) have shown very promising results in the field of software engineering with applications such as code refinement, code completion and generation. However, the task of time and space complexity classification from code has not been extensively explored due to a lack of datasets, with prior endeavors being limited to Java. In t

Hua Lan, Jinjie Hu, Zengfu Wang, Qiang Cheng

Motivated by the maneuvering target tracking with sensors such as radar and sonar, this paper considers the joint and recursive estimation of the dynamic state and the time-varying process noise covariance in nonlinear state space models. Due to the nonlinearity of the models and the non-conjugate prior, the state estimation problem is generally intractable

Zachary J. Wegert, Anthony P. Roberts, Vivien J. Challis

We present an extension of the projection method proposed by Challis et al. (Int J Solids Struct 45(14$\unicode{x2013}$15):4130$\unicode{x2013}$4146, 2008) for constrained level set-based topology optimisation that harnesses the Hilbertian velocity extension-regularisation framework. Our Hilbertian projection method chooses a normal velocity for the level se

Muhammad Noor Dwi Eldianto, Muhammad Febrian Rachmadi, Wisnu Jatmiko

White Matter Hyperintensities (WMH) are areas of the brain that have higher intensity than other normal brain regions on Magnetic Resonance Imaging (MRI) scans. WMH is often associated with small vessel disease in the brain, making early detection of WMH important. However, there are two common issues in the detection of WMH: high ambiguity and difficulty in

Chaojie Mo, Richard Johnston, Luciano Navarini, Furio Suggi Liverani

Coffee extraction involves many complex physical and transport processes extremely difficult to model. Among the many factors that will affect the final quality of coffee, the microstructure of the coffee matrix is one of the most critical ones. In this article, we use X-ray micro-computed (microCT) technique to capture the microscopic details of coffee matr

Sylvain Crovisier, Xiaodong Wang, Dawei Yang, Jinhua Zhang

Bonatti and da Luz have introduced the class of \emph{multi-singular hyperbolic} vector fields to characterize systems whose periodic orbits and singularities do not bifurcate under perturbation (called star vector fields). In this paper, we study the Sina\"{\i}-Ruelle-Bowen measures for multi-singular hyperbolic vector fields: in a $C^1$ open and $C^1$ dens

Yuanfei Gao, Zhiliang Yuan

Quantum key distribution (QKD) is an attractive technology for distributing secret encryption keys between distant users. The decoy-state technique has drastically improved its practicality and performance, and has been widely adopted in commercial systems. However, conventional intensity modulators can introduce security side channels in high speed QKD syst

Ying-nan Mao, Kechen Wang, Zeren Simon Wang

Two classes of far detectors have been proposed or are under operation at the LHC. The first class is a series of neutrino detectors that are sensitive to light active neutrinos via either charged-current or neutral-current interactions; exemplary ideas are FASER$\nu$, SND@LHC, and FLArE. Another type aims primarily at looking for displaced decays of long-li

Bolin Lai, Fiona Ryan, Wenqi Jia, Miao Liu

Egocentric gaze anticipation serves as a key building block for the emerging capability of Augmented Reality. Notably, gaze behavior is driven by both visual cues and audio signals during daily activities. Motivated by this observation, we introduce the first model that leverages both the video and audio modalities for egocentric gaze anticipation. Specifica

Jing Yang, Wei Yang

Subresultant is a powerful tool for developing various algorithms in computer algebra. Subresultants for polynomials in standard basis (i.e., power basis) have been well studied so far. With the popularity of basis-preserving algorithms, resultants and subresultants in non-standard basis are drawing more and more attention. In this paper, we develop a formul

Teruyuki Kitabayashi

Although the hybrid natural inflation is a successful inflation model, the symmetry breaking scale in the model should be large for a negative value of the running of the scalar spectral index. This study proposed a generalized hybrid natural inflation model that can realize a low-scale inflation with a negative value of the running of the scalar spectral in

Geng Chen, Tao Huang, Xiang Xu

In this paper, we prove the singularity formation for Poiseuille laminar flow of full Ericksen-Leslie system modeling nematic liquid crystal flows in dimension two. The singularity is due to the geometric effect at the origin.

Prasanth Chakka, Saurabh Joshi, Aniket Kate, Joshua Tobkin

Oracle networks feeding off-chain information to a blockchain are required to solve a distributed agreement problem since these networks receive information from multiple sources and at different times. We make a key observation that in most cases, the value obtained by oracle network nodes from multiple information sources are in close proximity. We define

Xiaoyu Guo, Xiang Wei, Qi Su, Huiqin Zhao

Semantic segmentation in rainy scenes is a challenging task due to the complex environment, class distribution imbalance, and limited annotated data. To address these challenges, we propose a novel framework that utilizes semi-supervised learning and pre-trained segmentation foundation model to achieve superior performance. Specifically, our framework levera

Taofeng Xie, Chentao Cao, Zhuoxu Cui, Yu Guo

MRI and PET are crucial diagnostic tools for brain diseases, as they provide complementary information on brain structure and function. However, PET scanning is costly and involves radioactive exposure, resulting in a lack of PET. Moreover, simultaneous PET and MRI at ultra-high-field are currently hardly infeasible. Ultra-high-field imaging has unquestionab

Ou Wu

Imbalance learning is a subfield of machine learning that focuses on learning tasks in the presence of class imbalance. Nearly all existing studies refer to class imbalance as a proportion imbalance, where the proportion of training samples in each class is not balanced. The ignorance of the proportion imbalance will result in unfairness between/among classe

Shuai Bian, Shouliang Qi, Chen Li, Yudong Yao

Deep learning has been applied to compressive sensing (CS) of images successfully in recent years. However, existing network-based methods are often trained as the black box, in which the lack of prior knowledge is often the bottleneck for further performance improvement. To overcome this drawback, this paper proposes a novel CS method using non-local prior

Huaiyu Jian, Xianduo Wang

A number of geometric problems, including affine hyperbolic spheres, Hilbert metrics and Minkowski type problems, are reduced to a singular Monge-Amp\`ere equation which can be written locally as a class of Monge-Amp\`ere equations with singularity at boundary. We estimate the boundary derivatives of all orders for the solutions to the class of singular equa

M. V. Dolgopolik

This two-part study is devoted to the analysis of the so-called exact augmented Lagrangians, introduced by Di Pillo and Grippo for finite dimensional optimization problems, in the case of optimization problems in Hilbert spaces. In the second part of our study we present applications of the general theory of exact augmented Lagrangians to several constrained

Frank M. Lee, B. A. Shadwick

We present a novel method for solving the linearized Vlasov--Poisson equation, based on analyticity properties of the equilibrium and initial condition through Cauchy-type integrals, that produces algebraic expressions for the distribution and field, i.e., the solution is expressed without integrals. Standard extant approaches involve deformations of the Bro

Changlin Yang, Alexei Ashikhmin, Xiaodong Wang, Zibin Zheng

A key constraint that limits the implementation of blockchain in Internet of Things (IoT) is its large storage requirement resulting from the fact that each blockchain node has to store the entire blockchain. This increases the burden on blockchain nodes, and increases the communication overhead for new nodes joining the network since they have to copy the e

Yafen Ye, Zhihu Xu, Jinhua Zhang, Weijie Chen

We propose a twin support vector quantile regression (TSVQR) to capture the heterogeneous and asymmetric information in modern data. Using a quantile parameter, TSVQR effectively depicts the heterogeneous distribution information with respect to all portions of data points. Correspondingly, TSVQR constructs two smaller sized quadratic programming problems (Q

Helen Shang, Yi Ding, Vidhya Venkateswaran, Kristin Boulier

Polygenic risk scores (PRS) summarize the combined effect of common risk variants and are associated with breast cancer risk in patients without identifiable monogenic risk factors. One of the most well-validated PRSs in breast cancer to date is PRS313, which was developed from a Northern European biobank but has shown attenuated performance in non-European

Zongyuan Yang, Baolin Liu, Yongping Xiong, Lan Yi

Removing degradation from document images not only improves their visual quality and readability, but also enhances the performance of numerous automated document analysis and recognition tasks. However, existing regression-based methods optimized for pixel-level distortion reduction tend to suffer from significant loss of high-frequency information, leading

Haowei Li, Haojie Wu, Wei Zheng, Wei Yi

We study an atom-cavity hybrid system where fermionic atoms in a one-dimensional lattice are subject to a cavity-induced dynamic gauge potential. The gauge coupling leads to highly-degenerate steady states in which the fermions accumulate to one edge of the lattice under an open boundary condition. Such a phenomenon originates from the many-body Liouvillian

Hrushikesh Mhaskar

For the past 30 years or so, machine learning has stimulated a great deal of research in the study of approximation capabilities (expressive power) of a multitude of processes, such as approximation by shallow or deep neural networks, radial basis function networks, and a variety of kernel based methods. Motivated by applications such as invariant learning,

T. Wang, C. Jiang, Q. Fang, X. Guo

Nonlinear photonic sources including semiconductor lasers have recently been utilized as ideal computation elements for information processing. They supply energy-efficient way and rich dynamics for classification and recognition tasks. In this work, we propose and numerically study the dynamics of complex photonic systems including high-$\beta$ laser elemen

Rong Chen

In 2012, L\'ev\^eque, Maffray, and Trotignon conjectured that each graph $G$ that contains no induced subdivision of $K_4$ is $4$-colorable. In this paper, we prove that this conjecture holds when $G$ contains a $K_{1,2,3}$.

Zijian Wang, Shuo Huang, Yujin Huang, Helei Cui

In recent years, on-device deep learning has gained attention as a means of developing affordable deep learning applications for mobile devices. However, on-device models are constrained by limited energy and computation resources. In the mean time, a poisoning attack known as sponge poisoning has been developed.This attack involves feeding the model with po

Valeri P. Frolov, Pavel Krtous, Andrei Zelnikov

In the present paper we discuss properties of a model of a ring wormhole, recently proposed by Gibbons and Volkov. Such a wormhole connects two flat spacetimes which are glued through discs of the radius $a$ bounded by the string with negative angle deficit $-2\pi$. The presence of the string's matter violating null energy condition makes the wormhole static

M. Manikandan, A. Ghosh, R. Mahendiran

Polycrystalline insulating ferromagnetic double perovskite La2CoMnO6 possessing monoclinic structure and a high ferromagnetic Curie temperature (TC = 222 K) was rapidly synthesized ( 30 min) by irradiating stoichiometric mixture of oxides with the microwave. The sample exhibits negative magnetostriction, i.e., contraction of length along the magnetic field d

Brenda B. Malabarba, K. P. Khemchandani, A. Martinez Torres

In this work we calculate the decay widths of $\phi(2170)$ to $\phi\eta$ and $\phi\eta^\prime$ by considering $\phi(2170)$ as a $\phi K\bar K$ state, with $K\bar K$ clustering as $f_0(980)$. These decay widths have been recently determined by the BESIII, BaBar and Belle collaborations with the aim of unraveling the nature of $\phi(2170)$. By analyzing the da

Bruce Warthon, Ivan Miranda, Iván Quispe Ccolque, Rafael Ponce

In this scientific article, the data on air pollution by PM2.5 particulate matter was evaluated in different locations in the city of Cusco with respect to the Environmental Quality Indexes (INCA) of the Ministry of the Environment of the Peruvian Government. The results show that air pollution in the city of Cusco is an environmental risk problem. More than

Chengshuai Shi, Cong Shen, Nicholas D. Sidiropoulos

Most existing studies on linear bandits focus on the one-dimensional characterization of the overall system. While being representative, this formulation may fail to model applications with high-dimensional but favorable structures, such as the low-rank tensor representation for recommender systems. To address this limitation, this work studies a general ten

Junda Ye, Zhongbao Zhang, Li Sun, Yang Yan

Sequential interaction networks (SIN) have been commonly adopted in many applications such as recommendation systems, search engines and social networks to describe the mutual influence between users and items/products. Efforts on representing SIN are mainly focused on capturing the dynamics of networks in Euclidean space, and recently plenty of work has ext

Xuan Xie, Jiayang Song, Zhehua Zhou, Fuyuan Zhang

Cyber-physical systems (CPSs) are now widely deployed in many industrial domains, e.g., manufacturing systems and autonomous vehicles. To further enhance the capability and applicability of CPSs, there comes a recent trend from both academia and industry to utilize learning-based AI controllers for the system control process, resulting in an emerging class o

Swagatika Dash

Multi-modal search engines have experienced significant growth and widespread use in recent years, making them the second most common internet use. While search engine systems offer a range of services, the image search field has recently become a focal point in the information retrieval community, as the adage goes, "a picture is worth a thousand words". Al

David Samuel, Andrey Kutuzov, Samia Touileb, Erik Velldal

We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the di

Jean-François de Kemmeter, Luca Gallo, Fabrizio Boncoraglio, Vito Latora

Social systems are characterized by the presence of group interactions and by the existence of both trust and distrust relations. Although there is a wide literature on signed social networks, where positive signs associated to the links indicate trust, friendship, agreement, while negative signs represent distrust, antagonism, and disagreement, very little

Andrew Romero-Wolf, Gregor Steinbruegge, Julie Castillo-Rogez, Corey J. Cochrane

We present a feasibility study for passive sounding of Uranian icy moons using Uranian Kilometric Radio (UKR) emissions in the 100 - 900 kHz band. We provide a summary description of the observation geometry, the UKR characteristics, and estimate the sensitivity for an instrument analogous to the Cassini Radio Plasma Wave Science (RPWS) but with a modified r

Furkan Berk Danisman, Ilyurek Kilic, Gizem Sarul, Sena Aktaş

The research was conducted to identify the factors that influence college students' satisfaction with their college experience. Firstly, the study was focused on the literature review to determine relevant factors that have been previously studied in the literature. Then, the survey analysis examined three main independent factors that have been found to be

Spencer Secord

We show that any $n$-absorbing ideal must be strongly $n$-absorbing, which is the first of Anderson and Badawi's three interconnected conjectures on absorbing ideals. We prove this by introducing and studying objects called maximal and semimaximal collections, which are tools for analysing the multiplicative ideal structure of commutative rings. We also make

Neelabhro Roy, Samie Mostafavi, James Gross

End-to-end learning for wireless communications has recently attracted much interest in the community, owing to the emergence of deep learning-based architectures for the physical layer. Neural network-based autoencoders have been proposed as potential replacements of traditional model-based transmitter and receiver structures. Such a replacement primarily p

Jonathan Brundan, Weiqiang Wang, Ben Webster

We introduce the nil-Brauer category and prove a basis theorem for its morphism spaces. This basis theorem is an essential ingredient required to prove that nil-Brauer categorifies the split iquantum group of rank one. As this iquantum group is a basic building block for $\imath$-quantum groups of higher rank, we expect that the nil-Brauer category will play

Joshua Pickard, Can Chen, Cooper Stansbury, Amit Surana

Hypergraphs and tensors extend classic graph and matrix theory to account for multiway relationships, which are ubiquitous in engineering, biological, and social systems. While the Kronecker product is a potent tool for analyzing the coupling of systems in graph or matrix contexts, its effectiveness in capturing multiway interactions remains elusive. In this

Yuan Chen, Dongbin Xiu

We present a numerical framework for learning unknown stochastic dynamical systems using measurement data. Termed stochastic flow map learning (sFML), the new framework is an extension of flow map learning (FML) that was developed for learning deterministic dynamical systems. For learning stochastic systems, we define a stochastic flow map that is a superpos

Zhong Zhou, Jan Niehues, Alex Waibel

In many humanitarian scenarios, translation into severely low resource languages often does not require a universal translation engine, but a dedicated text-specific translation engine. For example, healthcare records, hygienic procedures, government communication, emergency procedures and religious texts are all limited texts. While generic translation engi

Joanna Stepaniak, Damian Pszczel

A compilation of the experimental p+p data of neutral and charged kaon production has revealed a discrepancy between the observed $K^{0}_{s}$ yield and the average number of produced charged kaons $K^{0}_{s}=(K^{+}+K^{-})/2$. This widespread relation holds only for a colliding system that is an uniform population w.r. to the isospin (i.e. that consists of an

Jingyi Wu, James Owen Weatherall

We consider the duality between General Relativity and the theory of Einstein algebras, in the extended setting where one permits non-Hausdorff manifolds. We show that the duality breaks down, and then go on to discuss a sense in which general relativity, formulated using non-Hausdorff manifolds, exhibits excess structure when compared to Einstein algebras.

Patrick Emedom-Nnamdi, Abram L. Friesen, Bobak Shahriari, Nando de Freitas

Standard approaches to sequential decision-making exploit an agent's ability to continually interact with its environment and improve its control policy. However, due to safety, ethical, and practicality constraints, this type of trial-and-error experimentation is often infeasible in many real-world domains such as healthcare and robotics. Instead, control p

Wolfgang Altmannshofer, Pankaj Munbodh, Talise Oh

Lepton flavor violation is one of the cleanest probes of physics beyond the standard model. In this work, we explore the sensitivity of the process $e^+ e^- \to \tau \mu$ to new physics above the TeV scale at the proposed circular electron-positron colliders FCC-ee and CEPC. We compute the $e^+ e^- \to \tau \mu$ cross-section in the Standard Model Effective

Sriram S. K. S. Narayanan, Duvan Tellez-Castro, Sarang Sutavani, Umesh Vaidya

In this paper, we propose a novel data-driven approach for learning and control of quadrotor UAVs based on the Koopman operator and extended dynamic mode decomposition (EDMD). Building observables for EDMD based on conventional methods like Euler angles (to represent orientation) is known to involve singularities. To address this issue, we employ a set of ph

Christopher Lane, Yixuan Huang, Lin Hou, Jian-Xin Zhu

We predict the magnetic and electronic properties of a novel metal-organic framework. By combining density functional theory and density matrix renormalization group approaches, we find the diatomic Kagome crystal structure of the metal-semiquinoid framework (H$_2$NMe$_2$)$_2$M$_2$(Cl$_2$dhbq)$_3$ (M = Ti, V, Cr, Mn, Fe, Co, Ni, Cu, and Zn) to host a rich va

Naresh Ravichandran, Anders Lansner, Pawel Herman

We introduce a novel spiking neural network model for learning distributed internal representations from data in an unsupervised procedure. We achieved this by transforming the non-spiking feedforward Bayesian Confidence Propagation Neural Network (BCPNN) model, employing an online correlation-based Hebbian-Bayesian learning and rewiring mechanism, shown pre

Koichi Hattori, Kazunori Itakura, Sho Ozaki

We provide a pedagogical review article on fundamentals and applications of the quantum dynamics in strong electromagnetic fields in QED and QCD. The fundamentals include the basic picture of the Landau quantization and the resummation techniques applied to the class of higher-order diagrams that are enhanced by large magnitudes of the external fields. We th

Jaegyu Kim, Seongwoo Cho, Jiwon Yeom, Seongmun Eom

Piezoresponse force microscopy (PFM) has been widely used for nanoscale analysis of piezoelectric properties and ferroelectric domains. Although PFM is useful because of its simple and nondestructive features, PFM measurements can be obscured by non-piezoelectric effects that could affect the PFM signals or lead to ferroelectric-like behaviors in non-ferroel

Daniel Johnson, Trevor Maxfield, Yongxu Jin, Ronald Fedkiw

Various software efforts embrace the idea that object oriented programming enables a convenient implementation of the chain rule, facilitating so-called automatic differentiation via backpropagation. Such frameworks have no mechanism for simplifying the expressions (obtained via the chain rule) before evaluating them. As we illustrate below, the resulting er

Shangjia Zhang, Matt Kalscheur, Feng Long, Ke Zhang

Observations of substructure in protoplanetary disks have largely been limited to the brightest and largest disks, excluding the abundant population of compact disks which are likely sites of planet formation. Here, we reanalyze ~0.1'', 1.33 mm ALMA continuum observations of 12 compact protoplanetary disks in the Taurus star-forming region. By fitting visibi

Jeffrey S. Case, Aaron J. Tyrrell

We derive a sharp inequality relating the second and fourth elementary symmetric functions of the eigenvalues of a trace-free matrix and give two applications. First, we give a new proof of the classification of conformally flat hypersurfaces in spaceforms. Second, we construct a functional which characterizes rotational hypersurfaces and catenoids.

Zhenhua Yu, Peter R. N. Childs, Thrishantha Nanayakkara

My research objective is to explicitly bridge the gap between high computational performance and low power dissipation of robot on-board hardware by designing a bio-inspired tapered whisker neuromorphic computing (also called reservoir computing) system for offroad robot environment perception and navigation, that centres the interaction between a robot's bo

Anthony Constantinou, Neville K. Kitson, Yang Liu, Kiattikun Chobtham

Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation praovided by these algorithms enables transparency and explainability, which is necessary for decision making in critical real-world problems. Yet, causal ML has had limited impact in practice compared to

Zichang He, Ruslan Shaydulin, Shouvanik Chakrabarti, Dylan Herman

Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic algorithm, which it can approximate with sufficient depth. However, it is unclear to what extent the lessons from the adiabatic regime apply to QAOA as executed in practice with small to moderate depth. In this paper, we demonstrate that the intuition from the adiabatic algor

Michał Papaj

Altermagnets are a new class of magnetic materials, which exhibit large spin splitting, but due to the combined spin and real space group symmetry protection maintain zero net macroscopic magnetization. Such a characteristic may prove them to be superior in applications in superconducting heterostructures and thus here we investigate the Andreev reflection a

Ahmed Attia, Sven Leyffer, Todd Munson

Optimal design of experiments for Bayesian inverse problems has recently gained wide popularity and attracted much attention, especially in the computational science and Bayesian inversion communities. An optimal design maximizes a predefined utility function that is formulated in terms of the elements of an inverse problem, an example being optimal sensor p

Joshua Comden, Jing Wang, Andrey Bernstein

With the increasing adoption of distributed energy resources (DERs) in distribution networks, distributed energy resource management systems (DERMS) are becoming an attractive option to coordinate the control of DERs, especially primal-dual-based DERMS, which is a well-developed class. To help reduce the uncertainty in commercializing DERMS, we evaluate and

Mohamed K. M. Fadul, Donald R. Reising, Lakmali P. Weerasena

Increasing Internet of Things (IoT) deployments present a growing surface over which villainous actors can carry out attacks. This disturbing revelation is amplified by the fact that a majority of IoT devices use weak or no encryption at all. Specific Emitter Identification (SEI) is an approach intended to address this IoT security weakness. This work provid

Fabio V. Difonzo

Letting $P$ be a convex polytope in $\mathbb{R}^d$ with $n>d$ vertices, we study geometric and analytical properties of the set of generalized barycentric coordinates relative to any point $p\in P$. We prove that such sets are polytopes in $\mathbb{R}^n$ with at most $n-d-1$ vertices, and provide results about continuity and differentiability for the corresp

Brandon Harwood

This paper explores the potential for utilizing generative AI models in group-focused co-creative frameworks to enhance problem solving and ideation in business innovation and co-creation contexts, and proposes a novel prompting technique for conversational generative AI agents which employ methods inspired by traditional 'human-to-human' facilitation and in

Mark Connor, Michael O'Neill

This paper explores the potential opportunities, risks, and challenges associated with the use of large language models (LLMs) in sports science and medicine. LLMs are large neural networks with transformer style architectures trained on vast amounts of textual data, and typically refined with human feedback. LLMs can perform a large range of natural languag

Sebastian Lotter, Tom Bellmann, Sophie Marx, Mara Wesinger

Controlled drug delivery (CDD), the controlled release and delivery of therapeutic drugs inside the human body, is a promising approach to increase the efficacy of drug administration and reduce harmful side effects to the body. CDD has been a major research focus in the field of molecular communications (MC) with the goal to aid the design and optimization