October 2023 arXiv papers — page 34

Huihan Liu, Shivin Dass, Roberto Martín-Martín, Yuke Zhu

Robot learning methods have recently made great strides, but generalization and robustness challenges still hinder their widespread deployment. Failing to detect and address potential failures renders state-of-the-art learning systems not combat-ready for high-stakes tasks. Recent advances in interactive imitation learning have presented a promising framewor

Xiaoyuan Yi, Jing Yao, Xiting Wang, Xing Xie

Big models have greatly advanced AI's ability to understand, generate, and manipulate information and content, enabling numerous applications. However, as these models become increasingly integrated into everyday life, their inherent ethical values and potential biases pose unforeseen risks to society. This paper provides an overview of the risks and challen

Andi Peng, Mycal Tucker, Eoin Kenny, Noga Zaslavsky

Neural networks often learn task-specific latent representations that fail to generalize to novel settings or tasks. Conversely, humans learn discrete representations (i.e., concepts or words) at a variety of abstraction levels (e.g., "bird" vs. "sparrow") and deploy the appropriate abstraction based on task. Inspired by this, we train neural models to gener

Alexander F. Jercher, Luca Marchetti, Andreas G. A. Pithis

A major challenge at the interface of quantum gravity and cosmology is to explain the emergence of the large-scale structure of the Universe from Planck scale physics. In this letter, we extract the dynamics of scalar isotropic cosmological perturbations from full quantum gravity, as described by the causally complete Barrett-Crane group field theory model.

Anthony Leverrier

The goal of this note is to explain the reconciliation problem for continuous-variable quantum key distribution protocols with a discrete modulation. Such modulation formats are attractive since they significantly simplify experimental implementations compared to protocols with a Gaussian modulation. Previous security proofs that relied crucially on the Gaus

Karen Yeats, Stav Zalel

We give a framework for growth models on posets which simultaneously generalizes the Classical Sequential Growth models for posets from causal set theory and the tree growth models of natural growth and simple tree classes, the latter of which also appear as solutions of combinatorial Dyson-Schwinger equations in quantum field theory. We prove which cases of

Mengyi Gong, Rebecca Killick, Christopher Nemeth, John Quinton

Soil moisture dynamics provide an indicator of soil health that scientists model via drydown curves. The typical modelling process requires the soil moisture time series to be manually separated into drydown segments and then exponential decay models are fitted to them independently. Sensor development over recent years means that experiments that were previ

William Therrien, Olivier Lecompte, Alexandre Girard

Many advanced driver assistance schemes or autonomous vehicle controllers are based on a motion model of the vehicle behavior, i.e., a function predicting how the vehicle will react to a given control input. Data-driven models, based on experimental or simulated data, are very useful, especially for vehicles difficult to model analytically, for instance, gro

Aysin Tumay, Mustafa E. Aydin, Ali T. Koc, Suleyman S. Kozat

We introduce a novel ensemble approach for feature selection based on hierarchical stacking for non-stationarity and/or a limited number of samples with a large number of features. Our approach exploits the co-dependency between features using a hierarchical structure. Initially, a machine learning model is trained using a subset of features, and then the ou

Michel Benaïm, Oliver Tough

We introduce simple conditions ensuring that invariant distributions of a Feller Markov chain on a compact Riemannian manifold are absolutely continuous with a lower semi-continuous, continuous or smooth density with respect to the Riemannian measure. This is applied to Markov chains obtained by random composition of maps and to piecewise deterministic Marko

Tommaso Marchetti, Meridith Joyce, Christian Johnson, R. Michael Rich

The Blanco DECam Bulge Survey (BDBS) provides near-ultraviolet to near-infrared photometry for ~250 million unique stars. By combining BDBS photometry with the latest Gaia astrometry, we characterize the chemo-dynamics of red clump stars across the BDBS footprint, using an unprecedented sample size and sky coverage. We construct a sample of ~2.3 million red

Kota Fujimoto, Sakahisa Nagai, Nguyen Binh Minh, Hiroshi Fujimoto

To overcome the short flight duration of drones, research on in-flight inductive power transfer has been recognized as an essential solution. Thus, it is important to accurately estimate and control the attitude of the drones which operate close to the charging surface. To this end, this paper proposes an attitude estimation method based solely on the motor

Yuping Wang, Jier Chen

Forecasting vehicular motions in autonomous driving requires a deep understanding of agent interactions and the preservation of motion equivariance under Euclidean geometric transformations. Traditional models often lack the sophistication needed to handle the intricate dynamics inherent to autonomous vehicles and the interaction relationships among agents i

Evan Berkowitz, Aleksey Cherman, Theodore Jacobson

We construct symmetry-preserving lattice regularizations of 2d QED with one and two flavors of Dirac fermions, as well as the `3450' chiral gauge theory, by leveraging bosonization and recently-proposed modifications of Villain-type lattice actions. The internal global symmetries act just as locally on the lattice as they do in the continuum, the anomalies a

Henry H. H. Chen, Jiaming Lu

The prevailing principle of "Optimism in the Face of Uncertainty" advocates for the incorporation of an exploration bonus, generally assumed to be proportional to the inverse square root of the visit count ($1/\sqrt{n}$), where $n$ is the number of visits to a particular state-action pair. This approach, however, exclusively focuses on "uncertainty," neglect

Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy

Deep reinforcement learning methods exhibit impressive performance on a range of tasks but still struggle on hard exploration tasks in large environments with sparse rewards. To address this, intrinsic rewards can be generated using forward model prediction errors that decrease as the environment becomes known, and incentivize an agent to explore novel state

Behrad Taghavi, Ali Naseh, Kuroush Allameh

We study the classical Liouville field theory on Riemann surfaces of genus $g>1$ in the presence of vertex operators associated with branch points of orders $m_i>1$. In order to do so, we consider the generalized Schottky space $\mathfrak{S}_{g,n}(\boldsymbol{m})$ obtained as a holomorphic fibration over the Schottky space $\mathfrak{S}_g$ of the (compactifi

Fnu Suya, Anshuman Suri, Tingwei Zhang, Jingtao Hong

Numerous works study black-box attacks on image classifiers. However, these works make different assumptions on the adversary's knowledge and current literature lacks a cohesive organization centered around the threat model. To systematize knowledge in this area, we propose a taxonomy over the threat space spanning the axes of feedback granularity, the acces

Benhoummad Othmane, Rami Mohammed, Zaoual Atmane, Youssef Rochdi

Sialolithiasis rarely occurs in children; it is observed more commonly in adults. Various treatment modalities for sialolithiasis have been reported in literature; we report the case of 9 years old child, with no particular pathological history. For 4 years, he had a tumefaction of the left submandibular region, associated with painful blocking episodes of s

Anaelia Ovalle

Educational disparities within the Dominican Republic (DR) have long-standing origins rooted in economic, political, and social inequity. Addressing these challenges has necessarily called for capacity building with respect to educational materials, high-quality instruction, and structural resourcing. Generative AI tools like ChatGPT have begun to pique the

Marc Mosko

We describe a method to achieve distributed consensus in a Content Centric Network using the PAXOS algorithm. Consensus is necessary, for example, if multiple writers wish to agree on the current version number of a CCNx name or if multiple distributed systems wish to elect a leader for fast transaction processing. We describe two forms of protocols, one usi

Fengzhuo Zhang, Vincent Y. F. Tan, Zhaoran Wang, Zhuoran Yang

We design and analyze reinforcement learning algorithms for Graphon Mean-Field Games (GMFGs). In contrast to previous works that require the precise values of the graphons, we aim to learn the Nash Equilibrium (NE) of the regularized GMFGs when the graphons are unknown. Our contributions are threefold. First, we propose the Proximal Policy Optimization for G

Laura Cabello, Emanuele Bugliarello, Stephanie Brandl, Desmond Elliott

Pretrained machine learning models are known to perpetuate and even amplify existing biases in data, which can result in unfair outcomes that ultimately impact user experience. Therefore, it is crucial to understand the mechanisms behind those prejudicial biases to ensure that model performance does not result in discriminatory behaviour toward certain group

Alexander I. Bobenko, Carl O. R. Lutz

We introduce decorated piecewise hyperbolic and spherical surfaces and discuss their discrete conformal equivalence. A decoration is a choice of circle about each vertex of the surface. Our decorated surfaces are closely related to inversive distance circle packings, canonical tessellations of hyperbolic surfaces, and hyperbolic polyhedra. We prove the corre

Tali Khain, Michel Fruchart, Colin Scheibner, Thomas A. Witten

Chiral fluids - such as fluids under rotation or a magnetic field as well as synthetic and biological active fluids - flow in a different way than ordinary ones. Due to symmetries broken at the microscopic level, chiral fluids may have asymmetric stress and viscosity tensors, for example giving rise to a hydrostatic torque or non-dissipative (odd) and parity

Feng Wang, Zilong Chen, Guokang Wang, Yafei Song

In this paper, we propose the Masked Space-Time Hash encoding (MSTH), a novel method for efficiently reconstructing dynamic 3D scenes from multi-view or monocular videos. Based on the observation that dynamic scenes often contain substantial static areas that result in redundancy in storage and computations, MSTH represents a dynamic scene as a weighted comb

Qusai Khraisha, Sophie Put, Johanna Kappenberg, Azza Warraitch

Systematic reviews are vital for guiding practice, research, and policy, yet they are often slow and labour-intensive. Large language models (LLMs) could offer a way to speed up and automate systematic reviews, but their performance in such tasks has not been comprehensively evaluated against humans, and no study has tested GPT-4, the biggest LLM so far. Thi

Juan-Rafael Álvarez, Andrés Martínez Silva, Alejandra Valencia

In this work, we present an educational activity aimed at measuring the Wigner distribution functions of quantum states of light in the undergraduate laboratory. This project was conceived by students from various courses within the physics undergraduate curriculum, and its outcomes were used in an introductory Quantum Optics course at the Universidad de los

Hongbo Li, Tianwu Wang, Wenyin Wei, Kai Zhang

Terahertz scanning tunneling microscopy (THz-STM) has emerged as a potent technique for probing ultrafast nanoscale dynamics with exceptional spatiotemporal precision, whereby the acquisition of THz near-field waveforms holds paramount significance. While substantial efforts have been dedicated to retrieving the waveform utilizing the photoemission current o

Haiyuan Li, Yuelin Liu, Hari Madhukumar, Amin Emami

Multi-access edge computing provides localized resources within mobile networks to address the requirements of emerging latency-sensitive and computing-intensive applications. At the edge, dynamic requests necessitate sophisticated resource management for adaptive network slicing. This involves optimizing resource allocations, scaling functions, and load bal

Kota Fujimoto, Takumi Hamada, Hiroshi Fujimoto

This paper proposes a model-based control method in the wireless power transfer (WPT) system by operating a semi-bridgeless active rectifier (SBAR) to suppress the secondary coil current overshoot. By damping the current overshoot, it is possible to reduce the rectifier's rated current and decrease the rectifier's size, which is beneficial for the lightweigh

Bhavesh Ramkorun, Swapneal Jain, Adib Taba, Masoud Mahjouri-Samani

In dusty plasma environments, the spontaneous growth of nanoparticles from reactive gases has been extensively studied for over three decades, primarily focusing on hydrocarbons and silicate particles. Here, we introduce the growth of titanium dioxide, a wide band gap semiconductor, as dusty plasma nanoparticles. The resultant particles exhibited a spherical

Wenbo Li, Shiping Liu

We present a finer quantitative version of an observation due to Breuillard, Green, Guralnick and Tao which tells that for finite non-bipartite Cayley graphs, once the nontrivial eigenvalues of their normalized adjacency matrices are uniformly bounded away from $1$, then they are also uniformly bounded away from $-1$. Unlike previous works which depend heavi

Shrisha Bharadwaj, Yufeng Zheng, Otmar Hilliges, Michael J. Black

Our goal is to efficiently learn personalized animatable 3D head avatars from videos that are geometrically accurate, realistic, relightable, and compatible with current rendering systems. While 3D meshes enable efficient processing and are highly portable, they lack realism in terms of shape and appearance. Neural representations, on the other hand, are rea

Nouredine Medjoudj, Abdelkrim Moussaoui

We establish the existence and uniqueness of solutions for quasilinear singular Lane-Emden type systems subjected to Neumann boundary conditions. The approach is chiefly based on sub-supersolutions method.

Marilyn Pease, Mark Whitmeyer

We introduce a way to compare actions in decision problems. One action is safer than another if the set of beliefs at which the decision-maker prefers the safer action expands as the decision-maker becomes more risk averse. We provide a full characterization of this relation, show that it is equivalent to robust conceptions of single-crossing and second-orde

Rodrigo Gutiérrez-Cuevas, Dorian Bouchet, Julien de Rosny, Sébastien M. Popoff

Perturbations in complex media, due to their own dynamical evolution or to external effects, are often seen as detrimental. Therefore, a common strategy, especially for telecommunication and imaging applications, is to limit the sensitivity to those perturbations in order to avoid them. Here, we instead consider crashing straight into them in order to maximi

Anh Tran, Pieterjan Robbe, Theron Rodgers, Hojun Lim

Crystal plasticity finite element method (CPFEM) has been an integrated computational materials engineering (ICME) workhorse to study materials behaviors and structure-property relationships for the last few decades. These relations are mappings from the microstructure space to the materials properties space. Due to the stochastic and random nature of micros

Yuehong Xie

The CKM matrix is the only source of CP violation in the Standard Model.Heavy-flavour decays provide an ideal laboratory to test the CKM mechanism. Some recent highlights in CP violation in heavy-flavour decays from the LHCb and Belle II experiments are presented, including updated measurements of the CKM angles $\beta$, $\gamma$ and $\phi_s$, and new result

Kaiser Sun, Adina Williams, Dieuwke Hupkes

NLP models have progressed drastically in recent years, according to numerous datasets proposed to evaluate performance. Questions remain, however, about how particular dataset design choices may impact the conclusions we draw about model capabilities. In this work, we investigate this question in the domain of compositional generalization. We examine the pe

Yuchen Zeng, Kangwook Lee

Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method that leverages low-rank adaptation of weight matrices, has emerged as a prevalent technique for fine-tuning pre-trained models such as large language models and diffusion models. Despite its huge success in practice, the theoretical underpinnings of LoRA have largely remained unexplored. Th

Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin

Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning. While most of the work has focused on cooperation and collaboration between agents, little work explores competition, another important mechanism that promotes the development of society and economy. In this paper, we seek

Minoru Eto, Kentaro Nishimura, Muneto Nitta

Based on the chiral perturbation theory at the leading order, we show the presence of a new phase in rapidly rotating QCD matter with two flavors, that is a domain-wall Skyrmion phase. Based on the chiral Lagrangian with a Wess-Zumino-Witten (WZW) term responsible for the chiral anomaly and chiral vortical effect, it was shown that the ground state is a chir

Fabian Thiemann, Germán Sciaini, Alexander Kassen, Tyler S. Lott

Elucidating the interplay between nuclear and electronic degrees of freedom that govern the complex dielectric behavior of materials under intense photoexcitation is essential for tailoring optical properties on demand. However, conventional transient reflectivity experiments have been unable to differentiate between real and imaginary components of the diel

Peng Shi, J. C. Li, G. Zhuang, Zhifeng Cheng

The ITG and TEM instabilities with quasi-coherent spectra have been identified experimentally, by the newly developed far-forward collective scattering measurements in J-TEXT tokamak Ohmical plasmas. The ITG mode has characteristic frequencies in the range of 30-100kHz and wavenumber of k_\theta\rho_s<0.3. After the plasma density exceeds at critical value,

V. I. Korobov, A. V. Eskin, A. P. Martynenko, F. A. Martynenko

On the basis of variational method we study energy levels of pionic helium $(\pi-e-He)$ and kaonic helium $(K-e-He)$ with an electron in ground state and a meson in excited state with principal and orbital quantum numbers $n\sim l+1\sim 20$. Variational wave functions are taken in the Gaussian form. Matrix elements of the basic Hamiltonian and corrections to

Santiago D. Barrionuevo, Federico Fioravanti, Jorge M. Nuñez, Mauricio Llaver

Achieving versatile routes to generate crystalline carbon-based nanostructures has become a fervent pursuit in photocatalysis-related fields. We demonstrate that the direct electrooxidation of ethanol, performed on Ni foam, yields ultra-small and highly crystalline graphene-like structures named carbon quantum dots (CQDs). We perform simulations of various s

Roger D. Peng

The data science revolution has highlighted the varying roles that data analytic products can play in a different industries and applications. There has been particular interest in using analytic products coupled with algorithmic prediction models to aid in human decision-making. However, detailed descriptions of the decision-making process that leads to the

Guanhua Wang, Fang Yang, Jian Song, Zhu Han

The sixth-generation (6G) network is expected to achieve global coverage based on the space-air-ground integrated network, and the latest satellite network will play an important role in it. The introduction of inter-satellite links (ISLs) can significantly improve the throughput of the satellite network, and recently gets lots of attention from both academi

Aditya Dev

These notes provide a review of various neutrino mass model and their implications for particle physics and the Standard Model. We discuss how mass terms are incorporated into the Standard Model, including the Dirac mass term and the Majorana mechanism. We explore experimental evidence supporting the existence of non-zero neutrino mass and develop the formal

Gilles Puy, Spyros Gidaris, Alexandre Boulch, Oriane Siméoni

Self-supervised image backbones can be used to address complex 2D tasks (e.g., semantic segmentation, object discovery) very efficiently and with little or no downstream supervision. Ideally, 3D backbones for lidar should be able to inherit these properties after distillation of these powerful 2D features. The most recent methods for image-to-lidar distillat

Dan Deras, Mario Cadelano, Barbara Lanzoni, Francesco R. Ferraro

High-resolution Hubble Space Telescope (\textit{HST}) optical observations have been used to perform the deepest photometric study of the poorly studied Galactic globular cluster NGC 6284. The deep colour-magnitude diagram (CMD) that we obtained reaches 6 magnitudes below the main sequence turn-off. We provide the first determination of the gravitational cen

Florian Lux, Pascal Tilli, Sarina Meyer, Ngoc Thang Vu

Customizing voice and speaking style in a speech synthesis system with intuitive and fine-grained controls is challenging, given that little data with appropriate labels is available. Furthermore, editing an existing human's voice also comes with ethical concerns. In this paper, we propose a method to generate artificial speaker embeddings that cannot be lin

Mojtaba Abaie Shoushtary, Jose Maria Arnau, Jordi Tubella Murgadas, Antonio Gonzalez

Modern GPUs require an enormous register file (RF) to store the context of thousands of active threads. It consumes considerable energy and contains multiple large banks to provide enough throughput. Thus, a RF caching mechanism can significantly improve the performance and energy consumption of the GPUs by avoiding reads from the large banks that consume si

Wei Tao, Zhen-Jun Xiao

Within the nonrelativistic QCD (NRQCD) framework, we complete the three-loop calculations of the NRQCD renormalization constants and the matching coefficients, for the heavy flavor-changing temporal vector, spatial-spatial tensor, spatial-temporal axial-tensor currents, which are coupled to the $P$-wave $c\bar b$ mesons. We further study the ten decay consta

Florian Lux, Julia Koch, Sarina Meyer, Thomas Bott

For our contribution to the Blizzard Challenge 2023, we improved on the system we submitted to the Blizzard Challenge 2021. Our approach entails a rule-based text-to-phoneme processing system that includes rule-based disambiguation of homographs in the French language. It then transforms the phonemes to spectrograms as intermediate representations using a fa

Zhen Xiang, Zidi Xiong, Bo Li

Backdoor attack is a common threat to deep neural networks. During testing, samples embedded with a backdoor trigger will be misclassified as an adversarial target by a backdoored model, while samples without the backdoor trigger will be correctly classified. In this paper, we present the first certified backdoor detector (CBD), which is based on a novel, ad

Alexandra Jamchi Fugenfirov, Leonid Mytnik

We study a continuous time Mutually Catalytic Branching model on the $\mathbb{Z}^{d}$. The model describes the behavior of two different populations of particles, performing random walk on the lattice in the presence of branching, that is, each particle dies at a certain rate and is replaced by a random number of offspring. The branching rate of a particle i

Nian Si

In modern recommendation systems, the standard pipeline involves training machine learning models on historical data to predict user behaviors and improve recommendations continuously. However, these data training loops can introduce interference in A/B tests, where data generated by control and treatment algorithms, potentially with different distributions,

Vaughn Climenhaga

It is well-known that equilibrium measures for uniformly hyperbolic dynamical systems have a local product structure, which plays an important role in their mixing properties. Existing proofs of this fact rely either on transfer operators or on leafwise constructions, and in particular are not well-suited to the approach to thermodynamic formalism based on B

Salman Khan, Izzeddin Teeti, Andrew Bradley, Mohamed Elhoseiny

Interpretation and understanding of video presents a challenging computer vision task in numerous fields - e.g. autonomous driving and sports analytics. Existing approaches to interpreting the actions taking place within a video clip are based upon Temporal Action Localisation (TAL), which typically identifies short-term actions. The emerging field of Comple

Wenhan Yu, Terence Jie Chua, Jun Zhao

The efficient deployment and fine-tuning of foundation models are pivotal in contemporary artificial intelligence. In this study, we present a groundbreaking paradigm integrating Mobile Edge Computing (MEC) with foundation models, specifically designed to enhance local task performance on user equipment (UE). Central to our approach is the innovative Emulato

Terence Jie Chua, Wenhan Yu, Jun Zhao, Kwok-Yan Lam

The emergence of foundation models, including language and vision models, has reshaped AI's landscape, offering capabilities across various applications. Deploying and fine-tuning these large models, like GPT-3 and BERT, presents challenges, especially in the current foundation model era. We introduce Emulator-Assisted Tuning (EAT) combined with Parameter-Ef

Sukmin Cho, Jeongyeon Seo, Soyeong Jeong, Jong C. Park

Large language models (LLMs) enable zero-shot approaches in open-domain question answering (ODQA), yet with limited advancements as the reader is compared to the retriever. This study aims at the feasibility of a zero-shot reader that addresses the challenges of computational cost and the need for labeled data. We find that LLMs are distracted due to irrelev

L. Elisa Celis, Amit Kumar, Anay Mehrotra, Nisheeth K. Vishnoi

Biases with respect to socially-salient attributes of individuals have been well documented in evaluation processes used in settings such as admissions and hiring. We view such an evaluation process as a transformation of a distribution of the true utility of an individual for a task to an observed distribution and model it as a solution to a loss minimizati

Kai Mei, Yongfeng Zhang

This paper presents LightLM, a lightweight Transformer-based language model for generative recommendation. While Transformer-based generative modeling has gained importance in various AI sub-fields such as NLP and vision, generative recommendation is still in its infancy due to its unique demand on personalized generative modeling. Existing works on generati

Wei Dai, Taiki Yoda, Yuto Moritake, Masaaki Ono

Bending loss is one of the serious problems for constructing nanophotonic integrated circuits. Recently, many works reported that valley photonic crystals (VPhCs) enable significantly high transmission via 120-degree sharp bends. However, it is unclear whether the high bend-transmission results directly from the valley-photonic effects, which are based on th

A. Alonso-Izquierdo, W. Garcia Fuertes, N. S. Manton, J. Mateos Guilarte

The flow of shape eigenmodes of the small fluctuation operator around BPS 2-vortex solutions is calculated, as a function of the intervortex separation $2d$. For the rotationally-invariant 2-vortex, with $d = 0$, there are three discrete modes; the lowest is non-degenerate and the upper two are degenerate. As $d$ increases, the degeneracy splits, with one ei

Stephen Mak, Liming Xu, Tim Pearce, Michael Ostroumov

Collaborative vehicle routing occurs when carriers collaborate through sharing their transportation requests and performing transportation requests on behalf of each other. This achieves economies of scale, thus reducing cost, greenhouse gas emissions and road congestion. But which carrier should partner with whom, and how much should each carrier be compens

Bintao Cao, Wan Keng Cheong, Ngau Lam

We show that under a generic condition, the quadratic Gaudin Hamiltonians associated to $\mathfrak{gl}(p+m|q+n)$ are diagonalizable on any singular weight space in any tensor product of unitarizable highest weight $\mathfrak{gl}(p+m|q+n)$-modules. Moreover, every joint eigenbasis of the Hamiltonians can be obtained from some joint eigenbasis of the quadratic

Tonghang Han, Zhengguang Lu, Yuxuan Yao, Jixiang Yang

The quantum anomalous Hall effect (QAHE) is a robust topological phenomenon featuring quantized Hall resistance at zero magnetic field. We report the QAHE in a rhombohedral pentalayer graphene/monolayer WS2 heterostructure. Distinct from other experimentally confirmed QAHE systems, this system has neither magnetic element nor moir\'e superlattice effect. The

Igor Balla

We construct a bipartite generalization of Alon and Szegedy's nearly orthogonal vectors, thereby obtaining strong bounds for several extremal problems involving the Lov\'asz theta function, vector chromatic number, minimum semidefinite rank, nonnegative rank, and extension complexity of polytopes. In particular, we derive a couple of general lower bounds for

Michael Seidel, Yuhui Yang, Thorsten Schumacher, Yongheng Huo

Key requirements for quantum plasmonic nanocircuits are reliable single-photon sources, high coupling efficiency to the plasmonic structures and low propagation losses. Self-assembled epitaxially grown GaAs quantum dots are close to ideal stable, bright and narrowband single-photon emitters. Likewise, wet-chemically grown monocrystalline silver nanowires are

Jason M. TenBarge, James Juno, Gregory G. Howes

Particle energization due to magnetic reconnection is an important unsolved problem for myriad space and astrophysical plasmas. Electron energization in magnetic reconnection has traditionally been examined from a particle, or Lagrangian, perspective using particle-in-cell (PIC) simulations. Guiding-center analyses of ensembles of PIC particles have suggeste

Antariksha Mitra, Suman Ghosh

We examine the quasi normal modes of Ellis-Bronnikov wormholes embedded in a warped five dimensional braneworld background and compare with it's four dimensional counterpart. These scalar quasi normal frequencies are obtained using the WKB formula, Prony method and the direct integration method. The signature of the warped extra dimension shows up as two dis

R. S. Hutton, E. Vitral, E. Hamm, J. A. Hanna

Experiments reveal that structural transitions in thin sheets are mediated by the passage of transient and stable mobile localized elastic excitations. These ``crumples'' or ``d-cones'' nucleate, propagate, interact, annihilate, and escape. Much of the dynamics occurs on millisecond time scales. Nucleation sites correspond to regions where generators of the

Jonas Sievers, Thomas Blank

Electricity load forecasting is an essential task within smart grids to assist demand and supply balance. While advanced deep learning models require large amounts of high-resolution data for accurate short-term load predictions, fine-grained load profiles can expose users' electricity consumption behaviors, which raises privacy and security concerns. One so

Aleksandr V. Khmelev, Alexey V. Duplinsky, Ruslan M. Bakhshaliev, Egor I. Ivchenko

The Micius satellite is the pioneering initiative to demonstrate quantum teleportation, entanglement distribution, quantum key distribution (QKD), and quantum-secured communications experiments at the global scale. In this work, we report on the results of the 600-mm-aperture ground station design which has enabled the establishment of a quantum-secured link

Hai Yang, Yuchen Du, Tho V. Le, Joseph Y. J. Chow

With the rise in demand for local deliveries and e-commerce, robotic deliveries are being considered as efficient and sustainable solutions. However, the deployment of such systems can be highly complex due to numerous factors involving stochastic demand, stochastic charging and maintenance needs, complex routing, etc. We propose a model that uses continuous

Michael Chapman, Alexander Lubotzky

This paper is motivated by recent developments in group stability, high dimensional expansion, local testability of error correcting codes and topological property testing. In Part I, we formulate and motivate three stability problems: 1. Homomorphism stability: Are almost homomorphisms close to homomorphisms? 2. Covering stability: Are almost coverings of a

Michele Costola, Matteo Iacopini, Casper Wichers

A novel spatial autoregressive model for panel data is introduced, which incorporates multilayer networks and accounts for time-varying relationships. Moreover, the proposed approach allows the structural variance to evolve smoothly over time and enables the analysis of shock propagation in terms of time-varying spillover effects. The framework is applied to

Anjan S. Joshipura, Ketan M. Patel

We discuss a scenario in which the supergravity induced soft terms, conventionally used for breaking supersymmetry, also lead to non-zero Majorana neutrino masses. The soft terms lead to the spontaneous violation of the lepton number at the gravitino mass scale $m_{3/2}$ which in turn leads to (i) the Majorana masses of ${\cal O} (m_{3/2})$ for the right-han

Xiang Chen, Zhiheng Guo, Xijun Wang, Howard H. Yang

Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on multi-agent collaboration, especially in the context of the thriving development of pre-trained foundation models (PFM) and the evolving vision of 6G native artificial intelligence (AI). Ther

Luke Robitaille, Minh-Tâm Quang Trinh

A simple braid is a positive braid that can be drawn so that any two strands cross at most once. We prove that as $n \to \infty$, the proportion of simple braids on $n$ strands that have positive topological entropy tends toward $100\%$. Notably, such braids are either pseudo-Anosov or reducible with a pseudo-Anosov component. Our proof involves a method of

Bin Li, Wenshuai Liu, Wancheng Xie, Ning Zhang

In this paper, we study a digital twin (DT)-empowered integrated sensing, communication, and computation network. Specifically, the users perform radar sensing and computation offloading on the same spectrum, while unmanned aerial vehicles (UAVs) are deployed to provide edge computing service. We first formulate a multi-objective optimization problem to mini

Jorik Jooken

We study how few pairwise distinct longest cycles a regular graph can have under additional constraints. For each integer $r \geq 5$, we give exponential improvements for the best asymptotic upper bounds for this invariant under the additional constraint that the graphs are $r$-regular hamiltonian graphs. Earlier work showed that a conjecture by Haythorpe on

Yang Qin, Yuan Sun, Dezhong Peng, Joey Tianyi Zhou

Recently, image-text matching has attracted more and more attention from academia and industry, which is fundamental to understanding the latent correspondence across visual and textual modalities. However, most existing methods implicitly assume the training pairs are well-aligned while ignoring the ubiquitous annotation noise, a.k.a noisy correspondence (N

Luca Ambrogioni

Generative diffusion models have achieved spectacular performance in many areas of machine learning and generative modeling. While the fundamental ideas behind these models come from non-equilibrium physics, variational inference and stochastic calculus, in this paper we show that many aspects of these models can be understood using the tools of equilibrium

Lucas Buzaglo

Let $\Bbbk$ be an algebraically closed field of characteristic 0. We study some cohomological properties of Lie subalgebras of the Witt algebra $W = \operatorname{Der}(\Bbbk[t,t^{-1}])$ and the one-sided Witt algebra $W_{\geq -1} = \operatorname{Der}(\Bbbk[t])$. In the first part of the paper, we consider finite codimension subalgebras of $W_{\geq -1}$. We c

Siheon Ryee, Niklas Witt, Tim O. Wehling

The recent discovery of superconductivity in La$_3$Ni$_2$O$_7$ with $T_\mathrm{c} \simeq 80~\mathrm{K}$ under high pressure opens up a new route to high-$T_\mathrm{c}$ superconductivity. This material realizes a bilayer square lattice model featuring a strong interlayer hybridization unlike many unconventional superconductors. A key question in this regard c

Jingwen Cheng, Jianli Bai, Binbin Ruan, Pinyu Liu

Unconventional superconductivity in bulk materials under ambient pressure is extremely rare among the 3d transition metal compounds outside the layered cuprates and iron-based family. It is predominantly linked to highly anisotropic electronic properties and quasi-two-dimensional (2D) Fermi surfaces. To date, the only known example of a Co-based exotic super

Konstantin Hess, Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel

Treatment effect estimation in continuous time is crucial for personalized medicine. However, existing methods for this task are limited to point estimates of the potential outcomes, whereas uncertainty estimates have been ignored. Needless to say, uncertainty quantification is crucial for reliable decision-making in medical applications. To fill this gap, w

Daniel Kienzle, Julian Lorenz, Katja Ludwig, Rainer Lienhart

We present a novel method for precise 3D object localization in single images from a single calibrated camera using only 2D labels. No expensive 3D labels are needed. Thus, instead of using 3D labels, our model is trained with easy-to-annotate 2D labels along with the physical knowledge of the object's motion. Given this information, the model can infer the

Roland Gillen, Janina Maultzsch

Bottom-up synthesis from molecular precursors is a powerful route for the creation of novel synthetic carbon-based low-dimensional materials, such as planar carbon lattices. The wealth of conceivable precursor molecules introduces a significant number of degrees-of-freedom for the design of materials with defined physical properties. In this context, a prior

Nimisha Arora, Yogesh Kumar, Pintu Das

This work discusses the rich phase diagram of non-trivial chiral spin textures in confined ferromagnetic/heavy-metal (FM/HM) bilayer nanomagnets of circular cross-section. These spin textures are realized as a minimum-energy ground state during an external bias field sweep for a range of nanomagnet's diameter (d). Our study, based on micromagnetic simulation

Robert Burklund, Jeremy Hahn, Ishan Levy, Tomer M. Schlank

At each prime $p$ and height $n+1 \ge 2$, we prove that the telescopic and chromatic localizations of spectra differ. Specifically, for $\mathbb{Z}$ acting by Adams operations on $\mathrm{BP}\langle n \rangle$, we prove that the $T(n+1)$-localized algebraic $K$-theory of $\mathrm{BP}\langle n \rangle^{h\mathbb{Z}}$ is not $K(n+1)$-local. We also show that Ga

Stephen Mak, Liming Xu, Tim Pearce, Michael Ostroumov

Collaborative Vehicle Routing is where delivery companies cooperate by sharing their delivery information and performing delivery requests on behalf of each other. This achieves economies of scale and thus reduces cost, greenhouse gas emissions, and road congestion. But which company should partner with whom, and how much should each company be compensated?

Wei Bu, Sean Seet

Self-dual Yang-Mills theory admits an underlying infinite dimensional symmetry algebra, which has been obtained from mode expansion of Mellin transformed 4d scattering amplitudes and separately, Koszul duality on twistor space. In this paper, we propose to derive an explicit 2d realization of the algebra by performing a particular gauge transformation on the

Mourad Choulli

We establish in dimension $3$ a stability inequality for the problem of determining the potential in the Schr\"odinger equation from boundary measurements in the case where the potential belongs to $L^s$ with $s\in (2,3)$.

Zhiquan Tan, Kaipeng Zheng, Weiran Huang

Semi-supervised learning has made remarkable strides by effectively utilizing a limited amount of labeled data while capitalizing on the abundant information present in unlabeled data. However, current algorithms often prioritize aligning image predictions with specific classes generated through self-training techniques, thereby neglecting the inherent relat