April 2023 arXiv papers — page 100

Zhihao Lin, Yongtao Wang, Jinhe Zhang, Xiaojie Chu

Dynamic neural network is an emerging research topic in deep learning. With adaptive inference, dynamic models can achieve remarkable accuracy and computational efficiency. However, it is challenging to design a powerful dynamic detector, because of no suitable dynamic architecture and exiting criterion for object detection. To tackle these difficulties, we

Yongyun Chen, Qiusheng Gu, Junhui Fan, Xiaoling Yu

We study the radio galaxies with known redshift detected by the Fermi satellite after 10 years of data (4FGL-DR2). We use a one-zone leptonic model to fit the quasi-simultaneous multiwavelength data of these radio galaxies and study the distributions of the derived physical parameter as a function of black hole mass and accretion disk luminosity. The main re

Guowei Dai, Yong Zhang

We prove the existence of two smooth families of unbounded domains in $\mathbb{R}^{N+1}$ with $N\geq1$ such that \begin{equation} -\Delta u=\lambda u\,\, \text{in}\,\,\Omega, \,\, u=0,\,\,\partial_\nu u=\text{const}\,\,\text{on}\,\,\partial\Omega\nonumber \end{equation} admits a sign-changing solution. The domains bifurcate from the straight cylinder $B_1\ti

Thomas K. Reid, S. Pamir Alpay, Alexander V. Balatsky, Sanjeev K. Nayak

Topological insulators (TIs) are an important family of quantum materials that exhibit a Dirac point (DP) in the surface band structure but have a finite band gap in bulk. A large degree of spin-orbit interaction and low bandgap is a prerequisite for stabilizing DPs on selective atomically flat cleavage planes. Tuning of the DP in these materials has been su

Feixiang Ren

Human pose estimation has seen widespread use of transformer models in recent years. Pose transformers benefit from the self-attention map, which captures the correlation between human joint tokens and the image. However, training such models is computationally expensive. The recent token-Pruned Pose Transformer (PPT) solves this problem by pruning the backg

Adel Awad, Esraa Elkhateeb

Here we extend the approach developed in \cite{adel_2} to study the thermodynamics of Taub-NUT-AdS and dyonic Taub-NUT-AdS solutions. Furthermore, we investigate in details the possible phase structures of the dyonic Taub-NUT-AdS solution. We show that the first law, Gibbs-Duhem and Smarr's relations are all satisfied for both solutions. Our study of phase s

Yuzhao Chen, Zonghuan Li, Zhiyuan Hu, Nuno Vasconcelos

The problem of continual learning has attracted rising attention in recent years. However, few works have questioned the commonly used learning setup, based on a task curriculum of random class. This differs significantly from human continual learning, which is guided by taxonomic curricula. In this work, we propose the Taxonomic Class Incremental Learning (

Justin Pothoof, Robert J. E. Westbrook, Rajiv Giridharagopal, Madeleine D. Breshears

We use scanning probe microscopy to study ion migration in the formamidinium (FA)-containing halide perovskite semiconductor $Cs_{0.22}FA_{0.78}Pb(I_{0.85}Br_{0.15})_3$ in the presence and absence of chemical surface passivation. We measure the evolving contact potential difference (CPD) using scanning Kelvin probe microscopy (SKPM) following voltage poling.

Luis A. Delgadillo, O. G. Miranda

The precise determination of the leptonic $CP$-phase is one of the major goals for future generation long Baseline experiments. On the other hand, if new physics beyond the Standard Model exists, a robust determination of such a $CP$-phase may be a challenge. Moreover, it has been pointed out that, in this scenario, an apparent discrepancy in the $CP$-phase

Andrew Sabot, Vikas Natesh, H. T. Kung, Wei-Te Ting

We present the MEMA framework for the easy and quick derivation of efficient inference runtimes that minimize external memory accesses for matrix multiplication on TinyML systems. The framework accounts for hardware resource constraints and problem sizes in analytically determining optimized schedules and kernels that minimize memory accesses. MEMA provides

Yuchen He, Sung-Ha Kang, Wenjing Liao, Hao Liu

We propose an effective and robust algorithm for identifying partial differential equations (PDEs) with space-time varying coefficients from a single trajectory of noisy observations. Identifying unknown differential equations from noisy observations is a difficult task, and it is even more challenging with space and time varying coefficients in the PDE. The

Chen Zhao, Anqi Liu, Xiao Zhang, Xuewei Cao

Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phen

Yong Wang

In this paper, we define a generalized elliptic genus of an almost complex manifold with an extra complex bundle which generalize the elliptic genus in [10]. This generalized elliptic genus is a generalized Jacobi form. By this generalized Jacobi form, we can get some SL(2,Z) modular forms. By these SL(2,Z) modular forms, we get some interesting anomaly canc

S. V. Bolokhov, V. D. Ivashchuk

This review dealt with generalized Melvin solutions for simple finite-dimensional Lie algebras. Each solution appears in a model which includes a metric and $n$ scalar fields coupled to $n$ Abelian 2-forms with dilatonic coupling vectors determined by simple Lie algebra of rank $n$. The set of $n$ moduli functions $H_s(z)$ comply with $n$ non-linear (ordinar

Boyu Zhou, Boulat A. Bash, Saikat Guha, Christos N. Gagatsos

We address the problem of estimating the transmissivity of the pure-loss channel from the Bayesian point of view, i.e., we consider that some prior probability distribution function (PDF) on the unknown variable is available and we employ methods to compute the Bayesian minimum mean square error (MMSE). Specifically, we consider two prior PDFs: the two-point

Mohammad Reza Taesiri, Giang Nguyen, Sarra Habchi, Cor-Paul Bezemer

Image classifiers are information-discarding machines, by design. Yet, how these models discard information remains mysterious. We hypothesize that one way for image classifiers to reach high accuracy is to first zoom to the most discriminative region in the image and then extract features from there to predict image labels, discarding the rest of the image.

Yunlong Xu, Peizhen Yang, Zhengbin Tao

Complex networks represent system dynamics through the interactions of a set of anomalous time series. Consider the problem of computing correlations for highly correlated pairs of time series across sliding windows. Efficiently computing and updating the correlation matrix for user-defined sliding periods and thresholds enables large-scale time series netwo

Isidora Tourni, Georgios Grigorakis, Isidoros Marougkas, Konstantinos Dafnis

The advances of Generative AI models with interactive capabilities over the past few years offer unique opportunities for socioeconomic mobility. Their potential for scalability, accessibility, affordability, personalizing and convenience sets a first-class opportunity for poverty-stricken countries to adapt and modernize their educational order. As a result

Blake Mellor

The fundamental quandle is a complete invariant for unoriented tame knots \cite{JO, Ma} and non-split links \cite{FR}. The proof involves proving a relationship between the components of the fundamental quandle and the cosets of the peripheral subgroup(s) in the fundamental group of the knot or link. We extend these relationships to spatial graphs, and to $N

F. Pozo Nuñez, N. Gianniotis, K. L. Polsterer

We present a probabilistic cross-correlation approach to estimate time delays in the context of reverberation mapping (RM) of Active Galactic Nuclei (AGN). We reformulate the traditional interpolated cross-correlation method as a statistically principled model that delivers a posterior distribution for the delay. The method employs Gaussian processes as a mo

Gerardo L. Maldonado, Miguel Raggi Pérez, Edgardo Roldán-Pensado

In this note we give a negative answer to a question proposed by Almendra-Hern\'andez and Mart\'inez-Sandoval. Let $n\le m$ be positive integers and let $X$ and $Y$ be sets of sizes $n$ and $m$ in $\mathbb{R}^{n-1}$ such that every pair of points in $X\cup Y$ defines a unique distance. There is a natural order on $X\times Y$ induced by the distances between

Wataru Zaitsu, Mingzhe Jin

In the first half of 2023, text-generative artificial intelligence (AI), including ChatGPT, equipped with GPT-3.5 and GPT-4, from OpenAI, has attracted considerable attention worldwide. In this study, first, we compared Japanese stylometric features of texts generated by GPT (-3.5 and -4) and those written by humans. In this work, we performed multi-dimensio

Lingyun Ding, Richard M. McLaughlin

We investigate diffusion-driven flows in a parallel-plate channel domain with linear density stratification, which arise from the combined influence of gravity and diffusion in density-stratified fluids. We compute the time-dependent diffusion-driven flows and perturbed density field using eigenfunction expansions under the Boussinesq approximation. In chann

Teruo Nagase, Akiko Shima

Charts are oriented labeled graphs in a disk. Any simple surface braid (2-dimensional braid) can be described by using a chart. Also, a chart represents an oriented closed surface (called a surface-link) embedded in 4-space. In this paper, we investigate surface-links by using charts. In [11], [12], we gave an enumeration of the charts with two crossings. In

Jonathan M. Keith

The concept of measurability of functions on a charge space is generalised for functions taking values in a uniform space. Several existing forms of measurability generalise naturally in this context, and new forms of measurability are proposed. Conditions under which the various forms of measurability are logically equivalent are identified. Applying these

Ayon Sen, Gang Pan, Anton Mitrokhin, Ashraful Islam

Accurate camera-to-lidar calibration is a requirement for sensor data fusion in many 3D perception tasks. In this paper, we present SceneCalib, a novel method for simultaneous self-calibration of extrinsic and intrinsic parameters in a system containing multiple cameras and a lidar sensor. Existing methods typically require specially designed calibration tar

S. C. Burd, H. M. Knaack, R. Srinivas, C. Arenz

We show experimentally that a broad class of interactions involving quantum harmonic oscillators can be made stronger (amplified) using a unitary squeezing protocol. While our demonstration uses the motional and spin states of a single trapped $^{25}$Mg$^{+}$ ion, the scheme applies generally to Hamiltonians involving just a single harmonic oscillator as wel

Jun Dai, Roman V. Krems

Kernel models of potential energy surfaces (PES) for polyatomic molecules are often restricted by a specific choice of the kernel function. This can be avoided by optimizing the complexity of the kernel function. For regression problems with very expensive data, the functional form of the model kernels can be optimized in the Gaussian process (GP) setting th

Ryan Giordano, Martin Ingram, Tamara Broderick

Automatic differentiation variational inference (ADVI) offers fast and easy-to-use posterior approximation in multiple modern probabilistic programming languages. However, its stochastic optimizer lacks clear convergence criteria and requires tuning parameters. Moreover, ADVI inherits the poor posterior uncertainty estimates of mean-field variational Bayes (

Kyle Poe, Enrique Mallada, René Vidal

The study of theoretical conditions for recovering sparse signals from compressive measurements has received a lot of attention in the research community. In parallel, there has been a great amount of work characterizing conditions for the recovery both the state and the input to a linear dynamical system (LDS), including a handful of results on recovering s

Jihoon Suh, Takashi Tanaka

The global trend of energy deregulation has led to the market mechanism replacing some functionality of load frequency control (LFC). Accordingly, information exchange among participating generators and the market operator plays a crucial role in optimizing social utility. However, privacy has been an equally pressing concern in such settings. This conflict

Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao

We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question. We believe that current LLMs can answer causal questions with existing causal knowledge as combined domain experts. However, they are not yet able to provide satisfactory answers for discovering

Rakesh Chada, Zhaoheng Zheng, Pradeep Natarajan

We propose a self-supervised shared encoder model that achieves strong results on several visual, language and multimodal benchmarks while being data, memory and run-time efficient. We make three key contributions. First, in contrast to most existing works, we use a single transformer with all the encoder layers processing both the text and the image modalit

Shulei Cao

The current expansion of the Universe has been observed to be accelerating, and the widely accepted spatially-flat concordance model of general relativistic cosmology attributes this phenomenon to a constant dark energy, a cosmological constant, which is measured to comprise about 70% of the total energy budget of the current Universe. However, observational

Paul Sutcliffe

Q-lumps are spinning planar topological solitons with stationary solutions that satisfy first-order Bogomolny equations. Q-lump scattering has previously been studied only in the charge two sector, by approximating time evolution by motion in the moduli space of stationary solutions. In this paper, higher charge scattering is studied via motion on families o

Fernando Richter Vidal, Naghmeh Ivaki, Nuno Laranjeiro

Blockchain recently became very popular due to its use in cryptocurrencies and potential application in various domains (e.g., retail, healthcare, insurance). The smart contract is a key part of blockchain systems and specifies an agreement between transaction participants. Nowadays, smart contracts are being deployed carrying residual faults, including seve

Tomáš Ondro, Rudolf Gális

A new determination of the temperature of the intergalactic medium over $3.9 \leq z \leq 4.3$ is presented. We applied the curvature method on a sample of 10 high resolution quasar spectra from the Ultraviolet and Visual Echelle Spectrograph on the VLT/ESO. We measured the temperature at mean density by determining the temperature at the characteristic overd

Gurmeet Singh, Vikas Varshney, Veera Sundararaghavan

Vitrimers offer a promising sustainable alternative to conventional epoxies due to their recyclability. Vitrimers are covalent adaptive networks where some bonds can break and reform above the vitrimer transition temperature. While this can lead to desirable behavior such as malleability, this also leads to undesirable rheological behavior such as low-temper

M. Belén Arouxet, Aurelio F. Bariviera, Verónica Pastor, Victoria Vampa

Commodity futures constitute an attractive asset class for portfolio managers. Propelled by their low correlation with other assets, commodities begin gaining popularity among investors, as they allow to capture diversification benefits. After more than two decades of active investing experience, this paper examines the time and frequency of spillovers betwe

Yixuan Liu, Suyun Zhao, Li Xiong, Yuhan Liu

Federated Learning, as a popular paradigm for collaborative training, is vulnerable against privacy attacks. Different privacy levels regarding users' attitudes need to be satisfied locally, while a strict privacy guarantee for the global model is also required centrally. Personalized Local Differential Privacy (PLDP) is suitable for preserving users' varyin

Meng-Jhang Fong, Po-Hsuan Lin, Thomas R. Palfrey

Cursed Equilibrium of Eyster and Rabin (2005) has been a leading theory for explaining winner's-curse-type behavior in static Bayesian games, but it faces conceptual limitations when applied to dynamic games. Two recent extensions, Cursed Sequential Equilibrium (CSE) by Fong, Lin and Palfrey (2025) and Sequential Cursed Equilibrium (SCE) by Cohen and Li (202

Siyu Liu, Xunyuan Yin, Jinfeng Liu

This paper presents an efficient approach for state estimation of post-combustion CO2 capture plants (PCCPs) by using reduced-order neural network models. The method involves extracting lower-dimensional feature vectors from high-dimensional operational data of the PCCP and constructing a reduced-order process model using proper orthogonal decomposition (POD

Aly Sabri Abdalla, Vuk Marojevic

O-RAN establishes an advanced radio access network (RAN) architecture that supports inter-operable, multi-vendor, and artificial intelligence (AI) controlled wireless access networks. The unique components, interfaces, and technologies of O-RAN differentiate it from the 3GPP RAN. Because O-RAN supports 3GPP protocols, currently 4G and 5G, while offering addi

Taner Arsan, Sehnaz Sismanoglu Simsek, Onder Pekcan

In this study, Nobel Laureate Orhan Pamuk's works are chosen as examples of Turkish literature. By counting the number of letters and words in his texts, we find it possible to study his works statistically. It has been known that there is a geometrical order in text structures. Here the method based on the basic assumption of fractal geometry is introduced

Venkat Srinivasan, Darshan Gandhi, Urmish Thakker, Raghu Prabhakar

Large foundation language models have shown their versatility in being able to be adapted to perform a wide variety of downstream tasks, such as text generation, sentiment analysis, semantic search etc. However, training such large foundational models is a non-trivial exercise that requires a significant amount of compute power and expertise from machine lea

Saeid Ashraf Vaghefi, Qian Wang, Veruska Muccione, Jingwei Ni

Large Language Models (LLMs) have made significant progress in recent years, achieving remarkable results in question-answering tasks (QA). However, they still face two major challenges: hallucination and outdated information after the training phase. These challenges take center stage in critical domains like climate change, where obtaining accurate and up-

Junyao Wang, Hanning Chen, Mariam Issa, Sitao Huang

Cybersecurity has emerged as a critical challenge for the industry. With the large complexity of the security landscape, sophisticated and costly deep learning models often fail to provide timely detection of cyber threats on edge devices. Brain-inspired hyperdimensional computing (HDC) has been introduced as a promising solution to address this issue. Howev

Song Bo, Xunyuan Yin, Jinfeng Liu

Reinforcement learning (RL) is an area of significant research interest, and safe RL in particular is attracting attention due to its ability to handle safety-driven constraints that are crucial for real-world applications of RL algorithms. This work proposes a novel approach to RL training, called control invariant set (CIS) enhanced RL, which leverages the

Nick Galatos, Xiao Zhuang

We characterize all residuated lattices that have height equal to $3$ and show that the variety they generate has continuum-many subvarieties. More generally, we study unilinear residuated lattices: their lattice is a union of disjoint incomparable chains, with bounds added. We we give two general constructions of unilinear residuated lattices, provide an ax

Morteza Bodaghi, Kazem Esmailpour, Nima Refahati

In the past decade, the use of renewable energy for heating and residential and greenhouse cooling structures has gained much interest due to the energy crisis, population growth, and the quantity of demand. This paper investigates heat transport and thermodynamic equations for a residential and greenhouse structure to simulate and examine the performance of

Bangguo Yu, Hamidreza Kasaei, Ming Cao

This work focuses on the problem of visual target navigation, which is very important for autonomous robots as it is closely related to high-level tasks. To find a special object in unknown environments, classical and learning-based approaches are fundamental components of navigation that have been investigated thoroughly in the past. However, due to the dif

Huy Dao, Dung D. Le, Cuong Chu

State-of-the-art methods on conversational recommender systems (CRS) leverage external knowledge to enhance both items' and contextual words' representations to achieve high quality recommendations and responses generation. However, the representations of the items and words are usually modeled in two separated semantic spaces, which leads to misalignment is

Adam Rettig, Joonho Lee, Martin Head-Gordon

Hybrid density functional theory (DFT) remains intractable for large periodic systems due to the demanding computational cost of exact exchange. We apply the tensor hypercontraction (THC) (or interpolative separable density fitting) approximation to periodic hybrid DFT calculations with Gaussian-type orbitals. This is done to lower the computational scaling

Alexander N. Craddock, Yang Wang, Felipe Giraldo, Rourke Sekelsky

The generation of entangled photon pairs which are compatible with quantum devices and standard telecommunication channels are critical for the development of long range fiber quantum networks. Aside from wavelength, bandwidth matching and high fidelity of produced pairs are necessary for high interfacing efficiency. High-rate, robust entanglement sources th

Junyao Wang, Sitao Huang, Mohsen Imani

Brain-inspired hyperdimensional computing (HDC) has been recently considered a promising learning approach for resource-constrained devices. However, existing approaches use static encoders that are never updated during the learning process. Consequently, it requires a very high dimensionality to achieve adequate accuracy, severely lowering the encoding and

Zhongzheng Tian, Sheng Zhang, Gia-Wei Chern

We present a scalable machine learning (ML) framework for predicting intensive properties and particularly classifying phases of many-body systems. Scalability and transferability are central to the unprecedented computational efficiency of ML methods. In general, linear-scaling computation can be achieved through the divide and conquer approach, and the loc

Bangguo Yu, Hamidreza Kasaei, Ming Cao

Visual target navigation in unknown environments is a crucial problem in robotics. Despite extensive investigation of classical and learning-based approaches in the past, robots lack common-sense knowledge about household objects and layouts. Prior state-of-the-art approaches to this task rely on learning the priors during the training and typically require

Ionut Chifan, Michael Davis, Daniel Drimbe

In \cite{CDD22} we investigated the structure of $\ast$-isomorphisms between von Neumann algebras $L(\Gamma)$ associated with graph product groups $\Gamma$ of flower-shaped graphs and property (T) wreath-like product vertex groups as in \cite{CIOS21}. In this follow-up we continue the structural study of these algebras by establishing that these graph produc

Maher A. Dayeh, Eric J. Zirnstein, Pawel Swaczyna, David J. McComas

A Ribbon of enhanced energetic neutral atom (ENA) emissions was discovered by the Interstellar Boundary Explorer (IBEX) in 2009, redefining our understanding of the heliosphere boundaries and the physical processes occurring at the interstellar interface. The Ribbon signal is intertwined with that of a globally distributed flux (GDF) that spans the entire sk

Daniel Manu, Jingjing Yao, Wuji Liu, Xiang Sun

Recent advances in deep learning have accelerated its use in various applications, such as cellular image analysis and molecular discovery. In molecular discovery, a generative adversarial network (GAN), which comprises a discriminator to distinguish generated molecules from existing molecules and a generator to generate new molecules, is one of the premier

Amelie Royer, Ilia Karmanov, Andrii Skliar, Babak Ehteshami Bejnordi

Mixture of Experts (MoE) are rising in popularity as a means to train extremely large-scale models, yet allowing for a reasonable computational cost at inference time. Recent state-of-the-art approaches usually assume a large number of experts, and require training all experts jointly, which often lead to training instabilities such as the router collapsing

Haonan Wang, Li Yang

Topological mosaic pattern (TMP) can be formed in two-dimensional (2D) moir\'e superlattices, a set of periodic and spatially separated domains with distinct topologies give rise to periodic edge states on the domain walls. In this study, we demonstrate that these periodic edge states play a crucial role in determining global topological properties. By devel

Di Wu, Rehmat Ullah, Philip Rodgers, Peter Kilpatrick

Efficiently running federated learning (FL) on resource-constrained devices is challenging since they are required to train computationally intensive deep neural networks (DNN) independently. DNN partitioning-based FL (DPFL) has been proposed as one mechanism to accelerate training where the layers of a DNN (or computation) are offloaded from the device to t

Srikanth Sathyanarayana, Matteo Bernardini, Davide Modesti, Sergio Pirozzoli

Exascale High Performance Computing (HPC) represents a tremendous opportunity to push the boundaries of Computational Fluid Dynamics (CFD), but despite the consolidated trend towards the use of Graphics Processing Units (GPUs), programmability is still an issue. STREAmS-2 (Bernardini et al. Comput. Phys. Commun. 285 (2023) 108644) is a compressible solver fo

Uzma Hasan, Md Osman Gani

Learning causal relationships solely from observational data often fails to reveal the underlying causal mechanisms due to the vast search space of possible causal graphs, which can grow exponentially, especially for greedy algorithms using score-based approaches. Leveraging prior causal information, such as the presence or absence of causal edges, can help

Juntao Tan, Shelby Heinecke, Zhiwei Liu, Yongjun Chen

Sequential recommendation models, models that learn from chronological user-item interactions, outperform traditional recommendation models in many settings. Despite the success of sequential recommendation models, their robustness has recently come into question. Two properties unique to the nature of sequential recommendation models may impair their robust

Angel Felipe, Maria Jaenada, Pedro Miranda, Leandro Pardo

Model selection criteria are rules used to select the best statistical model among a set of candidate models, striking a trade-off between goodness of fit and model complexity. Most popular model selection criteria measure the goodness of fit trough the model log-likelihood function, yielding to non-robust criteria. This paper presents a new family of robust

P. Kosec, E. Kara, A. C. Fabian, F. Fürst

The accretion of matter onto black holes and neutron stars often leads to the launching of outflows that can greatly affect the environments surrounding the compact object. In supermassive black holes, these outflows can even be powerful enough to dictate the evolution of the entire host galaxy, and yet, to date, we do not understand how these so-called accr

Kun Qian, Ryan Shea, Yu Li, Luke Kutszik Fryer

Along with the development of systems for natural language understanding and generation, dialog systems have been widely adopted for language learning and practicing. Many current educational dialog systems perform chitchat, where the generated content and vocabulary are not constrained. However, for learners in a school setting, practice through dialog is m

Aditi Mitra, Hsiu-Chung Yeh, Fei Yan, Achim Rosch

Results are presented for a Floquet Ising chain with duality twisted boundary conditions, taking into account the role of weak integrability breaking in the form of four-fermion interactions. In the integrable case, a single isolated Majorana zero mode exists which is a symmetry in the sense that it commutes both with the Floquet unitary and the $Z_2$ symmet

Sergey Buterin, Sergey Vasilev

We suggest a new statement of the inverse spectral problem for Sturm--Liouville-type operators with constant delay. This inverse problem consists in recovering the coefficient (often referred to as potential) of the delayed term in the corresponding equation from the spectra of two boundary value problems with one common boundary condition. However, all stud

Yannis Coutouly, Emmanuel Godard

The Iterated Immediate Snapshot model (IIS) is a central model in the message adversary setting. We consider general message adversaries whose executions are arbitrary subsets of the executions of the IIS message adversary. We present a new topological approach for such general adversaries, based upon geometric simplicial complexes. We are able to define a t

Junrong Lin, Mahmudul Hasan, Pinar Acar, Jose Blanchet

Computational experiments are exploited in finding a well-designed processing path to optimize material structures for desired properties. This requires understanding the interplay between the processing-(micro)structure-property linkages using a multi-scale approach that connects the macro-scale (process parameters) to meso (homogenized properties) and micr

Joshua Rosser, Jacob Arkin, Siddharth Patki, Thomas M. Howard

In situations such as habitat construction, station inspection, or cooperative exploration, incorrect assumptions about the environment or task across the team could lead to mission failure. Thus it is important to resolve any ambiguity about the mission between teammates before embarking on a commanded task. The safeguards guaranteed by formal methods can b

Claudia Huaylla, Marcelo N Kuperman, Lucas A. Garibaldi

Networks are a convenient way to represent many interactions among different entities as they provide an efficient and clear methodology to evaluate and organize relevant data. While there are many features for characterizing networks there is a quantity that seems rather elusive: Complexity. The quantification of the complexity of networks is nowadays a fun

Lasse Peters, Andrea Bajcsy, Chih-Yuan Chiu, David Fridovich-Keil

Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on contingency planning, tailored to multi-agent scenarios in which a robot's actions impact the decisions of other age

Mahdi S. Hosseini, Babak Ehteshami Bejnordi, Vincent Quoc-Huy Trinh, Danial Hasan

Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the

Noah Martin, Fahad Dogar

Cloud providers are highly incentivized to reduce latency. One way they do this is by locating datacenters as close to users as possible. These "cloud edge" datacenters are placed in metropolitan areas and enable edge computing for residents of these cities. Therefore, which cities are selected to host edge datacenters determines who has the fastest access t

Francesca Rizzo

The period morphism of polarized hyper-K\"ahler manifolds of K3$^{[m]}$-type gives an embedding of each connected component of the moduli space of polarized hyper-K\"ahler manifolds of K3$^{[m]}$-type into their period space, which is the quotient of a Hermitian symmetric domain by an arithmetic group. Following work of Stellari and Gritsenko-Hulek-Sankaran,

H. S. Larssen, J. C. Vassilicos

We study fluctuations of all co-existing energy exchange/transfer/transport processes in stationary periodic turbulence including those which average to zero and are not present in average cascade theories. We use a Helmholtz decomposition of accelerations which leads to a decomposition of all terms in the K\'arm\'an-Howarth-Monin-Hill (KHMH) equation (scale

Zhibo Yang, Robert L. Kosut, K. Birgitta Whaley

We develop a Hamiltonian switching ansatz for bipartite control that is inspired by the Quantum Approximate Optimization Algorithm (QAOA), to mitigate environmental noise on qubits. We illustrate the approach with application to the protection of quantum gates performed on i) a central spin qubit coupling to bath spins through isotropic Heisenberg interactio

Lingyuan Ye

In this paper we provide a semantic and syntactic analysis of parametrised natural numbers object in coherent categories, or pr-coherent categories. Semantically, we show the definable functions in the initial pr-coherent category are exactly given by primitive recursive functions. We also show that any pr-coherent category supports the construction of bound

Rüdiger Kürsten, Jakob Mihatsch, Thomas Ihle

We consider two species of self-propelled point particles: A-particles and B-particles. The orientations between nearby particles are subject to pair interactions of different strength for A-A-, A-B-(=B-A-) and B-B-interactions, respectively. Even if all interactions involved are repelling, that is, if they locally favor anti-alignment between each pair of p

Aleksei Gerasimov, Miroslav Vořechovský

We propose an algorithm for an optimal adaptive selection of points from the design domain of input random variables that are needed for an accurate estimation of failure probability and the determination of the boundary between safe and failure domains. The method is particularly useful when each evaluation of the performance function g(x) is very expensive

Jakub Řada, Michal Zamboj

The paper is focused on the four-dimensional visualization of hypersurfaces represented by implicit equations without their parametrization. We describe a general method to find shadow boundaries in an arbitrary dimension and apply it in a three- and four-dimensional space. Furthermore, we design a system of polynomial equations to construct occluding contou

Pham Tran Anh Quang, Jérémie Leguay, Xu Gong, Xu Huiying

In modern SD-WAN networks, a global controller is able to steer traffic on different paths based on application requirements and global intents. However, existing solutions cannot dynamically tune the way bandwidth is shared between flows inside each overlay link, in particular when the available capacity is uncertain due to cross traffic. In this context, w

Jing Yang, Hanyuan Xiao, Wenbin Teng, Yunxuan Cai

Physically-based rendering (PBR) is key for immersive rendering effects used widely in the industry to showcase detailed realistic scenes from computer graphics assets. A well-known caveat is that producing the same is computationally heavy and relies on complex capture devices. Inspired by the success in quality and efficiency of recent volumetric neural re

Kevin Chang, Nathan Dahlin, Rahul Jain, Pierluigi Nuzzo

Over the past decade, neural network (NN)-based controllers have demonstrated remarkable efficacy in a variety of decision-making tasks. However, their black-box nature and the risk of unexpected behaviors pose a challenge to their deployment in real-world systems requiring strong guarantees of correctness and safety. We address these limitations by investig

Martin Koppenhöfer, Peter Groszkowski, A. A. Clerk

Quantum metrology protocols using entangled states of large spin ensembles attempt to achieve measurement sensitivities surpassing the standard quantum limit (SQL), but in many cases they are severely limited by even small amounts of technical noise associated with imperfect sensor readout. Amplification strategies based on time-reversed coherent spin-squeez

Mohammed Adil Saleem, Faraz Zaidi, Celine Rozenblat

One perspective to view the economic development of cities is through the presence of multinational firms; how subsidiaries of various organizations are set up throughout the globe and how cities are connected to each other through these networks of multinational firms. Analysis of these networks can reveal interesting economical and spatial trends, as well

Xue-Jing Luo, Shuo Wang, Zongwei Wu, Christos Sakaridis

The burgeoning field of camouflaged object detection (COD) seeks to identify objects that blend into their surroundings. Despite the impressive performance of recent models, we have identified a limitation in their robustness, where existing methods may misclassify salient objects as camouflaged ones, despite these two characteristics being contradictory. Th

Ulfeta A. Marovac, Aldina R. Avdić, Nikola Lj. Milošević

The Serbian language is a Slavic language spoken by over 12 million speakers and well understood by over 15 million people. In the area of natural language processing, it can be considered a low-resourced language. Also, Serbian is considered a high-inflectional language. The combination of many word inflections and low availability of language resources mak

Maximilian Schütte, Annika Eichler, Herbert Werner

The problem of robust controller synthesis for plants affected by structured uncertainty, captured by integral quadratic constraints, is discussed. The solution is optimized towards a worst-case white noise rejection specification, which is a generalization of the standard $\mathcal{H}_2$-norm to the robust setting including possibly non-LTI uncertainty. Arb

Jan Felipe van Diejen

A quantum $n$-particle model consisting of an open $q$-difference Toda chain with two-sided boundary interactions is placed on a finite integer lattice. The spectrum and eigenbasis are computed by establishing the equivalence with a previously studied $q$-boson model from which the quantum integrability is inherited. Specifically, the $q$-boson-Toda correspo

Matteo Acclavio, Davide Catta, Federico Olimpieri

In this paper we investigate the Curry-Howard correspondence for constructive modal logic in light of the gap between the proof equivalences enforced by the lambda calculi from the literature and by the recently defined winning strategies for this logic. We define a new lambda-calculus for a minimal constructive modal logic by enriching the calculus from the

Patrick Ebel, Vivien Sainte Fare Garnot, Michael Schmitt, Jan Dirk Wegner

Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface. Although modern deep learning methods can implicitly learn to ignore such occlusions, explicit cloud removal as pre-processing enables manual interpretation and allows training models when only few annotations are available. Cloud removal is

Chun Kit Wong, Manxi Lin, Alberto Raheli, Zahra Bashir

Examination of the umbilical artery with Doppler ultrasonography is performed to investigate blood supply to the fetus through the umbilical cord, which is vital for the monitoring of fetal health. Such examination involves several steps that must be performed correctly: identifying suitable sites on the umbilical artery for the measurement, acquiring the bl

Louis Commère, Jean Rouat

Visual to auditory sensory substitution devices convert visual information into sound and can provide valuable assistance for blind people. Recent iterations of these devices rely on depth sensors. Rules for converting depth into sound (i.e. the sonifications) are often designed arbitrarily, with no strong evidence for choosing one over another. The purpose

Thomas Simpson, Konstantinos Vlachas, Anthony Garland, Nikolaos Dervilis

Reduced Order Models (ROMs) are of considerable importance in many areas of engineering in which computational time presents difficulties. Established approaches employ projection-based reduction such as Proper Orthogonal Decomposition, however, such methods can become inefficient or fail in the case of parameteric or strongly nonlinear models. Such limitati

Juan D García-Muñoz, David J Fernández C, F Vergara-Méndez

The multiphoton algebras for one-dimensional Hamiltonians with infinite discrete spectrum, and for their associated kth-order SUSY partners are studied. In both cases, such an algebra is generated by the multiphoton annihilation and creation operators, as well as by Hamiltonians which are functions of an appropriate number operator. The algebras obtained tur