December 2023 arXiv papers — page 70

Konstantinos Voudouris, Ibrahim Alhas, Wout Schellaert, Matteo G. Mecattaf

The Animal-AI Environment is a unique game-based research platform designed to facilitate collaboration between the artificial intelligence and comparative cognition research communities. In this paper, we present the latest version of the Animal-AI Environment, outlining several major features that make the game more engaging for humans and more complex for

Xiao Tian, Rachael Hwee Ling Sim, Jue Fan, Bryan Kian Hsiang Low

Data valuation is concerned with determining a fair valuation of data from data sources to compensate them or to identify training examples that are the most or least useful for predictions. With the rising interest in personal data ownership and data protection regulations, model owners will likely have to fulfil more data deletion requests. This raises iss

Will Hide, Joe Thomas

We study the geometry and spectral theory of Weil-Petersson random surfaces with genus-$g$ and $n$ cusps in the large-$n$ limit. We show that for a random hyperbolic surface in $\mathcal{M}_{g,n}$ with $n$ large, the number of small Laplacian eigenvalues is linear in $n$ with high probability. By work of Otal and Rosas [41], this result is optimal up to a mu

Vladimir Rovenski

Weak contact metric manifolds, i.e., the linear complex structure on the contact distribution is replaced by a nonsingular skew-symmetric tensor, defined by the author and R. Wolak, allowed a new look at the theory of contact manifolds. In this paper we study the new structure of this type, called the weak nearly Sasakian structure. We find conditions that a

Zhixuan Liang, Yao Mu, Hengbo Ma, Masayoshi Tomizuka

Diffusion models have demonstrated strong potential for robotic trajectory planning. However, generating coherent trajectories from high-level instructions remains challenging, especially for long-range composition tasks requiring multiple sequential skills. We propose SkillDiffuser, an end-to-end hierarchical planning framework integrating interpretable ski

Matthias Rosynski, Alexandru Pop, Lucian Busoniu

We consider a problem in which the trajectory of a mobile 3D sensor must be optimized so that certain objects are both found in the overall scene and covered by the point cloud, as fast as possible. This problem is called target search and coverage, and the paper provides an end-to-end deep reinforcement learning (RL) solution to solve it. The deep neural ne

Pablo Krupa, Mario Zanon, Alberto Bemporad

This work presents a nonlinear control framework that guarantees asymptotic offset-free tracking of generic reference trajectories by learning a nonlinear disturbance model, which compensates for input disturbances and model-plant mismatch. Our approach generalizes the well-established method of using an observer to estimate a constant disturbance to allow t

Niclas Boehmer, Markus Brill, Alfonso Cevallos, Jonas Gehrlein

We provide the first large-scale data collection of real-world approval-based committee elections. These elections have been conducted on the Polkadot blockchain as part of their Nominated Proof-of-Stake mechanism and contain around one thousand candidates and tens of thousands of (weighted) voters each. We conduct an in-depth study of application-relevant q

Minh Nguyen, Tim Duong, Daniel Neuhauser

The projector augmented wave (PAW) method of Bl\"ochl linearly maps smooth pseudo wavefunctions to the highly oscillatory all-electron DFT orbitals. Compared to norm-conserving pseudopotentials (NCPP), PAW has the advantage of lower kinetic energy cutoffs and larger grid spacings at the cost of having to solve for non-orthogonal wavefunctions. We earlier dev

V. Hocdé, P. Moskalik, N. A. Gorynya, R. Smolec

The primary goal of this paper is to derive precise Fourier parameters of the radial velocity (RV) curves for fundamental and first-overtone Galactic Cepheids. For each star, we carefully selected RV measurements available in the literature which yield the highest precision of Fourier parameters. We performed a Fourier decomposition of the RV curves. We subt

S. N. Arjun, P. G. Romeo

Cohen and Taylor introduced Plesken Lie algebra as certain Lie algebra constructed using finite groups. Arjun and Romeo described the linear representation of these Lie algebras induced from group representation in [1]. Hence the authors posed the question as to what are the irreducible representations of Plesken Lie algebras and describes the irreducible re

Farivar Rajabi, J. J. McArthur

The rise of AI-powered classification techniques has ushered in a new era for data-driven Fault Detection and Diagnosis in smart building systems. While extensive research has championed supervised FDD approaches, the real-world application of unsupervised methods remains limited. Among these, cluster analysis stands out for its potential with Building Manag

Venkat Anantharam

The Shannon-Hagelbarger theorem states that the effective resistance across any pair of nodes in a resistive network is a concave function of the edge resistances. We give an information-theoretic proof of this result, building on the theory of the Gaussian free field. This also allows us to prove an extension of the result to determinants of matrices of cro

Benjamin Bordais, Daniel Neider, Rajarshi Roy

We investigate the complexity of LTL learning, which consists in deciding given a finite set of positive ultimately periodic words, a finite set of negative ultimately periodic words, and a bound B given in unary, if there is an LTL-formula of size less than or equal to B that all positive words satisfy and that all negative violate. We prove that this decis

Preeti Kharb, Eric G. Blackman, Eric Clausen-Brown, Mousumi Das

We report superluminal jet motion with an apparent speed of $\beta_\mathrm{app}=1.65\pm0.57$ in the radio-quiet (RQ) low ionisation nuclear emission line region (LINER) galaxy, KISSR872. This result comes from two epoch phase-referenced very long baseline interferometry (VLBI) observations at 5 GHz. The detection of bulk relativistic motion in the jet of thi

Vicu-Mihalis Maer, Levente Tamas, Lucian Busoniu

Global positioning systems can provide sufficient positioning accuracy for large scale robotic tasks in open environments. However, in underwater environments, these systems cannot be directly used, and measuring the position of underwater robots becomes more difficult. In this paper we first evaluate the performance of existing pose estimation techniques fo

V. Mastropietro

We review the theory of renormalization, including perturbative renormalization, regularized functional integrals, Renormalization Group and rigorous renormalization.

Chris Hokamp, Demian Gholipour Ghalandari, Parsa Ghaffari

We present an open-source Python library for building and using datasets where inputs are clusters of textual data, and outputs are sequences of real values representing one or more time series signals. The news-signals library supports diverse data science and NLP problem settings related to the prediction of time series behaviour using textual data feeds.

E. Filichkina, E. Yarovaya

We consider a time-continuous branching random walk on a one-dimensional lattice on which there is one center (lattice point) of particle generation, called branching source. The generation of particles in the branching source is described by a Markov branching process. Some number (finite or infinite, depending on the problem formulation) of absorbing sourc

Jordi Riu, Jan Nogué, Gerard Vilaplana, Artur Garcia-Saez

We propose a novel Reinforcement Learning (RL) method for optimizing quantum circuits using graph-theoretic simplification rules of ZX-diagrams. The agent, trained using the Proximal Policy Optimization (PPO) algorithm, employs Graph Neural Networks to approximate the policy and value functions. We demonstrate the capacity of our approach by comparing it aga

Sarah Recchia, Stefano Gabici

Recent measurements revealed the presence of several features in the cosmic ray spectrum. In particular, the proton and helium spectra exhibit a spectral hardening at $\approx$ 300 GV and a spectral steeping at $\approx$ 15 TV, followed by the well known knee-likefeature at $\approx$ 3 TV. The spectra of heavier nuclei also harden at $\approx$ 300 GV, while

Qi Mao, Lan Chen, Yuchao Gu, Zhen Fang

Recent diffusion-based image editing approaches have exhibited impressive editing capabilities in images with simple compositions. However, localized editing in complex scenarios has not been well-studied in the literature, despite its growing real-world demands. Existing mask-based inpainting methods fall short of retaining the underlying structure within t

Harshita Sharma, Pruthwik Mishra, Dipti Misra Sharma

Word problem Solving is a challenging NLP task that deals with solving mathematical problems described in natural language. Recently, there has been renewed interest in developing word problem solvers for Indian languages. As part of this paper, we have built a Hindi arithmetic word problem solver which makes use of verbs. Additionally, we have created verb

Greg Muller

We give a short and elementary proof that every Dynkin diagram admits finitely many (positive integral) friezes. This was originally proven by Gunawan-Muller using the geometry of cluster algebras. The proof here provides an explicit (albeit inefficient) bound on values.

Rong Li, Yichen Qin, Yang Li

Model misspecification is ubiquitous in data analysis because the data-generating process is often complex and mathematically intractable. Therefore, assessing estimation uncertainty and conducting statistical inference under a possibly misspecified working model is unavoidable. In such a case, classical methods such as bootstrap and asymptotic theory-based

Zeyinzi Jiang, Chaojie Mao, Yulin Pan, Zhen Han

Image diffusion models have been utilized in various tasks, such as text-to-image generation and controllable image synthesis. Recent research has introduced tuning methods that make subtle adjustments to the original models, yielding promising results in specific adaptations of foundational generative diffusion models. Rather than modifying the main backbon

Shanli Tan, Hao Cheng, Xiaohu Wu, Han Yu

Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models. Given the potential data heterogeneity, it is crucial to select appropriate collaborators for each FL participant (FL-PT) based on data complementarity. Recent studies have addressed this challenge. Similarly, it is imperative to consider the

Daniela Bubboloni, Costanza Catalano, Andrea Marino, Ana Silva

In this work we extend the concept of out/in-branchings spanning the vertices of a digraph (also called directed spanning trees) to temporal graphs, which are digraphs where arcs are available only at prescribed times. While the literature has focused on minimum weight/earliest arrival time Temporal Out-Branchings (TOB), we solve the problem for other optimi

Mohammad Rajabdorri, Lukas Sigrist, Enrique Lobato, Matthias C. M. Troffaes

This paper presents a data-driven methodology for estimating Under Frequency Load Shedding (UFLS) in small power systems. UFLS plays a vital role in maintaining system stability by shedding load when the frequency drops below a specified threshold following loss of generation. Using a dynamic System Frequency Response (SFR) model we generate different values

Hyeonsu B. Kang, David Chuan-En Lin, Nikolas Martelaro, Aniket Kittur

Nature is often used to inspire solutions for complex engineering problems, but achieving its full potential is challenging due to difficulties in discovering relevant analogies and synthesizing from them. Here, we present an end-to-end system, BioSpark, that generates biological-analogical mechanisms and provides an interactive interface to comprehend and s

Mohammad Rajabdorri, Enrique Lobato, Lukas Sigrist, Jamshid Aghaei

The conventional approach to solving the unit commitment problem involves discrete intervals at an hourly scale, particularly when integrating frequency dynamics to formulate a frequency-constrained unit commitment. To overcome this limitation, a novel continuous-time frequency-constrained unit commitment framework is proposed in this paper. In this approach

Roger Balsach, Domenico Bonocore, Anna Kulesza

The study of next-to-leading-power (NLP) corrections in soft emissions continues to attract interest both in QCD and in QED. Soft-photon spectra in particular provide a clean case-study for the experimental verification of the Low-Burnett-Kroll (LBK) theorem. In this paper we study the consistency of the LBK theorem in the context of an ambiguity arising fro

Zexi Liu, Bohan Tang, Ziyuan Ye, Xiaowen Dong

Hypergraphs play a pivotal role in the modelling of data featuring higher-order relations involving more than two entities. Hypergraph neural networks emerge as a powerful tool for processing hypergraph-structured data, delivering remarkable performance across various tasks, e.g., hypergraph node classification. However, these models struggle to capture glob

Ran Tao, Sheng Cheng, Xiaofeng Wang, Shenlong Wang

Model predictive control (MPC) has been applied to many platforms in robotics and autonomous systems for its capability to predict a system's future behavior while incorporating constraints that a system may have. To enhance the performance of a system with an MPC controller, one can manually tune the MPC's cost function. However, it can be challenging due t

Tudor Santejudean, Lucian Busoniu

We consider problems in which a mobile robot samples an unknown function defined over its operating space, so as to find a global optimum of this function. The path traveled by the robot matters, since it influences energy and time requirements. We consider a branch-and-bound algorithm called deterministic optimistic optimization, and extend it to the path-a

V. Mastropietro

Recently new methods have been introduced to investigate the non-renormalization properties of the anomalies at a non perturbative level and in presence of a lattice. The issue is relevant in a number of problems ranging from the anomaly-free construction of chiral lattice gauge theory with large cut-off to the universality properties observed in transport c

Ales Wodecki, Pavel Rytir, Vyacheslav Kungurtsev, Jakub Marecek

Multi-product pipelines are a highly efficient means of transporting liquids. Traditionally used to transport petroleum, its products and derivatives, they are now being repurposed to transport liquified natural gas admixed with hydrogen of various colors. We propose a novel mixed-integer linear programming (MILP) formulation, which optimizes efficiency whil

Francisco Troncoso-Pastoriza, Pablo Eguía-Oller, Rebeca P. Díaz-Redondo, Enrique Granada-Álvarez

Computer vision is used in this work to detect lighting elements in buildings with the goal of improving the accuracy of previous methods to provide a precise inventory of the location and state of lamps. Using the framework developed in our previous works, we introduce two new modifications to enhance the system: first, a constraint on the orientation of th

Geir Agnarsson, Elie Alhajjar, Aleyah Dawkins

In this article we compare the known dynamical polynomial time algorithm for the game-over attack strategy, to that of the brute force approach; of checking all the ordered rooted subtrees of a given tree that represents a given computer network. Our approach is purely enumerative and combinatorial in nature. We first revisit known results about a doubly exp

Joni Virta

Object data analysis is concerned with statistical methodology for datasets whose elements reside in an arbitrary, unspecified metric space. In this work we propose the object shape, a novel measure of shape/symmetry for object data. The object shape is easy to compute and interpret, owing to its intuitive interpretation as interpolation between two extreme

Henryk Fukś

We show how to construct a deterministic nearest-neighbour cellular automaton (CA) with four states which emulates diffusion on a one-dimensional lattice. The pseudo-random numbers needed for directing random walkers in the diffusion process are generated with the help of rule 30. This CA produces density profiles which agree very well with solutions of the

Size Wu, Wenwei Zhang, Lumin Xu, Sheng Jin

Detecting objects accurately from a large or open vocabulary necessitates the vision-language alignment on region representations. However, learning such a region-text alignment by obtaining high-quality box annotations with text labels or descriptions is expensive and infeasible. In contrast, collecting image-text pairs is simpler but lacks precise object l

Francisco Troncoso-Pastoriza, Pablo Eguía-Oller, Rebeca P. Díaz-Redondo, Enrique Granada-Álvarez

This paper introduces a complete method for the automatic detection, identification and localization of lighting elements in buildings, leveraging the available building information modeling (BIM) data of a building and feeding the BIM model with the new collected information, which is key for energy-saving strategies. The detection system is heavily improve

Thomas Lampe, Abbas Abdolmaleki, Sarah Bechtle, Sandy H. Huang

Reinforcement learning solely from an agent's self-generated data is often believed to be infeasible for learning on real robots, due to the amount of data needed. However, if done right, agents learning from real data can be surprisingly efficient through re-using previously collected sub-optimal data. In this paper we demonstrate how the increased understa

Pierre Cardaliaguet, Joe Jackson, Nikiforos Mimikos-Stamatopoulos, Panagiotis E. Souganidis

We study the convergence problem for mean field control, also known as optimal control of McKean-Vlasov dynamics. We assume that the data is smooth but not convex, and thus the limiting value function $\mathcal{U} :[0,T] \times \mathcal{P}_2(\mathbb{R}^d) \to \mathbb{R}$ is Lipschitz, but may not be differentiable. In this setting, the first and last named a

Lorenzo Barca, Francesco Knechtli, Michael Peardon, Stefan Schaefer

The computation of the glueball spectrum is particularly challenging due to the rapid decay of the signal-to-noise ratio of the correlation functions. To address this issue, advanced techniques such as gauge link smearing and the variational method are commonly employed to identify the spectrum before the signal diminishes significantly. However, a significa

Hjalte Frellesvig, Roger Morales, Matthias Wilhelm

We study geometries occurring in Feynman integrals that contribute to the scattering of black holes in the post-Minkowskian expansion. These geometries become relevant to gravitational-wave production during the inspiralling phase of binary black hole mergers through the classical conservative potential. At fourth post-Minkowskian order, a K3 surface is know

Jiahui Gao, Renjie Pi, Jipeng Zhang, Jiacheng Ye

Large language models (LLMs) have shown remarkable proficiency in human-level reasoning and generation capabilities, which encourages extensive research on their application in mathematical problem solving. However, current work has been largely focused on text-based mathematical problems, with limited investigation in problems involving geometric informatio

Edoardo Altamura

Hydrodynamic simulations have become irreplaceable in modern cosmology for exploring complex systems and making predictions to steer future observations. In Chapter 1, we begin with a philosophical discussion on the role of simulations in science. We argue that simulations can bridge the gap between empirical and fundamental knowledge. The validation of simu

Christopher W. Lynn, Qiwei Yu, Rich Pang, Stephanie E. Palmer

In the brain, fine-scale correlations combine to produce macroscopic patterns of activity. However, as experiments record from larger and larger populations, we approach a fundamental bottleneck: the number of correlations one would like to include in a model grows larger than the available data. In this undersampled regime, one must focus on a sparse subset

Bernd C. Kellner

In this note, we consider asymptotic products of binomial and multinomial coefficients and determine their asymptotic constants and formulas. Among them, special cases are the central binomial coefficients, the related Catalan numbers, and binomial coefficients in a row of Pascal's triangle. For the latter case, we show that it can also be derived from a lim

Luke Oeding

We explain a Macaulay2 implementation of a construction, which appeared in [Holweck-Oeding arXiv:2206.13662], of a graded algebra structure on the direct sum of a Lie algebra $\mathfrak{g}$ (typically $\mathfrak{sl}_n$) and a $\mathfrak{g}$-module (typically a subspace of an exterior algebra $\bigwedge^{\bullet}\mathbb{C}^n$). We implement brackets, a Killin

Ahmad Bazzi, Marwa Chafii

The following paper models a secure full duplex (FD) integrated sensing and communication (ISAC) scenario, where malicious eavesdroppers aim at intercepting the downlink (DL) as well as the uplink (UL) information exchanged between the dual functional radar and communication (DFRC) base station (BS) and a set of communication users. The DFRC BS, on the other

S. E. Campbell, G. Bollen, B. A. Brown, A. Dockery

We report the first mass measurement of the proton-halo candidate $^{22}$Al performed with the LEBIT facility's 9.4~T Penning trap mass spectrometer at FRIB. This measurement completes the mass information for the lightest remaining proton-dripline nucleus achievable with Penning traps. $^{22}$Al has been the subject of recent interest regarding a possible h

Jason Kumar, Danny Marfatia, Ningqiang Song

We consider the effect of isospin-violating dark matter-nucleon interactions on direct detection constraints in the regime of small dark matter mass and large scattering cross section. Isospin-violation can lead to both reductions in sensitivity (due to a reduced cross section for scattering with nuclei in the detector) and enhancements in sensitivity (due t

Tristan Bester, Benjamin Rosman, Steven James, Geraud Nangue Tasse

We present counting reward automata-a finite state machine variant capable of modelling any reward function expressible as a formal language. Unlike previous approaches, which are limited to the expression of tasks as regular languages, our framework allows for tasks described by unrestricted grammars. We prove that an agent equipped with such an abstract ma

Heqiang Wang, Jie Xu

The increasing demand for wireless communication underscores the need to optimize radio frequency spectrum utilization. An effective strategy for leveraging underutilized licensed frequency bands is cooperative spectrum sensing (CSS), which enable multiple secondary users (SUs) to collaboratively detect the spectrum usage of primary users (PUs) prior to acce

Gabriel Dale-Gau

Measurements at RHIC and the LHC show strongly enhanced baryon-to-meson yield ratios at intermediate transverse momenta ($p_{\rm{T}}$) in high-energy nuclear collisions compared to $p$+$p$ baseline. This enhancement is attributed to the following QGP effects: strong hydrodynamic flow and parton recombination. Jet probes have been used extensively to gain ins

Imants Platais, Johannes Sahlmann, Leo Girardi, Vera Kozhurina-Platais

We present results from the search for astrometric accelerations of stars in $\omega$ Centauri using 13 years of regularly-scheduled {\it Hubble Space Telescope} WFC3/UVIS calibration observations in the cluster core. The high-precision astrometry of $\sim$160\,000 sources was searched for significant deviations from linear proper motion. This led to the dis

Nandan Thakur, Luiz Bonifacio, Xinyu Zhang, Odunayo Ogundepo

Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) output by leveraging external knowledge sources to reduce factual hallucinations. However, prior work lacks a comprehensive evaluation of different language families, making it challenging to evaluate LLM robustness against errors in external retrieved knowledge. To overcome this, we est

Kim Youwang, Tae-Hyun Oh, Gerard Pons-Moll

We present Paint-it, a text-driven high-fidelity texture map synthesis method for 3D meshes via neural re-parameterized texture optimization. Paint-it synthesizes texture maps from a text description by synthesis-through-optimization, exploiting the Score-Distillation Sampling (SDS). We observe that directly applying SDS yields undesirable texture quality du

A Samuel Pottinger, Nivedita Biyani, Roland Geyer, Douglas J McCauley

Introduction: This multi-disciplinary case study details how an interactive decision support tool leverages game design to inform an international plastic pollution treaty. Design: Seeking to make our scientific findings more usable within the policy process, our interactive software supports manipulation of a mathematical model using techniques borrowed fro

Harry Ho-Yin Ng, Jin-Liang Jiang, Carlo Musolino, Christian Ecker

One of the main challenges in the numerical modeling of binary neutron-star (BNS) mergers is long-term simulations of the post-merger remnant over timescales of the order of seconds. When this modeling includes all the aspects of complex physics, the computational costs can easily become enormous. To address this challenge in part, we have developed a novel

Jasmine A. Berry

The maturation of cognition, from introspection to understanding others, has long been a hallmark of human development. This position paper posits that for AI systems to truly emulate or approach human-like interactions, especially within multifaceted environments populated with diverse agents, they must first achieve an in-depth and nuanced understanding of

Jakub Raczyński, Mateusz Lango, Jerzy Stefanowski

Providing natural language explanations for recommendations is particularly useful from the perspective of a non-expert user. Although several methods for providing such explanations have recently been proposed, we argue that an important aspect of explanation quality has been overlooked in their experimental evaluation. Specifically, the coherence between g

Harris Papadopoulos, George Anastassopoulos

Vesicoureteral Reflux (VUR) is a pediatric disorder in which urine flows backwards from the bladder to the upper urinary tract. Its detection is of great importance as it increases the risk of a Urinary Tract Infection, which can then lead to a kidney infection since bacteria may have direct access to the kidneys. Unfortunately the detection of VUR requires

Lu Shen, Yuriy Zakharov, Benjamin Henson, Nils Morozs

Underwater monitoring and surveillance systems are essential for underwater target detection, localization and classification. The aim of this work is to investigate the possibility of target detection by using data transmission between communication nodes in an underwater acoustic (UWA) network, i.e, re-using acoustic communication signals for target detect

Zachary Bradshaw

Recently, strong evidence has accumulated that some solutions to the Navier-Stokes equations in physically meaningful classes are not unique. The primary purpose of this paper is to establish necessary properties for the error of hypothetical non-unique Navier-Stokes flows under conditions motivated by the scaling of the equations. Our first set of results s

Louis Jouret, Adnane Saoud, Sorin Olaru

This paper presents a novel approach to ensure the safety of continuous-time linear dynamical systems controlled by a neural network (NN) based state-feedback. Our method capitalizes on the use of continuous piece-wise affine (PWA) activation functions (e.g. ReLU) which render the NN a PWA continuous function. By computing the affine regions of the latter an

Raúl Carballo-Rubio, Héloïse Delaporte, Astrid Eichhorn, Aaron Held

New physics beyond General Relativity can modify image features of black holes and horizonless spacetimes and increase the separation between photon rings. This motivates us to explore synthetic images consisting of two thin rings. Our synthetic images are parameterized by the separation as well as the relative flux density of the two rings. We perform fits

Joohyun Lee, Eun-jin Shin, Ji-hoon Kim, Paul R. Shapiro

Dark matter-deficient galaxies (DMDGs) discovered in the survey of ultra-diffuse galaxies (UDGs), in apparent conflict with standard CDM, may be produced by high-velocity galaxy-galaxy collisions, the $\textit{Mini-bullet}$ scenario. Recent observations of an aligned trail of $7-11$ UDGs near NGC1052, including DMDGs DF2 and DF4, suggesting a common formatio

Jiaxing Zhao, Pol Bernard Gossiaux, Taesoo Song, Elena Bratkovskaya

We investigate the charmonium and bottomonium production in $pp$ collisions using the Wigner densities formalism. The Wigner density of the quarkonia is approximated by analytical 3-D isotropic harmonic oscillator Wigner densities with the same root-mean-square radius given by the solution of the Schr\"odinger equation. This approach reproduces quite well th

Chenyang Qi, Zhengzhong Tu, Keren Ye, Mauricio Delbracio

Text-driven diffusion models have become increasingly popular for various image editing tasks, including inpainting, stylization, and object replacement. However, it still remains an open research problem to adopt this language-vision paradigm for more fine-level image processing tasks, such as denoising, super-resolution, deblurring, and compression artifac

Farnaz Kohankhaki, Kiarash Aghakasiri, Hongming Zhang, Ting-Han Wei

Monte Carlo Tree Search (MCTS) is an immensely popular search-based framework used for decision making. It is traditionally applied to domains where a perfect simulation model of the environment is available. We study and improve MCTS in the context where the environment model is given but imperfect. We show that the discrepancy between the model and the act

Luis S. Yagüe Bosch, Tim Ehret, Francesco Petiziol, Ennio Arimondo

A shortcut-to-adiabatic protocol for the realization of a fast and high-fidelity controlled-phase gate in Rydberg atoms is developed. The adiabatic state transfer, driven in the high-blockade limit, is sped up by compensating nonadiabatic transitions via oscillating fields that mimic a counterdiabatic Hamiltonian. High fidelities are obtained in wide paramet

Emil Hakan Leeb-Lundberg

We apply localization techniques to $A$-twisted $\mathcal{N}=(2,2)$ theories of vector multiplets on $S^{2}$. We derive formulae for $A$-model partition functions and correlators as integrals along a real contour, as opposed to a complex one. Using the correlator formula, we successfully recover the unstable instanton partition function of pure two-dimension

Francisco Troncoso-Pastoriza, Pablo Eguía-Oller, Rebeca P. Díaz-Redondo, Enrique Granada-Álvarez

In this paper we introduce a method that supports the detection, identification and localization of lamps in a building, with the main goal of automatically feeding its energy model by means of Building Information Modeling (BIM) methods. The proposed method, thus, provides useful information to apply energy-saving strategies to reduce energy consumption in

Jacob Knight, Paula García-Galindo, Johannes Pausch, Gunnar Pruessner

A wide array of biological systems can navigate in shallow gradients of chemoattractant with remarkable precision. Whilst previous approaches model such systems using coarse-grained chemical density profiles, we construct a dynamical model consisting of a chemotactic cell responding to discrete cue particles. For a cell without internal memory, we derive an

Hsiu-Yu Yang, Carina Silberer

Affordance knowledge is a fundamental aspect of commonsense knowledge. Recent findings indicate that world knowledge emerges through large-scale self-supervised pretraining, motivating our exploration of acquiring affordance knowledge from the visual domain. To this end, we augment an existing instructional video resource to create the new Causal Action-Effe

Christoph Tillmann, Aashka Trivedi, Sara Rosenthal, Santosh Borse

Offensive language such as hate, abuse, and profanity (HAP) occurs in various content on the web. While previous work has mostly dealt with sentence level annotations, there have been a few recent attempts to identify offensive spans as well. We build upon this work and introduce Muted, a system to identify multilingual HAP content by displaying offensive ar

Gergő Pintér, Balázs Lengyel

Barriers in cities, such as administrative boundaries, natural obstacles, railways or major roads are thought to induce segregation. However, the empirical knowledge about this phenomenon is limited. Here, we present a network science framework to assess barriers to urban mobility along their hierarchy, across residential areas and visited amenities. Using G

Yifan Wu, Rohit Jena, Mehmet Gulsun, Vivek Singh

Coronary angiography is the gold standard imaging technique for studying and diagnosing coronary artery disease. However, the resulting 2D X-ray projections lose 3D information and exhibit visual ambiguities. In this work, we aim to establish dense correspondence in multi-view angiography, serving as a fundamental basis for various clinical applications and

Daigo Ishikita, Yuya Haraguchi, Hiroko Aruga Katori

We have successfully synthesized four Mo$_3$O$_8$-type cluster Mott insulators (CMI) by intercalating lithium into nonmagnetic precursors to regulate the Mo$_3$ cluster valence. The resulting materials are Li$_{1+x}$$R$Mo$_3$O$_8$ ($R$ = Sc, Y, Lu) and Li$_x$Zn$_2$Mo$_3$O$_8$. Our magnetic susceptibility measurements revealed that these materials display cha

Cameron A. Smith, Ben Ashby

Mathematical models of infectious disease transmission typically neglect within-host dynamics. Yet within-host dynamics - including pathogen replication, host immune responses, and interactions with microbiota - are crucial not only for determining the progression of disease at the individual level, but also for driving within-host evolution and onwards tran

Cagatay Eskin, Dervis C. Vural

Aging, as defined in terms of the slope of the probability of death versus time (hazard curve), is a generic phenomenon observed in nearly all complex systems. Theoretical models of aging predict hazard curves that monotonically increase in time, in discrepancy with the peculiar ups and downs observed in empirically. Here we introduce the concept of co-aging

Grégory Berhuy

In this paper, we investigate the existence of self-dual MRD codes $C\subset L^n$, where $L/F$ is an arbitrary field extension of degree $m\geq n$. We then apply our results to the case of finite fields, and prove that if $m=n$ and $F=\mathbb{F}_q$, a self-dual MRD code exists if and only if $q\equiv n\equiv 3 \ [4].$

Timilehin B. Aderinola, Hananeh Younesian, Cathy Goulding, Darragh Whelan

$\textbf{Goal:}$ This study investigates the feasibility of monocular 2D markerless motion capture (MMC) using a single smartphone to measure jump height, velocity, flight time, contact time, and range of motion (ROM) during motor tasks. $\textbf{Methods:}$ Sixteen healthy adults performed three repetitions of selected tests while their body movements were r

M. I. Swayne, P. F. L. Maxted, A. H. M. J. Triaud, S. G. Sousa

Observations of low-mass stars have frequently shown a disagreement between observed stellar radii and radii predicted by theoretical stellar structure models. This ``radius inflation'' problem could have an impact on both stellar and exoplanetary science. We present the final results of our observation programme with the CHEOPS satellite to obtain high-prec

Lucas Heger, Kerem Akdogan, Matthias Schott

The effect of school closures on the spread of COVID-19 has been discussed among experts and the general public since those measures have been taken only a few months after the start of the pandemic in 2020. Within this study, the JuneGermany framework, is used to quantify the impact of school closures in the German state Rhineland Palatinate using an agent-

Philipp Altmann, Jonas Stein, Michael Kölle, Adelina Bärligea

Quantum computing (QC) in the current NISQ era is still limited in size and precision. Hybrid applications mitigating those shortcomings are prevalent to gain early insight and advantages. Hybrid quantum machine learning (QML) comprises both the application of QC to improve machine learning (ML) and ML to improve QC architectures. This work considers the lat

Yu Wang, Zhiwei Liu, Jianguo Zhang, Weiran Yao

The rise of Large Language Models (LLMs) has sparked interest in their application to sequential recommendation tasks as they can provide supportive item information. However, due to the inherent complexities of sequential recommendation, such as sequential patterns across datasets, noise within sequences, and the temporal evolution of user preferences, exis

Ericsson Tetteh Chenebuah, David Tetteh Chenebuah

The reliability with Machine Learning (ML) techniques in novel materials discovery often depend on the quality of the dataset, in addition to the relevant features used in describing the material. In this regard, the current study presents and validates a newly processed materials dataset that can be utilized for benchmark ML analysis, as it relates to the p

Or Hirschorn, Amir Jevnisek, Shai Avidan

Vector image representation is a popular choice when editability and flexibility in resolution are desired. However, most images are only available in raster form, making raster-to-vector image conversion (vectorization) an important task. Classical methods for vectorization are either domain-specific or yield an abundance of shapes which limits editability

Riccardo Senese, Jacob H. Robertson, Fabian H. L. Essler

We consider the probability distributions of the subsystem (staggered) magnetization in ordered and disordered models of quantum magnets in D dimensions. We focus on Heisenberg antiferromagnets and long-range transverse-field Ising models as particular examples. By employing a range of self-consistent time-dependent mean-field approximations in conjunction w

Lucas Heger, Kerem Akdogan, Matthias Schott

During the COVID-19 pandemic, a large variance of incidence rates on local level, e.g. cities and districts, within one country has been observed, while the same non-pharmaceutical measures have been taken to control the spread of the virus. This variance in incidence rates triggered the question, if the spread of incidence rates can be explained only by sta

David H. Lee, Anishalakshmi V. Palaparthi, Matthew C. Fontaine, Bryon Tjanaka

Diversity optimization seeks to discover a set of solutions that elicit diverse features. Prior work has proposed Novelty Search (NS), which, given a current set of solutions, seeks to expand the set by finding points in areas of low density in the feature space. However, to estimate density, NS relies on a heuristic that considers the k-nearest neighbors of

Renu Chaudhary, Kai Diethelm

This study reexamines diffusive representations for fractional integrals with the goal of pioneering new variants of such representations. These variants aim to offer highly efficient numerical algorithms for the approximate computation of fractional integrals. The approach seamlessly aligns with established techniques used in addressing problems involving i

Joshua G. Albert

We introduce a novel technique within the Nested Sampling framework to enhance efficiency of the computation of Bayesian evidence, a critical component in scientific data analysis. In higher dimensions, Nested Sampling relies on Markov Chain-based likelihood-constrained prior samplers, which generate numerous 'phantom points' during parameter space explorati

Riccardo Zuliani, Raffaele Soloperto, John Lygeros

We analyze the convergence properties of a robust adaptive model predictive control algorithm used to control an unknown nonlinear system. We show that by employing a standard quadratic stabilizing cost function, and by recursively updating the nominal model through kinky inference, the resulting controller ensures convergence of the true system to the origi

Charles Gale, Sangyong Jeon, Daniel Pablos, Mayank Singh

Minijets traversing through the QGP formed in heavy-ion collisions deposit significant amount of energy in the bulk medium. They also create gradients in temperatures which alter the flow profile and enhance entropy production. We study the effects of minijets in a simultaneous hydro + jet framework and find that inclusion of minijets requires recalibration