May 2024 arXiv papers — page 3

Praveen Mishra, Arun Singh Patel, Sanjay Kumar Chauhan, Anirban Chakraborti

In this paper, we have explored the charge transfer mechanism in transition metal dichalcogenide (TMDC) quantum dots (QDs) of molybdenum disulfide ($\rm{MoS_2}$) and tungsten disulfide ($\rm{WS_2}$). Rhodamine 6G (R6G), a dye from the rhodamine family, has been employed as the fluorescent molecule, with MoS$_2$ and WS$_2$ QDs acting as electron acceptors in

Joseph S. B. Mitchell, Linh Nguyen

The well-known \textsc{Watchman Route} problem seeks a shortest route in a polygonal domain from which every point of the domain can be seen. In this paper, we study the cooperative variant of the problem, namely the \textsc{$k$-Watchmen Routes} problem, in a simple polygon $P$. We look at both the version in which the $k$ watchmen must collectively see all

C. Georgy, F. Rizzuti, R. Hirschi, V. Varma

The treatment of convection remains a major weakness in the modelling of stellar evolution with one-dimensional (1D) codes. The ever increasing computing power makes now possible to simulate in 3D part of a star for a fraction of its life, allowing us to study the full complexity of convective zones with hydrodynamics codes. Here, we performed state-of-the-a

Nana Cabo Bizet, Oscar Loaiza-Brito, Yessenia Olguín-Trejo

We study the axio-dilaton and complex structure stabilization for a generic one-parameter Calabi-Yau (CY) compactification. We focus on near the conifold regions and consider a strongly warped metric. We analyze numerically the case of the mirror of the quintic CY. The axio-dilaton $\tau$ and complex structure $z$ moduli are stabilized simultaneously compari

Guilherme Franzmann

The identification of physical subsystems in quantum mechanics as compared to classical mechanics poses significant conceptual challenges, especially in the context of quantum gravity. Traditional approaches associate quantum systems with classical ones localized in spacetime, using either Hilbert space factors for finite-dimensional systems or local operato

Daniel A. Herrmann, Benjamin A. Levinstein

As large language models (LLMs) continue to demonstrate remarkable abilities across various domains, computer scientists are developing methods to understand their cognitive processes, particularly concerning how (and if) LLMs internally represent their beliefs about the world. However, this field currently lacks a unified theoretical foundation to underpin

Marta Vicentini, Ettore Bernardi, Matteo Bordin, Ekaterina Moreva

Unifying quantum theory and general relativity is the holy grail of contemporary physics. Nonetheless, the lack of experimental evidence driving this process led to a plethora of mathematical models with a substantial impossibility of discriminating among them or even establishing if gravity really needs to be quantized or if, vice versa, quantum mechanics m

Elias Stengel-Eskin, Peter Hase, Mohit Bansal

When answering questions, LLMs can convey not only an answer, but a level of confidence about the answer being correct. This includes explicit confidence markers (e.g. giving a numeric score) as well as implicit markers, like an authoritative tone or elaborating with additional knowledge. For LLMs to be trustworthy knowledge sources, the confidence they conv

Jiesong Lian, Yucong Huang, Chengdong Ma, Mingzhi Wang

For solving zero-sum games involving non-transitivity, a useful approach is to maintain a policy population to approximate the Nash Equilibrium (NE). Previous studies have shown that the Policy Space Response Oracles (PSRO) algorithm is an effective framework for solving such games. However, current methods initialize a new policy from scratch or inherit a s

Bernardo N. B. de Lima, Daniel Ungaretti, Maria Eulália Vares

This note was motivated by natural questions related to oriented percolation on a layered environment that introduces long range dependence. As a convenient tool, we are led to deal with questions on the strict decrease of the percolation parameter in the oriented setup when an extra dimension is added.

Rüdiger Valk, Daniel Moldt

Cycloids are particular Petri nets for modelling processes of actions and events, belonging to the fundaments of Petri's general systems theory. Defined by four parameters they provide an algebraic formalism to describe strongly synchronized sequential processes. To further investigate their structure, reduction systems of cycloids are defined in the sty

Daniel Duffy, Itay Griniasty, John Biggins, Cyrus Mostajeran

Shape-programmed sheets morph from one surface into another upon activation by stimuli such as illumination, and have attracted much interest for their potential engineering applications, especially in soft robotics. Complex shape changes can be achieved by patterning a simple local active deformation (e.g. isotropic swelling), to generate differential growt

Wenbo Chen, Haoruo Zhao, Mathieu Tanneau, Pascal Van Hentenryck

Recent years have witnessed increasing interest in optimization proxies, i.e., machine learning models that approximate the input-output mapping of parametric optimization problems and return near-optimal feasible solutions. Following recent work by (Nellikkath & Chatzivasileiadis, 2021), this paper reconsiders the optimality verification problem for optimiz

Zhen Qin, Yuxin Mao, Xuyang Shen, Dong Li

Linear attention mechanisms have gained prominence in causal language models due to their linear computational complexity and enhanced speed. However, the inherent decay mechanism in linear attention presents challenges when applied to multi-dimensional sequence modeling tasks, such as image processing and multi-modal learning. In these scenarios, the utiliz

Niloufar Zakariaei, Arman Rahmim, Eldad Haber

Dynamic Positron Emission Tomography (dPET) imaging and Time-Activity Curve (TAC) analyses are essential for understanding and quantifying the biodistribution of radiopharmaceuticals over time and space. Traditional compartmental modeling, while foundational, commonly struggles to fully capture the complexities of biological systems, including non-linear dyn

Dongho Shin, Yongyun Shin, Nao Hagiwara

We consider Bayesian estimation of a hierarchical linear model (HLM) from partially observed data, assumed to be missing at random, and small sample sizes. A vector of continuous covariates $C$ includes cluster-level partially observed covariates with interaction effects. Due to small sample sizes from 37 patient-physician encounters repeatedly measured at f

Alexander Lukin, Benjamin F. Schiffer, Boris Braverman, Sergio H. Cantu

The ability to efficiently prepare ground states of quantum Hamiltonians via adiabatic protocols is typically limited by the smallest energy gap encountered during the quantum evolution. This presents a key obstacle for quantum simulation and realizations of adiabatic quantum algorithms in large systems, particularly when the adiabatic gap vanishes exponenti

Xiaojun Jia, Tianyu Pang, Chao Du, Yihao Huang

Large language models (LLMs) are being rapidly developed, and a key component of their widespread deployment is their safety-related alignment. Many red-teaming efforts aim to jailbreak LLMs, where among these efforts, the Greedy Coordinate Gradient (GCG) attack's success has led to a growing interest in the study of optimization-based jailbreaking technique

Per Berglund, Giorgi Butbaia, Yang-Hui He, Elli Heyes

We apply reinforcement learning (RL) to generate fine regular star triangulations of reflexive polytopes, that give rise to smooth Calabi-Yau (CY) hypersurfaces. We demonstrate that, by simple modifications to the data encoding and reward function, one can search for CYs that satisfy a set of desirable string compactification conditions. For instance, we sho

Fatema Jannat Dihan, Saydul Akbar Murad

Monkeypox virus (MPXV) is a zoonotic virus that poses a significant threat to public health, particularly in remote parts of Central and West Africa. Early detection of monkeypox lesions is crucial for effective treatment. However, due to its similarity with other skin diseases, monkeypox lesion detection is a challenging task. To detect monkeypox, many rese

Ben Cottier, Robi Rahman, Loredana Fattorini, Nestor Maslej

The costs of training frontier AI models have grown dramatically in recent years, but there is limited public data on the magnitude and growth of these expenses. This paper develops a detailed cost model to address this gap, estimating training costs using three approaches that account for hardware, energy, cloud rental, and staff expenses. The analysis reve

Christian d'Elbée, Yatir Halevi, Will Johnson

We classify dp-minimal integral domains, building off the existing classification of dp-minimal fields and dp-minimal valuation rings. We show that if R is a dp-minimal integral domain, then R is a field or a valuation ring or arises from the following construction: there is a dp-minimal valuation overring O extending R, a proper ideal I in O, and a finite s

Pengyuan Lyu, Yulin Li, Hao Zhou, Weihong Ma

Text-rich images have significant and extensive value, deeply integrated into various aspects of human life. Notably, both visual cues and linguistic symbols in text-rich images play crucial roles in information transmission but are accompanied by diverse challenges. Therefore, the efficient and effective understanding of text-rich images is a crucial litmus

Konstantin Hess, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel

Estimating potential outcomes for treatments over time based on observational data is important for personalized decision-making in medicine. However, many existing methods for this task fail to properly adjust for time-varying confounding and thus yield biased estimates. There are only a few neural methods with proper adjustments, but these have inherent li

Chuqiao Lin, Vir B. Bulchandani, Shivaji L. Sondhi

Eigenstates of observables such as the Hamiltonian play a central role in quantum mechanics. Inspired by the pure Nash equilibria that arise in classical game theory, we propose ''Nash states'' of multiple observables as a generalization of eigenstates of single observables. This generalization is mathematically natural for many-body quantum systems, which p

Andrey Leonidov

A description of static equilibria in the noisy binary choice (Ising) game on complete and random graphs resulting from maximisation of the likelihood of system configurations is presented. An equivalence of such likelihood equilibria to the competitive Bayes-Nash quantal response expectation equilibria in the special case of consistent agents expectations i

Giuseppe De Palma, Saverio Giallorenzo, Jacopo Mauro, Matteo Trentin

We present FunLess, a Function-as-a-Service (FaaS) platform tailored for the private edge cloud system. FunLess responds to recent trends that advocate for extending the coverage of serverless computing to private edge cloud systems and enhancing latency, security, and privacy while improving resource usage. Unlike existing solutions that rely on containers

Josh Hoffmann, David Sloan

In this paper we present a framework in which the relational description of General Relativity can be used to smoothly continue cosmological dynamical systems through the Big Bang without invoking quantum gravity effects. Cosmological spacetimes contain as a key dynamical variable a notion of scale through the volume factor $\nu$. However no cosmological obs

Aria Chen, Tyler Cummins, Rishi De Francesco, Jate Greene

Fitch Cheney's 5-card trick was introduced in 1950. In 2013, Mulcahy invented a 4-card trick in which the cards are allowed to be displayed face down. We suggest our own invention: a 3-card trick in which the cards can be face down and also allowed to be placed both vertically and horizontally. We discuss the theory behind all the tricks and estimate the max

Sandip Halder, Snehal Mandal, Kalpataru Pradhan

We investigate the magnetic and transport properties of the $La_{1-x}Sr_{x}MnO_{3}$ (LSMO)/$Pr_{1-x}Ca_{x}MnO_{3}$ (PCMO) like ferromagnetic/antiferromagnetic superlattices in three dimensions using a two orbitals double exchange model incorporating the Jahn-Teller lattice distortions, superexchange interactions and long-range Coulomb interactions. In our si

Niklas Kolbe

A new coupling rule for the Lighthill-Whitham-Richards model at merging junctions is introduced that imposes the preservation of the ratio between inflow from a given road to the total inflow into the junction. This rule is considered both in the context of the original traffic flow model and a relaxation setting giving rise to two different Riemann solvers

Saif Mahmud, Devansh Agarwal, Ashwin Ajit, Qikang Liang

We introduce MunchSonic, an AI-powered active acoustic sensing system integrated into eyeglasses to track fine-grained dietary actions. MunchSonic emits inaudible ultrasonic waves from the eyeglass frame, with the reflected signals capturing detailed positions and movements of body parts, including the mouth, jaw, arms, and hands involved in eating. These si

Amr Alkhatib, Henrik Boström, Michalis Vazirgiannis

Characteristic rules have been advocated for their ability to improve interpretability over discriminative rules within the area of rule learning. However, the former type of rule has not yet been used by techniques for explaining predictions. A novel explanation technique, called CEGA (Characteristic Explanatory General Association rules), is proposed, whic

Charalampos Nikolis, Henrique Rubira, Fabian Schmidt

The renormalization group for large-scale structure (RG-LSS) describes the evolution of galaxy bias and stochastic parameters as a function of the cutoff $\Lambda$. In this work, we introduce interaction vertices that describe primordial non-Gaussianity into the Wilson-Polchinski framework, thereby extending the free theory to the interacting case. The prese

Zhizhi Zhou, Jiahuan Jiang, Yuanyuan Sun, Qing Qin

In this study, we successfully developed two-dimensional paper-based analytical devices using a hybrid technique of injection molding and embossing. This innovative approach involves passive or active delivery of molten wax onto a glass substrate through a sealed chip, facilitating wax stamp creation.

Alessandro Chiesa, Emilio Macaluso, Stefano Carretta

Many problems intractable on classical devices could be solved by algorithms explicitly based on quantum mechanical laws, i.e. exploiting quantum information processing. As a result, increasing efforts from different fields are nowadays directed to the actual realization of quantum devices. Here we provide an introduction to Quantum Information Processing, f

D. M. Gitman, A. L. Shelepin

In our previous works, we have proposed a quantum description of relativistic orientable objects by a scalar field on the Poincar\'{e} group. This description is, in a sense, a generalization of ideas used by Wigner, Casimir and Eckart back in the 1930's in constructing a non-relativistic theory of a rigid rotator. The present work is a continuation and deve

Robert Cardona, Eva Miranda, Daniel Peralta-Salas

In 1991, Moore [20] raised a question about whether hydrodynamics is capable of performing computations. Similarly, in 2016, Tao [25] asked whether a mechanical system, including a fluid flow, can simulate a universal Turing machine. In this expository article, we review the construction in [8] of a "Fluid computer" in dimension 3 that combines techniques in

J. -F. Ostiguy, C. M. Bhat

The Proton Improvement Plan phase II (PIP-II) project currently under construction at FNAL will replace the existing 400 MeV normal conducting linac with a new 800 MeV superconducting linac. The beam power in the downstream rapid-cycling Booster synchrotron will be doubled by raising the machine cycle frequency from 15 to 20 Hz and by increasing the injected

Marko Lalovic

This paper presents an $O^{*}(1.42^{n})$ time algorithm for the Maximum Cut problem on split graphs, along with a subexponential time algorithm for its decision variant.

Andreas Erik Gejl Madsen, Jesper Glückstad

This publication extends the HoloTile framework to three dimensions, introducing the ability to generate arbitrary dynamic patterns composed of extended depth-of-field non-diffractive beamlets with theoretically 100% diffraction efficiency. In particular, we demonstrate experimentally the generation of speckle-reduced reconstruction patterns, consisting of s

Nepomuk Ritz, Anxiang Ge, Elias Walter, Santiago Aguirre

We provide a detailed exposition of our computational framework designed for the accurate calculation of real-frequency dynamical correlation functions of the single-impurity Anderson model (AM) in the regime of weak to intermediate coupling. Using quantum field theory within the Keldysh formalism to directly access the self-energy and dynamical susceptibili

K. Sravan Kumar, João Marto

Quantum field theory (QFT) in Rindler spacetime is a gateway to understanding unitarity and information loss paradoxes in curved spacetime. Rindler coordinates map Minkowski spacetime onto regions with horizons, effectively dividing accelerated observers into causally disconnected sectors. Employing standard quantum field theory techniques and Bogoliubov tra

Josef Vonášek, Milan Straka, Rostislav Krč, Lenka Lasoňová

We present CWRCzech, Click Web Ranking dataset for Czech, a 100M query-document Czech click dataset for relevance ranking with user behavior data collected from search engine logs of Seznam$.$cz. To the best of our knowledge, CWRCzech is the largest click dataset with raw text published so far. It provides document positions in the search results as well as

Jean Barbier, Francesco Camilli, Marco Mondelli, Yizhou Xu

We consider a prototypical problem of Bayesian inference for a structured spiked model: a low-rank signal is corrupted by additive noise. While both information-theoretic and algorithmic limits are well understood when the noise is a Gaussian Wigner matrix, the more realistic case of structured noise still proves to be challenging. To capture the structure w

Yajie Duan, Javier Cabrera, Davit Sargsyan

In healthcare, clinical risks are crucial for treatment decisions, yet the analysis of their associations is often overlooked. This gap is particularly significant when balancing risks that are weighed against each other, as in the case of atrial fibrillation (AF) patients facing stroke and bleeding risks with anticoagulant medication. While traditional regr

Yi Yang, Qingwen Zhang, Kei Ikemura, Nazre Batool

Addressing hard cases in autonomous driving, such as anomalous road users, extreme weather conditions, and complex traffic interactions, presents significant challenges. To ensure safety, it is crucial to detect and manage these scenarios effectively for autonomous driving systems. However, the rarity and high-risk nature of these cases demand extensive, div

Eleanor Clifford, Adhithya Saravanan, Harry Langford, Cheng Zhang

Modern machine learning (ML) models are expensive IP and business competitiveness often depends on keeping this IP confidential. This in turn restricts how these models are deployed; for example, it is unclear how to deploy a model on-device without inevitably leaking the underlying model. At the same time, confidential computing technologies such as multi-p

Avinash Singh, R. S. Teja, T. J. Moriya, K. Maeda

We present a detailed investigation of photometric, spectroscopic, and polarimetric observations of the Type II SN 2023ixf. Earlier studies have provided compelling evidence for a delayed shock breakout from a confined dense circumstellar matter (CSM) enveloping the progenitor star. The temporal evolution of polarization in SN~2023ixf revealed three distinct

Michail Theologitis, Georgios Frangias, Georgios Anestis, Vasilis Samoladas

The ever-growing volume and decentralized nature of data, coupled with the need to harness it and extract knowledge, have led to the extensive use of distributed deep learning (DDL) techniques for training. These techniques rely on local training performed at distributed nodes using locally collected data, followed by a periodic synchronization process that

Muhammad Muneeb Saad, Mubashir Husain Rehmani, Ruairi O'Reilly

Generative Adversarial Networks (GANs) have high computational costs to train their complex architectures. Throughout the training process, GANs' output is analyzed qualitatively based on the loss and synthetic images' diversity and quality. Based on this qualitative analysis, training is manually halted once the desired synthetic images are generated. By ut

Linlin Yu, Bowen Yang, Tianhao Wang, Kangshuo Li

The fusion of raw sensor data to create a Bird's Eye View (BEV) representation is critical for autonomous vehicle planning and control. Despite the growing interest in using deep learning models for BEV semantic segmentation, anticipating segmentation errors and enhancing the explainability of these models remain underexplored. This paper introduces a compre

Linli Yao, Lei Li, Shuhuai Ren, Lean Wang

The visual projector, which bridges the vision and language modalities and facilitates cross-modal alignment, serves as a crucial component in MLLMs. However, measuring the effectiveness of projectors in vision-language alignment remains under-explored, which currently can only be inferred from the performance of MLLMs on downstream tasks. Motivated by the p

Hao Hu, Yiqin Yang, Jianing Ye, Chengjie Wu

Offline reinforcement learning (RL) is crucial for real-world applications where exploration can be costly or unsafe. However, offline learned policies are often suboptimal, and further online fine-tuning is required. In this paper, we tackle the fundamental dilemma of offline-to-online fine-tuning: if the agent remains pessimistic, it may fail to learn a be

Kuan-Yen Chou, Santhosh Prabhu, Giri Subramanian, Wenxuan Zhou

Data plane verification has grown into a powerful tool to ensure network correctness. However, existing methods with monolithic models have memory requirements tied to network sizes, and the existing method of scaling out is too limited in expressiveness to capture practical network features. In this paper, we describe Scylla, a general data plane verifier t

Matej Gazda, Samuel Kadoury, Jakub Gazda, Peter Drotar

Transthoracic Echocardiography (TTE) is a fundamental, non-invasive diagnostic tool in cardiovascular medicine, enabling detailed visualization of cardiac structures crucial for diagnosing various heart conditions. Despite its widespread use, TTE ultrasound imaging faces inherent limitations, notably the trade-off between field of view (FoV) and resolution.

Felix Mujkanovic, Ntumba Elie Nsampi, Christian Theobalt, Hans-Peter Seidel

Gaussian scale spaces are a cornerstone of signal representation and processing, with applications in filtering, multiscale analysis, anti-aliasing, and many more. However, obtaining such a scale space is costly and cumbersome, in particular for continuous representations such as neural fields. We present an efficient and lightweight method to learn the full

Alexander Duplinskiy, Jernej Frank, Kaden Bearne, A. I. Lvovsky

A widely tested approach to overcoming the diffraction limit in microscopy without disturbing the sample relies on substituting widefield sample illumination with a structured light beam. This gives rise to confocal, image-scanning and structured-illumination microscopy methods. On the other hand, as shown recently by Tsang and others, subdiffractional resol

Feiteng Fang, Yuelin Bai, Shiwen Ni, Min Yang

Large Language Models (LLMs) exhibit substantial capabilities yet encounter challenges, including hallucination, outdated knowledge, and untraceable reasoning processes. Retrieval-augmented generation (RAG) has emerged as a promising solution, integrating knowledge from external databases to mitigate these challenges. However, inappropriate retrieved passage

K. R. Rajagopal, C. Rodriguez

We present an asymptotic framework that rigorously generates nonlinear constitutive relations between stress and linearized strain for elastic bodies. Each of these relations arises as the leading order relationship satisfied by a one-parameter family of nonlinear constitutive relations between stress and nonlinear strain. The asymptotic parameter limits the

Agnes Totschnig, Rohit Vasishta, Adrian Vetta

A preference matrix $M$ has an entry for each pair of candidates in an election whose value $p_{ij}$ represents the proportion of voters that prefer candidate $i$ over candidate $j$. The matrix is rationalizable if it is consistent with a set of voters whose preferences are total orders. A celebrated open problem asks for a concise characterization of ration

Zhangchen Xu, Fengqing Jiang, Luyao Niu, Jinyuan Jia

In Federated Learning (FL), a set of clients collaboratively train a machine learning model (called global model) without sharing their local training data. The local training data of clients is typically non-i.i.d. and heterogeneous, resulting in varying contributions from individual clients to the final performance of the global model. In response, many co

Tianyang Xu, Shujin Wu, Shizhe Diao, Xiaoze Liu

Large language models (LLMs) often generate inaccurate or fabricated information and generally fail to indicate their confidence, which limits their broader applications. Previous work elicits confidence from LLMs by direct or self-consistency prompting, or constructing specific datasets for supervised finetuning. The prompting-based approaches have inferior

Wen-Pu Cai, Ming-Yang Li, Wu-Jun Li

Large language models~(LLMs) have recently demonstrated promising performance in many tasks. However, the high storage and computational cost of LLMs has become a challenge for deploying LLMs. Weight quantization has been widely used for model compression, which can reduce both storage and computational cost. Most existing weight quantization methods for LLM

Sajid Ahamed Mohammed Abdul, Prathyush P Menon, Debasish Ghose

Existing UAS Traffic Management (UTM) frameworks designate preplanned flight paths to uncrewed aircraft systems (UAS), enabling the UAS to deliver payloads. However, with increasing delivery demand between the source-destination pairs in the urban airspace, UAS will likely experience considerable congestion on the nominal paths. We propose a rule-based conge

Siddarth Venkatraman, Moksh Jain, Luca Scimeca, Minsu Kim

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data, $\mathbf{x}\sim p^{\rm post}(\mathbf{x})\propto p(\mathbf{x})r(\mathbf{x})$, in a mo

Xiaoke Wang, Xiaochen Yang, Rui Zhu, Jing-Hao Xue

Positive-unlabeled (PU) learning aims to train a classifier using the data containing only labeled-positive instances and unlabeled instances. However, existing PU learning methods are generally hard to achieve satisfactory performance on trifurcate data, where the positive instances distribute on both sides of the negative instances. To address this issue,

Yuanfeng Han, Boren Jiang, Gregory S. Chirikjian

This paper introduces a pair of low-cost, light-weight and compliant force-sensing gripping pads used for manipulating box-like objects with smaller-sized humanoid robots. These pads measure normal gripping forces and center of pressure (CoP). A calibration method is developed to improve the CoP measurement accuracy. A hybrid force-alignment-position control

Marco Calderini, Alessio Caminata, Irene Villa

Multivariate Cryptography is one of the candidates for Post-quantum Cryptography. Multivariate schemes are usually constructed by applying two secret affine invertible transformations $\mathcal S,\mathcal T$ to a set of multivariate polynomials $\mathcal{F}$ (often quadratic). The polynomials $\mathcal{F}$ possess a trapdoor that allows the legitimate user t

Vincent J. Esposito, Ryan C. Fortenberry, Christiaan Boersma, Louis J. Allamandola

Cyano-substituted polycyclic aromatic hydrocarbons (CN-PAHs) may contribute to the emission detected in the 7 - 9 $\mu$m (1430 - 1100 cm$^{-1}$) and 11 - 15 $\mu$m (900 - 670 cm$^{-1}$) regions of astronomical IR spectra. Anharmonic quantum chemical computations of 17 CN-PAH isomers for 4 small PAHs and Benzene reveal strong, broad absorption features across

Valentina Pyatkin, Bonnie Webber, Ido Dagan, Reut Tsarfaty

Superlatives are used to single out elements with a maximal/minimal property. Semantically, superlatives perform a set comparison: something (or some things) has the min/max property out of a set. As such, superlatives provide an ideal phenomenon for studying implicit phenomena and discourse restrictions. While this comparison set is often not explicitly def

Franciele M. da Silva, Luis C. N. Santos, V. B. Bezerra

In this work, we investigate some extensions of the Kiselev black hole solutions in the context of $f(\mathbb{T},\CMcal{T})$ gravity. By mapping the components of the Kiselev energy-momentum tensor into the anisotropic energy-momentum tensor and assuming a particular form of $f(\mathbb{T},\CMcal{T})$, we obtain exact solutions for the field equation in this

M. Ginolin, M. Rigault, M. Smith, Y. Copin

To get distances, Type Ia Supernovae magnitudes are corrected for their correlation with lightcurve width and colour. Here we investigate how this standardisation is affected by the SN environment, with the aim to reduce scatter and improve standardisation. We first study the SN Ia stretch distribution, as well as its dependence on environment, as characteri

Pavel Shumyatsky, Anitha Thillaisundaram

We show that a profinite group, in which the centralisers of non-trivial elements are metabelian, is either virtually pro-$p$ or virtually soluble of derived length at most 4. We furthermore show that a prosoluble group, in which the centralisers of non-trivial elements are soluble of bounded derived length, is either soluble or virtually pro-$p$.

Li-Tong Deng, Yukako Kezuka, Yong-Xiong Li, Meng Fai Lim

Let $A$ be an abelian variety defined over a global function field $F$, and let $p$ be a prime distinct from the characteristic of $F$. Let $F_\infty$ be a $p$-adic Lie extension of $F$ that contains the cyclotomic $\mathbb{Z}_p$-extension $F^{\mathrm{cyc}}$ of $F$. In this paper, we investigate the structure of the $p$-primary Selmer group $\mathrm{Sel}(A/F

Ciro Beneduce, Bruno Lepri, Massimiliano Luca

Predicting the locations an individual will visit in the future is crucial for solving many societal issues like disease diffusion and reduction of pollution. However, next-location predictors require a significant amount of individual-level information that may be scarce or unavailable in some scenarios (e.g., cold-start). Large Language Models (LLMs) have

Rachel Skipper, Anitha Thillaisundaram

We consider analogues of Grigorchuk-Gupta-Sidki (GGS-)groups acting on trees of growing degree; the so-called growing GGS-groups. These groups are not just infinite and do not possess the congruence subgroup property, but many of them are branch and have the $p$-congruence subgroup property, for a prime $p$. Among them, we find groups with maximal subgroups

Franck Tchinda, Joel Fotso Tachago, Joseph Dongho

In this paper, we are interested in reiterated periodic homogenization for a family of parabolic problems with nonstandard growth monotone operators leading to Orlicz spaces. The aim of this work is the determination of the global homogenized problem on the one hand and the macroscopic homogenized problem on the other hand, via the reiterated two-scale conve

Maria F. Davila R., Sven Groen, Fabian Panse, Wolfram Wingerath

In an era of rapidly advancing data-driven applications, there is a growing demand for data in both research and practice. Synthetic data have emerged as an alternative when no real data is available (e.g., due to privacy regulations). Synthesizing tabular data presents unique and complex challenges, especially handling (i) missing values, (ii) dataset imbal

Paulin Ferro, Paul Landel, Carla Landrodie, Simon Guillot

This paper presents a solver using the Level-Set method for incompressible two phase flows with surface tension. A one fluid approach is adopted where both phases share the same velocity and pressure field. The Level Set method has been coupled with the Ghost Fluid Method. An efficient and pragmatic solution is proposed to avoid interface displacement during

Evangelos Dimitriou, Edwin Fong, Jens Magelund Tarp, Karla Diaz-Ordaz

Bridging the gap between internal and external validity is crucial for heterogeneous treatment effect estimation. Randomised controlled trials (RCTs), favoured for their internal validity due to randomisation, often encounter challenges in generalising findings due to strict eligibility criteria. Observational studies, on the other hand, may provide stronger

Piotr Wojciech Mirowski, Juliette Love, Kory W. Mathewson, Shakir Mohamed

We interviewed twenty professional comedians who perform live shows in front of audiences and who use artificial intelligence in their artistic process as part of 3-hour workshops on ``AI x Comedy'' conducted at the Edinburgh Festival Fringe in August 2023 and online. The workshop consisted of a comedy writing session with large language models (LLMs), a hum

Satya N. Majumdar, Gregory Schehr

In this Chapter, we consider a model of $N$ independent random walkers, each of duration $t$, and each starting from the origin, on a lattice in $d$ dimensions. We focus on two observables, namely $D_N(t)$ and $C_N(t)$ denoting respectively the number of distinct and common sites visited by the walkers. For large $t$, where the lattice random walkers converg

Etienne Dreyer, Eilam Gross, Dmitrii Kobylianskii, Vinicius Mikuni

Detector simulation and reconstruction are a significant computational bottleneck in particle physics. We develop Particle-flow Neural Assisted Simulations (Parnassus) to address this challenge. Our deep learning model takes as input a point cloud (particles impinging on a detector) and produces a point cloud (reconstructed particles). By combining detector

Deyuan Li, Taesoo Daniel Lee, Marynel Vázquez, Nathan Tsoi

Multiclass neural network classifiers are typically trained using cross-entropy loss but evaluated using metrics derived from the confusion matrix, such as Accuracy, $F_\beta$-Score, and Matthews Correlation Coefficient. This mismatch between the training objective and evaluation metric can lead to suboptimal performance, particularly when the user's priorit

Abhishek Pathak, Arun Saini, Eduard Pozdeyev

This paper presents the final physics design of the Proton Improvement Plan-II (PIP-II) at Fermilab, focusing on the linear accelerator (Linac) and its beam transfer line. We address the challenges in longitudinal and transverse lattice design, specifically targeting collective effects, parametric resonances, and space charge nonlinearities that impact beam

V. M. Schäfer, Z. Niu, J. R. K. Cline, D. J. Young

Laser-cooled gases of atoms interacting with the field of an optical cavity are a powerful tool for quantum sensing and the simulation of open and closed quantum systems. They can display spontaneous self-organisation phase transitions, time crystals, new lasing mechanisms, squeezed states for quantum sensing, protection of quantum coherence, and dynamical p

Justin Norman, Francois Rivest

Efficient utilization of satellite resources in dynamic environments remains a challenging problem in satellite scheduling. This paper addresses the multi-satellite collection scheduling problem (m-SatCSP), aiming to optimize task scheduling over a constellation of satellites under uncertain conditions such as cloud cover. Leveraging Monte Carlo Tree Search

Denis V. Kurlov, Melina Luethi, Anatoliy I. Lotkov, Katharina Laubscher

We propose a universal set of single- and two-qubit quantum gates acting on a hybrid qubit formed by coupling a quantum dot spin qubit to a $\mathbb{Z}_{2m}$ parafermion qubit with arbitrary integer $m$. The special case $m=1$ reproduces the results previously derived for Majorana qubits. Our formalism utilizes Fock parafermions, facilitating a transparent t

Tiziana Cardinali, Serena Matucci, Paola Rubbioni

In this paper we investigate the asymptotic stability of a fourth-order PDE with a fading memory forcing term and boundary conditions arising from a flexible robotics model. We carry on our study by using an abstract formulation of the problem based on the $C_0$-semigroup. To achieve our objective, we first provide new results on the existence, uniqueness, c

Tom Vincent, Xinyun Liu, Daniel Johnson, Lars Mester

Scattering-type scanning near-field optical microscopy (s-SNOM) is a powerful technique for extreme subwavelength imaging and spectroscopy, with around 20 nm spatial resolution. But quantitative relationships between experiment and material properties requires modelling, which can be computationally and conceptually challenging. In this work, we present snom

Justin Cui, Wei-Lin Chiang, Ion Stoica, Cho-Jui Hsieh

Large Language Models (LLMs) require careful safety alignment to prevent malicious outputs. While significant research focuses on mitigating harmful content generation, the enhanced safety often come with the side effect of over-refusal, where LLMs may reject innocuous prompts and become less helpful. Although the issue of over-refusal has been empirically o

N. Schoinas, Y. Rath, S. Norimoto, W. Xie

We present an efficient machine learning based automated framework for the fast tuning of single-electron pump devices into current quantization regimes. It uses a sparse measurement approach based on an iterative active learning algorithm to take targeted measurements in the gate voltage parameter space. When compared to conventional parameter scans, our au

J. J. Sánchez-Gabites

Consider a flow in $\mathbb{R}^3$ and let $K$ be the biggest invariant subset of some compact region of interest $N \subseteq \mathbb{R}^3$. The set $K$ is often not computable, but the way the flow crosses the boundary of $N$ can provide indirect information about it. For example, classical tools such as Wa\.{z}ewski's principle or the Poincar\'e-Hopf theor

Domantas Burba, Mažena Mackoit Sinkevičienė, Viktor Novičenko, Emilia Witkowska

The spin-orbit coupling (SOC) affecting the center of mass of ultracold atoms can be simulated using a properly chosen periodic sequence of magnetic pulses. Yet such a method is generally accompanied by micro-motion which hinders a precise control of atomic dynamics and thus complicating practical applications. Here we show how to by-pass the micro-motion em

Hidetsugu Sakaguchi, Boris A. Malomed

We introduce a system of propagation equations for the fundamental-frequency (FF) and second-harmonic (SH) waves in the bulk waveguide with the effective fractional diffraction and quadratic (chi ^(2)) nonlinearity. The numerical solution produces families of ground-state (zero-vorticity) two-dimensional solitons in the free space, which are stable in exact

Eric R. Lowe, Stefan Stoll, J. P. Kestner

In this paper, we numerically optimize broadband pulse shapes that maximize Hahn echo amplitudes. Pulses are parameterized as neural networks (NN), nonlinear amplitude limited Fourier series (FS), and discrete time series (DT). These are compared to an optimized choice of the conventional hyperbolic secant (HS) pulse shape. A power constraint is included, as

Leandro Cagliero, Gonzalo Gutierrez

In the first part of the paper, we define the concept of a $G$-table of a $G$-(co)algebra and we compute the $G$-table of some $G$-(co)algebras (here a $G$-algebra is an algebra on which $G$ acts, semisimply, by algebra automorphisms). The $G$-table of a $G$-(co)algebra $A$ is a set of scalars that provides very precise and concise information about both the

Bertrand Eynard

Consider an algebraic equation $P(x,y)=0$ where $P\in \mathbb C[x,y] $ (or $\mathbb F[x,y]$ with $\mathbb F\subset \mathbb C$ a subfield) is a bivariate polynomial, it defines a plane algebraic curve. We provide an efficient method for computing integrals of the type $ \int_\gamma R(x,y)dx $ where $R(x,y)\in \mathbb C(x,y) $ is any rational fraction, and $y$

R. Winkler, U. Zülicke

We present a general, quantitative theory of electromagnetism and chirality in crystals. Symmetry is its guiding principle, enabling us to consider macroscopic multipole densities without reference to specific microscopic configurations. We use a formal analogy between space inversion $i$ and time inversion $θ$ to identify two complementary, comprehensive cl