February 2024 arXiv papers — page 164

Antonio Barrera, Patricia Román-Román, Francisco Torres-Ruiz

A stochastic diffusion process, whose mean function is a hyperbolastic curve of type I, is presented. Themain characteristics of the process are studied and the problem of maximum likelihood estimation forthe parameters of the process is considered. To this end, the firefly metaheuristic optimization algo-rithm is applied after bounding the parametric space

R. Frezzotti, G. Gagliardi, V. Lubicz, G. Martinelli

We determine, by means of lattice QCD calculations, the local form factors describing the $B_{s}\to \mu^{+}\mu^{-}\gamma$ decay. For this analysis we make use of the gauge configurations produced by the ETM Collaboration with $N_{f}=2+1+1$ flavour of Wilson-Clover twisted-mass fermions at maximal twist. To obtain the $B_{s}$ meson form-factors, we perform si

Osman M. Ahmed, Badruudin Zaheer Ahmad, Moncef Derouich

We present geomagnetic storms (GSs) selected from three solar cycles, spanning the years 1995 to 2022. We studied the development of the main phase of storms within disturbance storm time (Dst) amplitudes ranging from Dst =-64 nT to Dst=- 422 nT. In order to determine the solar wind (SW) parameters that mainly influence the main phase development of a GS, wh

G. I. Lykasov, A. I. Malakhov, A. A. Zaitsev

Inclusive spectra of the kaons produced in $Ar Sc$ collisions as functions of their transverse momentum $p_T$ at mid-rapidity have been calculated within the approach based on the assumption of the similarity of inclusive spectra of the hadrons produced in nucleus-nucleus collisions at their small transverse momenta in the mid-rapidity region taking into acc

Ruotong Wang, Lin Qiu, Justin Cranshaw, Amy X. Zhang

A recent surge in remote meetings has led to complaints of ``Zoom fatigue'' and ``collaboration overload,'' negatively impacting worker productivity and well-being. One way to alleviate the burden of meetings is to de-emphasize their synchronous participation by shifting work to and enabling sensemaking during post-meeting asynchronous activities. Towards th

Christine L. Borgman, Paul T. Groth

Sharing research data is necessary, but not sufficient, for data reuse. Open science policies focus more heavily on data sharing than on reuse, yet both are complex, labor-intensive, expensive, and require infrastructure investments by multiple stakeholders. The value of data reuse lies in relationships between creators and reusers. By addressing knowledge e

Nicolas Bonichon, Arnaud Casteigts, Cyril Gavoille, Nicolas Hanusse

The Freeze-Tag Problem, introduced in Arkin et al. (SODA'02) consists of waking up a swarm of $n$ robots, starting from a single active robot. In the basic geometric version, every robot is given coordinates in the plane. As soon as a robot is awakened, it can move towards inactive robots to wake them up. The goal is to minimize the wake-up time of the last

Tyler Lawson

If $E$ is a connective ring spectrum, then Pstragowski's category $Syn_E$ of $E$-synthetic spectra is generated by the bigraded spheres $S^{i,j}$. In particular, it is equivalent to the category of modules over a filtered ring spectrum.

Michael Huang, Vishal Gupta

We propose a novel family of decision-aware surrogate losses, called Perturbation Gradient (PG) losses, for the predict-then-optimize framework. The key idea is to connect the expected downstream decision loss with the directional derivative of a particular plug-in objective, and then approximate this derivative using zeroth order gradient techniques. Unlike

Ran Elgedawy, John Sadik, Anuj Gautam, Trinity Bissahoyo

In today's digital age, concerns about online security and privacy have become paramount. However, addressing these issues can be difficult, especially within the context of family relationships, wherein parents and children may have conflicting interests. In this environment, parents and children may turn to online security advice to determine how to procee

Milad Sefidgaran, Abdellatif Zaidi, Piotr Krasnowski

A major challenge in designing efficient statistical supervised learning algorithms is finding representations that perform well not only on available training samples but also on unseen data. While the study of representation learning has spurred much interest, most existing such approaches are heuristic; and very little is known about theoretical generaliz

Sruthi Gorantla, Sara Ahmadian

We investigate the problem of probably approximately correct and fair (PACF) ranking of items by adaptively evoking pairwise comparisons. Given a set of $n$ items that belong to disjoint groups, our goal is to find an $(\epsilon, \delta)$-PACF-Ranking according to a fair objective function that we propose. We assume access to an oracle, wherein, for each que

Sohee Kim, Jisu Kang, Dunam Kim, Seokju Lee

In this paper, we demonstrate that CLIP can also be adapted to downstream tasks where its vision-language alignment is suboptimally learned during pre-training on web-crawled data, all without requiring fine-tuning. We explore the case of monocular depth estimation, where CLIP's contrastive prior struggles to generalize, compared to its success in domains su

Sohom Bhattacharya, Rajarshi Mukherjee, Elizabeth Ogburn

Yule (1926) identified the issue of "nonsense correlations" in time series data, where dependence within each of two random vectors causes overdispersion -- i.e. variance inflation -- for measures of dependence between the two. During the near century since then, much has been written about nonsense correlations -- but nearly all of it confined to the time s

Xiao-Min Li, Qing-Fei Zhai, Hong-Xun Yi

In 1992, Gundersen (Complex Var. Elliptic Equ.20 (1992), no. 1-4, 99-106.) proposed the following famous open question: if two non-constant meromorphic functions share three values IM and share a fourth value CM, then do the functions necessarily share all four values CM? The open question is a long-standing question in the studies of the Nevanlinna$'$s valu

Sairam Sri Vatsavai, Venkata Sai Praneeth Karempudi, Ishan Thakkar

Several photonic microring resonators (MRRs) based analog accelerators have been proposed to accelerate the inference of integer-quantized CNNs with remarkably higher throughput and energy efficiency compared to their electronic counterparts. However, the existing analog photonic accelerators suffer from three shortcomings: (i) severe hampering of wavelength

Mingrui Li, Shuhong Liu, Heng Zhou, Guohao Zhu

We present SGS-SLAM, the first semantic visual SLAM system based on Gaussian Splatting. It incorporates appearance, geometry, and semantic features through multi-channel optimization, addressing the oversmoothing limitations of neural implicit SLAM systems in high-quality rendering, scene understanding, and object-level geometry. We introduce a unique semant

Arthur N. Montanari, Chao Duan, Adilson E. Motter

Functional observability and output controllability are properties that establish the conditions for the partial estimation and partial control of the system state, respectively. In the special case of full-state observability and controllability, the Popov-Belevitch-Hautus (PBH) tests provide conditions for the properties to hold based on the system eigensp

Orson Mengara

Diffusion models are state-of-the-art deep learning generative models that are trained on the principle of learning forward and backward diffusion processes via the progressive addition of noise and denoising. In this paper, we aim to fool audio-based DNN models, such as those from the Hugging Face framework, primarily those that focus on audio, in particula

Kolby Nottingham, Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Sameer Singh

Large language models (LLMs) have recently been used for sequential decision making in interactive environments. However, leveraging environment reward signals for continual LLM actor improvement is not straightforward. We propose Skill Set Optimization (SSO) for improving LLM actor performance through constructing and refining sets of transferable skills. S

Dat Phan-Trong, Hung The Tran, Alistair Shilton, Sunil Gupta

Black-box optimization is a powerful approach for discovering global optima in noisy and expensive black-box functions, a problem widely encountered in real-world scenarios. Recently, there has been a growing interest in leveraging domain knowledge to enhance the efficacy of machine learning methods. Partial Differential Equations (PDEs) often provide an eff

Antoine Magron, Anna Dai, Mike Zhang, Syrielle Montariol

Recent approaches in skill matching, employing synthetic training data for classification or similarity model training, have shown promising results, reducing the need for time-consuming and expensive annotations. However, previous synthetic datasets have limitations, such as featuring only one skill per sentence and generally comprising short sentences. In

Xiaohu Huang, Hao Zhou, Kun Yao, Kai Han

In this paper, we introduce FROSTER, an effective framework for open-vocabulary action recognition. The CLIP model has achieved remarkable success in a range of image-based tasks, benefiting from its strong generalization capability stemming from pretaining on massive image-text pairs. However, applying CLIP directly to the open-vocabulary action recognition

Ivan Dneprov, Maxim Grigoriev, Vyacheslav Gritzaenko

We elaborate on the recently proposed notion of a weak presymplectic gauge PDE. It is a $\mathbb{Z}$-graded bundle over the space-time manifold, equipped with a degree $1$ vector field and a compatible graded presymplectic structure. This geometrical data naturally defines a Lagrangian gauge field theory. Moreover, it encodes not only the Lagrangian of the t

Martin Kleppmann, Paul Frazee, Jake Gold, Jay Graber

Bluesky is a new social network built upon the AT Protocol, a decentralized foundation for public social media. It was launched in private beta in February 2023, and has grown to over 10 million registered users by October 2024. In this paper we introduce the architecture of Bluesky and the AT Protocol, and explain how the technical design of Bluesky is info

Larissa B. Little, Jennifer Coulter, Ruizhe Kang, Ilija Zeljkovic

YBa$_2$Cu$_3$O$_{7-\delta}$ (YBCO) has favorable macroscopic superconducting properties of $T_\mathrm{c}$ up to 93 K and $H_{c2}$ up to 150 T. However, its nanoscale electronic structure remains mysterious because bulk-like electronic properties are not preserved near the surface of cleaved samples for easy access by local or surface-sensitive probes. It has

Wedad Alharbi, Daniel Freeman, Dorsa Ghoreishi, Brody Johnson

A frame $(x_j)_{j\in J}$ for a Hilbert space $H$ allows for a linear and stable reconstruction of any vector $x\in H$ from the linear measurements $(\langle x,x_j\rangle)_{j\in J}$. However, there are many situations where some information in the frame coefficients is lost. In applications where one is using sensors with a fixed dynamic range, any measuremen

Ian DSouza, Chris Gordon

Scenarios such as the QCD axion with the Peccei-Quinn symmetry broken after inflation predict an enhanced matter power spectrum on sub-parsec scales. These theories lead to the formation of dense dark matter structures known as minihalos, which provide insights into early Universe dynamics and have implications for direct detection experiments. We examine th

Ahmed Ghita, Bjørk Antoniussen, Walter Zimmer, Ross Greer

The curation of large-scale datasets is still costly and requires much time and resources. Data is often manually labeled, and the challenge of creating high-quality datasets remains. In this work, we fill the research gap using active learning for multi-modal 3D object detection. We propose ActiveAnno3D, an active learning framework to select data samples f

Gesesew R. Habtie, Ramkrishna Das

This paper presents a comprehensive spectroscopic analysis of the quiescent stage of the recurrent nova RS Ophiuchi between its 2006 and 2021 outbursts. The spectra shows prominent low-ionization emission features, including hydrogen, helium, iron emissions, and TiO absorption features. The \ion{H}{$\alpha$} and \ion{H}{$\beta$} lines showed double-peaked em

Rafael I. Nepomechie, Francesco Ravanini, David Raveh

We introduce the notion of $su(2)$ spin-$s$ Dicke states, which are higher-spin generalizations of usual (spin-1/2) Dicke states. These multi-qudit states can be expressed as superpositions of $su(2s+1)$ qudit Dicke states. They satisfy a recursion formula, which we use to formulate an efficient quantum circuit for their preparation, whose size scales as $sk

Bastien Dubail

We investigate the mixing properties of a finite Markov chain in random environment defined as a mixture of a deterministic chain and a chain whose state space has been permuted uniformly at random. This work is the counterpart of a companion paper where we focused on a reversible model, which allowed for a few simplifications in the proof. We consider here

Gleb Ryzhakov, Svetlana Pavlova, Egor Sevriugov, Ivan Oseledets

This paper proposes a novel method, Explicit Flow Matching (ExFM), for training and analyzing flow-based generative models. ExFM leverages a theoretically grounded loss function, ExFM loss (a tractable form of Flow Matching (FM) loss), to demonstrably reduce variance during training, leading to faster convergence and more stable learning. Based on theoretica

Lorenzo Masoero, Mario Beraha, Thomas Richardson, Stefano Favaro

In online randomized experiments or A/B tests, accurate predictions of participant inclusion rates are of paramount importance. These predictions not only guide experimenters in optimizing the experiment's duration but also enhance the precision of treatment effect estimates. In this paper we present a novel, straightforward, and scalable Bayesian nonparamet

Arash Harirpoush, Amirhossein Rasoulian, Marta Kersten-Oertel, Yiming Xiao

Recent rising interests in patient-specific thoracic surgical planning and simulation require efficient and robust creation of digital anatomical models from automatic medical image segmentation algorithms. Deep learning (DL) is now state-of-the-art in various radiological tasks, and U-shaped DL models have particularly excelled in medical image segmentation

Sebastiano Stramaglia, Luca Faes, Jesus M. Cortes, Daniele Marinazzo

Transfer Entropy (TE), the primary method for determining directed information flow within a network system, can exhibit bias - either in deficiency or excess - during both pairwise and conditioned calculations, owing to high-order dependencies among the dynamic processes under consideration and the remaining processes in the system used for conditioning. He

Italo Salas, Francisca Guzmán-Lastra, Denisse Pastén, Ariel Norambuena

We investigate the behavior of active Brownian particles (ABP) within a temporal complex network framework approach. We focused on the node degree distribution, average path length, and average clustering coefficient across the P\'eclet number and packing fraction region. In the single phase or gas region, particle interactions mirror a random graph, and the

Vincent Roca, Grégory Kuchcinski, Jean-Pierre Pruvo, Dorian Manouvriez

In MRI studies, the aggregation of imaging data from multiple acquisition sites enhances sample size but may introduce site-related variabilities that hinder consistency in subsequent analyses. Deep learning methods for image translation have emerged as a solution for harmonizing MR images across sites. In this study, we introduce IGUANe (Image Generation wi

Xing Han, Huy Nguyen, Carl Harris, Nhat Ho

As machine learning models in critical fields increasingly grapple with multimodal data, they face the dual challenges of handling a wide array of modalities, often incomplete due to missing elements, and the temporal irregularity and sparsity of collected samples. Successfully leveraging this complex data, while overcoming the scarcity of high-quality train

Octavio Arizmendi, Saylé Sigarreta

In this manuscript we study how the vertex energy of a tree is affected when joined with a bipartite graph. We find an alternating pattern with respect to the coalescence vertex: the energy decreases for vertices located at odd distances and increases for those located at even distances.

David Radice, Ian Hawke

Observations of neutron star mergers have the potential to unveil detailed physics of matter and gravity in regimes inaccessible by other experiments. Quantitative comparisons to theory and parameter estimation require nonlinear numerical simulations. However, the detailed physics of energy and momentum transfer between different scales, and the formation an

Patrick Bareiß, Roman Klinger, Jeremy Barnes

Emotion classification in text is a challenging task due to the processes involved when interpreting a textual description of a potential emotion stimulus. In addition, the set of emotion categories is highly domain-specific. For instance, literature analysis might require the use of aesthetic emotions (e.g., finding something beautiful), and social media an

Fuqiang Wang

Hydrodynamics can describe majority of the measured azimuthal anisotropies in relativistic heavy-ion collisions. Many of the anisotropy measurements are contaminated by nonflow correlations (i.e., those unrelated to global event-wise correlations). Those nonflow contamination can cause incorrectness or compromise the accuracy of the physics extracted from da

Huy Nghiem, Umang Gupta, Fred Morstatter

The propagation of offensive content through social media channels has garnered attention of the research community. Multiple works have proposed various semantically related yet subtle distinct categories of offensive speech. In this work, we explore meta-earning approaches to leverage the diversity of offensive speech corpora to enhance their reliable and

Yatin Dandi, Emanuele Troiani, Luca Arnaboldi, Luca Pesce

We investigate the training dynamics of two-layer neural networks when learning multi-index target functions. We focus on multi-pass gradient descent (GD) that reuses the batches multiple times and show that it significantly changes the conclusion about which functions are learnable compared to single-pass gradient descent. In particular, multi-pass GD with

Tigany Zarrouk, Rina Ibragimova, Albert P. Bartók, Miguel A. Caro

An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous configurations through molecular dynamics or Monte Carlo structure optimization and selecting the one with the closest match to experiment. However, this inefficient process is not guar

Rowan Killip, Jason Murphy, Monica Visan

We prove that the small-data scattering map uniquely determines the nonlinearity for a wide class of gauge-invariant, intercritical nonlinear Schr\"odinger equations. We use the Born approximation to reduce the analysis to a deconvolution problem involving the distribution function for linear Schr\"odinger solutions. We then solve this deconvolution problem

Krzysztof Debicki, Lanpeng Ji, Svyatoslav Novikov

For $\{B_H(t)= (B_{H,1}(t), \ldots, B_{H,d}(t))^\top,t\ge0\}$, where $\{B_{H,i}(t),t\ge 0\}, 1\le i\le d$ are mutually independent fractional Brownian motions, we obtain the exact asymptotics of $$ \mathbb P (\exists t\ge 0: A B_{H}(t) - \mu t >\nu u), \ \ \ \ u\to\infty, $$ where $A$ is a non-singular $d\times d$ matrix and $\mu=(\mu_1,\ldots, \mu_d)^\top\i

Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo

In this paper, we introduce a new embedding model called M3-Embedding, which is distinguished for its versatility in \textit{Multi-Linguality}, \textit{Multi-Functionality}, and \textit{Multi-Granularity}. It provides a uniform support for the semantic retrieval of more than 100 working languages. It can simultaneously accomplish the three common retrieval f

M. Hofmann, D. Trabert, A. Geyer, N. Anders

We report on strong field ionization of S- and R-propylene oxide in circularly polarized two-color laser fields. We find that the relative helicity of the two single color laser fields affects the photoelectron circular dichroism (PECD). Further, we observe that PECD is modulated as a function of the sub-cycle release time of the electron. Our experimental o

Anna Yoo Jeong Ha, Josephine Passananti, Ronik Bhaskar, Shawn Shan

The advent of generative AI images has completely disrupted the art world. Distinguishing AI generated images from human art is a challenging problem whose impact is growing over time. A failure to address this problem allows bad actors to defraud individuals paying a premium for human art and companies whose stated policies forbid AI imagery. It is also cri

Ehud Haimov, Jonathan G. Hedley, Alexei A. Kornyshev

The mechanism behind mutual recognition of homologous DNA sequences prior to genetic recombination is one of the remaining puzzles in molecular biology. Leading models of homology recognition, based on classical electrostatics, neglect the short-range nonlocal screening effects arising from structured water around DNA, and hence may only provide insight for

Tomoaki Matsumoto

The formation of binary stars is highly influenced by magnetic fields, which play a crucial role in transporting angular momentum. We conducted three-dimensional numerical simulations of binary star accretion via a circumbinary disk, taking into account a magnetic field perpendicular to the disk and an infalling envelope. Our simulations reproduce the follow

Dmitri Maslov, Sergey Bravyi, Felix Tripier, Andrii Maksymov

Establishing an advantage for (white-box) computations by a quantum computer against its classical counterpart is currently a key goal for the quantum computation community. A quantum advantage is achieved once a certain computational capability of a quantum computer is so complex that it can no longer be reproduced by classical means, and as such, the quant

Anton Rodomanov, Ali Kavis, Yongtao Wu, Kimon Antonakopoulos

We develop universal gradient methods for Stochastic Convex Optimization (SCO). Our algorithms automatically adapt not only to the oracle's noise but also to the H\"older smoothness of the objective function without a priori knowledge of the particular setting. The key ingredient is a novel strategy for adjusting step-size coefficients in the Stochastic Grad

Kai Hugtenburg, Sara B. Tukachinsky

We compute the quantum cohomology relative to a Lagrangian submanifold in some complete intersections. For quadric hypersurfaces, we also give a full computation of the genus zero open Gromov-Witten invariants.

Patrick M. Harrington, Mingyu Li, Max Hays, Wouter Van De Pontseele

Quantum information processing at scale will require sufficiently stable and long-lived qubits, likely enabled by error-correction codes. Several recent superconducting-qubit experiments, however, reported observing intermittent spatiotemporally correlated errors that would be problematic for conventional codes, with ionizing radiation being a likely cause.

Nikita Gushchin, Sergei Kholkin, Evgeny Burnaev, Alexander Korotin

Schr\"odinger Bridges (SB) have recently gained the attention of the ML community as a promising extension of classic diffusion models which is also interconnected to the Entropic Optimal Transport (EOT). Recent solvers for SB exploit the pervasive bridge matching procedures. Such procedures aim to recover a stochastic process transporting the mass between d

Suneel Babu Chatla, Ruiqi Liu

Spatially distributed functional data are prevalent in many statistical applications such as meteorology, energy forecasting, census data, disease mapping, and neurological studies. Given their complex and high-dimensional nature, functional data often require dimension reduction methods to extract meaningful information. Inverse regression is one such appro

Stefan Steinerberger

Inspired by the bad scientist who keeps repeating an experiment 20 times to get a single outcome with $p < 0.05$, we consider matrices $A \in \mathbb{R}^{n \times n}$ whose rows are normalized in $\ell^2$ and for which $2^{-n}\sum_{x \in \left\{-1,1\right\}^n} \|Ax\|_{\ell^{\infty}}$ is large. They correspond to affine transformations of the discrete unit cu

Tianzhang Cai, Qichen Wang, Shuai Zhang, Özlem Tuğfe Demir

We develop a multi-agent reinforcement learning (MARL) algorithm to minimize the total energy consumption of multiple massive MIMO (multiple-input multiple-output) base stations (BSs) in a multi-cell network while preserving the overall quality-of-service (QoS) by making decisions on the multi-level advanced sleep modes (ASMs) and antenna switching of these

Gabriela Ciuperca

Based on the expectile loss function and the adaptive LASSO penalty, the paper proposes and studies the estimation methods for the accelerated failure time (AFT) model. In this approach, we need to estimate the survival function of the censoring variable by the Kaplan-Meier estimator. The AFT model parameters are first estimated by the expectile method and a

Rashid Iqbal, Mauro Biagi, Ahmed Zoha, Muhammad Ali Imran

Indoor visible light communication (VLC) is considered secure against attackers outside the confined area where the light propagates, but it is still susceptible to interception from inside the coverage area. A new technology, intelligent reflecting surfaces (IRS), has been recently introduced, offering a way to enhance physical layer security (PLS). Most re

Lingxiao Yang, Shutong Ding, Yifan Cai, Jingyi Yu

Recent advances in diffusion models attempt to handle conditional generative tasks by utilizing a differentiable loss function for guidance without the need for additional training. While these methods achieved certain success, they often compromise on sample quality and require small guidance step sizes, leading to longer sampling processes. This paper reve

N. Gavrielov

Nuclei in the $A\approx100$ region exhibit intricate shape-evolution and configuration crossing signatures. Exploring both even-even and their adjacent odd-mass nuclei gives further insight on the emergence of deformation and shape-phase transitions. We employ the algebraic frameworks of the interacting boson model with configuration mixing and the new inter

Vincenzo Barbuto, Claudio Savaglio, Roberto Minerva, Noel Crespi

Cities have undergone significant changes due to the rapid increase in urban population, heightened demand for resources, and growing concerns over climate change. To address these challenges, digital transformation has become a necessity. Recent advancements in Artificial Intelligence (AI) and sensing techniques, such as synthetic sensing, can elevate Digit

Xingyu Qu, Samuel Horvath

Model merging offers an efficient way to combine pre-trained neural networks but often suffers from inconsistent performance, especially when merging models with different initializations. We identify the ``vanishing feature'' phenomenon, where input-induced features diminish during propagation through the merged model, degrading performance. Through theoret

Felix Linker, David Basin

Social authentication has been suggested as a usable authentication ceremony to replace manual key authentication in messaging applications. Using social authentication, chat partners authenticate their peers using digital identities managed by identity providers. In this paper, we formally define social authentication, present a protocol called SOAP that la

Yakov Berchenko-Kogan, Evan S. Gawlik

The Whitney forms on a simplex $T$ admit high-order generalizations that have received a great deal of attention in numerical analysis. Less well-known are the shadow forms of Brasselet, Goresky, and MacPherson. These forms generalize the Whitney forms, but have rational coefficients, allowing singularities near the faces of $T$. Motivated by numerical probl

Rugved Chavan, Gabriel Hyman, Zoraiz Qureshi, Nivetha Jayakumar

Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its utilization remains limited. A key challenge in the quantitative analysis of dFDG-PET is characterizing a patient-specific blood input function, traditionally reliant on invasive arterial blood sampling. This r

Yeonwoo Rho

Handling multiplicity without losing much power has been a persistent challenge in various fields that often face the necessity of managing numerous statistical tests simultaneously. Recently, $p$-value combination methods based on heavy-tailed distributions, such as a Cauchy distribution, have received much attention for their ability to handle multiplicity

Jitendra Bhandari, Mohammed Nabeel, Likhitha Mankali, Ozgur Sinanoglu

This paper presents a novel defense strategy against static power side-channel attacks (PSCAs), a critical threat to cryptographic security. Our method is based on (1) carefully tuning high-Vth versus low-Vth cell selection during synthesis, accounting for both security and timing impact, and (2), at runtime, randomly switching the operation between these ce

Abdesslam Arhrib, Mohamed Krab, Souad Semlali

Charged Higgs bosons are common predictions in most extensions of the Standard Model (SM) Higgs sector. Therefore, their observation would elucidate the nature of the Higgs sector. Motivated by the ATLAS collaboration's latest analysis performed with $139~\text{fb}^{-1}$ of Run 2 data intended to search for charged Higgs boson, produced in top quark decay an

Roberto Maneiro-Catoira, Julio Brégains, José A. García-Naya, Luis Castedo

We present an innovative approach that simultaneously enables direct antenna frequency-hopped M-ary frequency shift keying (DAFH-MFSK) modulation and beamsteering through the use of time-modulated arrays (TMAs). The distinctive feature of our approach lies in the modulation of the TMA excitations with binary periodic sequences, which can be easily frequency-

Guo-Dong Zhang, Fu-Ming Chang, Paul M. Saffin, Qi-Xin Xie

Recently, it has been found that a $Q$-ball can amplify waves incident upon it, due to rotation in the internal space and the interaction of the two modes in the complex scalar field. While the spherically symmetric 3D case has been investigated previously, here we explore the 3D axi-symmetric case, which is numerically much more challenging. The difficulty

Zhiwen Jiang, Stephan Morgenthaler

We investigate the multiplicity model with m values of some test statistic independently drawn from a mixture of no effect (null) and positive effect (alternative), where we seek to identify, the alternative test results with a controlled error rate. We are interested in the case where the alternatives are rare. A number of multiple testing procedures filter

Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh

Whether embedding spaces use all their dimensions equally, i.e., whether they are isotropic, has been a recent subject of discussion. Evidence has been accrued both for and against enforcing isotropy in embedding spaces. In the present paper, we stress that isotropy imposes requirements on the embedding space that are not compatible with the presence of clus

Xiang Chen, Chenxi Wang, Yida Xue, Ningyu Zhang

Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination. The reliable detection of such hallucinations in MLLMs has, therefore, become a vital aspect of model evaluation and the safeguarding of practical application deployment. Prior research in this domain has been constrai

Jan Bíma

Given two metric spaces $\mathcal N \subseteq \mathcal M$ in inclusion and $0<p\leq 1$, we wish to determine the smallest constant $\mathfrak{t}_p (\mathcal N, \mathcal M)$ such that any Lipschitz map $f: \mathcal N \to Z$ into any $p$-Banach space $Z$ can be extended to a Lipschitz map $f' : \mathcal M \to Z$ satisfying $\operatorname{Lip} f' \leq \mathfrak

Ethan Wilson, Frederick Shic, Sophie Jörg, Eakta Jain

Advances in face swapping have enabled the automatic generation of highly realistic faces. Yet face swaps are perceived differently than when looking at real faces, with key differences in viewer behavior surrounding the eyes. Face swapping algorithms generally place no emphasis on the eyes, relying on pixel or feature matching losses that consider the entir

Kai Lion, Lorenzo Noci, Thomas Hofmann, Gregor Bachmann

The multi-modal nature of neural loss landscapes is often considered to be the main driver behind the empirical success of deep ensembles. In this work, we probe this belief by constructing various "connected" ensembles which are restricted to lie in the same basin. Through our experiments, we demonstrate that increased connectivity indeed negatively impacts

Charalambos Mitropoulos, Maria Kechagia, Chrysostomos Maschas, Sotiris Ioannidis

The usage of error handling in Solidity smart contracts is vital because smart contracts perform transactions that should be verified. Transactions that are not carefully handled, may lead to program crashes and vulnerabilities, implying financial loss and legal consequences. While Solidity designers attempt to constantly update the language with new feature

Vaclav Voracek

We study hypergraphs which represent finite quantum event structures. We contribute to results of graph theory, regarding bounds on the number of edges, given the number of vertices. We develop a missing one for 3-graphs of girth 4. As an application of the graph-theoretical approach to quantum structures, we show that the smallest orthoalgebra with an empty

Israel Quiros

We revisit the conformally coupled scalar gravitational theory. This is the simplest local-scale invariant theory of gravity which is linear in the curvature scalar. We demonstrate that, if incorporate local-scale symmetry into the variational procedure, it is not required that the trace of the stress-energy tensor of the matter fields vanished for this symm

Yuta Watanabe

In this paper, we investigate various positivity for singular Hermitian metrics such as Griffiths, $\omega$-trace and RC, where $\omega$ is a Hermitian metric, and show that these quasi-positivity notions induce $0$-th cohomology vanishing, rational conected-ness, etc. Here, $\omega$-trace positivity of smooth Hermitian metrics $h$ on holomorphic vector bund

Yan Zhao, Zhongyun Li, Yushan Pan, Jiaxing Wang

Generative Artificial Intelligence (AI), because of its emergent abilities, has empowered various fields, one typical of which is large language models (LLMs). One of the typical application fields of Generative AI is large language models (LLMs), and the natural language understanding capability of LLM is dramatically improved when compared with conventiona

Jingzhi Gong, Tao Chen

Learning and predicting the performance of given software configurations are of high importance to many software engineering activities. While configurable software systems will almost certainly face diverse running environments (e.g., version, hardware, and workload), current work often either builds performance models under a single environment or fails to

Yushan Jiang, Zijie Pan, Xikun Zhang, Sahil Garg

Recently, remarkable progress has been made over large language models (LLMs), demonstrating their unprecedented capability in varieties of natural language tasks. However, completely training a large general-purpose model from the scratch is challenging for time series analysis, due to the large volumes and varieties of time series data, as well as the non-

Mintong Kang, Nezihe Merve Gürel, Ning Yu, Dawn Song

Despite the impressive capabilities of large language models (LLMs) across diverse applications, they still suffer from trustworthiness issues, such as hallucinations and misalignments. Retrieval-augmented language models (RAG) have been proposed to enhance the credibility of generations by grounding external knowledge, but the theoretical understandings of

Pietro Pampili, Markus Pristovsek

We report about metalorganic vapour phase epitaxy of smooth nitrogen-polar AlN templates on vicinal (0001) sapphire substrates. The influence of V/III ratio, growth temperature, growth rate, as well as sapphire-nitridation time and temperature were studied. With 4{\deg} offcut sapphire, step-flow growth was possible only with V/III ratios below 2. However, o

Thomas Leguay, Théo Ladune, Pierrick Philippe, Olivier Déforges

We propose a lightweight learned video codec with 900 multiplications per decoded pixel and 800 parameters overall. To the best of our knowledge, this is one of the neural video codecs with the lowest decoding complexity. It is built upon the overfitted image codec Cool-chic and supplements it with an inter coding module to leverage the video's temporal redu

Yunfeng Zhang

A fruitful approach to studying the concentration of Laplace--Beltrami eigenfunctions on a compact manifold, as the eigenvalue tends to infinity, is to bound their restriction to submanifolds. In this paper, we adopt this approach in the setting of compact Lie groups and provide sharp restriction bounds for general Laplace--Beltrami eigenfunctions, as well a

Zaid Alyafeai, Khalid Almubarak, Ahmed Ashraf, Deema Alnuhait

Instruction tuning has emerged as a prominent methodology for teaching Large Language Models (LLMs) to follow instructions. However, current instruction datasets predominantly cater to English or are derived from English-dominated LLMs, resulting in inherent biases toward Western culture. This bias significantly impacts the linguistic structures of non-Engli

Bayode Ogunleye, Tonderai Maswera, Laurence Hirsch, Jotham Gaudoin

Topic modelling is a prominent task for automatic topic extraction in many applications such as sentiment analysis and recommendation systems. The approach is vital for service industries to monitor their customer discussions. The use of traditional approaches such as Latent Dirichlet Allocation (LDA) for topic discovery has shown great performances, however

Siddhartha Dalal, Vishal Misra

This paper introduces a novel Bayesian learning model to explain the behavior of Large Language Models (LLMs), focusing on their core optimization metric of next token prediction. We develop a theoretical framework based on an ideal generative text model represented by a multinomial transition probability matrix with a prior, and examine how LLMs approximate

Xiaobing Dai, Zewen Yang, Mengtian Xu, Fangzhou Liu

Consensus control in multi-agent systems has received significant attention and practical implementation across various domains. However, managing consensus control under unknown dynamics remains a significant challenge for control design due to system uncertainties and environmental disturbances. This paper presents a novel learning-based distributed contro

Zichen Zhu, Yang Xu, Lu Chen, Jingkai Yang

The rapid development of multimodal large language models (MLLMs) raises the question of how they compare to human performance. While existing datasets often feature synthetic or overly simplistic tasks, some models have already surpassed human expert baselines. In this paper, we present MULTI, a Chinese multimodal dataset derived from authentic examination

Gonçalo Gomes, Isabel Coutinho, Bruno Martins

Although the International Classification of Diseases (ICD) has been adopted worldwide, manually assigning ICD codes to clinical text is time-consuming, error-prone, and expensive, motivating the development of automated approaches. This paper describes a novel approach for automated ICD coding, combining several ideas from previous related work. We specific

Fatima Zahra Qachfar, Rakesh M. Verma

We examine the impact of homograph attacks on the Sentiment Analysis (SA) task of different Arabic dialects from the Maghreb North-African countries. Homograph attacks result in a 65.3% decrease in transformer classification from an F1-score of 0.95 to 0.33 when data is written in "Arabizi". The goal of this study is to highlight LLMs weaknesses' and to prio

Riccardo Grazzi, Julien Siems, Simon Schrodi, Thomas Brox

State of the art foundation models such as GPT-4 perform surprisingly well at in-context learning (ICL), a variant of meta-learning concerning the learned ability to solve tasks during a neural network forward pass, exploiting contextual information provided as input to the model. This useful ability emerges as a side product of the foundation model's massiv