October 2023 arXiv papers — page 171

Joanna A. Zielińska, Fons van der Laan, Andreas Norrman, René Reimann

The field of levitodynamics has made substantial advancements in manipulating the translational and rotational degrees of freedom of levitated nanoparticles. Notably, rotational degrees of freedom can now be cooled to millikelvin temperatures and driven into GHz rotational speeds. However, in the case of cylindrically symmetric nanorotors, only the rotations

João C. Getelina, Cai-Zhuang Wang, Thomas Iadecola, Yong-Xin Yao

We apply the adaptive variational quantum imaginary time evolution (AVQITE) method to prepare ground states of one-dimensional spin $S=1$ models. We compare different spin-to-qubit encodings (standard binary, Gray, unary, and multiplet) with regard to the performance and quantum resource cost of the algorithm. Using statevector simulations we study two well-

Zhiwen Fan, Panwang Pan, Peihao Wang, Yifan Jiang

In the field of novel-view synthesis, the necessity of knowing camera poses (e.g., via Structure from Motion) before rendering has been a common practice. However, the consistent acquisition of accurate camera poses remains elusive, and errors in pose extraction can adversely impact the view synthesis process. To address this challenge, we introduce PF-GRT,

Dimitrios Karamitros, Thomas McKelvey, Apostolos Pilaftsis

We analyse in detail the effect of varying entropy degrees of freedom on low-scale leptogenesis models. As an archetypal model, we consider the Tri-Resonant Leptogensis${}$ (TRL) scenario introduced recently by the authors, where the neutrino-Yukawa coupling matrix is dictated by an approximate $\mathbb{Z}_n$ discrete symmetry (with $n=3,6$). TRL models exhi

Jason Hartline, Darrell Hoy, Samuel Taggart

Equilibria in auctions can be very difficult to analyze, beyond the symmetric environments where revenue equivalence renders the analysis straightforward. This paper takes a robust approach to evaluating the equilibria of auctions. Rather than identify the equilibria of an auction under specific environmental conditions, it considers worst-case analysis, whe

Vojtěch Liška, Tereza Zemánková, Petr Jákl, Martin Šiler

Nanoparticles levitated in an optical trap provide a versatile platform to study mechanical oscillators in a controlled environment with tuneable parameters. Recently, it has become possible to couple two of these optomechanical oscillators. Here, we demonstrate the collective non-Hermitian dynamics of such a pair of non-conservatively coupled oscillators. W

Evgeny Stemasov, Jessica Hohn, Maurice Cordts, Anja Schikorr

3D printers enable end-users to design and fabricate unique physical artifacts but maintain an increased entry barrier and friction. End users must design tangible artifacts through intangible media away from the main problem space (ex-situ) and transfer spatial requirements to an abstract software environment. To allow users to evaluate dimensions, balance,

Qing-Hu Hou, Zhi-Wei Sun

During 2022--2023 Z.-W. Sun posed many conjectures on infinite series with summands involving generalized harmonic numbers. Motivated by this, we deduce $58$ series identities involving harmonic numbers, eight of which were previously conjectured by the second author. For example, we obtain that \[ \sum_{k=1}^{\infty} \frac{(-1)^k}{k^2{2k \choose k}{3k \choo

Gaëtan Fichet de Clairfontaine, Sara Buson, Leonard Pfeiffer, Stefano Marchesi

Recent observations are shedding light on the important role that active galactic nuclei (AGN) play in the production of high-energy neutrinos. In this study, we focus on one object, 5BZB J0630-2406, which is among the blazars recently proposed as associated with neutrino emission during the first 7-yr IceCube observations. Modelling the quasi-simultaneous,

H. Mohseni Sadjadi

This study investigates the emergence of dark energy during the matter-dominated era through spontaneous cosmological scalarization in the scalar-Ricci-Gauss-Bonnet model.Our model aligns with the conjecture that the speed of gravitational waves at low redshifts is nearly equal to the speed of light, implying that the Gauss-Bonnet invariant does not directly

Rahul Parhi, Michael Unser

We investigate the function-space optimality (specifically, the Banach-space optimality) of a large class of shallow neural architectures with multivariate nonlinearities/activation functions. To that end, we construct a new family of Banach spaces defined via a regularization operator, the $k$-plane transform, and a sparsity-promoting norm. We prove a repre

Michael S. Albergo, Nicholas M. Boffi, Michael Lindsey, Eric Vanden-Eijnden

Given a set of $K$ probability densities, we consider the multimarginal generative modeling problem of learning a joint distribution that recovers these densities as marginals. The structure of this joint distribution should identify multi-way correspondences among the prescribed marginals. We formalize an approach to this task within a generalization of the

Edward J. Oughton, David Amaglobeli, Marian Moszoro

Roughly 3 billion citizens remain offline, equating to approximately 40 percent of the global population. Therefore, providing Internet connectivity is an essential part of the Sustainable Development Goals (SDGs) (Goal 9). In this paper a high-resolution global model is developed to evaluate the necessary investment requirements to achieve affordable univer

Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen

Optimizing large language models (LLMs) for downstream use cases often involves the customization of pre-trained LLMs through further fine-tuning. Meta's open release of Llama models and OpenAI's APIs for fine-tuning GPT-3.5 Turbo on custom datasets also encourage this practice. But, what are the safety costs associated with such custom fine-tuning? We note

Simon Finster, Paul W. Goldberg, Edwin Lock

In markets with budget-constrained buyers, competitive equilibria need not be efficient in the utilitarian sense, or maximise the seller's revenue. We consider a setting with multiple divisible goods. Competitive equilibrium outcomes, and only those, are constrained utilitarian efficient, a notion of utilitarian efficiency that respects buyers' demands and b

Damien Masson, Sylvain Malacria, Géry Casiez, Daniel Vogel

We characterize and demonstrate how the principles of direct manipulation can improve interaction with large language models. This includes: continuous representation of generated objects of interest; reuse of prompt syntax in a toolbar of commands; manipulable outputs to compose or control the effect of prompts; and undo mechanisms. This idea is exemplified

Benjamin Grimmer, Zhichao Jia

We prove the first convergence guarantees for a subgradient method minimizing a generic Lipschitz function over generic Lipschitz inequality constraints. No smoothness or convexity (or weak convexity) assumptions are made. Instead, we utilize a sequence of recent advances in Lipschitz unconstrained minimization, which showed convergence rates of $O(1/\delta\

Alessandro Del Ponte, Audrey De Dominicis, Paolo Canofari

Background: Here we investigate whether releasing COVID-19 vaccines in limited quantities and at limited times boosted Italy's vaccination campaign in 2021. This strategy exploits insights from psychology and consumer marketing. Methods: We built an original dataset covering 200 days of vaccination data in Italy, including 'open day' events. Open-day events

Boaz Rafaely, Koby Alhaiany

Estimation of the direction-of-arrival (DoA) of a speaker in a room is important in many audio signal processing applications. Environments with reverberation that masks the DoA information are particularly challenging. Recently, a DoA estimation method that is robust to reverberation has been developed. This method identifies time-frequency bins dominated b

Zarif Ikram, Ling Pan, Dianbo Liu

Due to limited resources and fast economic growth, designing optimal transportation road networks with traffic simulation and validation in a cost-effective manner is vital for developing countries, where extensive manual testing is expensive and often infeasible. Current rule-based road design generators lack diversity, a key feature for design robustness.

Anna Langedijk, Hosein Mohebbi, Gabriele Sarti, Willem Zuidema

In recent years, many interpretability methods have been proposed to help interpret the internal states of Transformer-models, at different levels of precision and complexity. Here, to analyze encoder-decoder Transformers, we propose a simple, new method: DecoderLens. Inspired by the LogitLens (for decoder-only Transformers), this method involves allowing th

Eitetsu Ken, Satoru Kuroda

In [Mulmuley, 1987], Mulmuley gave an algorithm reducing the computation of the matrix rank function to that of determinants, of which the proof for the verification is elementary. In this article, we formalize this argument in the bounded arithmetic $LAP$; that is, we show that \[\det(AB)=\det(A)\det(B)\] for matrices $A,B$ with $mathbb{F}(X)$-coefficients

Matteo Fael, Johann Usovitsch

We present the QCD corrections of order $\alpha_s^3$ to the decay rate of $b \to u \ell \bar \nu_\ell$, with $\ell = e,\mu$, originating from diagrams with closed fermion loops and neglecting the mass of the up quark. Our calculation relies on integration-by-parts reduction of Feynman integrals with one propagator raised to a symbolic power in Kira and the n

Tung Phung, Victor-Alexandru Pădurean, Anjali Singh, Christopher Brooks

Generative AI and large language models hold great promise in enhancing programming education by automatically generating individualized feedback for students. We investigate the role of generative AI models in providing human tutor-style programming hints to help students resolve errors in their buggy programs. Recent works have benchmarked state-of-the-art

Alexander Robey, Eric Wong, Hamed Hassani, George J. Pappas

Despite efforts to align large language models (LLMs) with human intentions, widely-used LLMs such as GPT, Llama, and Claude are susceptible to jailbreaking attacks, wherein an adversary fools a targeted LLM into generating objectionable content. To address this vulnerability, we propose SmoothLLM, the first algorithm designed to mitigate jailbreaking attack

Érico Melo Silva

We show that certain functions whose nodal sets lie near a fixed nondegenerate minimal hypersurface satisfy a strong min-max principle for the Allen--Cahn energy which is analogous to the strong min-max principle for non-degenerate minimal hypersurfaces first proved by Brian White.

Diksha, Gunnemeda Eswar, Soumyajyoti Biswas

The dynamics of driven interfaces through disordered media is a common framework for a myriad of diverse systems starting from mode-I fracture, vortex lines in superconductors, magnetic domain walls to invading fluid in a porous medium to name a few. The dynamics, for a slow enough drive, progress through punctuated equilibria, showing an intermittent, scale

Marco Alberto Javarone, Fernando E. Rosas, Paolo Facchi, Saverio Pascazio

Here, we leverage recent advances in information theory to develop a novel method to characterise the dominant character of the high-order dependencies of quantum systems. To this end, we introduce the Q-information: an information-theoretic measure capable of distinguishing quantum states dominated by synergy or redundancy. We illustrate the measure by inve

G. Martinelli, S. Simula, L. Vittorio

We present an updated determination of the values of $\vert V_{cb} \vert$, $R(D^*)$ and $\vert V_{ub} \vert/\vert V_{cb} \vert$ based on the new data on semileptonic $B \to D^* \ell \nu_\ell$ decays by the Belle and Belle-II Collaborations and on the recent theoretical progress in the calculation of the form factors relevant for semileptonic $B \to D^* \ell

Matthew J. Filipovich, Aleksei Malyshev, A. I. Lvovsky

Diffractive optical neural networks (DONNs) have emerged as a promising optical hardware platform for ultra-fast and energy-efficient signal processing for machine learning tasks, particularly in computer vision. Previous experimental demonstrations of DONNs have only been performed using coherent light. However, many real-world DONN applications require con

Hayden Smotherman, Pedro H. Bernardinelli, Stephen K. N. Portillo, Andrew J. Connolly

We present the first set of trans-Neptunian objects (TNOs) observed on multiple nights in data taken from the DECam Ecliptic Exploration Project (DEEP). Of these 110 TNOs, 105 do not coincide with previously known TNOs and appear to be new discoveries. Each individual detection for our objects resulted from a digital tracking search at TNO rates of motion, u

Narutaka Ozawa

We answer the recent problem posed by Baudier, Braga, Farah, Vignati, and Willett that asks whether the $\ell_\infty$-direct sum of the matrix algebras embeds into the uniform Roe algebra or the quasi-local algebra of a uniformly locally finite metric space. The answers are no and yes, respectively. Hence the inclusion of the uniform Roe algebra into the qua

Ajay Suresha Sathya, Wilm Decre, Jan Swevers

We present PV-OSIMr, an efficient algorithm for computing the Delassus matrix (also known as the inverse operational space inertia matrix) for a kinematic tree, with the lowest order computational complexity known in literature. PV-OSIMr is derived by optimizing the Popov-Vereshchagin (PV) solver computations using the compositionality of the force and motio

Martin Schiemer, Clemens JS Schaefer, Jayden Parker Vap, Mark James Horeni

Continual learning is a desirable feature in many modern machine learning applications, which allows in-field adaptation and updating, ranging from accommodating distribution shift, to fine-tuning, and to learning new tasks. For applications with privacy and low latency requirements, the compute and memory demands imposed by continual learning can be cost-pr

Jeeban Kumar Nayak, Niladri Modak, Sayan Ghosh, Nirmalya Ghosh

We propose and experimentally demonstrate a differential microscopy method to obtain simultaneous amplitude, phase, and quantitative polarization gradient imaging in a single experimental embodiment. A full-field optical spatial differentiator is achieved in a relatively simple setup by placing a glass cover slip as a Signum phase mask in the Fourier plane o

Jonathan St-Onge, Giulio Burgio, Samuel F. Rosenblatt, Timothy M. Waring

Epidemic models study the spread of an undesired agent through a population, be it infectious diseases through a country, misinformation in online social media, or pests infesting a region. In combating these epidemics, we rely neither on global top-down interventions, nor solely on individual adaptations. Instead, interventions most commonly come from local

Pedro H. Bernardinelli, Hayden Smotherman, Zachary Langford, Stephen K. N. Portillo

We present a detailed study of the observational biases of the DECam Ecliptic Exploration Project's (DEEP) B1 data release and survey simulation software that enables direct statistical comparisons between models and our data. We inject a synthetic population of objects into the images, and then subsequently recover them in the same processing as our real de

Md. Ismail Hossain, M M Lutfe Elahi, Sameera Ramasinghe, Ali Cheraghian

In the knowledge distillation literature, feature-based methods have dominated due to their ability to effectively tap into extensive teacher models. In contrast, logit-based approaches, which aim to distill "dark knowledge" from teachers, typically exhibit inferior performance compared to feature-based methods. To bridge this gap, we present LumiNet, a nove

Oscar Sainz, Iker García-Ferrero, Rodrigo Agerri, Oier Lopez de Lacalle

Large Language Models (LLMs) combined with instruction tuning have made significant progress when generalizing to unseen tasks. However, they have been less successful in Information Extraction (IE), lagging behind task-specific models. Typically, IE tasks are characterized by complex annotation guidelines that describe the task and give examples to humans.

Riddam Rishu, Akshay Kakkar, Cheng Wang, Abdul Rahman

Building on previous work using reinforcement learning (RL) focused on identification of exfiltration paths, this work expands the methodology to include protocol and payload considerations. The former approach to exfiltration path discovery, where reward and state are associated specifically with the determination of optimal paths, are presented with these

Nicolas Matentzoglu, J. Harry Caufield, Harshad B. Hegde, Justin T. Reese

Aligning terminological resources, including ontologies, controlled vocabularies, taxonomies, and value sets is a critical part of data integration in many domains such as healthcare, chemistry, and biomedical research. Entity mapping is the process of determining correspondences between entities across these resources, such as gene identifiers, disease conc

Sergio Salinas-Fernández, Nancy Hitschfeld-Kahler

Polylla is a polygonal mesh algorithm that generates meshes with arbitrarily shaped polygons using the concept of terminal-edge regions. Until now, Polylla has been limited to 2D meshes, but in this work, we extend Polylla to 3D volumetric meshes. We present two versions of Polylla 3D. The first version generates terminal-edge regions, converts them into pol

Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon, Nikos Paragios

In medical imaging, segmentation models have known a significant improvement in the past decade and are now used daily in clinical practice. However, similar to classification models, segmentation models are affected by adversarial attacks. In a safety-critical field like healthcare, certifying model predictions is of the utmost importance. Randomized smooth

Pallav Kumar Bera, Vajendra Kumar, Samita Rani Pani, Om P. Malik

Protective relays can mal-operate for transmission lines connected to doubly fed induction generator (DFIG) based large capacity wind farms (WFs). The performance of distance relays protecting such lines is investigated and a statistical model based intelligent protection of the area between the grid and the WF is proposed in this article. The suggested meth

Thomas Titz Mite, Stefan Witzel

We construct the first example of a lattice on an irreducible Euclidean building that is not residually finite. Conjecturally, the normal subgroup theorem extends to this lattice making it virtually simple.

Jianhong Bai, Yuchen Yang, Huanpeng Chu, Hualiang Wang

Quantization has emerged as a promising direction for model compression. Recently, data-free quantization has been widely studied as a promising method to avoid privacy concerns, which synthesizes images as an alternative to real training data. Existing methods use classification loss to ensure the reliability of the synthesized images. Unfortunately, even i

Malcolm Perry, Maria J. Rodriguez

While static Love number vanish identically for Kerr black holes, we show that the corresponding dynamical tidal coefficients are generically non-zero and exhibit logarithmic behavior. The computational method employs a related but simpler scheme consistent with CFT descriptions, low-frequency regimes and post-Newtonian results. These coefficients are illust

Thorsten Händler

Large language models (LLMs) have revolutionized the field of artificial intelligence, endowing it with sophisticated language understanding and generation capabilities. However, when faced with more complex and interconnected tasks that demand a profound and iterative thought process, LLMs reveal their inherent limitations. Autonomous LLM-powered multi-agen

Ioannis Mademlis, Georgios Batsis, Adamantia Anna Rebolledo Chrysochoou, Georgios Th. Papadopoulos

Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. The large volume and high throughput of passengers, mailed parcels, etc., during rush hours practically make it a Big Data problem

Pål Forr Austnes, Celia García-Pareja, Fabio Nobile, Mario Paolone

Accurate and reliable electricity load forecasts are becoming increasingly important as the share of intermittent resources in the system increases. Distribution System Operators (DSOs) are called to accurately forecast their production and consumption to place optimal bids in the day-ahead market. Forecasts must account for the volatility of weather-paramet

William M Feldman, Inwon C. Kim, Norbert Požár

We introduce a toy model for rate-independent droplet motion on a surface with contact angle hysteresis based on the one-phase Bernoulli free boundary problem. We consider a notion of energy solutions and show existence by a minimizing movement scheme. The main result of the paper is on the PDE conditions satisfied by general energy solutions: we show that t

Benjamin Laufer, Jon Kleinberg, Karen Levy, Helen Nissenbaum

A broad current application of algorithms is in formal and quantitative measures of murky concepts -- like merit -- to make decisions. When people strategically respond to these sorts of evaluations in order to gain favorable decision outcomes, their behavior can be subjected to moral judgments. They may be described as 'gaming the system' or 'cheating,' or

Saniya Khan, Richard I. Anderson, Andrea Miglio, Benoît Mosser

We analyse Gaia EDR3 parallax systematics as a function of magnitude and sky location using a recently published catalogue of 12,500 asteroseismic red-giant star distances. We selected ~ 3500 red clump (RC) stars of similar chemical composition as the optimal subsample for this purpose. We perform a detailed assessment of systematic uncertainties relevant fo

Arianna Poli, Niklas Wagner, Max Fischer, Alessandro Toschi

We investigate the quasi-particle and transport properties of a model describing interacting Dirac and Weyl semimetals in the presence of local Hubbard repulsion $U$, where we explicitly include a deviation from the linearity of the energy-momentum dispersion through an intermediate-energy scale $\Lambda$. Our focus lies on the correlated phase of the semime

Jan N. Fuhg, Reese E. Jones, Nikolaos Bouklas

Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints and to overcome the challenging and time-consuming task of formulating phenomenological constitutive laws that can accurately capture the observed material response. However, even

Weiyong He

Given a symplectic class $[\omega]$ on a four torus $T^4$ (or a $K3$ surface), a folklore problem in symplectic geometry is whether symplectic forms in $[\omega]$ are isotropic to each other. We introduce a family of nonlinear Hodge heat flows on compact symplectic four manifolds to approach this problem, which is an adaption of nonlinear Hodge theory in sym

Robert M. Anderson, Haosui Duanmu

We propose two general equilibrium models, quota equilibrium and emission tax equilibrium. The government specifies quotas or taxes on emissions, then refrains from further action. Quota equilibrium exists; the allocation of emission property rights strongly impacts the distribution of welfare. If the only externality arises from total net emissions, quota e

Yasuaki Hiraoka, Ken Nakashima, Ippei Obayashi, Chenguang Xu

A fundamental challenge in multiparameter persistent homology is the absence of a complete and discrete invariant. To address this issue, we propose an enhanced framework that realizes a holistic understanding of a fully commutative quiver's representation via synthesizing interpretations obtained from intervals. Additionally, it provides a mechanism to tune

Robert Wilms

We give an alternative proof of the Faltings-Elkies bound on the average value of the Arakelov-Green function in pairs of a given set of $n$ points on a Riemann surface, which grows asymptotically like $O((\log n)/n)$. Our result is effective in terms of bounds of the Arakelov-Green function with respect to a given covering by local coordinates.

Haosen Ge, Hamsa Bastani, Osbert Bastani

Existing approaches to algorithmic fairness aim to ensure equitable outcomes if human decision-makers comply perfectly with algorithmic decisions. However, perfect compliance with the algorithm is rarely a reality or even a desirable outcome in human-AI collaboration. Yet, recent studies have shown that selective compliance with fair algorithms can amplify d

Tom Sherborne, Naomi Saphra, Pradeep Dasigi, Hao Peng

Sharpness-aware minimization (SAM) reports improving domain generalization by reducing the loss surface curvature in the parameter space. However, generalization during fine-tuning is often more dependent on the transferability of representations in the function space. Trust-region methods (TR) target this goal by regularizing representation curvature to red

M. G. Campitiello, A. Bonafede, A. Botteon, L. Lovisari

In this work, we investigated the interplay between the X-ray and radio emission of the cluster PSZ2G113.91-37.01 (z = 0.371) using the high-quality XMM-Newton observations of the CHEX-MATE project, and the images of the LoTSS-DR2. The cluster is undergoing a merger along the north-south axis, and shows a central radio halo and two radio relics, one in the s

Nir Lazarovich, Emily Stark

We prove that an infinite-ended group whose one-ended factors have finite-index subgroups and are in a family of groups with a nonzero multiplicative invariant is not quasi-isometrically rigid. Combining this result with work of the first author proves that a residually-finite multi-ended hyperbolic group is quasi-isometrically rigid if and only if it is vir

Jairo K. Mengue, Elismar R. Oliveira

We study the set of invariant idempotent probabilities for place dependent idempotent iterated function systems defined in compact metric spaces. Using well-known ideas from dynamical systems, such as the Ma\~{n}\'{e} potential and the Aubry set, we provide a complete characterization of the densities of such idempotent probabilities. As an application, we p

Yanming Wan, Jiayuan Mao, Joshua B. Tenenbaum

We introduce HandMeThat, a benchmark for a holistic evaluation of instruction understanding and following in physical and social environments. While previous datasets primarily focused on language grounding and planning, HandMeThat considers the resolution of human instructions with ambiguities based on the physical (object states and relations) and social (

Junqing Ji, Lili Ju, Xiaoping Zhang

In this paper, we present a deep surrogate model for learning the Green's function associated with the reaction-diffusion operator in rectangular domain. The U-Net architecture is utilized to effectively capture the mapping from source to solution of the target partial differential equations (PDEs). To enable efficient training of the model without relying o

Ari Karchmer

Carmosino et al. (2016) demonstrated that natural proofs of circuit lower bounds for $\Lambda$ imply efficient algorithms for learning $\Lambda$-circuits, but only over \textit{the uniform distribution}, with \textit{membership queries}, and provided $\AC^0[p] \subseteq \Lambda$. We consider whether this implication can be generalized to $\Lambda \not\supset

Zoltan A. Kocsis

We introduce a proof-theoretic approach to showing nondefinability of second-order intuitionistic connectives by quantifier-free schemata. We apply the method to prove that Taranovsky's "realizability disjunction" connective does not admit a quantifier-free definition, and use it to obtain new results and more nuanced information about the nondefinability of

Chih-Chen Chen, William Chen, Rodolfo Zevallos, John E. Ortega

The application of self-supervision to speech representation learning has garnered significant interest in recent years, due to its scalability to large amounts of unlabeled data. However, much progress, both in terms of pre-training and downstream evaluation, has remained concentrated in monolingual models that only consider English. Few models consider oth

Amar Fakhredine, Andrzej Wawro, Carmine Autieri

We investigate the magnetization and the Dzyaloshinskii-Moriya interactions (DMI) in Pt/Co/Re thin films in the case of perfect interfaces and upon the introduction of intermixing on both Co interfaces. Calculations were implemented on a series of systems with a varied number of cobalt atomic layers (ALs). Remarkably, the Re is able to introduce a DMI at the

Matthias Johann Steiner

For Arithmetization-Oriented ciphers and hash functions Gr\"obner basis attacks are generally considered as the most competitive attack vector. Unfortunately, the complexity of Gr\"obner basis algorithms is only understood for special cases, and it is needless to say that these cases do not apply to most cryptographic polynomial systems. Therefore, cryptogra

Ido Siovitz, Philipp Heinen, Niklas Rasch, Stefan Lannig

High-performance graphical processing units (GPU) are used for the repeated parallelised propagation of non-linear partial differential equations on large spatio-temporal grids. The main challenge results as a combination of the requirement of large grids for exploring scaling over several orders of magnitude, both in space and time, and the need for high st

Jiayuan Mao, Xuelin Yang, Xikun Zhang, Noah D. Goodman

Building machines that can reason about physical events and their causal relationships is crucial for flexible interaction with the physical world. However, most existing physical and causal reasoning benchmarks are exclusively based on synthetically generated events and synthetic natural language descriptions of causal relationships. This design brings up t

Amit Chakrabarti, Manuel Stoeckl

Adversarially robust streaming algorithms are required to process a stream of elements and produce correct outputs, even when each stream element can be chosen as a function of earlier algorithm outputs. As with classic streaming algorithms, which must only be correct for the worst-case fixed stream, adversarially robust algorithms with access to randomness

Tuwei Liao, C. Y. Zhao, Hong Wang, Shenghong Ju

The thermal modulator based on the near-field radiative heat transfer has wide applications in thermoelectric diodes, thermoelectric transistors, and thermal storage. However, the design of optimal near-field thermal radiation structure is a complex and challenging problem due to the tremendous number of degrees of freedom. In this work, we have proposed a d

Matteo Lulli, Antonino Marciano, Emanuele Zappala

The physical scalar product between spin-networks has been shown to be a fundamental tool in the theory of topological quantum neural networks (TQNN), which are quantum neural networks previously introduced by the authors in the context of quantum machine learning. However, the effective evaluation of the scalar product remains a bottleneck for the applicabi

Fernando Arias-Aragón, Vedran Brdar, Jérémie Quevillon

In this work we propose reactoscope, a novel experimental setup for axion-like particle (ALP) searches. Nuclear reactors produce a copious number of photons, a fraction of which could convert into ALPs via Primakoff process in the reactor core. The generated flux of ALPs leaves the nuclear power plant and its passage through a region with a strong magnetic f

Xian Yao Gwee, Isobel Claire Gormley, Michael Fop

Low-dimensional representation and clustering of network data are tasks of great interest across various fields. Latent position models are routinely used for this purpose by assuming that each node has a location in a low-dimensional latent space, and by enabling node clustering. However, these models fall short through their inability to simultaneously det

Yang Qiu, Aaron B. Wagner, Johannes Ballé, Lucas Theis

We introduce a distortion measure for images, Wasserstein distortion, that simultaneously generalizes pixel-level fidelity on the one hand and realism or perceptual quality on the other. We show how Wasserstein distortion reduces to a pure fidelity constraint or a pure realism constraint under different parameter choices and discuss its metric properties. Pa

Maksim D. Kiselev, Elena V. Gryzlova, Alexei N. Grum-Grzhimailo

The electron and ion properties observed in a photoionization inherit a symmetry properties of both a target and a radiation. Introducing a symmetry breaking in a photoionization process one can expect to observe a noticeable variation of the vector correlation parameters of either outgoing photoelectron or a residual ion. One of the ways to violate symmetry

Eveline Lehmann, Thomas Studer

Subset models provide a new semantics for justifcation logic. The main idea of subset models is that evidence terms are interpreted as sets of possible worlds. A term then justifies a formula if that formula is true in each world of the interpretation of the term. In this paper, we introduce a belief expansion operator for subset models. We study the main pr

Carolina Melo, Alfredo Nájera Chávez

Let $\mathcal{X}$ be a skew-symmetrizable cluster Poisson variety. The cluster complex $\Delta^+(\mathcal{X})$ was introduced by Gross, Hacking, Keel and Kontsevich. It codifies the theta functions on $\mathcal{X}$ that restrict to a character of a seed torus. Every seed ${ \bf s}$ for $\mathcal{X}$ determines a fan realization $\Delta^+_{\bf s}(\mathcal{X})

Khen Cohen, Tuval Kay

This paper presents a Multispectral imaging (MSI) approach that combines the use of a diffractive optical element, and a deep learning algorithm for spectral reconstruction. Traditional MSI techniques often face challenges such as high costs, compromised spatial or spectral resolution, or prolonged acquisition times. In contrast, our methodology uses a singl

Lennart Schulze, Hod Lipson

A robot self-model is a task-agnostic representation of the robot's physical morphology that can be used for motion planning tasks in the absence of a classical geometric kinematic model. In particular, when the latter is hard to engineer or the robot's kinematics change unexpectedly, human-free self-modeling is a necessary feature of truly autonomous agents

Pietro Traversa, Guilherme Ferraz de Arruda, Alexei Vazquez, Yamir Moreno

Metabolic networks are probably among the most challenging and important biological networks. Their study provides insight into how biological pathways work and how robust a specific organism is against an environment or therapy. Here we propose a directed hypergraph with edge-dependent vertex weight as a novel framework to represent metabolic networks. This

Xiangwei Yin, James A. Maxin, Dimitri V. Nanopoulos, Tianjun Li

We propose Generalized No-Scale Supergravity, the simplest scenario for Effective Super-Natural Supersymmetry, naturally solving the supersymmetry electroweak fine-tuning problem and including natural dark matter. A light right-handed slepton bulk region is realized in $\mathcal{F}$-$SU(5)$ and the pMSSM. The bulk may be beyond the LHC reach, though can be p

Andrei Alexandru, Ivan Horváth, Neel Bhattacharyya

Infrared (IR) dimension function $d_\text{IR}(\lambda)$ characterizes the space effectively utilized by QCD quarks at Dirac scale $\lambda$, and indirectly the space occupied by glue fields. It was proposed that its non-analytic behavior in thermal infrared phase reflects the separation of QCD system into an IR component and an independent bulk. Here we stud

Wenxin Jiang, Jason Jones, Jerin Yasmin, Nicholas Synovic

Developing and training deep learning models is expensive, so software engineers have begun to reuse pre-trained deep learning models (PTMs) and fine-tune them for downstream tasks. Despite the wide-spread use of PTMs, we know little about the corresponding software engineering behaviors and challenges. To enable the study of software engineering with PTMs,

Johan Andersson

Recently Sourmelidis proved that the discrete universality theorem is equivalent to the continuous universality theorem for zeta-functions. He treats both the zero-free universality theorem and the strong universality theorem. Unfortunately in the zero-free case his result is conditional on a Riemann hypothesis, and in the strong universality case he only pr

Aaron D. Mullen, Samuel E. Armstrong, Jeff Talbert, V. K. Cody Bumgardner

Machine learning classification problems are widespread in bioinformatics, but the technical knowledge required to perform model training, optimization, and inference can prevent researchers from utilizing this technology. This article presents an automated tool for machine learning classification problems to simplify the process of training models and produ

Shashank Motepalli, Luciano Freitas, Benjamin Livshits

Rollups have emerged as a promising solution to enhance blockchain scalability, offering increased throughput, reduced latency, and lower transaction fees. However, they currently rely on a centralized sequencer to determine transaction ordering, compromising the decentralization principle of blockchain systems. Recognizing this, there is a clear need for de

Wieland Morgenstern, Milena T. Bagdasarian, Anna Hilsmann, Peter Eisert

We propose a novel representation of virtual humans for highly realistic real-time animation and rendering in 3D applications. We learn pose dependent appearance and geometry from highly accurate dynamic mesh sequences obtained from state-of-the-art multiview-video reconstruction. Learning pose-dependent appearance and geometry from mesh sequences poses sign

Shawqi Al-Maliki, Adnan Qayyum, Hassan Ali, Mohamed Abdallah

Deep Neural Networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial examples -- input samples that have been perturbed to force DNN-based models to make errors. As a result, Adversarial Machine Learning (AdvML) has gained a lot of attention, and re

Xidong Wu, Jianhui Sun, Zhengmian Hu, Aidong Zhang

The minimax problems arise throughout machine learning applications, ranging from adversarial training and policy evaluation in reinforcement learning to AUROC maximization. To address the large-scale data challenges across multiple clients with communication-efficient distributed training, federated learning (FL) is gaining popularity. Many optimization alg

Benjamin Braun, Tara Gomes, Ezra Miller, Christopher O'Neill

Numerical semigroups with multiplicity $m$ are parameterized by integer points in a polyhedral cone $C_m$, according to Kunz. For the toric ideal of any such semigroup, the main result here constructs a free resolution whose overall structure is identical for all semigroups parametrized by the relative interior of a fixed face of $C_m$. The matrix entries of

Ahmed Fakhry, Raneem Khafagy, Adriaan-Alexander Ludl

Detecting and discovering new gene interactions based on known gene expressions and gene interaction data presents a significant challenge. Various statistical and deep learning methods have attempted to tackle this challenge by leveraging the topological structure of gene interactions and gene expression patterns to predict novel gene interactions. In contr

Andrey Churkin, Wangwei Kong, Mohammad Iman Alizadeh, Florin Capitanescu

Security-constrained transmission expansion planning (SCTEP) is an inherently complex problem that requires simultaneously solving multiple contingency states of the system (usually corresponding to N-1 security criterion). Existing studies focus on effectively finding optimal solutions; however, single optimal solutions are not sufficient to interpret the v

Zhengyan Zhu, Yuxiang Wu, Shengtai Fan, Yiliang Fan

The kagome lattice is very attractive as it can host many novel quantum states, such as the charge density wave, superconductivity, quantum spin liquid, etc. Meanwhile, iridates often exhibit a strong spin-orbit coupling (SOC) effect due to the large atomic mass of 5$d$ elements, which has important implications for both the energy bands and the pairing symm

Menghan Yu, Sourabh Kulhare, Courosh Mehanian, Charles B Delahunt

Acquiring large quantities of data and annotations is known to be effective for developing high-performing deep learning models, but is difficult and expensive to do in the healthcare context. Adding synthetic training data using generative models offers a low-cost method to deal effectively with the data scarcity challenge, and can also address data imbalan

Galina Weinstein

This paper explores the enduring black hole information and firewall paradoxes, challenges that have prompted many proposals, conjectures, and theories. Noteworthy among these are the ER = EPR conjecture and AdS/CFT correspondence, which suggest possible avenues toward the yet-to-be-realized unified theory of quantum gravity. This discourse offers a comprehe