October 2023 arXiv papers — page 89

Sherry Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel

Generative models trained on internet-scale data are capable of generating novel and realistic texts, images, and videos. A natural next question is whether these models can advance science, for example by generating novel stable materials. Traditionally, models with explicit structures (e.g., graphs) have been used in modeling structural relationships in sc

Duc-Vu Nguyen, Quoc-Nam Nguyen

In this paper, we evaluate the ability of large language models (LLMs) to perform multiple choice symbol binding (MCSB) for multiple choice question answering (MCQA) tasks in zero-shot, one-shot, and few-shot settings. We focus on Vietnamese, with fewer challenging MCQA datasets than in English. The two existing datasets, ViMMRC 1.0 and ViMMRC 2.0, focus on

Theodore Chambers, Michael Vierhauser, Ankit Agrawal, Michael Murphy

Small Unmanned Aerial Systems (sUAS) must meet rigorous safety standards when deployed in high-stress emergency response scenarios; however many reported accidents have involved humans in the loop. In this paper, we, therefore, present the HiFuzz testing framework, which uses fuzz testing to identify system vulnerabilities associated with human interactions.

Arnab Pal, Viktor Stojkoski, Trifce Sandev

By periodically returning a search process to a known or random state, random resetting possesses the potential to unveil new trajectories, sidestep potential obstacles, and consequently enhance the efficiency of locating desired targets. In this chapter, we highlight the pivotal theoretical contributions that have enriched our understanding of random resett

Nils Engler, Filip Lindskog

The distribution-free chain ladder of Mack justified the use of the chain ladder predictor and enabled Mack to derive an estimator of conditional mean squared error of prediction for the chain ladder predictor. Classical insurance loss models, i.e. of compound Poisson type, are not consistent with Mack's distribution-free chain ladder. However, for a sequenc

Ali Baheri

In inverse reinforcement learning (IRL), the central objective is to infer underlying reward functions from observed expert behaviors in a way that not only explains the given data but also generalizes to unseen scenarios. This ensures robustness against reward ambiguity where multiple reward functions can equally explain the same expert behaviors. While sig

Bibit Bianchini, Mathew Halm, Michael Posa

Robotic manipulation can greatly benefit from the data efficiency, robustness, and predictability of model-based methods if robots can quickly generate models of novel objects they encounter. This is especially difficult when effects like complex joint friction lack clear first-principles models and are usually ignored by physics simulators. Further, numeric

Michelle Cheng, Robert Laugwitz

In this expositional essay, we introduce some elements of the study of groups by analysing the braid pattern on a knitted blanket. We determine that the blanket features pure braids with a minimal number of crossings. Moreover, we determine polynomial invariants associated to the links obtained by closing the braid patterns of the blanket.

James Chok, Geoffrey M. Vasil

Recent years have witnessed the introduction and development of extremely fast rational function algorithms. Many ideas in this realm arose from polynomial-based linear-algebraic algorithms. However, polynomial approximation is occasionally ill-suited to specific challenging tasks arising in several situations. Some occasions require maximal efficiency in th

Usama Ikhlaq, Tahar Kechadi

This study addresses the vital role of data analytics in monitoring fertiliser applications in crop cultivation. Inaccurate fertiliser application decisions can lead to costly consequences, hinder food production, and cause environmental harm. We propose a solution to predict nutrient application by determining required fertiliser quantities for an entire se

Virginia Vassilevska Williams, Yinzhan Xu, Zixuan Xu

We provide a variety of lower bounds for the well-known shortcut set problem: how much can one decrease the diameter of a directed graph on $n$ vertices and $m$ edges by adding $O(n)$ or $O(m)$ of shortcuts from the transitive closure of the graph. Our results are based on a vast simplification of the recent construction of Bodwin and Hoppenworth [FOCS 2023]

Rishabh V. More, Eugene Pashkovski, Reid Patterson, Gareth H. McKinley

It is well known that inertia-free shearing flows of a viscoelastic fluid with curved streamlines, such as the torsional flow between a rotating cone and plate, or the flow in a Taylor-Couette geometry, can become unstable to a three-dimensional time-dependent instability at conditions exceeding a critical Weissenberg (Wi) number. However, the combined effec

Patrick Y. Wu, Jonathan Nagler, Joshua A. Tucker, Solomon Messing

Existing text scoring methods require a large corpus, struggle with short texts, or require hand-labeled data. We develop a text scoring framework that leverages generative large language models (LLMs) to (1) set texts against the backdrop of information from the near-totality of the web and digitized media, and (2) effectively transform pairwise text compar

Hai-Chen Wang, Ahmad W. Huran, Miguel A. L. Marques, Muralidhar Nalabothula

We study two-dimensional noble metal chalcogenides, with composition {Cu, Ag, Au}2{S, Se, Te}, crystallizing in a snub-square lattice. This is a semi-regular two-dimensional tesselation formed by triangles and squares that exhibits geometrical frustration. We use for comparison a square lattice, from which the snub-square tiling can be derived by a simple ro

Xandru Mifsud

We establish a sharp lower-bound for the number of conjugacy classes $k(A_n)$ in the alternating group $A_n$, for $n \geq 3$. Namely, we show that $k\left(A_n\right) \geq \frac{k\left(A_7\right)}{\log_2\left|A_7\right|} \cdot \log_2\left|A_n\right|$ with equality if, and only if, $n = 7$. The observations leading towards this result were obtained through a g

Pelin Ersin, Emma Hayes, Peter Matthews, Paramjyoti Mohapatra

Neural networks have become a powerful tool as surrogate models to provide numerical solutions for scientific problems with increased computational efficiency. This efficiency can be advantageous for numerically challenging problems where time to solution is important or when evaluation of many similar analysis scenarios is required. One particular area of s

Anders S. Kortegaard

Let $\mathscr{A}$ be an extension closed proper abelian subcategory of a triangulated category $\mathscr{T}$, with no negative 1 and 2 extensions. From this, two functors from $\Sigma\mathscr{A}\ast\mathscr{A}$ to $\mathscr{A}$ can be constructed giving a snake lemma mirroring that of homology without needing a t-structure. We generalize the concept of inter

Oussama Alyounes, Miguel Altamirano Cabrera, Dzmitry Tsetserukou

The growing demand for electric vehicles requires the development of automated car charging methods. At the moment, the process of charging an electric car is completely manual, and that requires physical effort to accomplish the task, which is not suitable for people with disabilities. Typically, the effort in the research is focused on detecting the positi

Jian-Ci Xiao

Let $K\subset\mathbb{R}^d$ be a self-similar set generated by an iterated function system $\{\varphi_i\}_{i=1}^m$ satisfying the strong separation condition and let $f$ be a contracting similitude with $f(K)\subset K$. We show that $f(K)$ is relative open in $K$ if all $\varphi_i$'s share a common contraction ratio and orthogonal part. We also provide a coun

Sebastian Grieninger, Dmitri E. Kharzeev, Ismail Zahed

We analyze the entanglement of a Schwinger pair created by a time-dependent pulse. In the semi-classical approximation, the pair creation by a pulse of external electric field is captured by a periodic worldline instanton. At strong gauge coupling, the gauge-gravity dual worldsheet instanton exhibits a falling wormhole in AdS. We identify the tunneling time

Amjad Al Taleb, Frederik Schiller, Denis V. Vyalikh, José Maria Pérez

Using a reactive molecular beam with high kinetic energy ($E_{kin}$) it is possible to speed gas-surface reactions involving high activation barriers ($E_{act}$), which would require elevated pressures ($P_0$) if a random gas with a Maxwell-Boltzmann distribution is used. By simply computing the number of molecules that overcome the activation barrier in a r

Philipp Schmidt, Luca Banszerus, Benedikt Frohn, Stefan Blien

We report on a ballistic and fully tunable Josephson junction system consisting of two parallel ribbons of graphene in contact with superconducting MoRe. By electrostatic gating of the two individual graphene ribbons we gain control over the real space distribution of the superconducting current density, which can be continuously tuned between both ribbons.

Reza Hadavian, Xiaoting Huang, Dmitri Truhachev, Kamal El-Sankary

We introduce a novel universal soft-decision decoding algorithm for binary block codes called ordered reliability direct error pattern testing (ORDEPT). Our results, obtained for a variety of popular short high-rate codes, demonstrate that ORDEPT outperforms state-of-the-art decoding algorithms of comparable complexity such as ordered reliability bits guessi

Yuan Peiwen, Zhu Changsheng

There has been an emergence of various models for long-term time series forecasting. Recent studies have demonstrated that a single linear layer, using Channel Dependent (CD) or Channel Independent (CI) modeling, can even outperform a large number of sophisticated models. However, current research primarily considers CD and CI as two complementary yet mutual

Yijian Meng, Ming Lai Chan, Rasmus B. Nielsen, Martin H. Appel

Deterministic photon sources allow long-term advancements in quantum optics. A single quantum emitter embedded in a photonic resonator or waveguide may be triggered to emit one photon at a time into a desired optical mode. By coherently controlling a single spin in the emitter, multi-photon entanglement can be realized. We demonstrate a deterministic source

Saleh Darzi, Kasra Ahmadi, Saeed Aghapour, Attila Altay Yavuz

The rise of quantum computers exposes vulnerabilities in current public key cryptographic protocols, necessitating the development of secure post-quantum (PQ) schemes. Hence, we conduct a comprehensive study on various PQ approaches, covering the constructional design, structural vulnerabilities, and offer security assessments, implementation evaluations, an

Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo

The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards. The second assumes that a reward model trained on these pointwise rewards can generalize from collected data to out-of-distribution dat

Bohao Tian, Shijun Zhang, Sirui Chen, Yuru Zhang

Flow, an optimal mental state merging action and awareness, significantly impacts our emotion, performance, and well-being. However, capturing its swift fluctuations on a fine timescale is challenging due to the sparsity of the existing flow detecting tools. Here we present a fine fingertip force control (F3C) task to induce flow, wherein the task challenge

William Good, Kinza Hasan, Allison Chevis, Huey-Wen Lin

We present the first calculation of the pion gluon moment from lattice QCD in the continuum-physical limit. The calculation is done using clover fermions for the valence action with three pion masses, 220, 310 and 690 MeV, and three lattice spacings, 0.09, 0.12, and 0.15 fm, using ensembles generated by MILC Collaboration with 2+1+1 flavors of highly improve

Siddhartha Laghuvarapu, Zhen Lin, Jimeng Sun

In drug discovery, it is vital to confirm the predictions of pharmaceutical properties from computational models using costly wet-lab experiments. Hence, obtaining reliable uncertainty estimates is crucial for prioritizing drug molecules for subsequent experimental validation. Conformal Prediction (CP) is a promising tool for creating such prediction sets fo

Olivier Truffinet, Karim Ammar, Jean-Philippe Argaud, Bertrand Bouriquet

The Linear Model of Co-regionalization (LMC) is a very general multitask gaussian process model for regression or classification. While its expressiveness and conceptual simplicity are appealing, naive implementations have cubic complexity in the product (number of datapoints $\times$ number of tasks), making approximations mandatory for most applications. H

Tatiana Zemskova, Margarita Kichik, Dmitry Yudin, Aleksei Staroverov

This paper presents an adaptive transformer model named SegmATRon for embodied image semantic segmentation. Its distinctive feature is the adaptation of model weights during inference on several images using a hybrid multicomponent loss function. We studied this model on datasets collected in the photorealistic Habitat and the synthetic AI2-THOR Simulators.

Arian Bërdëllima, Naim L. Braha

Given an infinite matrix $M=(m_{nk})$ we study a family of sequence spaces $\ell_M^p$ associated with it. When equipped with a suitable norm $\|\cdot\|_{M,p}$ we prove some basic properties of the Banach spaces of sequences $(\ell_M^p,\|\cdot\|_{M,p})$. In particular we show that such spaces are separable and strictly/uniformly convex for a considerably larg

Bin Fan

In this paper, we consider an inverse space-dependent source problem for a time-fractional diffusion equation. To deal with the ill-posedness of the problem, we transform the problem into an optimal control problem with total variational (TV) regularization. In contrast to the classical Tikhonov model incorporating $L^2$ penalty terms, the inclusion of a TV

Ruben Khachaturyan, Yijing Yang, Sheng-Han Teng, Benjamin Udofia

Surprisingly little is known about the microscopic processes that govern ferroelectric switching in orthorhombic ferroelectrics. To study microscopic switching processes we combine ab initio-based molecular dynamics simulations and data science on the prototypical material BaTiO$_3$. We reveal two different field regimes: For moderate field strengths, the sw

J. S. Dowker

It is shown that the functional determinant ($\sim$ effective action) for a scalar field propagating on the mixed signature product of unit spheres, S$^q\times$S$^p$, according to the GJMS operator, depends, if $d$ is odd, only on $d=p+q$ and on whether $p$ is even or odd. In the first case the effective action equals twice the standard quantity on S$^d$ and

G. Sacco, A. Kapetanović, D. Poljak, M. Zhadobov

With the proliferation of 5G wireless networks, the population is increasingly exposed to frequencies approaching the millimeter-wave (mmW) range. Human ears are among the most exposed body parts. This paper proposes an analysis of the ear exposure in the near field using an anatomical model in presence of different electromagnetic (EM) sources (vertical dip

Mingzhang Yin, Ruijiang Gao, Weiran Lin, Steven M. Shugan

Designing products to meet consumers' preferences is essential for a business's success. We propose the Gradient-based Survey (GBS), a discrete choice experiment for multiattribute product design. The experiment elicits consumer preferences through a sequence of paired comparisons for partial profiles. GBS adaptively constructs paired comparison questions ba

Milena Horvath, Sudipta Dhar, Arpita Das, Matthew D. Frye

Bose-Einstein condensates of ultracold atoms serve as low-entropy sources for a multitude of quantum-science applications, ranging from quantum simulation and quantum many-body physics to proof-of-principle experiments in quantum metrology and quantum computing. For stability reasons, in the majority of cases the energetically lowest-lying atomic spin state

M. C. Braidotti, A. Vinante, M. Cromb, A. Sandakumar

In 1971, Zel'dovich predicted the amplification of electromagnetic (EM) waves scattered by a rotating metallic cylinder, gaining mechanical rotational energy from the body. Since then, this phenomenon has been believed to be unobservable with electromagnetic fields due to technological difficulties in meeting the condition of amplification, that is, the cyli

Philipp Borchert, Jochen De Weerdt, Kristof Coussement, Arno De Caigny

We introduce CORE, a dataset for few-shot relation classification (RC) focused on company relations and business entities. CORE includes 4,708 instances of 12 relation types with corresponding textual evidence extracted from company Wikipedia pages. Company names and business entities pose a challenge for few-shot RC models due to the rich and diverse inform

Shinya Kanemura, Kunio Kaneta

Gravitational waves have become an irreplaceable tool for exploring the post-inflationary universe. Their cosmological and astrophysical origins have been attracting numerous attention. In this Letter, we point out a novel source of ultra-high frequency gravitational waves: the decay of particles produced during the reheating era. We highlight the decay of t

Moshir Harsh, Leonhard Götz Vulpius, Peter Sollich

Partial measurements of biochemical reaction networks are ubiquitous and limit our ability to reconstruct the topology of the reaction network and the strength of the interactions amongst both the observed and the unobserved molecular species. Here, we show how we can utilize noisy time series of such partially observed networks to determine which species of

Guangyi Liu, Arash Amini, Vivek Pandey, Nader Motee

We introduce a novel data-driven method to mitigate the risk of cascading failures in delayed discrete-time Linear Time-Invariant (LTI) systems. Our approach involves formulating a distributionally robust finite-horizon optimal control problem, where the objective is to minimize a given performance function while satisfying a set of distributionally chances

Shengqiang Zhang, Philipp Wicke, Lütfi Kerem Şenel, Luis Figueredo

The convergence of embodied agents and large language models (LLMs) has brought significant advancements to embodied instruction following. Particularly, the strong reasoning capabilities of LLMs make it possible for robots to perform long-horizon tasks without expensive annotated demonstrations. However, public benchmarks for testing the long-horizon reason

Zhenhui Peng, Qiaoyi Chen, Zhiyu Shen, Xiaojuan Ma

Online design communities, where members exchange free-form views on others' designs, offer a space for beginners to learn visual design. However, the content of these communities is often unorganized for learners, containing many redundancies and irrelevant comments. In this paper, we propose a computational approach for leveraging online design communities

Jacob Seifert, Yifeng Shao, Allard P. Mosk

Computational imaging is increasingly vital for a broad spectrum of applications, ranging from biological to material sciences. This includes applications where the object is known and sufficiently sparse, allowing it to be described with a reduced number of parameters. When no explicit parameterization is available, a deep generative model can be trained to

Alessandro Scirè

This work concerns the long-term dynamics of a spatiotemporal many-body deterministic model that exhibits emergence and self-organization, and which has been recently proposed as a new paradigm for Artificial Life. Collective structures emerge in the form of dynamic networks, created by bursts of spatiotemporal activity (avalanches) at the edge of a synchron

Zhaoyu Chen, Bo Li, Kaixun Jiang, Shuang Wu

Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Many intelligent systems, such as electronic payment and identity verification, rely on face forgery detection. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery d

Roland Donninger

This is an expository article that describes the spectral-theoretic aspects in the study of the stability of self-similar blowup for nonlinear wave equations. The linearization near a self-similar solution leads to a genuinely nonself-adjoint operator which is difficult to analyze. The main goal of this article is to provide an accessible account to the only

Akiharu Nakagawa, Tomoharu Kurayama, Hiroshi Sudou, Gabor Orosz

We present astrometric very long baseline interferometry (VLBI) studies of AGB stars. To understand the properties and evolution of AGB stars, distances are an important parameter. The distribution and kinematics of their circumstellar matter are also revealed with the VLBI method. We used the VERA array to observe 22\,GHz H$_2$O masers in various subclasses

Jingze Lu, Yuxiang Zhang, Wenchao Wang, Zengqiang Shang

Current spoofing speech detection systems need more convincing evidence. In this paper, the flaws of rhythm information inherent in the TTS-generated speech are analyzed to increase the reliability of detection systems. TTS models take text as input and utilize acoustic models to predict rhythm information, which introduces artifacts in the rhythm informatio

Jeffrey S. Case, Andrea Malchiodi

We show that the GJMS operators of a special Einstein product factor as a composition of second- and fourth-order differential operators. In particular, our formula applies to the Riemannian product $H^{\ell} \times S^{d-\ell}$. We also show that there is an integer $D = D(k,\ell)$ such that if $d \geq D$, then for any special Einstein product $N^\ell \times

Afnan Alostaz, Oliver Gueckstock, Jungwei Tong, Jana Kredl

Linear terahertz time-domain spectroscopy (THz-TDS) is a sensitive probe for material characterization including thickness measurements of thin layers. These applications critically rely on a sufficiently large bandwidth, which is not straightforwardly available in typical THz-TDS systems. Here, we introduce a hybrid THz-emitter concept based on a spintronic

Zheye Deng, Weiqi Wang, Zhaowei Wang, Xin Liu

Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning, yet constructing them through human annotations can be costly. As a result, various automatic methods have been proposed to construct CSKG with larger semantic coverage. However, these unsupervised approaches introduce spurious noise that can lower the quality of the resulting CSKG,

Jinbo Xing, Menghan Xia, Yong Zhang, Haoxin Chen

Animating a still image offers an engaging visual experience. Traditional image animation techniques mainly focus on animating natural scenes with stochastic dynamics (e.g. clouds and fluid) or domain-specific motions (e.g. human hair or body motions), and thus limits their applicability to more general visual content. To overcome this limitation, we explore

Chenchen Ma, Jing Ouyang, Chun Wang, Gongjun Xu

Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure become multifaceted, multidimensional item response theory (MIRT) provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. However, the computational challenge of

Joel T. Clemmer, Joseph M. Monti, Jeremy B. Lechman

The high-pressure compaction of three dimensional granular packings is simulated using a bonded particle model (BPM) to capture linear elastic deformation. In the model, grains are represented by a collection of point particles connected by bonds. A simple multibody interaction is introduced to control Poisson's ratio and the arrangement of particles on the

Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Ru Li

Knowledge graph entity typing (KGET) aims at inferring plausible types of entities in knowledge graphs. Existing approaches to KGET focus on how to better encode the knowledge provided by the neighbors and types of an entity into its representation. However, they ignore the semantic knowledge provided by the way in which types can be clustered together. In t

Abhishek Vivekanandan, Ahmed Abouelazm, Philip Schörner, J. Marius Zöllner

Accurately forecasting the motion of traffic actors is crucial for the deployment of autonomous vehicles at a large scale. Current trajectory forecasting approaches primarily concentrate on optimizing a loss function with a specific metric, which can result in predictions that do not adhere to physical laws or violate external constraints. Our objective is t

James Forster

In this paper we present a framework of key algorithms and data-structures for efficiently generating timetables for any number of AGVs from any given positioning on any given graph to accomplish any given demands as long as a few easily satisfiable assumptions are met. Our proposed algorithms provide guaranteed solutions in predictable polynomial running-ti

Francesco Montefusco, Morten Gram Pedersen

To obtain explicit understanding of the behavior of dynamical systems, geometrical methods and slow-fast analysis have proved to be highly useful. Such methods are standard for smooth dynamical systems, and increasingly used for continuous, non-smooth dynamical systems. However, they are much less used for random dynamical systems, in particular for hybrid m

Feng Luo, Jinxi Xiang, Jun Zhang, Xiao Han

The recent use of diffusion prior, enhanced by pre-trained text-image models, has markedly elevated the performance of image super-resolution (SR). To alleviate the huge computational cost required by pixel-based diffusion SR, latent-based methods utilize a feature encoder to transform the image and then implement the SR image generation in a compact latent

M. F. Holst, M. Sigrist, K. V. Samokhin

We study the effects of interband pairing in two-band s-wave and d-wave superconductors with D4h symmetry in both time-reversal invariant as well as time-reversal symmetry breaking states. The presence of interband pairing qualitatively changes the nodal structure of the superconductor: nodes can (dis)appear, merge, and leave high-symmetry locations when int

Mateusz Pyla, Kamil Deja, Bartłomiej Twardowski, Tomasz Trzciński

Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. Their power comes from the expressiveness of neural networks and Bayesian inference which make them suitable in the context of continual learning. We delve into the mechanics behind B

Pascal Kündig, Fabio Sigrist

Latent Gaussian process (GP) models are flexible probabilistic non-parametric function models. Vecchia approximations are accurate approximations for GPs to overcome computational bottlenecks for large data, and the Laplace approximation is a fast method with asymptotic convergence guarantees to approximate marginal likelihoods and posterior predictive distr

O. M. Guerra-Alvarado, N. van der Marel, J. Di Francesco, L. W. Looney

Understanding the formation of substructures in protoplanetary disks is vital for gaining insights into dust growth and the process of planet formation. Studying these substructures in highly embedded Class 0 objects using the Atacama Large Millimeter/submillimeter Array (ALMA), however, poses significant challenges. Nonetheless, it is imperative to do so to

Yuhan Yang, Youlong Wu, Yuning Jiang, Yuanming Shi

Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry. This paper studies distributed learning in wireless data center networks, which contain a central edge server and multiple edge workers to col

Hua-sheng Xie, Xue-yun Wang

Fusion reactivity represents the integration of fusion cross-sections and the velocity distributions of two reactants. In this study, we investigate the upper bound of fusion reactivity for a non-thermal reactant coexisting with a thermal Maxwellian background reactant while maintaining a constant total energy. Our optimization approach involves fine-tuning

Gui-Long Jiang, Wen-Qiang Liu, Hai-Rui Wei

Quantum circuits of a general quantum gate acting on multiple $d$-level quantum systems play a prominent role in multi-valued quantum computation. We first propose a new recursive Cartan decomposition of semi-simple unitary Lie group $U(3^n)$ (arbitrary $n$-qutrit gate). Note that the decomposition completely decomposes an n-qutrit gate into local and non-lo

Carlos D. F. J. Bernardes, César Rodrigo

The landing and takeoff operations for an airport at any given day are described in terms of the capacity envelopes associated to runway system configurations, of the scheduled flights along the day and of predefined delay tolerances for both types of operations. Assuming the inter-arrival times and service times are random variables with known quadratic rat

Shiang Hu, Jie Ruan, Nicolas Langer, Jorge Bosch-Bayard

The brain projects require the collection of massive electrophysiological data, aiming to the longitudinal, sectional, or populational neuroscience studies. Quality metrics automatically label the data after centralized preprocessing. However, although the waveforms-based metrics are partially useful, they may be unreliable by neglecting the spectral profile

Maia Fraser, Sheila Sandon, Bingyu Zhang

Using SFT techniques, Eliashberg, Kim and Polterovich (2006) proved that if $\pi R_2^2 \leq K \leq \pi R_1^2$ for some integer $K$ then there is no contact squeezing in $\mathbb{R}^{2n} \times S^1$ of the prequantization of the ball of radius $R_1$ into the prequantization of the ball of radius $R_2$. This result was extended to the case of balls of radius $

Nidhi Saini, Patrick Das Gupta

Fast radio bursts (FRBs) are radio transients of extragalactic origin lasting for about a few to several milli-seconds. We have analyzed both non-CHIME and CHIME FRB data. To circumvent the absence of measured fluence and flux density of FRBs belonging to the CHIME catalog, we have devised a novel approach that utilizes the ratio of the lower limits of the f

Floris Holstege, Bram Wouters, Noud van Giersbergen, Cees Diks

Out-of-distribution generalization in neural networks is often hampered by spurious correlations. A common strategy is to mitigate this by removing spurious concepts from the neural network representation of the data. Existing concept-removal methods tend to be overzealous by inadvertently eliminating features associated with the main task of the model, ther

Edgardo Franzin, Stefano Liberati, Vania Vellucci

We study gravitational and test-field perturbations for the two possible families of spherically symmetric black-hole mimickers that smoothly interpolate between regular black holes and horizonless compact objects accordingly to the value of a regularization parameter. One family can be described by the Bardeen-like metrics, and the other by the Simpson-Viss

Yunfan Li, Peng Hu, Dezhong Peng, Jiancheng Lv

The core of clustering is incorporating prior knowledge to construct supervision signals. From classic k-means based on data compactness to recent contrastive clustering guided by self-supervision, the evolution of clustering methods intrinsically corresponds to the progression of supervision signals. At present, substantial efforts have been devoted to mini

Lumen Eek, Anouar Moustaj, Malte Röntgen, Vincent Pagneux

The Hatano-Nelson and the non-Hermitian Su-Schrieffer-Heeger model are paradigmatic examples of non-Hermitian systems that host non-trivial boundary phenomena. In this work, we use recently developed graph-theoretical tools to design systems whose isospectral reduction -- akin to an effective Hamiltonian -- has the form of either of these two models. In the

Georgios I. Papayiannis

The problem of asset liability management (ALM) is a classic problem of the financial mathematics and of great interest for the banking institutions and insurance companies. Several formulations of this problem under various model settings have been studied under the Mean-Variance (MV) principle perspective. In this paper, the ALM problem is revisited under

Laura Weidinger, Maribeth Rauh, Nahema Marchal, Arianna Manzini

Generative AI systems produce a range of risks. To ensure the safety of generative AI systems, these risks must be evaluated. In this paper, we make two main contributions toward establishing such evaluations. First, we propose a three-layered framework that takes a structured, sociotechnical approach to evaluating these risks. This framework encompasses cap

James D. Wells

Invited presentation on issues at the intersection of science and politics in the United States, delivered at the 150 year anniversary of the French Physical Society on the 6th of July 2023 in Paris.

Phillip Kearns, Bruno Jedynak, John Lipor

We consider the problem of active learning in the context of spatial sampling for level set estimation (LSE), where the goal is to localize all regions where a function of interest lies above/below a given threshold as quickly as possible. We present a finite-horizon search procedure to perform LSE in one dimension while optimally balancing both the final es

Shaoxiong Duan, Yining Shi, Wei Xu

In this paper, we investigate the inherent capabilities of transformer models in learning arithmetic algorithms, such as addition and parity. Through experiments and attention analysis, we identify a number of crucial factors for achieving optimal length generalization. We show that transformer models are able to generalize to long lengths with the help of t

Mahsa Ghavami, Babak Ghaffarzadeh Bakhshayesh, Mohammad Haeri, Giacomo Como

This paper introduces a consensus-based generalized multi-population aggregative game coordination approach with application to electric vehicles charging under transmission line constraints. The algorithm enables agents to seek an equilibrium solution while considering the limited infrastructure capacities that impose coupling constraints among the users. T

Weichen Wu, Jisu Kim, Alessandro Rinaldo

The prevailing statistical approach to analyzing persistence diagrams is concerned with filtering out topological noise. In this paper, we adopt a different viewpoint and aim at estimating the actual distribution of a random persistence diagram, which captures both topological signal and noise. To that effect, Chazel and Divol (2019) proved that, under gener

Michele Bolognesi, Robert Laterveer

Let S be a K3 surface obtained as triple cover of a quadric branched along a genus 4 curve. Using the relation with cubic fourfolds, we show that S has finite dimensional motive, in the sense of Kimura. We also establish the Kuga-Satake Hodge conjecture for S, as well as Voisin'conjecture concerning zero-cycles. As a consequence, we obtain Kimura finite dime

T. J. Gray, A. E. Stuchbery, J. Dobaczewski, A. Blazhev

The $g$ factors of $11/2^-$ isomers in semimagic $^{109}$Sn and $^{111}$Sn (isomeric lifetimes $\tau = 2.9(3)$ ns and $\tau = 14.4(7)$ ns, respectively) were measured by an extension of the Time Differential Perturbed Angular Distribution technique, which uses \LaBr detectors and the hyperfine fields of a gadolinium host to achieve precise measurements in a

Claudio Bonanno, Alessandro Nada, Davide Vadacchino

Calculations of topological observables in lattice gauge theories with traditional Monte Carlo algorithms have long been known to be a difficult task, owing to the effects of long autocorrelations times. Several mitigation strategies have been put forward, including the use of open boundary conditions and methods such as parallel tempering. In this contribut

Pascal Pernot

Binwise Variance Scaling (BVS) has recently been proposed as a post hoc recalibration method for prediction uncertainties of machine learning regression problems that is able of more efficient corrections than uniform variance (or temperature) scaling. The original version of BVS uses uncertainty-based binning, which is aimed to improve calibration condition

Natalie B. Hogg

Problems with the cosmological constant model of dark energy motivate the investigation of alternative scenarios. I make the first measurement of the dark energy equation of state using the hierarchical strong lensing time delay likelihood provided by TDCOSMO. I find that the combination of seven TDCOSMO lenses and 33 SLACS lenses is only able to provide a w

Renzhi Wang, Jing Li, Piji Li

Diffusion models have garnered considerable interest in the field of text generation. Several studies have explored text diffusion models with different structures and applied them to various tasks, including named entity recognition and summarization. However, there exists a notable disparity between the "easy-first" text generation process of current diffu

Roberto Passante, Lucia Rizzuto

In this review we investigate several aspects and features of spatial field correlations for the massless scalar field and the electromagnetic field, both in stationary and nonstationary conditions, and show how they manifest in two- and many-body static and dynamic dispersion interactions (van der Waals and Casimir-Polder). We initially analyze the spatial

Xianghai Zhou, Haiyan Su

In this present paper we consider a full divergence-free of high order virtual finite element algorithm to approximate the stationary inductionless magnetohydrodynamic model on polygonal meshes. More precisely, we choice appropriate virtual spaces and necessary degrees of freedom for velocity and current density to guarantee that their final discrete formats

Zhenwei Lin, Jingfan Xia, Qi Deng, Luo Luo

We consider decentralized gradient-free optimization of minimizing Lipschitz continuous functions that satisfy neither smoothness nor convexity assumption. We propose two novel gradient-free algorithms, the Decentralized Gradient-Free Method (DGFM) and its variant, the Decentralized Gradient-Free Method$^+$ (DGFM$^{+}$). Based on the techniques of randomized

Jiří Rosický, Giacomo Tendas

Following the classical approach of Birkhoff, we suggest an enriched version of enriched universal algebra. Given a suitable base of enrichment $\mathcal V$, we define a language $\mathbb L$ to be a collection of $(X,Y)$-ary function symbols whose arities are taken among the objects of $\mathcal V$. The class of $\mathbb L$-terms is constructed recursively f

Rui Zheng, Wei Shen, Yuan Hua, Wenbin Lai

The success of AI assistants based on language models (LLMs) hinges crucially on Reinforcement Learning from Human Feedback (RLHF), which enables the generation of responses more aligned with human preferences. As universal AI assistants, there's a growing expectation for them to perform consistently across various domains. However, previous work shows that

Yixin Wu, Rui Wen, Michael Backes, Pascal Berrang

Large-scale pre-trained models are increasingly adapted to downstream tasks through a new paradigm called prompt learning. In contrast to fine-tuning, prompt learning does not update the pre-trained model's parameters. Instead, it only learns an input perturbation, namely prompt, to be added to the downstream task data for predictions. Given the fast develop

Maciej Beręsewicz

This paper proposes a~simple, yet powerful, method for balancing distributions of covariates for causal inference based on observational studies. The method makes it possible to balance an arbitrary number of quantiles (e.g., medians, quartiles, or deciles) together with means if necessary. The proposed approach is based on the theory of calibration estimato

David Miyamoto

Given a Lie groupoid, we can form its orbit space, which carries a natural diffeology. More generally, we have a quotient functor from the Hilsum-Skandalis category of Lie groupoids to the category of diffeological spaces. We introduce the notion of a lift-complete Lie groupoid, and show that the quotient functor restricts to an equivalence of the categories

Armin Haberl, Jürgen Fleiß, Dominik Kowald, Stefan Thalmann

aTrain is an open-source and offline tool for transcribing audio data in multiple languages with CPU and NVIDIA GPU support. It is specifically designed for researchers using qualitative data generated from various forms of speech interactions with research participants. aTrain requires no programming skills, runs on most computers, does not require an inter