May 2023 arXiv papers — page 100

Chiara Amorino, Arnaud Gloter

Our research delves into the balance between maintaining privacy and preserving statistical accuracy when dealing with multivariate data that is subject to \textit{componentwise local differential privacy} (CLDP). With CLDP, each component of the private data is made public through a separate privacy channel. This allows for varying levels of privacy protect

Xiaoman Zhang, Chaoyi Wu, Ziheng Zhao, Weixiong Lin

Medical Visual Question Answering (MedVQA) presents a significant opportunity to enhance diagnostic accuracy and healthcare delivery by leveraging artificial intelligence to interpret and answer questions based on medical images. In this study, we reframe the problem of MedVQA as a generation task that naturally follows the human-machine interaction and prop

Jegug Ih, Eliza M. -R. Kempton, Emily A. Whittaker, Madeline Lessard

Recently, the first JWST measurement of thermal emission from a rocky exoplanet was reported. The inferred dayside brightness temperature of TRAPPIST-1 b at 15 $\mu$m is consistent with the planet having no atmosphere and therefore no mechanism by which to circulate heat to its nightside. In this Letter, we compare the measured secondary eclipse depth of TRA

Xin Guo, Binnan Wang, Ruixun Zhang, Chaoyi Zhao

Signatures are iterated path integrals of continuous and discrete-time processes, and their universal nonlinearity linearizes the problem of feature selection in time series data analysis. This paper studies the consistency of signature using Lasso regression, both theoretically and numerically. We establish conditions under which the Lasso regression is con

Stefania Druga, Amy J. Ko

What role can AI play in supporting and constraining creative coding by families? To investigate these questions, we built a Wizard of Oz platform to help families engage in creative coding in partnership with a researcher-operated AI Friend. We designed a 3 week series of programming activities with ten children, 7 to 12 years old, and nine parents. Using a

Hanna Ziesche, Leonel Rozo

Robots often rely on a repertoire of previously-learned motion policies for performing tasks of diverse complexities. When facing unseen task conditions or when new task requirements arise, robots must adapt their motion policies accordingly. In this context, policy optimization is the \emph{de facto} paradigm to adapt robot policies as a function of task-sp

Naomi Andrew, Yassine Guerch, Sam Hughes, Monika Kudlinska

We prove that residually finite mapping tori of polynomially growing automorphisms of hyperbolic groups, groups hyperbolic relative to finitely many virtually polycyclic groups, right-angled Artin groups (when the automorphism is untwisted), and right-angled Coxeter groups have the cheap rebuilding property of Abert, Bergeron, Fraczyk, and Gaboriau. In parti

Krzysztof Jodłowski

The dark axion portal is a dimension-5 coupling between an axion-like particle (ALP), a photon, and a dark photon, which is one of the targets of the intensity frontier searches looking for $\sim\,$sub-GeV long-lived particles (LLPs). In this work, we re-examine the limits set by existing detectors such as CHARM and NuCal, and by future experiments such as F

Nam Ho Koh, Joseph Plata, Joyce Chai

Application Tracking Systems (ATS) have allowed talent managers, recruiters, and college admissions committees to process large volumes of potential candidate applications efficiently. Traditionally, this screening process was conducted manually, creating major bottlenecks due to the quantity of applications and introducing many instances of human bias. The

Shehzaad Dhuliawala, Mrinmaya Sachan, Carl Allen

We present a latent variable model for classification that provides a novel probabilistic interpretation of neural network softmax classifiers. We derive a variational objective to train the model, analogous to the evidence lower bound (ELBO) used to train variational auto-encoders, that generalises the softmax cross-entropy loss. Treating inputs to the soft

Nathanael Arkor, Dylan McDermott

We establish a relative monadicity theorem for relative monads with dense roots in a virtual equipment, specialising to a relative monadicity theorem for enriched relative monads. In particular, for a dense $\mathbb V$-functor $j \colon A \to E$, a $\mathbb V$-functor $r \colon D \to E$ is $j$-monadic if and only if $r$ admits a left $j$-relative adjoint and

Om Prakash, Shikha Patel, Habibul Islam

Let $p$ be a prime and $\mathbb{F}_q$ be the finite field of order $q=p^m$. In this paper, we study $\mathbb{F}_q\mathcal{R}$-skew cyclic codes where $\mathcal{R}=\mathbb{F}_q+u\mathbb{F}_q$ with $u^2=u$. To characterize $\mathbb{F}_q\mathcal{R}$-skew cyclic codes, we first establish their algebraic structure and then discuss the dual-containing properties b

Rohan Anil, Andrew M. Dai, Orhan Firat, Melvin Johnson

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM

Kristina Oganesyan

We show the univalence of $T$-symmetric Suffridge type polynomials $S_4^{(T)}$ in the unit disk, confirming thereby the conjecture proposed by Dmitrishin, Gray, and Stokolos in their recent paper. The result also implies the quasi-extremality of $S_n^{(T)}$ in the sense of Ruscheweyh.

Qiaochu Chen, Arko Banerjee, Çağatay Demiralp, Greg Durrett

Many data extraction tasks of practical relevance require not only syntactic pattern matching but also semantic reasoning about the content of the underlying text. While regular expressions are very well suited for tasks that require only syntactic pattern matching, they fall short for data extraction tasks that involve both a syntactic and semantic componen

Michal Yarom, Yonatan Bitton, Soravit Changpinyo, Roee Aharoni

Automatically determining whether a text and a corresponding image are semantically aligned is a significant challenge for vision-language models, with applications in generative text-to-image and image-to-text tasks. In this work, we study methods for automatic text-image alignment evaluation. We first introduce SeeTRUE: a comprehensive evaluation set, span

Alexander Shmakov, Kevin Greif, Michael Fenton, Aishik Ghosh

High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before measurements can be compared to certain theoretical predictions or measurements from other detectors. Methods to solve this \textit{inverse problem} of mapping detector observations to

Kirti Joshi

This paper lays the foundation of the Theory of Arithmetic Teichmuller Spaces of Number Fields by explicitly constructing many arithmetically inequivalent avatars of a fixed number field. This paper also constructs a topological space of such avatars and describes its symmetries. Notably amongst these symmetries is a global Frobenius morphism which changes t

Yifan Zhang, Jingqin Yang, Zhiquan Tan, Yang Yuan

Semi-supervised learning has emerged as a pivotal approach for leveraging scarce labeled data alongside abundant unlabeled data. Despite significant progress, prevailing SSL methods predominantly enforce consistency between different augmented views of individual samples, thereby overlooking the rich relational structure inherent within a mini-batch. In this

Jack Tacchi, Chiara Boldrini, Andrea Passarella, Marco Conti

Human social behaviour has been observed to adhere to certain structures. One such structure, the Ego Network Model (ENM), has been found almost ubiquitously in human society. Recently, this model has been extended to include signed connections. While the unsigned ENM has been rigorously observed for decades, the signed version is still somewhat novel and la

Yoonchang Sung, Peter Stone

Motion planning seeks a collision-free path in a configuration space (C-space), representing all possible robot configurations in the environment. As it is challenging to construct a C-space explicitly for a high-dimensional robot, we generally build a graph structure called a roadmap, a discrete approximation of a complex continuous C-space, to reason about

Miguel S. Costa, Vasco Goncalves, Aaditya Salgarkar, Joao Vilas Boas

We propose and explore the Regge limit for correlation functions of five local primary operators in conformal field theories. After reviewing some features of Regge theory for flat-space scattering amplitudes, we analyse the analytic structure of conformal blocks both in position and Mellin space in the Regge limit and propose an extension of conformal Regge

Benedetta Ferrario, Margherita Zanella

We construct stationary statistical solutions of a deterministic unforced nonlinear Schr\"odinger equation, by perturbing it by a linear damping $\gamma u$ and a stochastic force whose intensity is proportional to $\sqrt \gamma$, and then letting $\gamma\to 0^+$. We prove indeed that the family of stationary solutions $\{U_\gamma\}_{\gamma>0}$ of the perturb

Subhankar Banerjee, Sennur Ulukus, Anthony Ephremides

We consider a time slotted communication network with a base station (BS) and a user. At each time slot a fresh update packet arrives at the BS with probability $p>0$. When the BS transmits an update packet for the first time, it goes through with a success probability of $q_1$. In all subsequent re-transmissions, the packet goes through with a success proba

Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath

We study the node classification problem on feature-decorated graphs in the sparse setting, i.e., when the expected degree of a node is $O(1)$ in the number of nodes, in the fixed-dimensional asymptotic regime, i.e., the dimension of the feature data is fixed while the number of nodes is large. Such graphs are typically known to be locally tree-like. We intr

Ethan C. McGarrigle, Kris T. Delaney, Leon Balents, Glenn H. Fredrickson

We report the first numerical prediction of a "spin microemulsion" -- a phase with undulating spin domains resembling classical bicontinuous oil-water-surfactant emulsions -- in two-dimensional systems of spinor Bose-Einstein condensates with isotropic Rashba spin-orbit coupling. Using field-theoretic numerical simulations, we investigated the melting of a l

Reilly Browne, Rezaul Chowdhury, Shih-Yu Tsai, Yimin Zhu

We present two cache-oblivious sorting-based convex hull algorithms in the Binary Forking Model. The first is an algorithm for a presorted set of points which achieves $O(n)$ work, $O(\log n)$ span, and $O(n/B)$ serial cache complexity, where $B$ is the cache line size. These are all optimal worst-case bounds for cache-oblivious algorithms in the Binary Fork

Thomas Altstidl, David Dobre, Björn Eskofier, Gauthier Gidel

Certified defenses against adversarial attacks offer formal guarantees on the robustness of a model, making them more reliable than empirical methods such as adversarial training, whose effectiveness is often later reduced by unseen attacks. Still, the limited certified robustness that is currently achievable has been a bottleneck for their practical adoptio

Yating Wu, William Sheffield, Kyle Mahowald, Junyi Jessy Li

Automated text simplification, a technique useful for making text more accessible to people such as children and emergent bilinguals, is often thought of as a monolingual translation task from complex sentences to simplified sentences using encoder-decoder models. This view fails to account for elaborative simplification, where new information is added into

Maarten Golterman, Kim Maltman, Santiago Peris

Using standard mathematical methods for asymptotic series and the large-$\beta_0$ approximation, we define a Minimum Distance between the Fixed-Order perturbative series and the Contour-Improved perturbative series in the strong coupling $\alpha_s$ for finite-energy sum rules as applied to hadronic $\tau$ decays. This distance is similar, but not identical,

Mohamed Awadalla, François Bouffard

The objective of electric power system operators is to determine cost-effective operating points by resolving optimization problems that include physical and engineering constraints. As empirical evidence and operator experience indicate, only a small portion of these constraints are found to be binding during operations. Several optimization-based methods h

Yassir Fathullah, Guoxuan Xia, Mark Gales

Efficiently and reliably estimating uncertainty is an important objective in deep learning. It is especially pertinent to autoregressive sequence tasks, where training and inference costs are typically very high. However, existing research has predominantly focused on tasks with static data such as image classification. In this work, we investigate Ensemble

Sergio Pelaez, Gaurav Verma, Barbara Ribeiro, Philip Shapira

Labeling data is essential for training text classifiers but is often difficult to accomplish accurately, especially for complex and abstract concepts. Seeking an improved method, this paper employs a novel approach using a generative language model (GPT-4) to produce labels and rationales for large-scale text analysis. We apply this approach to the task of

Pedro G. S. Fernandes

We present analytic stationary and axially-symmetric black hole solutions to the semiclassical Einstein equations that are sourced by the trace anomaly. We also find that the same spacetime geometry satisfies the field equations of a subset of Horndeski theories featuring a conformally coupled scalar field. We explore various properties of these solutions, a

Esra Ceylan, Jiehua Chen, Sanjukta Roy

We study four NP-hard optimal seat arrangement problems [Bodlaender et al., 2020a], which each have as input a set of n agents, where each agent has cardinal preferences over other agents, and an n-vertex undirected graph (called seat graph). The task is to assign each agent to a distinct vertex in the seat graph such that either the sum of utilities or the

Barbara Brune, Jonathan Flossdorf, Carsten Jentsch

The Erd\"os Renyi graph is a popular choice to model network data as it is parsimoniously parametrized, straightforward to interprete and easy to estimate. However, it has limited suitability in practice, since it often fails to capture crucial characteristics of real-world networks. To check the adequacy of this model, we propose a novel class of goodness-o

Jorge Medina, Andrew D. White

Evolutionary symbolic regression (SR) fits a symbolic equation to data, which gives a concise interpretable model. We explore using SR as a method to propose which data to gather in an active learning setting with physical constraints. SR with active learning proposes which experiments to do next. Active learning is done with query by committee, where the Pa

Kayla Boggess, Sarit Kraus, Lu Feng

As multi-agent reinforcement learning (MARL) systems are increasingly deployed throughout society, it is imperative yet challenging for users to understand the emergent behaviors of MARL agents in complex environments. This work presents an approach for generating policy-level contrastive explanations for MARL to answer a temporal user query, which specifies

Madhura Ghosh Dastidar, Aprameyan Desikan, Vidya Praveen Bhallamudi

Quantum metrology aims at achieving enhanced performance in measuring unknown parameters by utilizing quantum resources. Thus, quantum metrology is an important application of quantum technologies. Photonic systems can implement these metrological tasks with simpler experimental techniques. We present a scheme for improved parameter estimation by introducing

Mutian Shen, Gerardo Ortiz, Yang-Yu Liu, Martin Weigel

We consider the effect of perturbing a single bond on ground-states of nearest-neighbor Ising spin-glasses, with a Gaussian distribution of the coupling constants, across various two and three-dimensional lattices and regular random graphs. Our results reveal that the ground-states are strikingly susceptible to such changes. Altering the strength of only a s

Joseph Pierre Anderson, Anter El-Azab

Progress toward a first-principles theory of plasticity and work-hardening is currently impeded by an insufficient picture of dislocation kinetics (the dynamic effect of driving forces in a given dislocation theory). This is because present methods ignore the short-range interaction of dislocations. This work presents a kinetic theory of continuum dislocatio

Shagun Jhaver, Alice Qian Zhang, Quanze Chen, Nikhila Natarajan

Social media platforms moderate content for each user by incorporating the outputs of both platform-wide content moderation systems and, in some cases, user-configured personal moderation preferences. However, it is unclear (1) how end users perceive the choices and affordances of different kinds of personal content moderation tools, and (2) how the introduc

Meixi Chen, Martin Lysy, David Moorman, Reza Ramezan

Modelling the dynamics of interactions in a neuronal ensemble is an important problem in functional connectivity research. One popular framework is latent factor models (LFMs), which have achieved notable success in decoding neuronal population dynamics. However, most LFMs are specified in discrete time, where the choice of bin size significantly impacts inf

Sumit Rout, Nitica Sakharwade, Some Sankar Bhattacharya, Ravishankar Ramanathan

In communication complexity-like problems, previous studies have shown either an exponential quantum advantage or an unbounded quantum advantage with an exponentially large input set $\Theta(2^{n})$ bits with respect to classical communication $\Theta(n)$ bits. In the former, the quantum and classical separation grows exponentially in input while the latter'

Tribhuban Parida, Sandeep Chatterjee

We propose that the transverse momentum ($p_T$) differential splitting of directed flow ($\Delta v_1$) between proton and anti-proton can serve as a sensitive observable to extract the baryon diffusion coefficient ($\kappa_B$) of the hot and dense strongly interacting matter produced in relativistic heavy ion collisions. We use relativistic dissipative hydro

Collin Beaudoin, Koustubh Phalak, Swaroop Ghosh

Identification and verification of molecular properties such as side effects is one of the most important and time-consuming steps in the process of molecule synthesis. For example, failure to identify side effects before submission to regulatory groups can cost millions of dollars and months of additional research to the companies. Failure to identify side

Moshe Marcus

Consider operators $L_{V}:=\Delta + V$ in a bounded Lipschitz domain $\Omega\subset \mathbb{R}^N$. Assume that $V\in C^\alpha(\Omega)$ satisfies $|V(x)| \leq \bar a\,\mathrm{dist}(x,\partial\Omega)^{-2}$ in $\Omega$ and that $L_V$ has a (minimal) ground state $\Phi_V$ in $\Omega$. We derive a representation formula for signed supersolutions (or subsolutions)

A. -K. Peters, R. Capilla, V. C. Coroamă, R. Heldal

Research shows that the global society as organized today, with our current technological and economic system, is impossible to sustain. We are living in the Anthropocene, an era in which human activities in highly industrialized countries are responsible for overshooting several planetary boundaries, with poorer communities contributing least to the problem

Archisman Ghosh, Jose Maria Bermudo Mera, Angshuman Karmakar, Debayan Das

The hard mathematical problems that assure the security of our current public-key cryptography (RSA, ECC) are broken if and when a quantum computer appears rendering them ineffective for use in the quantum era. Lattice based cryptography is a novel approach to public key cryptography, of which the mathematical investigation (so far) resists attacks from quan

Philipp Kroenert, Yannick Wunderlich, Farah Afzal, Annika Thiel

A truncated partial-wave analysis is performed for $\eta$-photoproduction using the polarization observables $\sigma_0, \Sigma, T, E, F$ and $G$. Different truncation orders are analyzed for six energy bins within the range of $E^{lab}_{\gamma} \in [750, 1250]$ MeV. Bayesian statistics is combined with truncated partial-wave analysis for the first time to in

Yu Ding, Yirui Cong, Xiangke Wang

In this article, we focus on the cooperative state estimation problem of a multi-agent system. Each agent is equipped with absolute and relative measurements. The purpose of this research is to make each agent generate its own state estimation with only local measurement information and local communication with neighborhood agents using Set Membership Filter

Jorge A. León, Yanghui Liu, Samy Tindel

The Malliavin differentiability of a SDE plays a crucial role in the study of density smoothness and ergodicity among others. For Gaussian driven SDEs the differentiability property is now well established. In this paper, we consider the Malliavin differentiability for the Euler scheme of such SDEs. We will focus on SDEs driven by fractional Brownian motions

Peng-cheng Cao, Qiong Liu, Neng-Hui Liao, Qian-cheng Yang

We have collected a catalog of 1095 debris disks with properties and classification (resolved, planet, gas) information. From the catalog, we defined a less biased sample with 612 objects and presented the distributions of their stellar and disk properties to search for correlations between disks and stars. We found debris disks were widely distributed from

Natalia Chepiga, Nicolas Laflorencie

The quantum critical properties of interacting fermions in the presence of disorder are still not fully understood. While it is well known that for Dirac fermions, interactions are irrelevant to the non-interacting infinite randomness fixed point (IRFP), the problem remains largely open in the case of Majorana fermions which further display a much richer dis

Shubh Agrawal, Jean-Baptiste Ruffio, Quinn M. Konopacky, Bruce Macintosh

While radial velocity surveys have demonstrated that the population of gas giants peaks around $3~\text{au}$, the most recent high-contrast imaging surveys have only been sensitive to planets beyond $\sim~10~\text{au}$. Sensitivity at small angular separations from stars is currently limited by the variability of the point spread function. We demonstrate how

Eilam Shapira, Omer Madmon, Reut Apel, Moshe Tennenholtz

Recent advances in Large Language Models (LLMs) have spurred interest in designing LLM-based agents for tasks that involve interaction with human and artificial agents. This paper addresses a key aspect in the design of such agents: predicting human decisions in off-policy evaluation (OPE). We focus on language-based persuasion games, where an expert aims to

Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang

Inspired by the superior language abilities of large language models (LLM), large vision-language models (LVLM) have been recently explored by integrating powerful LLMs for improving the performance on complex multimodal tasks. Despite the promising progress on LVLMs, we find that LVLMs suffer from the hallucination problem, i.e. they tend to generate object

Lane Lawley, Christopher J. MacLellan

We present a system for interpretable, symbolic, interactive task learning from dialog using a GPT model as a conversational front-end. The learned tasks are represented as hierarchical decompositions of predicate-argument structures with scoped variable arguments. By using a GPT model to convert interactive dialog into a semantic representation, and then re

Igor Sfiligoi, Daniel McDonald, Rob Knight, Frank Würthwein

GitHub is a popular repository for hosting software projects, both due to ease of use and the seamless integration with its testing environment. Native GitHub Actions make it easy for software developers to validate new commits and have confidence that new code does not introduce major bugs. The freely available test environments are limited to only a few po

Wanli Ma, Oktay Karakus, Paul L. Rosin

Semi-supervised learning has been well developed to help reduce the cost of manual labelling by exploiting a large quantity of unlabelled data. Especially in the application of land cover classification, pixel-level manual labelling in large-scale imagery is labour-intensive, time-consuming and expensive. However, existing semi-supervised learning methods pa

Raúl E. Curto, Maria Infusino

The realizability problem is a well-known problem in the analysis of complex systems, which can be modeled as an infinite-dimensional moment problem. More precisely, as a truncated $K-$moment problem where $K$ is the space of all possible configurations of the components of the considered system. The power of this reformulation has been already exploited in

Jake Clarkson, Kyle Y Lin

Consider a two-person zero-sum search game between a hider and a searcher. The hider hides among $n$ discrete locations, and the searcher successively visits individual locations until finding the hider. Known to both players, a search at location $i$ takes $t_i$ time units and detects the hider -- if hidden there -- independently with probability $\alpha_i$

Reilly Browne

We present linear-time algorithms for both maximum hidden set and minimum convex cover in funnel polygons. These algorithms show that funnel polygons are "homestead" polygons, i.e. polygons for which the hidden set number and the convex cover number coincide. We extend the algorithm to apply to maximum hidden vertex set and use the result to give a 2-approxi

Emily Kendall, Mateja Gosenca, Richard Easther

Ultralight dark matter (ULDM) is an interesting alternative to the cold dark matter (CDM) paradigm. Due to the extremely low mass of the constituent particle ($\sim 10^{-22}$ eV), ULDM can exhibit quantum effects up to kiloparsec scales. In particular, runaway collapse in the centres of ULDM halos is prevented by quantum pressure, providing a possible resolu

Kristiina Rahkema, Dietmar Pfahl

Although using third-party libraries is common practice when writing software, vulnerabilities may be found even in well-known libraries. Detected vulnerabilities are often fixed quickly in the library code. The easiest way to include these fixes in a dependent software application, is to update the used library version. Package managers provide automated so

Maoran Zhu, Yuanxin Wu

Inertial-based navigation refers to the navigation methods or systems that have inertial information or sensors as the core part and integrate a spectrum of other kinds of sensors for enhanced performance. Through a series of papers, the authors attempt to explore information blending of inertial-based navigation by a polynomial optimization method. The basi

Hanno Jacobs, Philipp Mertsch, Vo Hong Minh Phan

Observations of the vicinity of a variety of galactic gamma-ray sources have indicated a local suppression of diffusivity of cosmic rays by up to three orders of magnitude. However, the impact of these low-diffusion zones on \emph{global} properties of cosmic-ray transport is however only poorly understood. Here, we argue that cosmic-ray nuclear ratios, like

Chongzhi Wang, Haibin Shao, Dewei Li

Synchronization and desynchronization are the two ends on the spectrum of emergent phenomena that somehow often coexist in biological, neuronal, and physical networks. However, previous studies essentially regard their coexistence as a partition of the network units: those that are in relative synchrony and those that are not. In real-world systems, desynchr

James Bernhard

In this paper, we state and prove a simple geometric interpretation of the degrees of freedom of a chi-square distribution. The general geometric idea goes back at least to Fisher in the 1920s, but the exact result does not appear to have been explicitly stated or proved prior to this paper.

David Hyland, Julian Gutierrez, Michael Wooldridge

We introduce and study a computational version of the principal-agent problem -- a classic problem in Economics that arises when a principal desires to contract an agent to carry out some task, but has incomplete information about the agent or their subsequent actions. The key challenge in this setting is for the principal to design a contract for the agent

Marco Manzoni, Dario Tagliaferri, Stefano Tebaldini, Marouan Mizmizi

Networked sensing refers to the capability of properly orchestrating multiple sensing terminals to enhance specific figures of merit, e.g., positioning accuracy or imaging resolution. Regarding radio-based sensing, it is essential to understand \textit{when} and \textit{how} sensing terminals should be orchestrated, namely the best cooperation that trades be

Michele Colombo, Giacomo Boracchi, Simone Melzi

Shape matching is a fundamental problem in computer graphics with many applications. Functional maps translate the point-wise shape-matching problem into its functional counterpart and have inspired numerous solutions over the last decade. Nearly all the solutions based on functional maps rely on the eigenfunctions of the Laplace-Beltrami Operator (LB) to de

Hiroaki Nishikawa

In this short note, we discuss the circumstances that can lead to a failure to observe the design order of discretization error convergence in accuracy verification when solving a time-dependent problem. In particular, we discuss the problem of failing to observe the design order of spatial accuracy with an extremely small time step. The same problem is enco

Raluca M. Balan, Xiao Liang

In this article, we study the continuity in law of the solutions of two linear multiplicative SPDEs (the parabolic Anderson model and the hyperbolic Anderson model) with respect to the spatial parameter of the noise. The solution is interpreted in the Skorohod sense, using Malliavin calculus. We consider two cases: (i) the regular noise, whose spatial covari

Anchun Gui, Jinqiang Ye, Han Xiao

It has become a popular paradigm to transfer the knowledge of large-scale pre-trained models to various downstream tasks via fine-tuning the entire model parameters. However, with the growth of model scale and the rising number of downstream tasks, this paradigm inevitably meets the challenges in terms of computation consumption and memory footprint issues.

Xiongchao Chen, Bo Zhou, Huidong Xie, Xueqi Guo

Myocardial perfusion imaging (MPI) by single-photon emission computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. Reducing the dose of the injected tracer is essential for lowering the patient's radiation exposure, but it will lead to increased image noise. Additionally, the latest dedicated cardiac SPECT scanners typic

Melissa Diamond, Damiano F. G. Fiorillo, Gustavo Marques-Tavares, Irene Tamborra

The metastable hypermassive neutron star produced in the coalescence of two neutron stars can copiously produce axions that radiatively decay into $\mathcal{O}(100)$~MeV photons. These photons can form a fireball with characteristic temperature smaller than $1\rm\, MeV$. By relying on X-ray observations of GW170817/GRB 170817A with CALET CGBM, Konus-Wind, an

Xiongchao Chen, Bo Zhou, Huidong Xie, Xueqi Guo

Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of ischemic heart diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads to increased image noise. Limited-angle (LA) SPECT enables faster scanning and reduced hardware costs but results in lower reconstruction accuracy. Additionally, computed tomograp

Wei-Jian Li, Shi-Chang Xiao, Da-Fei Sun, Chang-De Gong

Realizing controllable room-temperature ferromagnetism in carbon-based materials is one of recent prospects. The magnetism in graphene nanostructures reported previously is mostly formed near the vacancies, zigzag edges, or impurities by breaking the local sublattice imbalance, though a bulk chiral spin-density-wave ground state is also reported at van Hove

Susanne Ulmer, Do Tran Van, Stephan F. Huckemann

It is well known, that Fr\'echet means on non-Euclidean spaces may exhibit nonstandard asymptotic rates depending on curvature. Even for distributions featuring standard asymptotic rates, there are non-Euclidean effects, altering finite sampling rates up to considerable sample sizes. These effects can be measured by the variance modulation function proposed

Christopher Brav, Nick Rozenblyum

The Deligne conjecture (many times a theorem) endows Hochschild cochains of a linear category with the structure of an $E_2$-algebra, that is, of an algebra over the little 2-disks operad. In this paper, we prove the cyclic Deligne conjecture, stating that for a linear category equipped with a Calabi-Yau structure (a kind of non-commutative orientation), the

N. M. Pham, V. N. Duarte

The nonlinear collisional dynamics of coupled driven plasma waves in the presence of background dissipation is studied analytically within kinetic theory. Sufficiently near marginal stability, phase space correlations are poorly preserved and time delays become unimportant. The system is then shown to be governed by two first-order coupled autonomous differe

Atli Thor Sigurgeirsson, Simon King

Reference-based Text-to-Speech (TTS) models can generate multiple, prosodically-different renditions of the same target text. Such models jointly learn a latent acoustic space during training, which can be sampled from during inference. Controlling these models during inference typically requires finding an appropriate reference utterance, which is non-trivi

Weitao Chen, Hongbin Xu, Zhipeng Zhou, Yang Liu

The core of Multi-view Stereo(MVS) is the matching process among reference and source pixels. Cost aggregation plays a significant role in this process, while previous methods focus on handling it via CNNs. This may inherit the natural limitation of CNNs that fail to discriminate repetitive or incorrect matches due to limited local receptive fields. To handl

Ujash Joshi, Michael Guerzhoy

We apply convolutional neural networks (CNN) to the problem of image orientation detection in the context of determining the correct orientation (from 0, 90, 180, and 270 degrees) of a consumer photo. The problem is especially important for digitazing analog photographs. We substantially improve on the published state of the art in terms of the performance o

Laura Río-Martín, Michael Dumbser

This paper presents a high-order discontinuous Galerkin finite element method to solve the barotropic version of the conservative symmetric hyperbolic and thermodynamically compatible (SHTC) model of compressible two-phase flow, introduced by Romenski et al., in multiple space dimensions. In the absence of algebraic source terms, the model is endowed with a

P. Kucharczyk, A. Golombek, M. Schleberger, A. Wucher

A time resolved experiment to investigate the ultrafast dynamics following an ion impact onto a solid surface requires an ultrashort ion pump pulse in combination with a properly synchronized and time resolved probe. In order to realize such an experiment, we have investigated a strategy to use femtosecond laser photoionization of atoms entrained in a pulsed

Chenming Zhang

We aims to provide an interpretation of the the design background, motivation, and key innovations of FreeRider. The technique utilized by FreeRider enables tags to transform codewords present in commodity signals into another valid ones from the same codebook during reflection. As a result, the backscattered signal remains valid as commodity radios such as

Abineet Parichha, Shiv Sethi

The Weakly Interacting Massive Particles(WIMPs) have long been the favored CDM candidate in the standard $\Lambda$CDM model. However, owing to great improvement in the experimental sensitivity in the past decade, some parameter space of the SUSY-based WIMP model is ruled out. In addition, WIMP as the CDM particle is also at variance with other astrophysical

Xingyao Wang, Hao Peng, Reyhaneh Jabbarvand, Heng Ji

Fine-tuning pre-trained language models (LMs) is essential for enhancing their capabilities. Existing techniques commonly fine-tune on input-output pairs (e.g., instruction tuning) or with numerical rewards that gauge the output quality (e.g., RLHF). We explore LMs' potential to learn from textual interactions (LETI) that not only check their correctness wit

Stefano Bonaccorsi, Bernard Hanzon, Giulia Lombardi

In the literature, finite mixture models are described as linear combinations of probability distribution functions having the form $\displaystyle f(x) = \Lambda \sum_{i=1}^n w_i f_i(x)$, $x \in \mathbb{R}$, where $w_i$ are positive weights, $\Lambda$ is a suitable normalising constant and $f_i(x)$ are given probability density functions. The fact that $f(x)

Irfan Glogić

We continue our work \cite{Glo22a} on the analysis of spatially global stability of self-similar blowup profiles for semilinear wave equations in the radial case. In this paper we study the Yang-Mills equations in $(1+d)$-dimensional Minkowski space. For $d \geq 5$, which is the energy supercritical case, we consider an explicitly known equivariant self-simi

Guy R. Y. Coleman, Angus Macintyre, Michael J. Walsh, William T. Salter

Site-specific weed control (SSWC) can provide considerable reductions in weed control costs and herbicide usage. Despite the promise of machine vision for SSWC systems and the importance of ground speed in weed control efficacy, there has been little investigation of the role of ground speed and camera characteristics on weed detection performance. Here, we

Samuel Jaques, Arthur G. Rattew

Quantum random-access memory (QRAM) is a mechanism to access data (quantum or classical) based on addresses which are themselves a quantum state. QRAM has a long and controversial history, and here we survey and expand arguments and constructions for and against. We use two primary categories of QRAM from the literature: (1) active, which requires external i

Georgios Palaiokrassas, Sandro Scherrers, Iason Ofeidis, Leandros Tassiulas

Since the inception of permissionless blockchains with Bitcoin in 2008, it became apparent that their most well-suited use case is related to making the financial system and its advantages available to everyone seamlessly without depending on any trusted intermediaries. Smart contracts across chains provide an ecosystem of decentralized finance (DeFi), where

Wenfang Sun, Yingjun Du, Xiantong Zhen, Fan Wang

Meta-learning algorithms are able to learn a new task using previously learned knowledge, but they often require a large number of meta-training tasks which may not be readily available. To address this issue, we propose a method for few-shot learning with fewer tasks, which we call MetaModulation. The key idea is to use a neural network to increase the dens

Soma Onishi, Shoya Meguro

Tabular data is the most widely used data format in machine learning (ML). While tree-based methods outperform DL-based methods in supervised learning, recent literature reports that self-supervised learning with Transformer-based models outperforms tree-based methods. In the existing literature on self-supervised learning for tabular data, contrastive learn

Zuhao Yang, Yingfang Yuan, Yang Xu, Shuo Zhan

Measuring the distance between machine-produced and human language is a critical open problem. Inspired by empirical findings from psycholinguistics on the periodicity of entropy in language, we propose FACE, a set of metrics based on Fourier Analysis of the estimated Cross-Entropy of language, for measuring the similarity between model-generated and human-w

Ping Yang, Junyu Lu, Ruyi Gan, Junjie Wang

We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis. Our approach converts the text-based IE tasks as the token-pair problem, which uniformly disassembles all extraction

Janus Capellan Aban, Chuan-Ren Chen, Chrisna Setyo Nugroho

Recent measurements of $R_{D}$ and $R_{D^{*}}$ by the LHCb collaboration show deviations from their respective Standard Model values. These semileptonic $B$ meson decays, associated with $b\rightarrow c \tau \bar{\nu}$ transition, are pointing toward new physics beyond the Standard Model via leptonic flavor universality violation. In this paper, we show that