May 2023 arXiv papers — page 12

Baptiste Anselme Martin, Thomas Ayral, François Jamet, Marko J. Rančić

Matrix Product States (MPS) and Operators (MPO) have been proven to be a powerful tool to study quantum many-body systems but are restricted to moderately entangled states as the number of parameters scales exponentially with the entanglement entropy. While MPS can efficiently find ground states of 1D systems, their capacities are limited when simulating the

Vaibhav Kumar, Hana Koorehdavoudi, Masud Moshtaghi, Amita Misra

We propose CHRT (Control Hidden Representation Transformation) - a controlled language generation framework that steers large language models to generate text pertaining to certain attributes (such as toxicity). CHRT gains attribute control by modifying the hidden representation of the base model through learned transformations. We employ a contrastive-learn

Rui Ye, Mingkai Xu, Jianyu Wang, Chenxin Xu

This work considers the category distribution heterogeneity in federated learning. This issue is due to biased labeling preferences at multiple clients and is a typical setting of data heterogeneity. To alleviate this issue, most previous works consider either regularizing local models or fine-tuning the global model, while they ignore the adjustment of aggr

Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone

Automatic melody-to-lyric generation is a task in which song lyrics are generated to go with a given melody. It is of significant practical interest and more challenging than unconstrained lyric generation as the music imposes additional constraints onto the lyrics. The training data is limited as most songs are copyrighted, resulting in models that underfit

Ilya Kolpakov, Thijs Smulders

Radiative tunneling recombination mechanism is observed in an InP nanowire solar cell at low temperatures. A link between observed radiative tunneling and field-emission dominated electrical transport is established through the characteristic tunneling energy. Plasmon-phonon interaction is found to play an important role in solar cell performance

Suraj, Shankar Kumar Selvaraja

We report $\approx$400\% enhancement in PZT Pockels coefficient on DFT simulation of lattice strain due to phonon mode softening.The simulation showed a relation between the rumpling and the Pockels coefficient divergence that happens at -8\% and 25\% strain developed in PZT film.The simulation was verified experimentally by RF sputter deposited PZT film on

Ousmane Ly

We propose an analytical formulation for the Scanning Gate Microscopy (SGM) response to local tips with arbitrary strength in two terminal nano-structures. The real space resolved conductance is expressed in terms of the unperturbed quantities underlying the scattering problem. Providing a non-dynamical approach for obtaining the SGM maps, the proposed expre

Catalin Mitelut, Ben Smith, Peter Vamplew

The rapid advancement of artificial intelligence (AI) systems suggests that artificial general intelligence (AGI) systems may soon arrive. Many researchers are concerned that AIs and AGIs will harm humans via intentional misuse (AI-misuse) or through accidents (AI-accidents). In respect of AI-accidents, there is an increasing effort focused on developing alg

Christopher Maulén, Claudio Muñoz

The Fourth order $\phi^4$ model generalizes the classical $\phi^4$ model of quantum field theory, sharing the same kink solution. It is also the dispersive counterpart of the well-known parabolic Cahn-Hilliard equation. Mathematically speaking, the kink is characterized by a fourth-order nonnegative linear operator with a simple kernel at the origin but no s

Benjamin D. Smith, Bahar Babaei, Andal Narayanan, Lindsay J. LeBlanc

Coherent microwave-to-optical conversion is crucial for transferring quantum information generated in the microwave domain to optical frequencies, where propagation losses can be minimised. Among the various physical platforms that have realized coherent microwave-to-optical transduction, those that use atoms as transducers have shown rapid progress in recen

Francesco Cesa, Hannes Pichler

We develop a model for quantum computation with Rydberg atom arrays, which only relies on global driving, without the need of local addressing of the qubits: any circuit is executed by a sequence of global, resonant laser pulses on a static atomic arrangement. We present two constructions: for the first, the circuit is imprinted in the trap positions of the

C. Gauvin-Ndiaye, C. Lahaie, Y. M. Vilk, A. -M. S. Tremblay

The two-particle self-consistent approach (TPSC) is a method for the one-band Hubbard model that can be both numerically efficient and reliable. However, TPSC fails to yield physical results deep in the renormalized classical regime of the bidimensional Hubbard model where the spin correlation length becomes exponentially large. We address the limitations of

Zichen Wang, Rishab Balasubramanian, Hui Yuan, Chenyu Song

We propose the first study of adversarial attacks on online learning to rank. The goal of the adversary is to misguide the online learning to rank algorithm to place the target item on top of the ranking list linear times to time horizon $T$ with a sublinear attack cost. We propose generalized list poisoning attacks that perturb the ranking list presented to

Abhishek Ghosh, Khushboo Agarwal, Sergio Gonzalez Munoz, Prashant Bisht

The present study demonstrates a large enhancement in the Seebeck coefficient and ultralow thermal conductivity (TE) in Sb$_2$Te$_3$-AgSbTe$_2$ nanocomposite thin film. The addition of Ag leads to the in-situ formation of AgSbTe$_2$ secondary phase nanoaggregates in the Sb$_2$Te$_3$ matrix during the growth resulting in a large Seebeck coefficient and reduct

Sergey V. Ulyanov, Fabio Ghisi, Ichiro Kurawaki, Viktor S. Ulyanov

There are important algorithms built upon a mixture of basic techniques described; for example, the Fast Fourier Transform (FFT) employs both Divide-and-Conquer and Transform-and-Conquer techniques. In this article, the evolution of a quantum algorithm (QA) is examined from an information theory viewpoint. The complex vector entering the quantum algorithmic

Yaoyiran Li, Ching-Yun Chang, Stephen Rawls, Ivan Vulić

Research on text-to-image generation (TTI) still predominantly focuses on the English language due to the lack of annotated image-caption data in other languages; in the long run, this might widen inequitable access to TTI technology. In this work, we thus investigate multilingual TTI (termed mTTI) and the current potential of neural machine translation (NMT

Albert Xue, Jingyou Rao, Sriram Sankararaman, Harold Pimentel

New biological assays like Perturb-seq link highly parallel CRISPR interventions to a high-dimensional transcriptomic readout, providing insight into gene regulatory networks. Causal gene regulatory networks can be represented by directed acyclic graph (DAGs), but learning DAGs from observational data is complicated by lack of identifiability and a combinato

Gustavo de Carvalho Bertoli, Gabriel Victor C. Fernandes, Pedro H. Borges Monici, César H. de Araujo Guibo

Internet of Things (IoT) devices are increasingly pervasive and essential components in enabling new applications and services. However, their widespread use also exposes them to exploitable vulnerabilities and flaws that can lead to significant losses. In this context, ensuring robust cybersecurity measures is essential to protect IoT devices from malicious

Xiao Liu, Da Yin, Chen Zhang, Yansong Feng

Causal reasoning, the ability to identify cause-and-effect relationship, is crucial in human thinking. Although large language models (LLMs) succeed in many NLP tasks, it is still challenging for them to conduct complex causal reasoning like abductive reasoning and counterfactual reasoning. Given the fact that programming code may express causal relations mo

Johannes K. Fichte, Markus Hecher, Michael Morak, Patrick Thier

In this paper, we introduce a novel algorithm to solve projected model counting (PMC). PMC asks to count solutions of a Boolean formula with respect to a given set of projection variables, where multiple solutions that are identical when restricted to the projection variables count as only one solution. Inspired by the observation that the so-called "treewid

Nicolo Bellarmino, Giorgio Bozzini, Riccardo Cantoro, Francesco Castelletti

The SARS-CoV-2 coronavirus emerged in 2019, causing a COVID-19 pandemic that resulted in 7 million deaths out of 770 million reported cases over the next four years. The global health emergency called for unprecedented efforts to monitor and reduce the rate of infection, pushing the study of new diagnostic methods. In this paper, we introduce a cheap, fast,

Peter K. Friz, Terry Lyons, Anna Seigal

The signature of a rectifiable path is a tensor series in the tensor algebra whose coefficients are definite iterated integrals of the path. The signature characterises the path up to a generalised form of reparametrisation. It is a classical result of K. T. Chen that the log-signature (the logarithm of the signature) is a Lie series. A Lie series is polynom

Hugo Lóio, Miguel Gonçalves, Pedro Ribeiro, Eduardo V. Castro

We have found the first instance of a third-order topological Anderson insulator (TOTAI). This disorder-induced topological phase is gapped and characterized by a quantized octupole moment and topologically protected corner states, as revealed by a detailed numerically exact analysis. We also find that the disorder-induced transition into the TOTAI phase can

Sougata Mallick, Yanis Sassi, Nicholas Figueiredo Prestes, Sachin Krishnia

Despite significant advances in the last decade regarding the room temperature stabilization of skyrmions or their current induced dynamics, the impact of local material inhomogeneities still remains an important issue that impedes to reach the regime of steady state motion of these spin textures. Here, we study the spin-torque driven motion of skyrmions in

Kamil Bujel, Yonatan Gideoni, Chaitanya K. Joshi, Pietro Liò

Much work has been devoted to devising architectures that build group-equivariant representations, while invariance is often induced using simple global pooling mechanisms. Little work has been done on creating expressive layers that are invariant to given symmetries, despite the success of permutation invariant pooling in various molecular tasks. In this wo

Hengchao Chen, Xin Chen, Mohamad Elmasri, Qiang Sun

Gradient Descent (GD) has been proven effective in solving various matrix factorization problems. However, its optimization behavior with large initial values remains less understood. To address this gap, this paper presents a novel theoretical framework for examining the convergence trajectory of GD with a large initialization. The framework is grounded in

Bo Jiang, Shuxian Luo, Xiao Wang, Chuanfu Li

Learning based feature matching methods have been commonly studied in recent years. The core issue for learning feature matching is to how to learn (1) discriminative representations for feature points (or regions) within each intra-image and (2) consensus representations for feature points across inter-images. Recently, self- and cross-attention models have

Philippe Laban, Jesse Vig, Wojciech Kryscinski, Shafiq Joty

Text simplification research has mostly focused on sentence-level simplification, even though many desirable edits - such as adding relevant background information or reordering content - may require document-level context. Prior work has also predominantly framed simplification as a single-step, input-to-output task, only implicitly modeling the fine-graine

Eytan Singher, Shachar Itzhaky

E-graphs are a prominent data structure that has been increasing in popularity in recent years due to their expanding range of applications in various formal reasoning tasks. Often, they are used for equality saturation, a process of deriving consequences through repeatedly applying universally quantified equality formulas via term rewriting. They handle equ

Yanhong A. Liu, Scott D. Stoller, Yi Tong, Bo Lin

This paper presents a language, Alda, that supports all of logic rules, sets, functions, updates, and objects as seamlessly integrated built-ins. The key idea is to support predicates in rules as set-valued variables that can be used and updated in any scope, and support queries using rules as either explicit or implicit automatic calls to an inference funct

Jiuhn Song, Seonghoon Park, Honggyu An, Seokju Cho

Neural radiance fields (NeRF) shows powerful performance in novel view synthesis and 3D geometry reconstruction, but it suffers from critical performance degradation when the number of known viewpoints is drastically reduced. Existing works attempt to overcome this problem by employing external priors, but their success is limited to certain types of scenes

Albie Chan, Zheng Shi, Luca Dellantonio, Wolfgang Dür

Variational quantum eigensolvers (VQEs) are successful algorithms for studying physical systems on quantum computers. Recently, they were extended to the measurement-based model of quantum computing, bringing resource graph states and their advantages into the realm of quantum simulation. In this work, we incorporate such ideas into traditional VQE circuits.

Manuel Bernardino del Pino, Tomás Chacón Rebollo, Macarena Gómez Mármol

We present in this paper the results of a research motivated by the need of a very fast solution of thermal flow in solar receivers. These receivers are composed by a large number of parallel pipes with the same geometry. We have introduced a reduced Schwarz algorithm that skips the computation in a large part of the pipes. The computation of the temperature

Vivek Nair, Christian Rack, Wenbo Guo, Rui Wang

Motion tracking "telemetry" data lies at the core of nearly all modern virtual reality (VR) and metaverse experiences. While generally presumed innocuous, recent studies have demonstrated that motion data actually has the potential to uniquely identify VR users. In this study, we go a step further, showing that a variety of private user information can be in

Ishaan Gulrajani, Tatsunori B. Hashimoto

Despite a growing interest in diffusion-based language models, existing work has not shown that these models can attain nontrivial likelihoods on standard language modeling benchmarks. In this work, we take the first steps towards closing the likelihood gap between autoregressive and diffusion-based language models, with the goal of building and releasing a

Phung Van Dong

It is shown that the solution for $B-L$ gauge symmetry with $B-L=-4,-4,+5$ assigned for three right-handed neutrinos respectively, reveals a novel scotogenic mechanism with implied matter parity for neutrino mass generation and dark matter stability. Additionally, the world with two-component dark matter is hinted.

Daniel Ritter, Fredrik Nordvall Forsberg, Stefanie Rinderle-Ma

Enterprise Application Integration deals with the problem of connecting heterogeneous applications, and is the centerpiece of current on-premise, cloud and device integration scenarios. For integration scenarios, structurally correct composition of patterns into processes and improvements of integration processes are crucial. In order to achieve this, we for

Jialu Li, Mohit Bansal

Vision-and-Language Navigation (VLN) requires the agent to follow language instructions to navigate through 3D environments. One main challenge in VLN is the limited availability of photorealistic training environments, which makes it hard to generalize to new and unseen environments. To address this problem, we propose PanoGen, a generation method that can

Colin A. Grambow, Hayley Weir, Nathaniel L. Diamant, Gabriele Scalia

Macrocyclic peptides are an emerging therapeutic modality, yet computational approaches for accurately sampling their diverse 3D ensembles remain challenging due to their conformational diversity and geometric constraints. Here, we introduce RINGER, a diffusion-based transformer model using a redundant internal coordinate representation that generates three-

Jun Wu, Xuesong Ye

The proliferation of fake news poses a serious threat to society, as it can misinform and manipulate the public, erode trust in institutions, and undermine democratic processes. To address this issue, we present FakeSwarm, a fake news identification system that leverages the swarming characteristics of fake news. To extract the swarm behavior, we propose a n

Ernie Chu, Shuo-Yen Lin, Jun-Cheng Chen

In this study, we present an efficient and effective approach for achieving temporally consistent synthetic-to-real video translation in videos of varying lengths. Our method leverages off-the-shelf conditional image diffusion models, allowing us to perform multiple synthetic-to-real image generations in parallel. By utilizing the available optical flow info

F. Demachi, Y. Fukui, R. I. Yamada, K. Tachihara

We investigated the giant molecular clouds (GMCs) in M74 (NGC 628), using data obtained from the PHANGS project. We applied the GMC Types according to the activity of star formation: Type I without star formation, Type II with H$\alpha$ luminosity ($L_\mathrm{H\alpha}$) less than $10^{37.5}~\mathrm{erg~s^{-1}}$, and Type III with $L_\mathrm{H\alpha}$ greater

Kenneth I. Aycock, Nuno Rebelo, Brent A. Craven

The superelastic constitutive model implemented in the commercial finite element code ABAQUS is verified using the method of exact solutions (MES). An analytical solution for uniaxial strain is first developed under a set of simplifying assumptions including von Mises-like transformation surfaces, symmetric transformation behavior, and monotonic loading. Num

Shida Wang, Zhong Li, Qianxiao Li

We prove an inverse approximation theorem for the approximation of nonlinear sequence-to-sequence relationships using recurrent neural networks (RNNs). This is a so-called Bernstein-type result in approximation theory, which deduces properties of a target function under the assumption that it can be effectively approximated by a hypothesis space. In particul

Han Yan, Owen Benton, Andriy H. Nevidomskyy, Roderich Moessner

The hallmark of highly frustrated systems is the presence of many states close in energy to the ground state. Fluctuations between these states can preclude the emergence of any form of order and lead to the appearance of spin liquids. Even on the classical level, spin liquids are not all alike: they may have algebraic or exponential correlation decay, and v

J. Lorenzana, G. Seibold

We investigate order parameter fluctuations in the Hubbard model within a time-dependent Gutzwiller approach. While in the weak coupling limit we find that the amplitude fluctuations are short-lived due to a degeneracy with the energy of the edge of the quasiparticle continua (and in agreement with Hartree-Fock + RPA theory), these are shifted below the edge

Zhen Lin, Shubhendu Trivedi, Jimeng Sun

Large language models (LLMs) specializing in natural language generation (NLG) have recently started exhibiting promising capabilities across a variety of domains. However, gauging the trustworthiness of responses generated by LLMs remains an open challenge, with limited research on uncertainty quantification (UQ) for NLG. Furthermore, existing literature ty

Raphael Steiner

A set $\mathcal{G}$ of planar graphs on the same number $n$ of vertices is called simultaneously embeddable if there exists a set $P$ of $n$ points in the plane such that every graph $G \in \mathcal{G}$ admits a (crossing-free) straight-line embedding with vertices placed at points of $P$. A conflict collection is a set of planar graphs of the same order wit

Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen

Many common types of data can be represented as functions that map coordinates to signal values, such as pixel locations to RGB values in the case of an image. Based on this view, data can be compressed by overfitting a compact neural network to its functional representation and then encoding the network weights. However, most current solutions for this are

Danilo de Oliveira, Navin Raj Prabhu, Timo Gerkmann

In large part due to their implicit semantic modeling, self-supervised learning (SSL) methods have significantly increased the performance of valence recognition in speech emotion recognition (SER) systems. Yet, their large size may often hinder practical implementations. In this work, we take HuBERT as an example of an SSL model and analyze the relevance of

Andrea Cini, Danilo Mandic, Cesare Alippi

Relationships among time series can be exploited as inductive biases in learning effective forecasting models. In hierarchical time series, relationships among subsets of sequences induce hard constraints (hierarchical inductive biases) on the predicted values. In this paper, we propose a graph-based methodology to unify relational and hierarchical inductive

Lingxiao Yang, Xuewen Dong, Sheng Gao, Qiang Qu

As a promising implementation model of payment channel network (PCN), payment channel hub (PCH) could achieve high throughput by providing stable off-chain transactions through powerful hubs. However, existing PCH schemes assume hubs preplaced in advance, not considering payment requests' distribution and may affect network scalability, especially network lo

Bin Xiao, Murat Simsek, Burak Kantarci, Ala Abu Alkheir

Table Detection (TD) is a fundamental task to enable visually rich document understanding, which requires the model to extract information without information loss. However, popular Intersection over Union (IoU) based evaluation metrics and IoU-based loss functions for the detection models cannot directly represent the degree of information loss for the pred

Lauren D. Liao, Emilie Højbjerre-Frandsen, Alan E. Hubbard, Alejandro Schuler

Although randomized controlled trials (RCTs) are a cornerstone of comparative effectiveness, they typically have much smaller sample size than observational studies because of financial and ethical considerations. Therefore there is interest in using plentiful historical data (either observational data or prior trials) to reduce trial sizes. Previous estimat

Damien Scieur

Despite the impressive numerical performance of the quasi-Newton and Anderson/nonlinear acceleration methods, their global convergence rates have remained elusive for over 50 years. This study addresses this long-standing issue by introducing a framework that derives novel, adaptive quasi-Newton and nonlinear/Anderson acceleration schemes. Under mild assumpt

Yu Zhang, Di Jin, Ran Tivony, Nir Kampf

Transient electric fields across cell bilayer membranes can lead to electroporation, as well as to cell fusion, and have been extensively studied. We find that transmembrane electric fields similar to those in cells can lead to a massive, reversible modulation--by up to 200-fold--of the interfacial energy dissipation between surfaces sliding across the lipid

Simon Gazagnes, Valentin Mauerhofer, Danielle A. Berg, Jeremy Blaizot

Observations of low-ionization state (LIS) metal lines provide crucial insights into the interstellar medium of galaxies, yet, disentangling the physical processes responsible for the emerging line profiles is difficult. This work investigates how mock spectra generated using a single galaxy in a radiation-hydrodynamical simulation can help us interpret obse

Madison Van Dyk, Jochen Koenemann

While many time-dependent network design problems can be formulated as time-indexed formulations with strong relaxations, the size of these formulations depends on the discretization of the time horizon and can become prohibitively large. The recently-developed dynamic discretization discovery (DDD) method allows many time-dependent problems to become more t

Lucas B. Vieira, Simon Milz, Giuseppe Vitagliano, Costantino Budroni

We introduce a framework to compute upper bounds for temporal correlations achievable in open quantum system dynamics, obtained by repeated measurements on the system. As these correlations arise by virtue of the environment acting as a memory resource, such bounds are witnesses for the minimal dimension of an effective environment compatible with the observ

Suraj, Shankar Kumar Selvaraja

In this work, we have demonstrated a novel method to increase the electro-optic interaction in an intensity modulator at the C-band by optimizing the growth methodology of PZT with the metal (Ti/Pt) as a base material and the PZT poling architecture. Here, we have used a patterned Pt layer for PZT deposition instead of a buffer layer. By optimizing the PZT g

Chiara Boccato, Andreas Deuchert, David Stocker

We consider a homogeneous Bose gas in the Gross-Pitaevskii limit at temperatures that are comparable to the critical temperature for Bose-Einstein condensation in the ideal gas. Our main result is an upper bound for the grand canonical free energy in terms of two new contributions: (a) the free energy of the interacting condensate is given in terms of an eff

Navdeep Arya, Sandeep K. Goyal

The sustained intense experimental activity around atomic spectroscopy and the resulting high-precision measurements of atomic spectral lines attract interest in Lamb shift as a witness for noninertial effects in quantum systems. We investigate the Lamb shift in a two-level system undergoing uniform circular motion and coupled to a quantum electromagnetic fi

S. Mironov, A. Shtennikova

Considering an anisotropic cosmological background is an interesting and simultaneously challenging problem of theoretical physics, since we not only assume a high degree of anisotropy in the early stages of the Universe, but also observe it to a small degree until now. In this paper we have constructed the unconstrained action for the perturbations above Bi

Ilker Oguz, Junjie Ke, Qifei Wang, Feng Yang

Neural networks (NN) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations. Optics-based platforms, using technologies such as silicon photonics and spatial light modulators, offer promising avenues for achieving this goal. However, training multipl

Ewan Forbes, Matthew D. Horner, Andrew Hallam, Joseph Barker

In this paper we explore the properties of a 1-dimensional spin chain in the presence of chiral interactions, focusing on the system's transition to distinct chiral phases for various values of the chiral coupling. By employing the mean field theory approximation we establish a connection between this chiral system and a Dirac particle in the curved spacetim

Peter Klimek, Ahmet Aykac, Stefan Thurner

Concerns about the integrity of Turkey's elections have increased with the recent transition from a parliamentary democracy to an executive presidency under Recep Tayyip Erdogan. Election forensics tools are used to identify statistical traces of certain types of electoral fraud, providing important information about the integrity and validity of democratic

Davide Nadalini, Manuele Rusci, Luca Benini, Francesco Conti

Enabling On-Device Learning (ODL) for Ultra-Low-Power Micro-Controller Units (MCUs) is a key step for post-deployment adaptation and fine-tuning of Deep Neural Network (DNN) models in future TinyML applications. This paper tackles this challenge by introducing a novel reduced precision optimization technique for ODL primitives on MCU-class devices, leveragin

Flaminia Fortuni, Emiliano Merlin, Adriano Fontana, Carlo Giocoli

We present FORECAST, a new flexible and adaptable software package that performs forward modeling of the output of any cosmological hydrodynamical simulations to create a wide range of realistic synthetic astronomical images. With customizable options for filters, field of view size and survey parameters, it allows users to tailor the synthetic images to the

Kanishk Gandhi, Dorsa Sadigh, Noah D. Goodman

Strategic reasoning enables agents to cooperate, communicate, and compete with other agents in diverse situations. Existing approaches to solving strategic games rely on extensive training, yielding strategies that do not generalize to new scenarios or games without retraining. Large Language Models (LLMs), with their ability to comprehend and generate compl

Viraj Prabhu, Sriram Yenamandra, Prithvijit Chattopadhyay, Judy Hoffman

We propose an automated algorithm to stress-test a trained visual model by generating language-guided counterfactual test images (LANCE). Our method leverages recent progress in large language modeling and text-based image editing to augment an IID test set with a suite of diverse, realistic, and challenging test images without altering model weights. We ben

Timm Intemann, Iris Pigeot

The association between multidimensional exposure patterns and outcomes is commonly investigated by first applying cluster analysis algorithms to derive patterns and then estimating the associations. However, errors in the underlying continuous, possibly skewed, exposure variables lead to misclassified exposure patterns and therefore to biased effect estimat

Yuqing Yang, Qipeng Guo, Xiangkun Hu, Yue Zhang

Recent works have introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE), since AMR provides a useful interpretation of complex semantic structures and helps to capture long-distance dependency. However, in these works AMR is used only implicitly, for instance, as additional features or training signals.

Haniyeh Barghi, Xiaotong Cheng, Setareh Maghsudi

We present a novel approach to address the multi-agent sparse contextual linear bandit problem, in which the feature vectors have a high dimension $d$ whereas the reward function depends on only a limited set of features - precisely $s_0 \ll d$. Furthermore, the learning follows under information-sharing constraints. The proposed method employs Lasso regress

Blake A. Myers, Lucas Jaggernauth, Thomas M. Metz, Matthew Q. Hill

Common and important applications of person identification occur at distances and viewpoints in which the face is not visible or is not sufficiently resolved to be useful. We examine body shape as a biometric across distance and viewpoint variation. We propose an approach that combines standard object classification networks with representations based on lin

Jintang Li, Huizhe Zhang, Ruofan Wu, Zulun Zhu

While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensionality to learn informative and discriminative full-precision representations, raising concerns about computation, memory footprint, and energy consumption burden (largely overloo

D. Tejada

In this work of divulgation, the global behavior of the Lyness equation is described. Results are obtained for different values of the parameter $\alpha$.

Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li

Competitions for shareable and limited resources have long been studied with strategic agents. In reality, agents often have to learn and maximize the rewards of the resources at the same time. To design an individualized competing policy, we model the competition between agents in a novel multi-player multi-armed bandit (MPMAB) setting where players are sel

S. Mohammadreza Ebrahimi, Farid Norouzi, Hossein Dastres, Reza Faieghi

This paper focuses on sensor fault detection and compensation for robotic manipulators. The proposed method features a new adaptive observer and a new terminal sliding mode control law established on a second-order integral sliding surface. The method enables sensor fault detection without the need to know the bounds on fault value and/or its derivative. It

Takafumi Kai, Keiya Ishiguro, Hiroshi Okada, Hajime Otsuka

We examine symmetries of chiral four-dimensional vacua of Type IIB flux compactifications with vanishing superpotential $W=0$. We find that the ${\cal N}=1$ supersymmetric MSSM-like and Pati-Salam vacua possess enhanced discrete symmetries in the effective action below the mass scale of stabilized complex structure moduli and dilaton. Furthermore, a generati

José Camacho-Mateu, Aniello Lampo, Matteo Sireci, Miguel Ángel Muñoz

During the last decades macroecology has identified broad-scale patterns of abundances and diversity of microbial communities and put forward some potential explanations for them. However, these advances are not paralleled by a full understanding of the dynamical processes behind them. In particular, abundance fluctuations of different species are found to b

Chenyi Liu, Vaneet Aggarwal, Tian Lan, Nan Geng

Robust network design, which aims to guarantee network availability under various failure scenarios while optimizing performance/cost objectives, has received significant attention. Existing approaches often rely on model-based mixed-integer optimization that is hard to scale or employ deep learning to solve specific engineering problems yet with limited gen

Tobias Haug, Soovin Lee, M. S. Kim

Stabilizer entropies (SEs) are measures of nonstabilizerness or `magic' that quantify the degree to which a state is described by stabilizers. SEs are especially interesting due to their connections to scrambling, localization and property testing. However, applications have been limited so far as previously known measurement protocols for SEs scale exponent

Luca Teodori, Kfir Blum

Massive elliptical galaxies, that serve as lenses in gravitational lensing time delay measurements of the Hubble parameter $H_0$, often reside in a host group. We consider degeneracies in the modeling of the group halo. When the group effect on imaging can be summarized by its flexion (the next order term beyond shear in the tidal expansion), the posterior l

Tivas Gupta, Mallesh M Pai, Max Resnick

The current Proposer-Builder Separation (PBS) equilibrium has several builders with different backgrounds winning blocks consistently. This paper considers how that equilibrium will shift when transactions are sold privately via order flow auctions (OFAs) rather than forwarded directly to the public mempool. We discuss a novel model that highlights the augme

Lorenz Zwick, Lucio Mayer

We revisit the precessing black hole binary model, a candidate to explain the bizarre quasi-periodic optical flares in OJ-287's light curve, from first principles. We deviate from existing work in three significant ways: 1) Including crucial aspects of relativistic dynamics related to the accretion disc's gravitational moments. 2) Adopting a model-agnostic p

Federico Buseghin, Nicolò Forcillo, Nicola Garofalo

In this note we prove a sub-Riemannian maximum modulus theorem in a Carnot group. Using a nontrivial counterexample, we also show that such result is best possible, in the sense that in its statement one cannot replace the right-invariant horizontal gradient with the left-invariant one.

Taisiya Glushkova, Chrysoula Zerva, André F. T. Martins

Although neural-based machine translation evaluation metrics, such as COMET or BLEURT, have achieved strong correlations with human judgements, they are sometimes unreliable in detecting certain phenomena that can be considered as critical errors, such as deviations in entities and numbers. In contrast, traditional evaluation metrics, such as BLEU or chrF, w

Bastien Liétard, Mikaela Keller, Pascal Denis

Lexical Semantic Change is the study of how the meaning of words evolves through time. Another related question is whether and how lexical relations over pairs of words, such as synonymy, change over time. There are currently two competing, apparently opposite hypotheses in the historical linguistic literature regarding how synonymous words evolve: the Law o

ATLAS Collaboration

This Letter presents the measurement of the fiducial and differential cross-sections of the electroweak production of a $Z \gamma$ pair in association with two jets. The analysis uses 140 fb$^{-1}$ of LHC proton-proton collision data taken at $\sqrt{s}$=13 TeV recorded by the ATLAS detector during the years 2015-2018. Events with a $Z$ boson candidate decayi

Omer Nivron, Raghul Parthipan, Damon J. Wischik

We propose the Taylorformer for random processes such as time series. Its two key components are: 1) the LocalTaylor wrapper which adapts Taylor approximations (used in dynamical systems) for use in neural network-based probabilistic models, and 2) the MHA-X attention block which makes predictions in a way inspired by how Gaussian Processes' mean predictions

Gihyun Lee

In this paper, we prove a version of the logarithmic Sobolev inequality of fractional order on noncommutative $n$-tori for any dimension $n\geq 2$.

Giacomo De Nicola, Göran Kauermann

Quantifying the number of deaths caused by the COVID-19 crisis has been an ongoing challenge for scientists, and no golden standard to do so has yet been established. We propose a principled approach to calculate age-adjusted yearly excess mortality, and apply it to obtain estimates and uncertainty bounds for 30 countries with publicly available data. The re

Jiarui Gong, Donghyeok Kim, Hokyung Jang, Fikadu Alema

The $\beta$-Ga$_2$O$_3$ has exceptional electronic properties with vast potential in power and RF electronics. Despite the excellent demonstrations of high-performance unipolar devices, the lack of p-type doping in $\beta$-Ga$_2$O$_3$ has hindered the development of Ga$_2$O$_3$-based bipolar devices. The approach of p-n diodes formed by polycrystalline p-typ

Kyeongsu Choi, Robert Haslhofer, Or Hershkovits

We consider the profile function of ancient ovals and of noncollapsed translators. Recall that pioneering work of Angenent-Daskalopoulos-Sesum (JDG '19, Annals '20) gives a sharp $C^0$-estimate and a quadratic concavity estimate for the profile function of two-convex ancient ovals, which are crucial in their papers as well as a slew of subsequent papers on a

Joseph Vulakh

An important step towards the classification of finite-dimensional pointed Hopf algebras is the classification of finite-dimensional Nichols algebras arising from braided vector spaces of group type. This question is fundamentally linked with the structure of algebraic objects called racks. Of particular interest to this classification is the type D conditio

Doyeon Kim, Eunji Ko, Hyunsu Kim, Yunji Kim

Portrait stylization, which translates a real human face image into an artistically stylized image, has attracted considerable interest and many prior works have shown impressive quality in recent years. However, despite their remarkable performances in the image-level translation tasks, prior methods show unsatisfactory results when they are applied to the

Yufei Cai

This undergraduate thesis focuses on calculating maximum likelihood estimates of parameters in the generalized Gamma distribution using the SeLF algorithm. As an extension of the Gamma distribution, the generalized Gamma distribution can better fit real data and has been widely applied. The research begins by exploring the definition of the generalized Gamma

Jeff Achter, Lian Duan, Xiyuan Wang

The classical Mordell-Weil theorem implies that an abelian variety $A$ over a number field $K$ has only finitely many $K$-rational torsion points. This finitude of torsion still holds even over the cyclotomic extension $K^{\rm cyc}=K\mathbb{Q}^{\mathrm{ab}}$ by a result of Ribet. In this article, we consider the finiteness of torsion points of an abelian var

Luca Basso, Michael Titze, Jacob Henshaw, Pauli Kehayias

Color centers in diamond are one of the most promising tools for quantum information science. Of particular interest is the use of single-crystal diamond membranes with nanoscale-thickness as hosts for color centers. Indeed, such structures guarantee a better integration with a variety of other quantum materials or devices, which can aid the development of d

Krishna Kumar

Machine Learning systems have achieved outstanding performance in different domains. In this paper machine learning methods have been applied to classification task to classify music genre. The code shows how to extract features from audio files and classify them using supervised learning into 2 genres namely classical and metal. Algorithms used are Logistic