July 2023 arXiv papers — page 157

Polina M. Sheverdyaeva, Conor Hogan, Anna Sgarlata, Laura Fazi

Thin Ge layers deposited on Si(105) form a stable single-domain film structure with large terraces and rebonded-step surface termination, thus realizing an extended and ordered Ge/Si planar hetero-junction. At the coverage of four Ge monolayers angle-resolved photoemission spectroscopy reveals the presence of two-dimensional surface and film bands displaying

Csenge Petak, Lapo Frati, Melissa H. Pespeni, Nick Cheney

In environments that vary frequently and unpredictably, bet-hedgers can overtake the population. Diversifying bet-hedgers have a diverse set of offspring so that, no matter the conditions they find themselves in, at least some offspring will have high fitness. In contrast, conservative bet-hedgers have a set of offspring that all have an in-between phenotype

Bin Li, Carsten Jentsch, Emmanuel Müller

Detecting abnormal patterns that deviate from a certain regular repeating pattern in time series is essential in many big data applications. However, the lack of labels, the dynamic nature of time series data, and unforeseeable abnormal behaviors make the detection process challenging. Despite the success of recent deep anomaly detection approaches, the myst

Haodong Wang, Donglin Ma

A near-eye display device (NED) is a visual optical system that places a miniature display in front of the human eye to provide an immersive viewing experience. NEDs have been playing an irreplaceable role in both early military flight applications and today's civil and entertainment applications. In this paper, we propose an easy-to-machine design of a

Zhenhan Huang, Fumihide Tanaka

On-chain data (metrics) of blockchain networks, akin to company fundamentals, provide crucial and comprehensive insights into the networks. Despite their informative nature, on-chain data have not been utilized in reinforcement learning (RL)-based systems for cryptocurrency (crypto) portfolio management (PM). An intriguing subject is the extent to which the

Stefano Gabici

Massive stars blow powerful winds and eventually explode as supernovae. By doing so, they inject energy and momentum in the circumstellar medium, which is pushed away from the star and piles up to form a dense and expanding shell of gas. The effect is larger when many massive stars are grouped together in bound clusters or associations. Large cavities form a

Yingji Li, Mengnan Du, Xin Wang, Ying Wang

As the representation capability of Pre-trained Language Models (PLMs) improve, there is growing concern that they will inherit social biases from unprocessed corpora. Most previous debiasing techniques used Counterfactual Data Augmentation (CDA) to balance the training corpus. However, CDA slightly modifies the original corpus, limiting the representation d

Hannah Scholten, Herbert Köstler, Anne Slawig

Purpose: To examine whether an advanced gradient pre-emphasis approach based on the gradient system transfer function (GSTF) can mitigate artifacts caused by residual unbalanced gradients in Cartesian balanced steady-state free precession (bSSFP) imaging with non-linear line-ordering. Theory and Methods: We implemented a gradient pre-emphasis based on the GS

Wei Zhang, Ping Zhang, Jian Dong, Yongkang Wang

The effectiveness of ad creatives is greatly influenced by their visual appearance. Advertising platforms can generate ad creatives with different appearances by combining creative elements provided by advertisers. However, with the increasing number of ad creative elements, it becomes challenging to select a suitable combination from the countless possibili

Kouji Nakamura

A gauge-invariant perturbation theory on a generic background spacetime is developing from 2003 and ``zero-mode problem'' for linear metric perturbations was proposed as the essential problem of this theory. In the perturbation theory on the Schwarzschild background spacetime, $l=0,1$ modes correspond to the above ``zero-mode'' and the gauge-

Bhaskar Bagchi, Basudeb Datta

In this paper we study the structure of cellular pseudomanifolds (aka abstract polytopes). These are natural combinatorial generalisations of polytopal spheres (i.e., boundary complexes of convex polytopes). This class is closed under natural notions of duality and product. We show that they are also closed under an operation of direct product. Any cellular

Alex Gabel, Victoria Klein, Riccardo Valperga, Jeroen S. W. Lamb

The problem of detecting and quantifying the presence of symmetries in datasets is useful for model selection, generative modeling, and data analysis, amongst others. While existing methods for hard-coding transformations in neural networks require prior knowledge of the symmetries of the task at hand, this work focuses on discovering and characterizing unkn

Franco Marchesoni-Acland, Gabriele Facciolo

This work proposes a strategy for training models while annotating data named Intelligent Annotation (IA). IA involves three modules: (1) assisted data annotation, (2) background model training, and (3) active selection of the next datapoints. Under this framework, we open-source the IAdet tool, which is specific for single-class object detection. Additional

Chun-Hui Wang

Let F be the usual real field. Let W be a symplectic vector space over F. It is known that there are two different Weil representations of a Meteplectic covering group $\widetilde{Sp}(W)$. By some twisted actions, we reorganize them into a representation of $\widetilde{Sp}^{\pm}(W)$, a covering group over a subgroup $Sp^{\pm}(W)$ of $GSp(W)$. Based on the wo

Franco Marchesoni-Acland, Jean-Michel Morel, Josselin Kherroubi, Gabriele Facciolo

Even though data annotation is extremely important for interpretability, research and development of artificial intelligence solutions, most research efforts such as active learning or few-shot learning focus on the sample efficiency problem. This paper studies the neglected complementary problem of getting annotated data given a predictor. For the simple bi

Paul Stoewer, Achim Schilling, Andreas Maier, Patrick Krauss

The human brain possesses the extraordinary capability to contextualize the information it receives from our environment. The entorhinal-hippocampal plays a critical role in this function, as it is deeply engaged in memory processing and constructing cognitive maps using place and grid cells. Comprehending and leveraging this ability could significantly augm

Sun-Goo Lee, Seong-Han Kim, Wook-Jae Lee

Bound states in the continuum (BICs) theoretically have the ability to confine electromagnetic waves in limited regions with infinite radiative quality ($Q$) factors. However, in practical experiments, resonances can only exhibit finite $Q$ factors due to unwanted scattering losses caused by fabrication imperfections. Recently, it has been shown that ultrahi

Tuomas Lappi, Heikki Mäntysaari, Hannu Paukkunen, Mirja Tevio

Measurements of Deep Inelastic Scattering (DIS) provide a powerful tool to probe the fundamental structure of protons and other nuclei. The DIS cross sections can be expressed in terms of structure functions which are conventionally expressed in terms of parton distribution functions (PDFs) that obey the DGLAP evolution equations. However, it is also possibl

Alejandro José Uría-Álvarez, Juan José Esteve-Paredes, Manuel Antonio García-Blázquez, Juan José Palacios

Here we describe an efficient numerical implementation of the Bethe-Salpeter equation to obtain the excitonic spectrum of semiconductors. This is done on the electronic structure calculated either at the simplest tight-binding level or through density funcional theory calculations based on local orbitals. We use a simplified model for the electron-electron i

Vu-Duc Ngo, Tuan-Cuong Vuong, Thien Van Luong, Hung Tran

Internet of things (IoT) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT devices are highly vulnerable to cyber-attacks, which may result in security breaches and data leakages. To effectively prevent these attacks, a variety of machine learning-based network

Muhammad Fahad, Jérôme Darmont

Business Intelligence constitutes a set of methodologies and tools aiming at querying, reporting, on-line analytic processing (OLAP), generating alerts, performing business analytics, etc. When in need to perform these tasks collectively by different collaborators, we need a Collaborative Business Intelligence (CBI) platform. CBI plays a significant role in

Max Cohen, Maurice Charbit, Sylvain Le Corff

As sequential neural architectures become deeper and more complex, uncertainty estimation is more and more challenging. Efforts in quantifying uncertainty often rely on specific training procedures, and bear additional computational costs due to the dimensionality of such models. In this paper, we propose to decompose a classification or regression task in t

Shuyi Wang, Guido Zuccon

Federated online learning to rank (FOLTR) aims to preserve user privacy by not sharing their searchable data and search interactions, while guaranteeing high search effectiveness, especially in contexts where individual users have scarce training data and interactions. For this, FOLTR trains learning to rank models in an online manner -- i.e. by exploiting u

Alex Barbier-Chebbah, Christian L. Vestergaard, Jean-Baptiste Masson

This paper addresses the exploration-exploitation dilemma inherent in decision-making, focusing on multi-armed bandit problems. The problems involve an agent deciding whether to exploit current knowledge for immediate gains or explore new avenues for potential long-term rewards. We here introduce a novel algorithm, approximate information maximization (AIM),

François Parreau

We show that any non-mixing automorphism of a standard probability space has a factor disjoint from all mixing automorphism.

Elia Cereda, Alessandro Giusti, Daniele Palossi

Palm-sized nano-drones are an appealing class of edge nodes, but their limited computational resources prevent running large deep-learning models onboard. Adopting an edge-fog computational paradigm, we can offload part of the computation to the fog; however, this poses security concerns if the fog node, or the communication link, can not be trusted. To tack

Sandor Szedmak, Riikka Huusari, Tat Hong Duong Le, Juho Rousu

In this paper we propose a novel variable selection method for two-view settings, or for vector-valued supervised learning problems. Our framework is able to handle extremely large scale selection tasks, where number of data samples could be even millions. In a nutshell, our method performs variable selection by iteratively selecting variables that are highl

Mingjie Lu, Yuanxian Huang, Ji Liu, Jinzhang Peng

Understanding driving scenarios is crucial to realizing autonomous driving. Previous works such as map learning and BEV lane detection neglect the connection relationship between lane instances, and traffic elements detection tasks usually neglect the relationship with lane lines. To address these issues, the task is presented which includes 4 sub-tasks, the

Nasrin Rahimi, A. Murat Tekalp

Perception-distortion trade-off is well-understood for single-image super-resolution. However, its extension to video super-resolution (VSR) is not straightforward, since popular perceptual measures only evaluate naturalness of spatial textures and do not take naturalness of flow (temporal coherence) into account. To this effect, we propose a new measure of

Auguste Tetenoire, J. I. Juaristi, M. Alducin

The role played by electronic and phononic excitations in the femtosecond laser induced desorption and oxidation of CO coadsorbed with O on Ru(0001) is investigated using ab initio molecular dynamics with electronic friction. To this aim, simulations that account for both kind of excitations and that only consider electronic excitations are performed. Result

Joshua Petrack, David Soloveichik, David Doty

The field of chemical computation attempts to model computational behavior that arises when molecules, typically nucleic acids, are mixed together. Thermodynamic binding networks (TBNs) is a highly abstracted model that focuses on which molecules are bound to each other in a "thermodynamically stable" sense. Stability is measured based only on how ma

Xinbai. Li, Jiaxuan. Luo, Zebo. Tang, Xin. Wu

Extensive studies have been conducted in the past few decades to investigate potential signatures of higher-order QED effects in high-energy electromagnetic scattering processes. In our previous work, we have identified evidence of higher-order corrections in the total cross-section for the Breit-Wheeler process in relativistic heavy-ion collisions. However,

Hussam Ghanem, Massinissa Atmani, Christophe Cruz

The use of knowledge graphs (KGs) enhances the accuracy and comprehensiveness of the responses provided by a conversational agent. While generating answers during conversations consists in generating text from these KGs, it is still regarded as a challenging task that has gained significant attention in recent years. In this document, we provide a review of

Mikhail Kostylev

We carried out numerical simulations of propagation of spin waves (magnons in quantum language) in a yttrium-iron garnet film. The numerical model is based on an original formalism. We demonstrated that a potential barrier for magnons, created by an Oersted field of a dc current flowing through a wire sitting on top of the film, is able to act as an electric

Cédric Picron, Tinne Tuytelaars

Many two-stage instance segmentation heads predict a coarse 28x28 mask per instance, which is insufficient to capture the fine-grained details of many objects. To address this issue, PointRend and RefineMask predict a 112x112 segmentation mask resulting in higher quality segmentations. Both methods however have limitations by either not having access to neig

Varsha Behrunani, Marta Zagorowska, Mathias Hudoba de Badyn, Francesco Ricca

Mitigating the energy use in buildings, together with satisfaction of comfort requirements are the main objectives of efficient building control systems. Augmenting building energy systems with batteries can improve the energy use of a building, while posing the challenge of considering battery degradation during control operation. We demonstrate the perform

Jian Guan, Minlie Huang

Despite the huge progress in myriad generation tasks, pretrained language models (LMs) such as GPT2 still tend to generate repetitive texts with maximization-based decoding algorithms for open-ended generation. We attribute their overestimation of token-level repetition probabilities to the learning bias: LMs capture simple repetitive patterns faster with th

Sumiyoshi Abe, Norikazu Suzuki, Dmitrii A. Tayurskii

The Omori-Utsu law shows the temporal power-law-like decrease of the frequency of earthquake aftershocks and, interestingly, is found in a variety of complex systems/phenomena exhibiting catastrophes. Now, it may be interpreted as a characteristic response of such systems to large events. Here, hierarchical dynamics with the fast and slow degrees of freedom

Pavel Exner, David Spitzkopf

We investigate the spectrum of a soft quantum waveguide in two dimensions of the generalized `bookcover' shape, that is, Schrödinger operator with the potential in the form of a ditch consisting of a finite curved part and straight asymptotes which are parallel or almost parallel pointing in the same direction. We show how the eigenvalues accumulate when

Kim Siang Khaw, Cheng Chen, Massimo Giovannozzi, Tianqi Hu

Permanent electric dipole moments (EDMs) are excellent probes of physics beyond the Standard Model, especially on new sources of CP violation. The muon EDM has recently attracted significant attention due to discrepancies in the magnetic anomaly of the muon, as well as potential violations of lepton-flavor universality in B-meson decays. At the Paul Scherrer

Yiyang Liao, Lei Zhang, Ziye Jia, Chao Dong

In recent years, there is an increasing demand for unmanned aerial vehicles (UAVs) to complete multiple applications. However, as unmanned equipments, UAVs lead to some security risks to general civil aviations. In order to strengthen the flight management of UAVs and guarantee the safety, UAVs can be equipped with automatic dependent surveillance-broadcast

Filip Cano Córdoba, Samuel Judson, Timos Antonopoulos, Katrine Bjørner

Principled accountability for autonomous decision-making in uncertain environments requires distinguishing intentional outcomes from negligent designs from actual accidents. We propose analyzing the behavior of autonomous agents through a quantitative measure of the evidence of intentional behavior. We model an uncertain environment as a Markov Decision Proc

Camilla Sammartino, Yair Shokef, Bat-El Pinchasik

Liquid diodes are surface structures that facilitate the flow of liquids in a specific direction. When these structures are within the capillary regime, they promote liquid transport without the need for external forces. In nature, they are used to increase water collection and uptake, reproduction, and feeding. While nature offers various one-dimensional ch

Azamat Yeldesbay, Silvia Daun

We present a software tool -- extended Dynamic Causal Modelling for Phase Coupling (eDCM PC) -- that is able to estimate effective connectivity between any kind of oscillating systems, e.g. distant brain regions, using the phase information obtained from experimental signals. With the help of a transformation function eDCM PC can measure observable independe

Sumiyoshi Abe, Norikazu Suzuki

The concept of memory is of central importance for characterizing complex systems and phenomena. Presence of long-term memories indicates how their dynamics can be less sensitive to initial conditions compared to the chaotic cases. On the other hand, it is empirically known that the Feller-Pareto distribution, which decays as the power law i.e. the scale-inv

Roberta Di Gennaro, Francesco Malaspina

We introduce and study a notion of Castelnuovo-Mumford regularity suitable for rational normal scroll surfaces. In this setting we prove analogs of some classical properties. We prove splitting criteria for coherent sheaves and a characterization of Ulrich bundles. Finally we study logarithmic bundles associated to arrangements of lines and rational curves.

Theodor Pribulla, Tamás Borkovits, Rahul Jayaraman, Saul Rappaport

We have found that the 2+2 quadruple star system BU CMi is currently the most compact quadruple system known, with an extremely short outer period of only 121 days. The previous record holder was TIC 219006972 (Kostov et al. 2023), with a period of 168 days. The quadruple nature of BU CMi was established by Volkov et al. (2021), but they misidentified the ou

Joonkyo Shim, Hyunsoo Yoon

Deepfakes, malicious visual contents created by generative models, pose an increasingly harmful threat to society. To proactively mitigate deepfake damages, recent studies have employed adversarial perturbation to disrupt deepfake model outputs. However, previous approaches primarily focus on generating distorted outputs based on only predetermined target at

Simin Ma, Junghwan Lee, Nicoleta Serban, Shihao Yang

Tailoring treatment for individual patients is crucial yet challenging in order to achieve optimal healthcare outcomes. Recent advances in reinforcement learning offer promising personalized treatment recommendations; however, they rely solely on current patient observations (vital signs, demographics) as the patient's state, which may not accurately rep

Alemdar Hasanov

This paper addresses the analysis of a boundary feedback system involving a non-homogeneous Euler-Bernoulli beam governed by the equation $m(x)u_{tt}+μ(x)u_{t}$$+\left(r(x)u_{xx}\right)_{xx}=0$, subject to the initial $u(x,0)=u_0(x)$, $u_t(x,0)=v_0(x)$ and boundary conditions $u(0,t)=0$, $\left (-r(x)u_{xx}(x,t)\right )_{x=0}=-k^{-}_r u_{x}(0,t)-k^{-}_a u_{x

Yanshi Sun, Wei Cao, Momiao Zhou, Zhiguo Ding

This paper considers a cognitive radio inspired uplink communication scenario, where one primary user is allocated with one dedicated resource block, while $M$ secondary users compete with each other to opportunistically access the primary user's channel. Two new designs of NOMA schemes, namely hybrid successive interference cancellation with power adapt

Constantinos Varsos, Giorgos Flouris, Marina Bitsaki

We consider misinformation games, i.e., multi-agent interactions where the players are misinformed with regards to the game that they play, essentially having an \emph{incorrect} understanding of the game setting, without being aware of their misinformation. In this paper, we introduce and study a new family of misinformation games, called Noisy games, where

Marko Đurasević, Mateja Đumić, Rebeka Čorić, Francisco Javier Gil-Gala

The container relocation problem is a combinatorial optimisation problem aimed at finding a sequence of container relocations to retrieve all containers in a predetermined order by minimising a given objective. Relocation rules (RRs), which consist of a priority function and relocation scheme, are heuristics commonly used for solving the mentioned problem du

Yanshi Sun, Zhiguo Ding

Recently, stochastic geometry has been applied to provide tractable performance analysis for low earth orbit (LEO) satellite networks. However, existing works mainly focus on analyzing the ``coverage probability'', which provides limited information. To provide more insights, this paper provides a more fine grained analysis on LEO satellite networks

Rafał Filipów, Adam Kwela, Jacek Tryba

We generalize the classical Olivier's theorem which says that for any convergent series $\sum_n a_n$ with positive nonincreasing real terms the sequence $(n a_n)$ tends to zero. Our results encompass many known generalizations of Olivier's theorem and give some new instances. The generalizations are done in two directions: we either drop the monotoni

M. A. Gali Labarias, O. A. Nieves, S. T. Keenan, E. E. Mitchell

Inductance plays a crucial role in the design and optimization of superconducting quantum interference devices (SQUIDs) for quantum sensing applications, since it dictates the sensitivity and coupling ratio with other circuit elements. In high-temperature superconductors the kinetic inductance, which depends on both geometry and temperature, becomes a domina

Aniket Vashishtha, Kabir Ahuja, Sunayana Sitaram

While understanding and removing gender biases in language models has been a long-standing problem in Natural Language Processing, prior research work has primarily been limited to English. In this work, we investigate some of the challenges with evaluating and mitigating biases in multilingual settings which stem from a lack of existing benchmarks and resou

Hsueh-I Lu

Since Jacobson [FOCS89] initiated the investigation of succinct graph encodings 35 years ago, there has been a long list of results on balancing the generality of the class, the speed, the succinctness of the encoding, and the query support. Let Cn denote the set consisting of the graphs in a class C that with at most n vertices. A class C is nontrivial if t

Roozbeh Farhoodi, Phil Wilkes, Anirudh M. Natarajan, Samantha Ing-Esteves

Since they became observable, neuron morphologies have been informally compared with biological trees but they are studied by distinct communities, neuroscientists, and ecologists. The apparent structural similarity suggests there may be common quantitative rules and constraints. However, there are also reasons to believe they should be different. For exampl

Adrian Miranda

Weak structures abound in higher category theory, but are often suitably equivalent to stricter structures that are easier to understand. We extend strictification for tricategories and trihomomorphisms to trinatural transformations, trimodifications and perturbations. Along the way we distinguish between the operational coherences, which are possible to str

Ahmed Zahari, Sania Asif

Due to the immense importance of BiHom Type algebras and cohomology of various algebraic structures, this paper is devoted to defining the BiHom-associative dialgebra, its derivation, generalized derivation, and quasi-derivation. We provided the complete classification of these derivations of $2-$ and $3$-dimensional BiHom-associative dialgebras. We further

Jérémie Brieussel, Tianyi Zheng

We determine Furstenberg entropy spectra of ergodic stationary actions of $SL(d,\mathbb{R})$ and its lattices. The constraints on entropy spectra are derived from a refinement of the Nevo-Zimmer projective factor theorem. The realisation part is achieved by means of building Poisson bundles over stationary random subgroups.

Nazmul Islam, Seokjoo Shin

Differentiating malware is important to determine their behaviors and level of threat; as well as to devise defensive strategy against them. In response, various anti-malware systems have been developed to distinguish between different malwares. However, most of the recent malware families are Artificial Intelligence (AI) enable and can deceive traditional a

Zhiqi Li, Zhiding Yu, David Austin, Mingsheng Fang

This technical report summarizes the winning solution for the 3D Occupancy Prediction Challenge, which is held in conjunction with the CVPR 2023 Workshop on End-to-End Autonomous Driving and CVPR 23 Workshop on Vision-Centric Autonomous Driving Workshop. Our proposed solution FB-OCC builds upon FB-BEV, a cutting-edge camera-based bird's-eye view percepti

Abeer Alasmari, Abdulrahman Al-Khalifah, Ahmed BaHammam, Hesham Alodah

Background: The epidemic of nonalcoholic fatty liver disease (NAFLD) and its metabolic effects present a serious public health concern. We hypothesized that the Ramadan fasting model (RFM), which involves fasting from dawn to dusk for a month, could provide potential therapeutic benefits and mitigate NAFLD. Accordingly, we aimed to validate this hypothesis u

Po-Hsueh Tseng, Kentaro Nishida, Pang-Han Wu, Yu-Lung Tang

Optical bistability is fundamental for all-optical switches, but typically requires high-Q cavities with micrometer sizes. Through boosting nonlinearity with photo-thermo-optical effects, we achieve bistability in a silicon Mie resonator with a volume size of 10-3 um3 and Q-factor < 10, both are record-low. Furthermore, bistable scattering naturally leads to

Ryan Faulkner, Luke Haub, Simon Ratcliffe, Ian Reid

LiDAR scanning for surveying applications acquire measurements over wide areas and long distances, which produces large-scale 3D point clouds with significant local density variations. While existing 3D semantic segmentation models conduct downsampling and upsampling to build robustness against varying point densities, they are less effective under the large

Junjie Wu, Dit-Yan Yeung

Despite their promising performance across various natural language processing (NLP) tasks, current NLP systems are vulnerable to textual adversarial attacks. To defend against these attacks, most existing methods apply adversarial training by incorporating adversarial examples. However, these methods have to rely on ground-truth labels to generate adversari

Jun Shi, Hongyu Kan, Shulan Ruan, Ziqi Zhu

Recently, deep learning methods have been widely used for tumor segmentation of multimodal medical images with promising results. However, most existing methods are limited by insufficient representational ability, specific modality number and high computational complexity. In this paper, we propose a hybrid densely connected network for tumor segmentation,

Minsu Ko, Sergey V. Uchaikin, Boris I. Ivanov, JinMyeong Kim

The axion search experiments in the microwave frequency range require high sensitive detectors with intrinsic noise close to quantum noise limit. Josephson parametric amplifiers (JPAs) are the most valuable candidates for the role of the first stage amplifier in the measurement circuit of the microwave frequency range, as they are well-known in superconducti

Cheng-Hau Yang, Kumar Saurabh, Guglielmo Scovazzi, Claudio Canuto

The accurate and efficient simulation of Partial Differential Equations (PDEs) in and around arbitrarily defined geometries is critical for many application domains. Immersed boundary methods (IBMs) alleviate the usually laborious and time-consuming process of creating body-fitted meshes around complex geometry models (described by CAD or other representatio

Sin-Ei Takahasi, Kiyoshi Shirayanagi, Makoto Tsukada

In this paper, we provide a complete classification of 2-dimensional endo-commutative straight algebras of type I over any field. An endo-commutative algebra is a non-associative algebra in which the square mapping preserves multiplication. Type I denotes a distinguishing characteristic of its structure matrix of rank 2. We list all multiplication tables of

Xiaotian Sun, Xinyu Wang, Wanqian Li, Lei Wang

Crossbar-based PIM DNN accelerators can provide massively parallel in-situ operations. A specifically designed compiler is important to achieve high performance for a wide variety of DNN workloads. However, some key compilation issues such as parallelism considerations, weight replication selection, and array mapping methods have not been solved. In this wor

Hiroki Kawai, Yohko Tsuboi, Wataru B. Iwakiri, Yoshitomo Maeda

We report on the results of our simultaneous observations of three large stellar flares with soft X-rays (SXRs) and an H$\mathrmα$ emission line from two binary systems of RS CVn type. The energies released in the X-ray and H$\mathrmα$ emissions during the flares were $10^{36}$--$10^{38}$ and $10^{35}$--$10^{37}$ erg, respectively. It renders the set of the

Chuanyu Pan, Guowei Yang, Taijiang Mu, Yu-Kun Lai

With the booming of virtual reality (VR) technology, there is a growing need for customized 3D avatars. However, traditional methods for 3D avatar modeling are either time-consuming or fail to retain similarity to the person being modeled. We present a novel framework to generate animatable 3D cartoon faces from a single portrait image. We first transfer an

Tatsuya Ishibashi, Kosuke Ono, Noriyuki Kugo, Yuji Sato

In this report, we describe the technical details of our approach for the Ego4D Long-Term Action Anticipation Challenge 2023. The aim of this task is to predict a sequence of future actions that will take place at an arbitrary time or later, given an input video. To accomplish this task, we introduce three improvements to the baseline model, which consists o

Jieliang Hong

For the range-R bond percolation in d=4,5,6, we obtain a lower bound for the critical probability p_c for R large, agreeing with the conjectured asymptotics and thus complementing the corresponding results of Van der Hofstad-Sakai (2005) for d>6, and Frei-Perkins (2016), Hong (2021) for d<4. The proof follows by showing the extinction of the associated SIR e

Helen Carson, Jason J. Ford, Michael Milford

While substantial progress has been made in the absolute performance of localization and Visual Place Recognition (VPR) techniques, it is becoming increasingly clear from translating these systems into applications that other capabilities like integrity and predictability are just as important, especially for safety- or operationally-critical autonomous syst

Yan Wang, Rong Wu

For a number $l\geq 2$, let ${\cal{G}}_l$ denote the family of graphs which have girth $2l+1$ and have no odd hole with length greater than $2l+1$. Wu, Xu and Xu conjectured that every graph in $\bigcup_{l\geq 2} {\cal{G}}_{l}$ is $3$-colorable. Chudnovsky et al., Wu et al., and Chen showed that every graph in ${\cal{G}}_2$, ${\cal{G}}_3$ and $\bigcup_{l\geq

Meng Lv, Jiangtao Wang, Kim Siang Khaw

Muons have established a unique and pivotal role in both fundamental physics and applied sciences. Given that a typical muon experiment spans roughly ten muon lifetimes, the optimal muon source should operate at around 50\,kHz in pulsed mode. However, existing muon facilities operate in either the 25-50\,Hz pulsed mode or continuous beam (DC) mode, which res

Brendan King, Jeffrey Flanigan

There has been significant interest in zero and few-shot learning for dialogue state tracking (DST) due to the high cost of collecting and annotating task-oriented dialogues. Recent work has demonstrated that in-context learning requires very little data and zero parameter updates, and even outperforms trained methods in the few-shot setting (Hu et al. 2022)

Ryota Tajima

Let $g \in S_{k}(Γ_{0}(N))$ be a normalized newform and $f$ be a harmonic Maass form that is good for $g$. The holomorphic part of $f$ is called a mock modular form and denoted by $f^{+}$. For odd prime $p$, K. Bringmann, P. Guerzhoy, and B. Kane obtained a $p$-adic modular form of level $pN$ from $f^{+}$ and a certain $p$-adic constant $α_{g}(f)$. When $g$

Ming Zhong, Siru Ouyang, Minhao Jiang, Vivian Hu

Structured chemical reaction information plays a vital role for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design. Despite the importance of extracting structured reactions from scientific literature, data annotation for this purpose is cost-prohibitive due to the significant labor required from domain experts. Con

Zizhuo Li, Jiayi Ma

Accurately matching local features between a pair of images is a challenging computer vision task. Previous studies typically use attention based graph neural networks (GNNs) with fully-connected graphs over keypoints within/across images for visual and geometric information reasoning. However, in the context of feature matching, considerable keypoints are n

Jonathan Pilault, Can Liu, Mohit Bansal, Markus Dreyer

Prompts have been shown to be an effective method to adapt a frozen Pretrained Language Model (PLM) to perform well on downstream tasks. Prompts can be represented by a human-engineered word sequence or by a learned continuous embedding. In this work, we investigate conditional and compositional differentiable prompting. We propose a new model, Prompt Produc

John Bistline, Geoffrey Blanford, Maxwell Brown, Dallas Burtraw

If goals set under the Paris Agreement are met, the world may hold warming well below 2 C; however, parties are not on track to deliver these commitments, increasing focus on policy implementation to close the gap between ambition and action. Recently, the US government passed its most prominent piece of climate legislation to date, the Inflation Reduction A

Chen Qiu, Yulin Wu, Weixin Huang, Botao Liu

Many recent practical and theoretical breakthroughs focus on adversarial team multi-player games (ATMGs) in ex ante correlation scenarios. In this setting, team members are allowed to coordinate their strategies only before the game starts. Although there existing algorithms for solving extensive-form ATMGs, the size of the game tree generated by the previou

Kyungkeun Kang, Chanhong Min

We study the Stokes system with the localized boundary data in the half-space. We are concerned with the local regularity of its solution near the boundary away from the support of the given boundary data which are product forms of each spatial variable and the temporal variable. We first show that if the boundary data are smooth in time, the corresponding s

Athul Pradeepkumar Girija

Aerocapture is a technique which uses atmospheric drag to decelerate a spacecraft and achieve nearly fuel-free orbit insertion from an interplanetary trajectory. The present study performs a historical review of the field, and a bibliometric data analysis of the literature from 1980 to 2023. The data offers insights into the evolution of the field, current s

Yili Zhang, Hanyan Huang, Mei Xiong, Zengquan Yao

High-dimensional complex multi-parameter problems are prevalent in engineering, exceeding the capabilities of traditional surrogate models designed for low/medium-dimensional problems. These models face the curse of dimensionality, resulting in decreased modeling accuracy as the design parameter space expands. Furthermore, the lack of a parameter decoupling

Rui Wang, Zhiming Zhou, Tao Zhang, Ling Wang

Branch-and-bound is a typical way to solve combinatorial optimization problems. This paper proposes a graph pointer network model for learning the variable selection policy in the branch-and-bound. We extract the graph features, global features and historical features to represent the solver state. The proposed model, which combines the graph neural network

Ivan Nevirkovets, Oleg Mukhanov

We report the fabrication and testing, at 4.2 K, of an SISFS device, where S, F, and I denote a superconductor (Nb), a ferromagnetic material (permalloy), and an insulator (AlOx), respectively. The F layer covers about one half of the top electrode of the SIS Josephson junction and is positioned off-center. Electric current, I _tr, along the S electrode can

Giuseppe Petrillo, Jiancang Zhuang

The existence of magnitude dependence in earthquake triggering has been reported. Such a correlation is linked to the issue of seismic predictability and remains under intense debate whether it is physical or is caused by incomplete data due to short-term aftershocks missing. Working firstly with a synthetic catalogue generated by a numerical model that capt

Yuta Fujishige, Yuki Tsujimaru, Shunsuke Inenaga, Hideo Bannai

The directed acyclic word graph (DAWG) of a string $y$ of length $n$ is the smallest (partial) DFA which recognizes all suffixes of $y$ with only $O(n)$ nodes and edges. In this paper, we show how to construct the DAWG for the input string $y$ from the suffix tree for $y$, in $O(n)$ time for integer alphabets of polynomial size in $n$. In so doing, we first

Zhiwei Chen

Gravitational waves emitted from stellar binary black hole (sBBH) mergers can be gravitationally lensed by intervening galaxies and detected by future ground-based detectors. A large amount of effort has been put into the estimation of the detection rate of lensed sBBH originating from the evolution of massive binary stars (EMBS channel). However, sBBHs prod

Zhen Zhu, Yijun Li, Weijie Lyu, Krishna Kumar Singh

We investigate how to generate multimodal image outputs, such as RGB, depth, and surface normals, with a single generative model. The challenge is to produce outputs that are realistic, and also consistent with each other. Our solution builds on the StyleGAN3 architecture, with a shared backbone and modality-specific branches in the last layers of the synthe

Tristan Deleu, Yoshua Bengio

While Markov chain Monte Carlo methods (MCMC) provide a general framework to sample from a probability distribution defined up to normalization, they often suffer from slow convergence to the target distribution when the latter is highly multi-modal. Recently, Generative Flow Networks (GFlowNets) have been proposed as an alternative framework to mitigate thi

Yunhui Guo, Youren Zhang, Yubei Chen, Stella X. Yu

Given an image set without any labels, our goal is to train a model that maps each image to a point in a feature space such that, not only proximity indicates visual similarity, but where it is located directly encodes how prototypical the image is according to the dataset. Our key insight is to perform unsupervised feature learning in hyperbolic instead of

Kuntal Kumar Pal, Michael Gamon, Nirupama Chandrasekaran, Silviu Cucerzan

In community question-answering platforms, tags play essential roles in effective information organization and retrieval, better question routing, faster response to questions, and assessment of topic popularity. Hence, automatic assistance for predicting and suggesting tags for posts is of high utility to users of such platforms. To develop better tag predi

Yoshihiko Abe, Tetsutaro Higaki, Junichiro Kawamura, Tatsuo Kobayashi

We construct a model in which the hierarchies of the quark and lepton masses and mixing are explained by the $Γ_6^\prime$ modular flavor symmetry. The hierarchies are realized by the Froggatt-Nielsen-like mechanism due to the residual $Z^T_6$ symmetry, approximately unbroken at $τ\sim i\infty.$ We argue that the $Γ_6^{(\prime)}$ symmetry is the minimal possi