October 2022 arXiv papers — page 61

Rajiv Krishnakumar, Mathias Soeken, Martin Roetteler, William J. Zeng

In this paper, we present Q# implementations for arbitrary single-variabled fixed-point arithmetic operations for a gate-based quantum computer based on lookup tables (LUTs). In general, this is an inefficent way of implementing a function since the number of inputs can be large or even infinite. However, if the input domain can be bounded and there can be s

A. Adhikari, A. Wierzbicka, A. Lysak, P. Sybilski

The development of CdMgO ternary alloy with a single cubic phase is challenging but meaningful work for technological advancement. In this work, we have grown a series of Cd1-xMgxO ternary random alloys with various Mg concentrations (x = 0, 30, 32, 45, and 55%) on quartz substrate by plasma-assisted molecular beam epitaxy (PA-MBE) technique. The structural

Václav Rozhoň, Bernhard Haeupler, Christoph Grunau

We give a simple, local process for nodes in an undirected graph to form non-adjacent clusters that (1) have at most a polylogarithmic diameter and (2) contain at least half of all vertices. Efficient deterministic distributed clustering algorithms for computing strong-diameter network decompositions and other key tools follow immediately. Overall, our proce

Patrick Jauernig, Domagoj Jakobovic, Stjepan Picek, Emmanuel Stapf

Fuzzing is an automated software testing technique broadly adopted by the industry. A popular variant is mutation-based fuzzing, which discovers a large number of bugs in practice. While the research community has studied mutation-based fuzzing for years now, the algorithms' interactions within the fuzzer are highly complex and can, together with the randomn

Qiyu Chen, Jean-Marc Schlenker

Let $N$ be a geodesically convex subset in a convex co-compact hyperbolic manifold $M$ with incompressible boundary. We assume that each boundary component of $N$ is either a boundary component of $\partial_\infty M$, or a smooth, locally convex surface in $M$. We show that $N$ is uniquely determined by the boundary data defined by the conformal structure on

Shang Gao, Ganesh Pokharel, Andrew F. May, Joseph A. M. Paddison

Competition among exchange interactions is able to induce novel spin correlations on a bipartite lattice without geometrical frustration. A prototype example is the spiral spin liquid, which is a correlated paramagnetic state characterized by sub-dimensional degenerate propagation vectors. Here, using spectral graph theory, we show that spiral spin liquids o

Tong Nie, Guoyang Qin, Yunpeng Wang, Jian Sun

Traffic speed is central to characterizing the fluidity of the road network. Many transportation applications rely on it, such as real-time navigation, dynamic route planning, and congestion management. Rapid advances in sensing and communication techniques make traffic speed detection easier than ever. However, due to sparse deployment of static sensors or

Chia-Man Hung, Shaohong Zhong, Walter Goodwin, Oiwi Parker Jones

We present a novel approach to path planning for robotic manipulators, in which paths are produced via iterative optimisation in the latent space of a generative model of robot poses. Constraints are incorporated through the use of constraint satisfaction classifiers operating on the same space. Optimisation leverages gradients through our learned models tha

Takehiro Ito, Yuni Iwamasa, Naonori Kakimura, Yusuke Kobayashi

In this paper, we consider a transformation of $k$ disjoint paths in a graph. For a graph and a pair of $k$ disjoint paths $\mathcal{P}$ and $\mathcal{Q}$ connecting the same set of terminal pairs, we aim to determine whether $\mathcal{P}$ can be transformed to $\mathcal{Q}$ by repeatedly replacing one path with another path so that the intermediates are als

Bin Wang, Chen Zhang, Yan Zhang, Yiming Chen

Dialogue summarization is abstractive in nature, making it suffer from factual errors. The factual correctness of summaries has the highest priority before practical applications. Many efforts have been made to improve faithfulness in text summarization. However, there is a lack of systematic study on dialogue summarization systems. In this work, we first pe

Margarida Mendes Lopes, Rita Pardini, Sofia Tirabassi

Kawamata has shown that the quasi-Albanese map of a quasi-projective variety with log-irregularity equal to the dimension and log-Kodaira dimension 0 is birational. In this note we show that under these hypotheses the quasi-Albanese map is proper in codimension 1 as conjectured by Iitaka.

Marcus Waurick, Hans Zwart

We characterise asymptotic stability of port-Hamiltonian systems by means of matrix conditions using well-known resolvent criteria from $C_0$-semigroup theory. The idea of proof is based on a recent characterisation of exponential stability established in [Trostorff, Waurick, arXiv:2201.10367], which was inspired by a structural observation concerning port-H

Martino Borello, Paolo Santonastaso, Ferdinando Zullo

In this paper we introduce left ideal low-rank parity-check codes by using group algebras and we finally use them to extend ROLLO-I KEM.

Kun Zhou, Yeyun Gong, Xiao Liu, Wayne Xin Zhao

Sampling proper negatives from a large document pool is vital to effectively train a dense retrieval model. However, existing negative sampling strategies suffer from the uninformative or false negative problem. In this work, we empirically show that according to the measured relevance scores, the negatives ranked around the positives are generally more info

Ran Wang

Consider the stochastic partial differential equation $$ \frac{\partial }{\partial t}u_t(\mathbf{x})= -(-\Delta)^{\frac{\alpha}{2}}u_t(\mathbf{x}) +b\left(u_t(\mathbf{x})\right)+\sigma\left(u_t(\mathbf{x})\right) \dot F(t, \mathbf{x}), \ \ \ t\ge0, \mathbf{x}\in \mathbb R^d, $$ where $-(-\Delta)^{\frac{\alpha}{2}}$ denotes the fractional Laplacian with the p

Farhad Rezazadeh, Sarang Kahvazadeh, Mohammadreza Mosahebfard

The quantum machine learning (QML) paradigms and their synergies with network slicing can be envisioned to be a disruptive technology on the cusp of entering to era of sixth-generation (6G), where the mobile communication systems are underpinned in the form of advanced tenancy-based digital use-cases to meet different service requirements. To overcome the ch

Yahav Alon, Michael Krivelevich

We prove that for every $\varepsilon > 0$ there is $c_0$ such that if $G\sim G(n,c/n)$, $c\ge c_0$, then with high probability $G$ can be covered by at most $(1+\varepsilon)\cdot \frac{1}{2}ce^{-c} \cdot n$ vertex disjoint paths, which is essentially tight. This is equivalent to showing that, with high probability, at most $(1+\varepsilon)\cdot \frac{1}{2}ce

Y. Tokiwa, P. Opletal, H. Sakai, K. Kubo

We investigate the effect of easy-axis metamagnetic crossover on superconductivity in UTe$_2$ along the $a$-axis through measurements of AC susceptibility, magnetization, and the magnetocaloric effect. In ultra-clean single crystals, we identify a field-induced phase transition within the superconducting state at 5.6 T, driven by metamagnetism. This transiti

Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu

Knowledge distillation is one of the primary methods of transferring knowledge from large to small models. However, it requires massive task-specific data, which may not be plausible in many real-world applications. Data augmentation methods such as representation interpolation, token replacement, or augmentation with models are applied to tackle this proble

Hung D. Nguyen, Anand U. Oza

We study the long time statistics of a walker in a hydrodynamic pilot-wave system, which is a stochastic Langevin dynamics with an external potential and memory kernel. While prior experiments and numerical simulations have indicated that the system may reach a statistically steady state, its long-time behavior has not been studied rigorously. For a broad cl

Yuki Arase, Satoru Uchida, Tomoyuki Kajiwara

Controllable text simplification is a crucial assistive technique for language learning and teaching. One of the primary factors hindering its advancement is the lack of a corpus annotated with sentence difficulty levels based on language ability descriptions. To address this problem, we created the CEFR-based Sentence Profile (CEFR-SP) corpus, containing 17

A. K. Bera, S. M. Yusuf, S. K. Saha, M. Kumar

Understanding exotic forms of magnetism in quantum spin systems is an emergent topic of modern condensed matter physics. Quantum dynamics can be described by particle-like carriers of information, known-as quasiparticles that appear from the collective behaviour of the underlying system. Spinon excitations, governing the excitations of quantum spin-systems,

K. A. Bronnikov, V. D. Ivashchuk, V. V. Khruschov

We consider the current results in search for and description of temporal variations of fundamental physical constants (FPCs) obtained under laboratory and astrophysical conditions. On the basis of fixed values of base constants, those FPCs have been chosen that can exhibit variations of greatest interest from the viewpoints of physics and metrology. An anal

S. Pfalzner

Observations show that individual protoplanetary disk lifetimes vary from \mbox{$<$ 1 Myr} to $\gg$ 20 Myr. The disk lifetime distribution is currently unknown. For the example of a Gaussian distribution of the disk lifetime, I suggest a simple method for deducing such a disk lifetimes distribution. The median disk lifetimes inferred with this method is also

E. M. Tursunov, M. Z. Saidov, M. M. Begijonov

The spatial structure of the lowest 0$_1^+$, 0$_2^+$, 2$_1^+$ and 2$_2^+$ states of the $^{12}$C nucleus is studied within the 3$\alpha$ model with the Buck, Friedrich, and Wheatley $\alpha \alpha$ potential with Pauli forbidden states in the $S$ and $D$ waves. The Pauli forbidden states in the three-body system are treated by the exact orthogonalization met

Nihar Sahoo, Himanshu Gupta, Pushpak Bhattacharyya

With the rise of online hate speech, automatic detection of Hate Speech, Offensive texts as a natural language processing task is getting popular. However, very little research has been done to detect unintended social bias from these toxic language datasets. This paper introduces a new dataset ToxicBias curated from the existing dataset of Kaggle competitio

Hong Xuan, Xi Chen

Visual-Semantic Embedding (VSE) is a prevalent approach in image-text retrieval by learning a joint embedding space between the image and language modalities where semantic similarities would be preserved. The triplet loss with hard-negative mining has become the de-facto objective for most VSE methods. Inspired by recent progress in deep metric learning (DM

Haoyan Wei, Dandan Lyu, Wei Hu, C. T. Wu

In modern engineering designs, advanced materials (e.g., fiber/particle-reinforced polymers, metallic alloys, laminar composites, etc.) are widely used, where microscale heterogeneities such as grains, inclusions, voids, micro-cracks, and interfaces significantly affect the macroscopic constitutive behaviors. Obviously, an accurate description of the multisc

H. Garzón, Adrián Rodríguez, G. C. de Elía

Hot Jupiters (HJs) are giant planets with orbital periods of the order of a few days with semimajor axis within $\sim$0.1 au. Several theories have been invoked in order to explain the origin of this type of planets, one of them being the high-eccentricity migration. This migration can occur through different high-eccentricity mechanisms. Our investigation f

Shengyuan Hou, Jushi Kai, Haotian Xue, Bingyu Zhu

Recent works have revealed that Transformers are implicitly learning the syntactic information in its lower layers from data, albeit is highly dependent on the quality and scale of the training data. However, learning syntactic information from data is not necessary if we can leverage an external syntactic parser, which provides better parsing quality with w

Ravindra S. Ghodake, Pankaj Doshi, Ashish V. Orpe

We have studied the gravity driven flow of spherical shaped, millimetric sized granular material coated with aspherical, micron-sized, near frictionless lubricant particles. Experiments were performed on an inclined plane using two different sized particles for varying concentrations of the lubricant. The particle volumetric flow rate exhibits a non-monotoni

Khalid N. Elmadani, Francois Meyer, Jan Buys

The paper describes the University of Cape Town's submission to the constrained track of the WMT22 Shared Task: Large-Scale Machine Translation Evaluation for African Languages. Our system is a single multilingual translation model that translates between English and 8 South / South East African Languages, as well as between specific pairs of the African lan

Sakil Ahamed, Debanjana Mitra

In this article, we study the control aspects of the one-dimensional compressible Navier-Stokes equations with Maxwell's law linearized around a constant steady state with zero velocity. We consider the linearized system with Dirichlet boundary conditions and with interior controls. We prove that the system is not null controllable at any time using localize

Yuki Akiyama, Minh Vu, Konstantinos Slavakis

This paper introduces a solution to the problem of selecting dynamically (online) the ``optimal'' p-norm to combat outliers in linear adaptive filtering without any knowledge on the probability density function of the outliers. The proposed online and data-driven framework is built on kernel-based reinforcement learning (KBRL). To this end, novel Bellman map

Margarida Pereira, Guillermo Currás-Lorenzo, Álvaro Navarrete, Akihiro Mizutani

The Bennett-Brassard 1984 (BB84) protocol is the most widely implemented quantum key distribution (QKD) scheme. However, despite enormous theoretical and experimental efforts in the past decades, the security of this protocol with imperfect sources has not yet been rigorously established. In this work, we address this shortcoming and prove the security of th

Jivnesh Sandhan, Rathin Singha, Narein Rao, Suvendu Samanta

Sanskrit Word Segmentation (SWS) is essential in making digitized texts available and in deploying downstream tasks. It is, however, non-trivial because of the sandhi phenomenon that modifies the characters at the word boundaries, and needs special treatment. Existing lexicon driven approaches for SWS make use of Sanskrit Heritage Reader, a lexicon-driven sh

Mridweeka Singh, Kuntal Misra, Devendra K. Sahu, Bhavya Ailawadhi

We present optical photometric and spectroscopic analysis of a Type Iax supernova (SN) 2020rea situated at the brighter luminosity end of Type Iax supernovae (SNe). The light curve decline rate of SN~2020rea is $\Delta$m$_{15}$(g) = 1.31$\pm$0.08 mag which is similar to SNe 2012Z and 2005hk. Modelling the pseudo bolometric light curve with a radiation diffus

Brett McInnes

It is well known that an asymptotically flat four-dimensional Kerr black hole has a smaller (specific) entropy than a Schwarzschild black hole of the same mass. We show here that the same is true if the temperature, rather than the mass, is held fixed; and we also show that an asymptotically AdS$_5$-Kerr black hole has a smaller specific entropy than an AdS$

Kazuto Nakashima, Yumi Iwashita, Ryo Kurazume

3D LiDAR sensors are indispensable for the robust vision of autonomous mobile robots. However, deploying LiDAR-based perception algorithms often fails due to a domain gap from the training environment, such as inconsistent angular resolution and missing properties. Existing studies have tackled the issue by learning inter-domain mapping, while the transferab

Hiroshi Nozaki, Masashi Shinohara, Sho Suda

A finite set of the Euclidean space is called an $s$-distance set provided the number of Euclidean distances in the set is $s$. Determining the largest possible $s$-distance set for the Euclidean space of a given dimension is challenging. This problem was solved only when dealing with small values of $s$ and dimensions. Lison\v{e}k (1997) achieved the classi

Akira Kusaba, Shugo Nitta, Kenji Shiraishi, Tetsuji Kuboyama

To develop a quantitative reaction simulator, data assimilation was performed using high-resolution time-of-flight mass spectrometry (TOF-MS) data applied to GaN metalorganic vapor phase epitaxy system. Incorporating ab initio knowledge into the optimization successfully reproduces not only the concentration of CH$_4$ (an impurity precursor) as an objective

Haonan Zhang, Chuanchuan Yang, Bin Dai

In this paper, the wireless hierarchical federated learning (HFL) is revisited by considering physical layer security (PLS). First, we establish a framework for this new problem. Then, we propose a practical finite blocklength (FBL) coding scheme for the wireless HFL in the presence of PLS, which is self-secure when the coding blocklength is lager than a cer

Takuya Midooka, Misaki Mizumoto, Ken Ebisawa

Contemporary radiation-magnetohydrodynamic simulation of the AGNs predicts presence of the hot and strong accretion disk wind, which gets unstable far from the central region and turns into gas clumps. These inner-wind and outer clumps may be actually observed as the ultrafast outflows (UFOs) and the clumpy absorbers, respectively. We may call this picture a

Junaid Akram, Awais Akram, Rutvij H. Jhaveri, Mamoun Alazab

We leverage blockchain technology for drone node authentication in internet of drone things (IoDT). During the authentication procedure, the credentials of drone nodes are examined to remove malicious nodes from the system. In IoDT, drones are responsible for gathering data and transmitting it to cluster heads (CHs) for further processing. The CH collects an

Ife Adebara, AbdelRahim Elmadany, Muhammad Abdul-Mageed, Alcides Alcoba Inciarte

Language identification (LID) is a crucial precursor for NLP, especially for mining web data. Problematically, most of the world's 7000+ languages today are not covered by LID technologies. We address this pressing issue for Africa by introducing AfroLID, a neural LID toolkit for $517$ African languages and varieties. AfroLID exploits a multi-domain web data

Eva Wisse, Pietro Tedeschi, Savio Sciancalepore, Roberto Di Pietro

The recent worldwide introduction of RemoteID (RID) regulations forces all Unmanned Aircrafts (UAs), a.k.a. drones, to broadcast in plaintext on the wireless channel their identity and real-time location, for accounting and monitoring purposes. Although improving drones' monitoring and situational awareness, the RID rule also generates significant privacy co

Douglas B. West, Xuding Zhu

A graph is $\ell$-reconstructible if it is determined by its multiset of induced subgraphs obtained by deleting $\ell$ vertices. We prove that strongly regular graphs with at least six vertices are $2$-reconstructible.

Kouichi Okunishi, Hiroshi Ueda, Tomotoshi Nishino

We propose the entanglement bipartitioning approach to design an optimal network structure of the tree-tensor-network (TTN) for quantum many-body systems. Given an exact ground-state wavefunction, we perform sequential bipartitioning of spin-cluster nodes so as to minimize the mutual information or the maximum loss of the entanglement entropy associated with

Teng Liu, Fa Zhao, Pengfei Lu, Qifeng Lao

A novel optical method for distinguishing chiral molecules is proposed and validated within a quantum simulator employing a trapped-ion qudit. This approach correlates the sign disparity of the dipole moment of chiral molecules with distinct cyclic evolution trajectories, yielding the unity population contrast induced by the different non-Abelian holonomies

Rodolfo Aguilar Aguilar, Oğuz Şavk

We call a non-trivial homology sphere a Kirby-Ramanujam sphere if it bounds both a homology plane and a Mazur or Po\'enaru manifold. In 1980, Kirby found the first example by proving that the boundary of the Ramanujam surface bounds a Mazur manifold and it has remained a single example since then. By tracing their initial step, we provide the first additiona

Ka Ho Wong, Ameya Patwardhan, Peter Abbamonte, Fahad Mahmood

We demonstrate that the photo-electron counting rate, $P^{(2)}$, measured in two electron coincidence spectroscopy (2$e$-ARPES) experiments, provides unprecedented insight into the nature of topological superconductivity. In particular, we show that the spin dependence of $P^{(2)}$ allows one to detect superconducting spin-triplet correlations that are induc

Jeahan Jung, Minseok Choi

We develop a novel deep learning method for uncertainty quantification in stochastic partial differential equations based on Bayesian neural network (BNN) and Hamiltonian Monte Carlo (HMC). A BNN efficiently learns the posterior distribution of the parameters in deep neural networks by performing Bayesian inference on the network parameters. The posterior di

Pengfei Li, Rong Shi, Peize Lin, Xinguo Ren

Plasmon excitations in graphene, silicene and germanene are studied using linear-response time dependent density functional theory within the random phase approximation (RPA). Here, we examine both the plasmon dispersion behavior and lifetime of extrinsic and intrinsic plasmons for these three materials. For extrinsic plasmons, we found that their properties

Xuanli He, Chen Chen, Lingjuan Lyu, Qiongkai Xu

The collection and availability of big data, combined with advances in pre-trained models (e.g. BERT), have revolutionized the predictive performance of natural language processing tasks. This allows corporations to provide machine learning as a service (MLaaS) by encapsulating fine-tuned BERT-based models as APIs. Due to significant commercial interest, the

Changfeng Gui, Yeyao Hu, Qinfeng Li

In this paper, we prove that for any bounded set of finite perimeter $\Omega \subset \mathbb{R}^n$, we can choose smooth sets $E_k \Subset \Omega$ such that $E_k \rightarrow \Omega$ in $L^1$ and \begin{align} \label{moregeneralapproximation} \limsup_{i \rightarrow \infty} P(E_i) \le P(\Omega)+C_1(n) \mathscr{H}^{n-1}(\partial \Omega \cap \Omega^1). \end{alig

Xu Zhang, Gaopei Pan, Bin-Bin Chen, Heqiu Li

We show that for magic-angle twisted bilayer graphene (TBG) away from charge neutrality, although quantum Monte Carlo (QMC) simulations suffer from the sign problem, the computational complexity is at most polynomial at certain integer fillings. For even integer fillings, this polynomial complexity survives even if an extra inter-valley attractive interactio

Sulakashna, Rupam Barman

Let $D_\lambda^d$ denote the family of monomial deformations of diagonal hypersurface over a finite field $\mathbb{F}_q$ given by \begin{align*} D_\lambda^d: X_1^d+X_2^d+\cdots+X_n^d=\lambda d X_1^{h_1}X_2^{h_2}\cdots X_n^{h_n}, \end{align*} where $d,n\geq2$, $h_i\geq1$, $\sum_{i=1}^n h_i=d$, and $\gcd(d,h_1,h_2,\ldots, h_n)=1$. The Dwork hypersurface is the

Shiwali Mohan, Matthew Klenk

Architectures that implement the Common Model of Cognition - Soar, ACT-R, and Sigma - have a prominent place in research on cognitive modeling as well as on designing complex intelligent agents. In this paper, we explore how computational models of analogical processing can be brought into these architectures to enable concept acquisition from examples obtai

Yupeng Hou, Wayne Xin Zhao, Yaliang Li, Ji-Rong Wen

To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years. Although GSL models are frequently deployed in privacy-sensitive scenarios, the user privacy protection of neural GSL models has not drawn much attention. To comprehensively understand the privacy protection

Josiah Ross, Luke Yoffe, Alon Albalak, William Yang Wang

Transfer learning is an exciting area of Natural Language Processing that has the potential to both improve model performance and increase data efficiency. This study explores the effects of varying quantities of target task training data on sequential transfer learning in the dialog domain. We hypothesize that a model can utilize the information learned fro

Surgan Jandial, Yash Khasbage, Arghya Pal, Vineeth N Balasubramanian

The inadvertent stealing of private/sensitive information using Knowledge Distillation (KD) has been getting significant attention recently and has guided subsequent defense efforts considering its critical nature. Recent work Nasty Teacher proposed to develop teachers which can not be distilled or imitated by models attacking it. However, the promise of con

I. Prasetyo, I. H. Belfaqih, A. B. Wahidin, A. Suroso

In this paper, we employ one variant of the Generalized Uncertainty Principle (GUP) model, i.e., the Kempf-Mangano-Mann (KMM) model, and discuss the impact of GUP on the EoS of nuclear and neutron star matter based on the Relativistic Mean Field (RMF) model. We input the result in the Serrano-Li\v{s}ka (SL) gravity theory to discuss the corresponding Neutron

Guowen Li, Lingyu Ren, Yangyang Fu, Zhiyao Yang

Modern Building Automation Systems (BASs), as the brain that enables the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as optimized automation via outsourced cloud analytics and increased building-grid integrations. However, increased connectivity and accessibility come

Abel Pacheco-Ortega, Walterio Mayol-Cuevas

We present AROS, a one-shot learning approach that uses an explicit representation of interactions between highly-articulated human poses and 3D scenes. The approach is one-shot as the method does not require re-training to add new affordance instances. Furthermore, only one or a small handful of examples of the target pose are needed to describe the interac

Zihao Wu, Luis C. Ho, Ming-Yang Zhuang

It is commonly accepted that radio-loud active galactic nuclei are hosted exclusively by giant elliptical galaxies. We analyze high-resolution optical Hubble Space Telescope images of a sample of radio galaxies with extended double-lobed structures associated with disk-like optical counterparts. After systematically evaluating the probability of chance align

Cheol Jun Cho, Peter Wu, Abdelrahman Mohamed, Gopala K. Anumanchipalli

Recent self-supervised learning (SSL) models have proven to learn rich representations of speech, which can readily be utilized by diverse downstream tasks. To understand such utilities, various analyses have been done for speech SSL models to reveal which and how information is encoded in the learned representations. Although the scope of previous analyses

Vardhan Dongre, Abhinav Thimma Reddy, Nikhitha Reddeddy

DeepFake Audio, unlike DeepFake images and videos, has been relatively less explored from detection perspective, and the solutions which exist for the synthetic speech classification either use complex networks or dont generalize to different varieties of synthetic speech obtained using different generative and optimization-based methods. Through this work,

Chiu-Chu Melissa Liu, Song Yu

We continue the mathematical development of the open/closed correspondence proposed by Mayr and Lerche-Mayr. Given an open geometry on a toric Calabi-Yau 3-orbifold $\mathcal{X}$ relative to a framed Aganagic-Vafa outer brane $(\mathcal{L},f)$, we construct a toric Calabi-Yau 4-orbifold $\widetilde{\mathcal{X}}$ and identify its genus-zero Gromov-Witten inva

Wangjie Jiang, Zhihao Ye, Zijing Ou, Ruihui Zhao

Chinese Spelling Correction (CSC) is gaining increasing attention due to its promise of automatically detecting and correcting spelling errors in Chinese texts. Despite its extensive use in many applications, like search engines and optical character recognition systems, little has been explored in medical scenarios in which complex and uncommon medical enti

Weiyu Guo, Zhaoshuo Li, Yongkui Yang, Zheng Wang

Stereo depth estimation is of great interest for computer vision research. However, existing methods struggles to generalize and predict reliably in hazardous regions, such as large uniform regions. To overcome these limitations, we propose Context Enhanced Path (CEP). CEP improves the generalization and robustness against common failure cases in existing so

Pedro Castro, Tae-Kyun Kim

Learning based 6D object pose estimation methods rely on computing large intermediate pose representations and/or iteratively refining an initial estimation with a slow render-compare pipeline. This paper introduces a novel method we call Cascaded Pose Refinement Transformers, or CRT-6D. We replace the commonly used dense intermediate representation with a s

Steven Charlton, Herbert Gangl, Li Lai, Ce Xu

In this paper, we shall prove two conjectures of Z.-W. Sun concerning Ap\'ery-like series. One of the series is alternating whereas the other one is not. Our main strategy is to convert the series (resp.~the alternating series) to log-sine-cosine (resp.~log-sinh-cosh) integrals. Then we express all these integrals in terms of single-valued Bloch-Wigner-Ramak

Zhijing Wan, Zhixiang Wang, CheukTing Chung, Zheng Wang

Large-scale datasets have played a crucial role in the advancement of computer vision. However, they often suffer from problems such as class imbalance, noisy labels, dataset bias, or high resource costs, which can inhibit model performance and reduce trustworthiness. With the advocacy of data-centric research, various data-centric solutions have been propos

Jun Jiang, Yunnan Li, Yunhe Sheng

A difference Lie group is a Lie group equipped with a difference operator, equivalently a crossed homomorphism with respect to the adjoint action. In this paper, first we introduce the notion of a representation of a difference Lie group, and establish the relation between representations of difference Lie groups and representations of difference Lie algebra

Lanrui Wang, Jiangnan Li, Zheng Lin, Fandong Meng

Empathy, which is widely used in psychological counselling, is a key trait of everyday human conversations. Equipped with commonsense knowledge, current approaches to empathetic response generation focus on capturing implicit emotion within dialogue context, where the emotions are treated as a static variable throughout the conversations. However, emotions c

Lilit Avetisyan, Chengxin Zhang, Sue Bai, Ehsan Moradi Pari

Micro-mobility is promising to contribute to sustainable cities in the future with its efficiency and low cost. To better design such a sustainable future, it is necessary to understand the trends and challenges. Thus, we examined people's opinions on micro-mobility in the US and the EU using Tweets. We used topic modeling based on advanced natural language

Shogo Masaki, Daichi Kashino, Shogo Ishikawa, Yen-Ting Lin

We propose a novel subhalo abundance matching (SHAM) model that uses the virial mass of the main progenitor of each (sub)halo $M_{\rm prog}$ as a proxy of the galaxy stellar mass $M_*$ at the time of observation. This $M_{\rm prog}$ model predicts the two-point correlation functions depending on the choice of the epoch $z_{\rm prog}$ at which $M_\mathrm{prog

D. M. Gokhfeld, S. V. Semenov, M. I. Petrov, I. V. Nemtsev

A misalignment of anisotropic crystallites causes small values of anisotropy and decreases the critical current density of textured polycrystalline superconductors. To relate the crystallite misalignment and out-plane anisotropy, the magnetic properties of the textured Bi2223 polycrystalline superconductor were investigated. A distribution of orientation ang

Sirui Li, Kok Wai Wong, Dengya Zhu, Chun Che Fung

Representation learning of knowledge graphs aims to embed entities and relations into low-dimensional vectors. Most existing works only consider the direct relations or paths between an entity pair. It is considered that such approaches disconnect the semantic connection of multi-relations between an entity pair, and we propose a convolutional and multi-rela

P. Klose, M. Laine, S. Procacci

We revisit the framework of axion-like inflation in view of the possibility that the coupling of the inflaton to a non-Abelian topological charge density could lead to the generation of a rapidly thermalizing heat bath. Both dispersive (mass) and absorptive (friction) effects are included. For phenomenologically viable parameters, the system remains in a wea

Damian Pavlyshyn, Iain M. Johnstone, Jacqueline L. Sztepanacz

It is now well documented that genetic covariance between functionally related traits leads to an uneven distribution of genetic variation across multivariate trait combinations, and possibly a large part of phenotype-space that is inaccessible to evolution. How the size of this nearly-null genetic space translates to the broader phenome level is unknown. Hi

Xingwei He, Yeyun Gong, A-Long Jin, Weizhen Qi

Commonsense generation aims to generate a realistic sentence describing a daily scene under the given concepts, which is very challenging, since it requires models to have relational reasoning and compositional generalization capabilities. Previous work focuses on retrieving prototype sentences for the provided concepts to assist generation. They first use a

Mayu Otani, Yale Song, Yang Wang

With the broad growth of video capturing devices and applications on the web, it is more demanding to provide desired video content for users efficiently. Video summarization facilitates quickly grasping video content by creating a compact summary of videos. Much effort has been devoted to automatic video summarization, and various problem settings and appro

Wenfang Yao, Kaiwen Meng, Minghua Li, Xiaoqi Yang

This paper is devoted to the study of a newly introduced tool, projectional coderivatives and the corresponding calculus rules in finite dimensions. We show that when the restricted set has some nice properties, more specifically, is a smooth manifold, the projectional coderivative can be refined as a fixed-point expression. We will also improve the generali

Wangchunshu Zhou, Canwen Xu, Julian McAuley

Intermediate-task transfer can benefit a wide range of NLP tasks with properly selected source datasets. However, it is computationally infeasible to experiment with all intermediate transfer combinations, making choosing a useful source task a challenging problem. In this paper, we anticipate that task-specific parameters updated in parameter-efficient tuni

Hari Mohan Pandey

This paper reviews various Evolutionary Approaches applied to the domain of Evolutionary Robotics with the intention of resolving difficult problems in the areas of robotic design and control. Evolutionary Robotics is a fast-growing field that has attracted substantial research attention in recent years. The paper thus collates recent findings along with som

Kaiyuan Chen, Alexander Thomas, Hanming Lu, William Mullen

We propose a federated Function-as-a-Service (FaaS) execution model that provides secure and stateful execution in both Cloud and Edge environments. The FaaS workers, called Paranoid Stateful Lambdas (PSLs), collaborate with one another to perform large parallel computations. We exploit cryptographically hardened and mobile bundles of data, called DataCapsul

Daniel Reijsbergen, Aung Maw, Zheng Yang, Tien Tuan Anh Dinh

Users today expect more security from services that handle their data. In addition to traditional data privacy and integrity requirements, they expect transparency, i.e., that the service's processing of the data is verifiable by users and trusted auditors. Our goal is to build a multi-user system that provides data privacy, integrity, and transparency for a

Minduli Wijayatunga, Roberto Armellin, Harry Holt, Laura Pirovano

Space debris have been becoming exceedingly dangerous over the years as the number of objects in orbit continues to rise. Active debris removal (ADR) missions have garnered significant attention as an effective way to mitigate this collision risk. This research focuses on developing a multi-ADR mission that utilizes controlled reentry and deorbiting. The mis

Jin-Hua Xie, Yan-Quan Feng, Young Soo Kwon

A Cayley digraph $\rm{Cay}(G,S)$ of a group $G$ with respect to a subset $S$ of $G$ is called a CI-digraph if for any Cayley digraph $\rm{Cay}(G,T)$ isomorphic to $\rm{Cay}(G,S)$, there is an $\alpha\in \rm{Aut}(G)$ such that $S^\alpha=T$. For a positive integer $m$, $G$ is said to have the $m$-DCI property if all Cayley digraphs of $G$ with out-valency $m$

Apiwit Kittiratpattana, Tom Reichert, Pengcheng Li, Ayut Limphirat

Isospin triggering allows to distinguish coalescence from thermal production of light clusters in heavy ion collisions. Triggering on $Y(\pi^-) - Y(\pi^+)$ allows to select very neutron or proton rich final states. The deuteron (cluster) production with coalescence ($d \propto n \cdot p$) leads then to an inverse parabolic dependence of the deuteron yield on

Arnav Kumar Jain, Shivakanth Sujit, Shruti Joshi, Vincent Michalski

Learning world models from their sensory inputs enables agents to plan for actions by imagining their future outcomes. World models have previously been shown to improve sample-efficiency in simulated environments with few objects, but have not yet been applied successfully to environments with many objects. In environments with many objects, often only a sm

Saeed Maleki, Adhiti Raman, Yang Cheng, John Crassidis

This work provides a theoretical analysis for optimally solving the pose estimation problem using total least squares for vector observations from landmark features, which is central to applications involving simultaneous localization and mapping. First, the optimization process is formulated with observation vectors extracted from point-cloud features. Then

Jian Su, Haiqin Wang, Zhongyu Yan, Xinpeng Xu

Hydra tissue fragments excised freshly from Hydra body bend spontaneously to some quasi-stable shape in several minutes. We propose that the spontaneous bending is driven mechanically by supracellular actomyosin bundles inherited from parent Hydra. An active-laminated-plate model is constructed, from which we predict that the fragment shape characterized by

Mert Kosan, Zexi Huang, Sourav Medya, Sayan Ranu

Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, there is an increasing need to explain GNN predictions since GNNs are black-box machine learning models. One way to address this is counterfactual reasoning where the objective is to c

Wenqi Zhang, Yongliang Shen, Yanna Ma, Xiaoxia Cheng

Math word problem solver requires both precise relation reasoning about quantities in the text and reliable generation for the diverse equation. Current sequence-to-tree or relation extraction methods regard this only from a fixed view, struggling to simultaneously handle complex semantics and diverse equations. However, human solving naturally involves two

Ran Tian, Ankur P. Parikh

We present Amos, a stochastic gradient-based optimizer designed for training deep neural networks. It can be viewed as an Adam optimizer with theoretically supported, adaptive learning-rate decay and weight decay. A key insight behind Amos is that it leverages model-specific information to determine the initial learning-rate and decaying schedules. When used

Deepan Muthirayan, Chinmay Maheshwari, Pramod P. Khargonekar, Shankar Sastry

We study the problem of online learning in two-sided non-stationary matching markets, where the objective is to converge to a stable match. In particular, we consider the setting where one side of the market, the arms, has fixed known set of preferences over the other side, the players. While this problem has been studied when the players have fixed but unkn

Yuecai Han, Xudong Zheng

We investigate the problem of pricing derivatives under a fractional stochastic volatility model. We obtain an approximate expression of the derivative price where the stochastic volatility can be composed of deterministic functions of time and fractional Ornstein-Uhlenbeck process. Numerical simulations are given to illustrate the feasibility and operabilit

Kaiyuan Chen, Jiachen Yuan, Nikhil Jha, Jeffrey Ichnowski

Fog Robotics renders networked robots with greater mobility, on-demand compute capabilities and better energy efficiency by offloading heavy robotics workloads to nearby Edge and distant Cloud data centers. However, as the de-facto standard for implementing fog robotics applications, Robot Operating System (ROS) and its successor ROS2 fail to provide fog rob