October 2022 arXiv papers — page 139

O. M. Sotnikov, E. A. Stepanov, M. I. Katsnelson, F. Mila

The development of the quantum skyrmion concept is aimed at expanding the scope of the fundamental research and practical applications for classical topologically-protected magnetic textures, and potentially paves the way for creating new quantum technologies. Undoubtedly, this calls for establishing a connection between a classical skyrmion and its quantum

Abhishek Ghose

We present convincing empirical evidence for an effective and general strategy for building accurate small models. Such models are attractive for interpretability and also find use in resource-constrained environments. The strategy is to learn the training distribution and sample accordingly from the provided training data. The distribution learning algorith

Wei-Chen Wang, Jonas Mueller

Mislabeled examples are a common issue in real-world data, particularly for tasks like token classification where many labels must be chosen on a fine-grained basis. Here we consider the task of finding sentences that contain label errors in token classification datasets. We study 11 different straightforward methods that score tokens/sentences based on the

Chenliang Zhou, Fangcheng Zhong, Cengiz Oztireli

Recently introduced Contrastive Language-Image Pre-Training (CLIP) bridges images and text by embedding them into a joint latent space. This opens the door to ample literature that aims to manipulate an input image by providing a textual explanation. However, due to the discrepancy between image and text embeddings in the joint space, using text embeddings a

Jitao Xu, Hongbo Li, Minghao Yin

The 0-1 Multidimensional Knapsack Problem (MKP) is a classical NP-hard combinatorial optimization problem with many engineering applications. In this paper, we propose a novel algorithm combining evolutionary computation with the exact algorithm to solve the 0-1 MKP. It maintains a set of solutions and utilizes the information from the population to extract

Yan Dolinsky

In this paper, we obtain a duality result for the exponential utility maximization problem where trading is subject to quadratic transaction costs and the investor is required to liquidate her position at the maturity date. As an application of the duality, we treat utility-based hedging in the Bachelier model. For European contingent claims with a quadratic

Yao Lu, Jide Zhang, Su Zheng, Zhen Li

In this paper, two approximate 3*3 multipliers are proposed and the synthesis results of the ASAP-7nm process library justify that they can reduce the area by 31.38% and 36.17%, and the power consumption by 36.73% and 35.66% compared with the exact multiplier, respectively. They can be aggregated with a 2*2 multiplier to produce an 8*8 multiplier with low er

Misaki Ohta

In a previous paper, I found that the Weyl group $W(F_4)$ and Barns-Wall Lattice $BW_{16}$ can be constructed using the rank $2$ tensor of the quaternion. In the present paper, I describe how I were able to construct an algebra, which is the subalgebra of the direct product of Hurwitz Quaternionic integers $\mathscr{H}^4$, isomorphic to the automorphism $\te

Haoming Jiang, Tianyu Cao, Zheng Li, Chen Luo

E-commerce query understanding is the process of inferring the shopping intent of customers by extracting semantic meaning from their search queries. The recent progress of pre-trained masked language models (MLM) in natural language processing is extremely attractive for developing effective query understanding models. Specifically, MLM learns contextual te

Yuzhi Yang, Zhaoyang Zhang, Zhaohui Yang

This paper investigates a communication-efficient split learning (SL) over multiple-input multiple-output (MIMO) communication system. In particular, we mathematically decompose the inter-layer connection of a neural network (NN) to a series of linear precoding and combining transformations using over-the-air computation (OAC), which synergistically form a l

Bora Jin, Amy H. Herring, David Dunson

In this paper we predict sea surface salinity (SSS) in the Arctic Ocean based on satellite measurements. SSS is a crucial indicator for ongoing changes in the Arctic Ocean and can offer important insights about climate change. We particularly focus on areas of water mistakenly flagged as ice by satellite algorithms. To remove bias in the retrieval of salinit

Min Li, Zhimin Wang, Caimei Liu, Peizhi Lu

This work presents the design and performance study of compact strips of plastic scintillator with WLS-fiber readout in a dimension of 0.1 * 0.02 * 2 m3, which evaluates as a candidate for cosmic-ray muon detector for JUNO-TAO. The strips coupling with 3-inch PMTs are measured and compared between the single-end and double-end readout options first, and the

Dawei Gao, Qinghua Guo, Guisheng Liao, Yonina C. Eldar

In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have be

Sat byul Seo, Jianzhong Su

A synapse acts on neural transmission through a chemical process called synapses fusion between pre-synaptic and post-synaptic terminals. Presynaptic terminals release neurotransmitters either in response to action potential or spontaneously independent of presynaptic activity. However, it is still unclear the mechanism of evoked and spontaneous neuro-transm

Zeal Shah, Simone Fobi, Gabriel Cadamuro, Jay Taneja

Governments and international organizations the world over are investing towards the goal of achieving universal energy access for improving socio-economic development. However, in developing settings, monitoring electrification efforts is typically inaccurate, infrequent, and expensive. In this work, we develop and present techniques for high-resolution mon

Savithramma R M, R Sumathi, Sudhira H S

Population and economic growth of urban areas have led to intensive use of private vehicles, thereby increasing traffic volume and congestion on roads. The traffic management in the city is a challenge for concerned authorities, and the signalized intersections are the primary interest of traffic management. Interpreting traffic patterns and current traffic

Yixiang Shan, Jielong Yang, Xing Liu, Yixing Gao

Graph neural networks (GNNs) have achieved great success in many scenarios with graph-structured data. However, in many real applications, there are three issues when applying GNNs: graphs are unknown, nodes have noisy features, and graphs contain noisy connections. Aiming at solving these problems, we propose a new graph neural network named as GL-GNN. Our

Sneihil Gopal, David Griffith, Richard A. Rouil, Chunmei Liu

5G New Radio (NR) promises to support diverse services such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC). This requires spectrum, most of which is occupied by 4G Long Term Evolution (LTE). Hence, network operators are expected to deploy 5G using the existing LTE infrastru

Dominic Coey, Kenneth Hung

We study the problem of selecting the best $m$ units from a set of $n$ as $m / n \to \alpha \in (0, 1)$, where noisy, heteroskedastic measurements of the units' true values are available and the decision-maker wishes to maximize the aggregate true value of the units selected. Given a parametric prior distribution, the empirical Bayes decision rule incurs $O_

Hongyang Chen, Kaisheng Ma

The deep learning (DL)-based methods of low-level tasks have many advantages over the traditional camera in terms of hardware prospects, error accumulation and imaging effects. Recently, the application of deep learning to replace the image signal processing (ISP) pipeline has appeared one after another; however, there is still a long way to go towards real

Cong Chen, Lang Tong

Wholesale market participation of storage with state-of-charge (SoC) dependent bids results in a non-convex cost in a multi-interval economic dispatch, which requires a mixed-integer linear program in the market clearing. We show that the economic dispatch can be convexified to the standard linear program when the SoC-dependent bid satisfies the equal decrem

Yan Peng

We study the existence of scalar fields outside neutral reflecting shells. We consider static massive scalar fields non-minimally coupled to the Gauss-Bonnet invariant. We analytically investigated properties of scalar fields through the scalar field equation. In the small scalar field mass regime, we derive a compact resonance formula for the allowed masses

Abdolmajid Erfani, Vanessa Frias-Martinez

In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefit

Hanqiu Chen, Yahya Alhinai, Yihan Jiang, Eunjee Na

Dynamic graph neural network (DGNN) is becoming increasingly popular because of its widespread use in capturing dynamic features in the real world. A variety of dynamic graph neural networks designed from algorithmic perspectives have succeeded in incorporating temporal information into graph processing. Despite the promising algorithmic performance, deployi

Jie Liu, Jingjing Wang, Peng Zhang, Chunmao Wang

Currently, many face forgery detection methods aggregate spatial and frequency features to enhance the generalization ability and gain promising performance under the cross-dataset scenario. However, these methods only leverage one level frequency information which limits their expressive ability. To overcome these limitations, we propose a multi-scale wavel

Joseph Kapusta

An overview is presented of the many open, interesting questions regarding the behavior of matter at large temperatures and chemical potentials.

Jin-Nan Wei, Zi-Ke Liu, Jun-Jie Wei, Bin-Bin Zhang

The observed spectral lags of gamma-ray bursts (GRBs) have been widely used to explore possible violations of Lorentz invariance. However, these studies were generally performed by concentrating on the rough time lag of a single highest-energy photon and ignoring the intrinsic time lag at the source. A new way to test nonbirefringent Lorentz-violating effect

Shosuke Sasaki

Light-absorbing materials are widely used, and their optical properties are an important factor. Snell's law does not hold in materials that partially absorb light. Hence, the optical path in refraction is calculated from Maxwell's law. We used it to obtain the deviation angle when a light passes through a prism made of light absorbing material. As a result,

Yinpeng Dong, Shouwei Ruan, Hang Su, Caixin Kang

Recent studies have demonstrated that visual recognition models lack robustness to distribution shift. However, current work mainly considers model robustness to 2D image transformations, leaving viewpoint changes in the 3D world less explored. In general, viewpoint changes are prevalent in various real-world applications (e.g., autonomous driving), making i

Ondrej Sykora, Phitchaya Mangpo Phothilimthana, Charith Mendis, Amir Yazdanbakhsh

Analytical hardware performance models yield swift estimation of desired hardware performance metrics. However, developing these analytical models for modern processors with sophisticated microarchitectures is an extremely laborious task and requires a firm understanding of target microarchitecture's internal structure. In this paper, we introduce GRANITE, a

Hiroshi Inazawa

In this paper, we present a neural network system related to about memory and recall that consists of one neuron group (the "cue ball") and a one-layer neural net (the "recall net"). This system realizes the bidirectional memorization learning between one cue neuron in the cue ball and the neurons in the recall net. It can memorize many patterns and recall t

Farhad Aghili

In this work, we present a hybrid simulator for space docking and robotic proximity operations methodology. This methodology also allows for the emulation of a target robot operating in a complex environment by using an actual robot. The emulation scheme aims to replicate the dynamic behavior of the target robot interacting with the environment, without deal

Souvik Dey

Given a commutative Noetherian ring $R$ with total ring of fractions $Q(R)$, and a finitely generated $R$-submodule $M$ of $Q(R)$, we prove an equality between trace ideal, and certain annihilator of Ext and Tor of $M$. As a consequence, we answer in one-dimensional local analytically unramified case, a question raised by the present author and R. Takahashi.

Can Cui, Shu Yang, Brian J Reich, David A Gill

Marine conservation preserves fish biodiversity, protects marine and coastal ecosystems, and supports climate resilience and adaptation. Despite the importance of establishing marine protected areas (MPAs), research on the effectiveness of MPAs with different conservation policies is limited due to the lack of quantitative MPA information. In this paper, lev

HongYu Shi, BoLin Li, Wei. E. I. Sha, ZhiHao Lan

Topological waveguides have been extensively studied for their robust transmission properties immune to defects and their application potentials for microwave and terahertz integrated circuits. In this work, by using grounded planar valley-Hall photonic topological insulators, a high-efficiency topological directional coupler fed directly by microstrip line

Yuehua Su, Hongyun Wu, Kun Cao, Chao Zhang

As charge carrier of the macroscopic superconductivity, the Cooper pair is a composite particle of two paired electrons, which has both center-of-mass and inner-pair degrees of freedom. In most cases, these two different degrees of freedom can be well described by the macroscopic Ginzburg-Landau theory and the microscopic Bardeen-Cooper-Schrieffer (BCS) theo

Mingjie Shao, Wing-Kin Ma, Junbin Liu, Zihao Huang

In this paper we study the expectation maximization (EM) technique for one-bit MIMO-OFDM detection (OMOD). Arising from the recent interest in massive MIMO with one-bit analog-to-digital converters, OMOD is a massive-scale problem. EM is an iterative method that can exploit the OFDM structure to process the problem in a per-iteration efficient fashion. In th

Cong Ma, Yaping Zhang, Mei Tu, Xu Han

End-to-end text image translation (TIT), which aims at translating the source language embedded in images to the target language, has attracted intensive attention in recent research. However, data sparsity limits the performance of end-to-end text image translation. Multi-task learning is a non-trivial way to alleviate this problem via exploring knowledge f

Wedad Alharbi, Salah Alshabhi, Daniel Freeman, Dorsa Ghoreishi

A frame $(x_j)_{j\in J}$ for a Hilbert space $H$ is said to do phase retrieval if for all distinct vectors $x,y\in H$ the magnitude of the frame coefficients $(|\langle x, x_j\rangle|)_{j\in J}$ and $(|\langle y, x_j\rangle|)_{j\in J}$ distinguish $x$ from $y$ (up to a unimodular scalar). We consider the weaker condition where the magnitude of the frame coef

Tao Zhong, Zhixiang Chi, Li Gu, Yang Wang

In this paper, we tackle the problem of domain shift. Most existing methods perform training on multiple source domains using a single model, and the same trained model is used on all unseen target domains. Such solutions are sub-optimal as each target domain exhibits its own specialty, which is not adapted. Furthermore, expecting single-model training to le

Weixiang Zhao, Yanyan Zhao, Xin Lu, Bing Qin

As a critical step to achieve human-like chatbots, empathetic response generation has attained increasing interests. Previous attempts are incomplete and not sufficient enough to elicit empathy because they only focus on the initial aspect of empathy to automatically mimic the feelings and thoughts of the user via other-awareness. However, they ignore to mai

Yi Shi, Jiang Wu, Shixuan Zhao, Gangyao Gao

Multi-scale detection plays an important role in object detection models. However, researchers usually feel blank on how to reasonably configure detection heads combining multi-scale features at different input resolutions. We find that there are different matching relationships between the object distribution and the detection head at different input resolu

Musaddiq Al Ali, Amjad Y. Sahib, Muazez Al Ali

Due to the heavy burden on medical institutes and computer-aided image diagnostics (CAD) have been gaining importance in diagnostic medicine to aid the medical staff to attain better service for the patients. Breast cancer is a fatal disease that can be treated successfully if it is detected early. Quantum neural network (QNN) has been introduced by many res

Chen Cui, Kai Jiang, Yun Liu, Shi Shu

Large sparse linear algebraic systems can be found in a variety of scientific and engineering fields, and many scientists strive to solve them in an efficient and robust manner. In this paper, we propose an interpretable neural solver, the Fourier Neural Solver (FNS), to address them. FNS is based on deep learning and Fast Fourier transform. Because the erro

Yu-Mei Wu, Zu-Cheng Chen, Qing-Guo Huang, Xingjiang Zhu

Composed of ultralight bosons, fuzzy dark matter provides an intriguing solution to challenges that the standard cold dark matter model encounters on sub-galactic scales. The ultralight dark matter with mass $m\sim10^{-23} \rm{eV}$ will induce a periodic oscillation in gravitational potentials with a frequency in the nanohertz band, leading to observable eff

Indu Panigrahi, Ryan Manzuk, Adam Maloof, Ruth Fong

Most computer vision research focuses on datasets containing thousands of images of commonplace objects. However, many high-impact datasets, such as those in medicine and the geosciences, contain fine-grain objects that require domain-expert knowledge to recognize and are time-consuming to collect and annotate. As a result, these datasets contain few labeled

Hong Wang, Shukun Wu

We improve the $L^{p}\rightarrow L^p$ restriction estimate in $\mathbb{R}^3$ to the range $p>3+3/14$, based on some Kakeya type incidence estimates and the refined decoupling theorem.

Scott Pratt, Dmytro Oliinychenko, Chris Plumberg

Baryon annihilations during the hadronic stage of heavy-ion collisions affects final-state baryon and antibaryon yields and final-state correlations of baryons and antibaryons. Understanding annihilation is important for addressing questions about the chemistry at the beginning of the hadronic stage, and for interpreting charge-balance correlations involving

E. J. Callaghan, B. L. Goldblum, J. A. Brown, T. A. Laplace

The proton light yield of liquid scintillators is an important property in the context of their use in large-scale neutrino experiments, with direct implications for neutrino-proton scattering measurements and the discrimination of fast neutrons from inverse beta-decay coincidence signals. This work presents the first measurement of the proton light yield of

Robert A. Lang, Aadithya Ganeshram, Artur F. Izmaylov

Accurately solving the electronic structure problem through the variational quantum eigensolver (VQE) is hindered by the available quantum resources of current and near-term devices. One approach to relieving the circuit depth requirements for VQE is to "pre-process" the electronic Hamiltonian by a similarity transformation incorporating some degree of elect

Md Mazharul Islam, Shamiul Alam, Md Shafayat Hossain, Ahmedullah Aziz

The precession of a ferromagnet leads to the injection of spin current and heat into an adjacent non-magnetic material. Besides, spin-orbit entanglement causes an additional charge current injection. Such a device has been recently proposed where a quantum-spin hall insulator (QSHI) in proximity to a ferromagnetic insulator (FI) and superconductor (SC) leads

Hodari-Sadiki Hubbard-James, D. Xavier Lesley, Todd J. Henry, Leonardo A. Paredes

As part of a comprehensive effort to characterize the nearest stars, the CHIRON echelle spectrograph on the CTIO/SMARTS 1.5m telescope is being used to acquire high resolution (R = 80000) spectra of K dwarfs within 50 parsecs. This paper provides spectral details about 35 K dwarfs from five benchmark sets with estimated ages spanning 20 Myr -- 5.7 Gyr. Four

Hailong Dao, Souvik Dey, Monalisa Dutta

A local Cohen--Macaulay ring is called Ulrich-split if any short exact sequence of Ulrich modules split. In this paper we initiate the study of Ulrich split rings. We prove several necessary or sufficient criteria for this property, linking it to syzygies of the residue field and cohomology annihilator. We characterize Ulrich split rings of small dimensions.

Alon Albalak, Akshat Shrivastava, Chinnadhurai Sankar, Adithya Sagar

Multi-task learning (MTL), instruction tuning, and prompting have recently been shown to improve the generalizability of large language models to new tasks. However, the benefits of such methods are less well-documented in smaller language models, with some studies finding contradictory results. In this work, we explore and isolate the effects of (i) model s

M. G. Dainotti, D. Levine, D. Warren, N. Fraija

Gamma-ray bursts (GRBs) are extremely high-energy events that can be observed at very high redshift. In addition to gamma rays, they can emit in X-ray, optical, and sometimes radio wavelengths. Here, following the approach in Srinivasaragavan et al. (2020); Dainotti et al. (2021b,c), and Dainotti et al (2022, submitted), we consider 82 GRBs from Dainotti et

Haoran Zhu, Maryam Majzoubi, Arihant Jain, Anna Choromanska

The goal of lifelong learning is to continuously learn from non-stationary distributions, where the non-stationarity is typically imposed by a sequence of distinct tasks. Prior works have mostly considered idealistic settings, where the identity of tasks is known at least at training. In this paper we focus on a fundamentally harder, so-called task-agnostic

Thomas Sinclair, Naveen Vivek

The goal of this short note is to point out three observations around the Grothendieck norm and semidefinite programming. The first is that the Grothendieck norm captures the difficulty of relating the off-diagonal entries of a real, symmetric matrix to a probabilistic correlation, the second is that there is an interesting ``Fourier''-type duality between t

Yan-Hong Bao, Dong-Xing Fu, Yu Ye, James J. Zhang

We study and classify the 2-unitary operads of Gelfand-Kirillov dimension three.

Shitao Liu, Antonio Pierrottet, Scott Scruggs

We consider the inverse hyperbolic problem of recovering all spatial dependent coefficients, which are the wave speed, the damping coefficient, potential coefficient and gradient coefficient, in a second-order hyperbolic equation defined on an open bounded domain with smooth enough boundary. We show that by appropriately selecting finite pairs of initial con

Aleksa Milojevic

In this paper, we investigate the connectivity of friends-and-strangers graphs, which were introduced by Defant and Kravitz in 2020. We begin by considering friends-and-strangers graphs arising from two random graphs and consider the threshold probability at which such graphs attain maximal connectivity. We slightly improve the lower bounds on the threshold

Jing Li, Andrew H. Comstock, Dali Sun, Xiaoshan Xu

We demonstrate a nonlinear Hall effect due to the boundary spin accumulation in Pt films grown on Al2O3 substrates. This Hall effect and the previously demonstrated Hanle magnetoresistance provide a complete picture of the spin-precession control of the spin and charge transport at the boundary of a spin-orbit coupled material, which we refer to as spin-Hall

Thomas Sinclair

We survey the model theory of operator systems and C$^*$-algebras.

Dharma KC, Venkata Ravi Kiran Dayana, Meng-Lin Wu, Venkateswara Rao Cherukuri

Transformers are a popular choice for classification tasks and as backbones for object detection tasks. However, their high latency brings challenges in their adaptation to lightweight object detection systems. We present an approximation of the self-attention layers used in the transformer architecture. This approximation reduces the latency of the classifi

D. H. Ryan, Sergey L. Bud'ko, Brinda Kuthanazhi, Paul C. Canfield

$^{151}$Eu M\"ossbauer spectroscopy shows that yttrium substitution in mixed-valent $\rm EuPd_3S_4$ drives the initial 50:50 mix of Eu$^{3+}$ and Eu$^{2+}$ towards pure Eu$^{2+}$, whereas lanthanum substitution has the opposite effect, but only for substitution levels above 50\%. We find that total valence electron count and chemical pressure effects cannot

Hua Li, Wenya Luo, Zhidong Bai, Huanchao Zhou

This paper proposes an improved linear discriminant analysis called spectrally-corrected and regularized LDA (SRLDA). This method integrates the design ideas of the sample spectrally-corrected covariance matrix and the regularized discriminant analysis. With the support of a large-dimensional random matrix analysis framework, it is proved that SRLDA has a li

Se Jung Kwon, Jeonghoon Kim, Jeongin Bae, Kang Min Yoo

There are growing interests in adapting large-scale language models using parameter-efficient fine-tuning methods. However, accelerating the model itself and achieving better inference efficiency through model compression has not been thoroughly explored yet. Model compression could provide the benefits of reducing memory footprints, enabling low-precision c

Tadahisa Funaki, Patrick van Meurs, Sunder Sethuraman, Kenkichi Tsunoda

We derive the hydrodynamic limit of Glauber-Kawasaki dynamics. The Kawasaki part is simple and describes independent movement of the particles with hard core exclusive interactions. It is speeded up in a diffusive space-time scaling. The Glauber part describes the birth and death of particles. It is set to favor two levels of particle density with a preferen

Robin K. S. Hankin

Objects in the {\tt stl map} class of {\tt C++} associate a value to each of a set of keys. Accessing values or keys of such an object is problematic in the R programming language because the value-key pairs are not stored in a well-defined order. This document motivates and discusses the concept of "disordered vector" as implemented by the {\tt disordR} pac

Lin Song, Pan Zhao, Neng Wan, Naira Hovakimyan

This paper presents a novel approach for achieving safe stochastic optimal control in networked multi-agent systems (MASs). The proposed method incorporates barrier states (BaSs) into the system dynamics to embed safety constraints. To accomplish this, the networked MAS is factorized into multiple subsystems, and each one is augmented with BaSs for the centr

Wibson W. G. Silva, José Holanda

We study analytically the Rashba-Edelstein magnetoresistance (REMR) in a structure made from an insulator ferromagnet, such as yttrium iron garnet (YIG), and a 2D material (2DM) with direct and inverse Rashba-Edelstein effects, such as SLG and MoS$_2$. Our results represent an efficient way of analyzing the Rashba-Edelstein effects.

Yuxuan Shu, Xiao Gu, Guang-Zhong Yang, Benny Lo

The success of most advanced facial expression recognition works relies heavily on large-scale annotated datasets. However, it poses great challenges in acquiring clean and consistent annotations for facial expression datasets. On the other hand, self-supervised contrastive learning has gained great popularity due to its simple yet effective instance discrim

Gianluca Brero, Alon Eden, Darshan Chakrabarti, Matthias Gerstgrasser

We introduce a reinforcement learning framework for economic design where the interaction between the environment designer and the participants is modeled as a Stackelberg game. In this game, the designer (leader) sets up the rules of the economic system, while the participants (followers) respond strategically. We integrate algorithms for determining follow

Zezhou Huang, Eugene Wu

Data analytics over normalized databases typically requires computing and materializing expensive joins (wide-tables). Factorized query execution models execution as message passing between relations in the join graph and pushes aggregations through joins to reduce intermediate result sizes. Although this accelerates query execution, it only optimizes a sing

Xin Li

We introduce \emph{Information Topology}: a framework that unifies information theory and algebraic topology by treating \emph{cycle closure} as the primitive operation of inference. The starting point is the \emph{dot-cycle dichotomy}, which separates pointwise, order-sensitive fluctuations (dots) from order-invariant, predictive structure (cycles). Algebra

Zhiyu Chen, Shiyang Li, Charese Smiley, Zhiqiang Ma

With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching. The community is experiencing the shift of the challenge from how to model language to the imitation of complex reasoning abilities like human beings. In this work, we investigate the applica

Eduardo D. Sontag, Debojyoti Biswas, Noah J. Cowan

For a general class of translationally invariant systems with a specific category of nonlinearity in the output, this paper presents necessary and sufficient conditions for global observability. Critically, this class of systems cannot be stabilized to an isolated equilibrium point by dynamic output feedback. These analyses may help explain the active sensin

Marcelo Hernández Caro, Steen Ryom-Hansen

We study the representation theory of the Soergel calculus algebra $ A_w := \mbox{End}_{{\mathcal D}_{(W,S)}} (\underline{w}) $ over $\mathbb C$ in type $\tilde{A}_1$. We generalize the recent isomorphism between the nil-blob algebra ${\mathbb{NB}}_n$ and $ A_w $ to deal with the two-parameter blob algebra. Under this generalization, the two parameters corre

Leandro R. F. Lima, Caio Lewenkopf

We study the suppression of the conductance quantization in quantum spin Hall systems by a combined effect of electronic interactions and edge disorder, that is ubiquitous in exfoliated and CVD grown 2D materials. We show that the interplay between the electronic localized states due to edge defects and electron-electron interactions gives rise to local magn

Sijie Yu, James S. Bullock, Alexander B. Gurvich, Zachary Hafen

We investigate the formation of Milky-Way-mass galaxies using FIRE-2 LCDM cosmological zoom-in simulations by studying the orbital evolution of stars formed in the main progenitor of the galaxy, from birth to the present day. We classify in situ stars as isotropic spheroid, thick-disc, and thin-disc according to their orbital circularities and show that thes

Alex Contarino, Christine Schubert Kabban, Chancellor Johnstone, Fairul Mohd-Zaid

Artificial neural networks (ANNs) are popular tools for accomplishing many machine learning tasks, including predicting continuous outcomes. However, the general lack of confidence measures provided with ANN predictions limit their applicability. Supplementing point predictions with prediction intervals (PIs) is common for other learning algorithms, but the

Yizhou Chen, Xiaoyun Gong, Xiang Ji

Since numbers in the computer are represented with a fixed number of bits, loss of accuracy during calculation is unavoidable. At high precision where more bits (e.g. 64) are allocated to each number, round-off errors are typically small. On the other hand, calculating at lower precision, such as half (16 bits), has the advantage of being much faster. This r

Hanshen Xiao, Jun Wan, Srinivas Devadas

Training large neural networks with meaningful/usable differential privacy security guarantees is a demanding challenge. In this paper, we tackle this problem by revisiting the two key operations in Differentially Private Stochastic Gradient Descent (DP-SGD): 1) iterative perturbation and 2) gradient clipping. We propose a generic optimization framework, cal

Qiaosi Wang, Ashok K. Goel

New developments are enabling AI systems to perceive, recognize, and respond with social cues based on inferences made from humans' explicit or implicit behavioral and verbal cues. These AI systems, equipped with an equivalent of human's Theory of Mind (ToM) capability, are currently serving as matchmakers on dating platforms, assisting student learning as t

Siddhartha Brahma, Polina Zablotskaia, David Mimno

Transformers allow attention between all pairs of tokens, but there is reason to believe that most of these connections - and their quadratic time and memory - may not be necessary. But which ones? We evaluate the impact of sparsification patterns with a series of ablation experiments. First, we compare masks based on syntax, lexical similarity, and token po

Ceren B. Dag, Simeon I. Mistakidis, Amos Chan, H. R. Sadeghpour

In quantum chaotic systems, the spectral form factor (SFF), defined as the Fourier transform of the two-level spectral correlation function, is known to follow random matrix theory (RMT), namely a 'ramp' followed by a 'plateau' in sufficiently late times. Recently, a generic early-time deviation from the RMT behavior, which we call the 'bump', was shown to e

Ivo Koch, Nina Pardal, Vinicius Fernandes dos Santos

For a fixed property (graph class) ${\Pi}$, given a graph G and an integer k, the ${\Pi}$-deletion problem consists in deciding if we can turn $G$ into a graph with the property ${\Pi}$ by deleting at most $k$ edges. The ${\Pi}$-deletion problem is known to be NP-hard for most of the well-studied graph classes, such as chordal, interval, bipartite, planar, c

Noam Malali, Yosi Keller

We present a deep learning approach for learning the joint semantic embeddings of images and captions in a Euclidean space, such that the semantic similarity is approximated by the L2 distances in the embedding space. For that, we introduce a metric learning scheme that utilizes multitask learning to learn the embedding of identical semantic concepts using a

Weijie Gan, Chunwei Ying, Parna Eshraghi, Tongyao Wang

Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep unfolding (DU) for image reconstruction. DEQ models-implicit neural networks with effectively infinite number of layers-were shown to achieve state-of-the-art image reconstruction without the memory complexity associated with DU. While the performance of DEQ has been widely investig

Mark Van der Merwe, Dmitry Berenson, Nima Fazeli

Many manipulation tasks require the robot to control the contact between a grasped compliant tool and the environment, e.g. scraping a frying pan with a spatula. However, modeling tool-environment interaction is difficult, especially when the tool is compliant, and the robot cannot be expected to have the full geometry and physical properties (e.g., mass, st

Alexander Kato, Andrei Nomerotski, Boris B. Blinov

We propose and demonstrate a new method for Doppler cooling trapped-ion crystals where the distribution of micromotion amplitudes may be large and uneven. The technique uses pulses of Doppler cooling light synchronized with the trap RF that selectively target ions when their velocity is near a node, leading to more uniform cooling across a crystal by a singl

Muhammad Asaduzzaman, Simon Catterall, Yannick Meurice, Ryo Sakai

We show how to apply renormalization group algorithms incorporating entanglement filtering methods and a loop optimization to a tensor network which includes Grassmann variables which represent fermions in an underlying lattice field theory. As a numerical test a variety of quantities are calculated for two dimensional Wilson--Majorana fermions and for the t

A. V. Afanasjev, S. Teeti, A. Taninah

The analysis of the present understanding of collective rotation in very neutron-rich nuclei is presented. It is shown that collective rotation can lead to the increase of stability of rotational states with increasing spin. The detailed investigation of rotational excitations in very proton-rich nuclei confirms this conclusion and indicate that experimental

Charity Woodrum, Christina C. Williams, Marcia Rieke, Joel Leja

We explore how the presence of detectable molecular gas depends on the inferred star formation histories (SFHs) in 8 massive, quiescent galaxies at $\mathrm{z\sim0.7}$. Half of the sample have clear detections of molecular gas, traced by CO(2-1). We find that the molecular gas content is unrelated to the rate of star formation decline prior to the most recen

Jeremiah Blocki, Elena Grigorescu, Tamalika Mukherjee, Samson Zhou

We develop a framework for efficiently transforming certain approximation algorithms into differentially-private variants, in a black-box manner. Specifically, our results focus on algorithms A that output an approximation to a function f of the form $(1-a)f(x)-k \leq A(x) \leq (1+a)f(x)+k$, where $k \in \mathbb{R}_{\geq 0}$ denotes additive error and $a \in

Patrick Jentsch, Chiu Fan Lee

Active matter is not only relevant to living matter and diverse nonequilibrium systems, but also constitutes a fertile ground for novel physics. Indeed, dynamic renormalization group (DRG) analyses have uncovered many new universality classes (UCs) in polar active fluids (PAFs) - an archetype of active matter systems. However, due to the inherent technical d

Seyed Mojtaba Marvasti-Zadeh, Devin Goodsman, Nilanjan Ray, Nadir Erbilgin

This paper provides a comprehensive review of past and current advances in the early detection of bark beetle-induced tree mortality from three primary perspectives: bark beetle & host interactions, RS, and ML/DL. In contrast to prior efforts, this review encompasses all RS systems and emphasizes ML/DL methods to investigate their strengths and weaknesses. W

Osman Asif Malik, Vivek Bharadwaj, Riley Murray

We show how to develop sampling-based alternating least squares (ALS) algorithms for decomposition of tensors into any tensor network (TN) format. Provided the TN format satisfies certain mild assumptions, resulting algorithms will have input sublinear per-iteration cost. Unlike most previous works on sampling-based ALS methods for tensor decomposition, the

Pavlo Maksyutenko, Rafael Martin-Domenech, Elettra Piacentino, Karin I. Oberg

Benzonitrile (c-C6H5CN) has been recently detected in cold and dense regions of the interstellar medium (ISM), where it has been used as a signpost of a rich aromatic organic chemistry that might lead to the production of polycyclic aromatic hydrocarbons (PAHs). One possible origin of this benzonitrile is interstellar ice chemistry involving benzene (c-C6H6)

Jwala Dhamala, Varun Kumar, Rahul Gupta, Kai-Wei Chang

Several prior works have shown that language models (LMs) can generate text containing harmful social biases and stereotypes. While decoding algorithms play a central role in determining properties of LM generated text, their impact on the fairness of the generations has not been studied. We present a systematic analysis of the impact of decoding algorithms

Maria Attarian, Advaya Gupta, Ziyi Zhou, Wei Yu

Cognitive planning is the structural decomposition of complex tasks into a sequence of future behaviors. In the computational setting, performing cognitive planning entails grounding plans and concepts in one or more modalities in order to leverage them for low level control. Since real-world tasks are often described in natural language, we devise a cogniti