May 2023 arXiv papers — page 173

Rohan Saha

With the advent of e-commerce platforms, reviews are crucial for customers to assess the credibility of a product. The star ratings do not always match the review text written by the customer. For example, a three star rating (out of five) may be incongruous with the review text, which may be more suitable for a five star review. A clustering approach can be

Yue Guan

Although ankle injuries resulting from postural instability are frequently observed during high-speed and intense physical activities, most current research has been limited to static or quasi-static models of the lower limb, or has focused solely on the ankle joint itself. In this study, to explain the kinetic mechanism underlying postural instability and a

Fabio Pavanello, Cedric Marchand, Ian O'Connor, Regis Orobtchouk

This special session paper introduces the Horizon Europe NEUROPULS project, which targets the development of secure and energy-efficient RISC-V interfaced neuromorphic accelerators using augmented silicon photonics technology. Our approach aims to develop an augmented silicon photonics platform, an FPGA-powered RISC-V-connected computing platform, and a comp

Nabajeet Barman, Yuriy Reznik, Maria G. Martini

In modern-era video streaming systems, videos are streamed and displayed on a wide range of devices. Such devices vary from large-screen UHD and HDTVs to medium-screen Desktop PCs and Laptops to smaller-screen devices such as mobile phones and tablets. It is well known that a video is perceived differently when displayed on different devices. The viewing exp

Xiuyi Qin, So Hirata

A unified theory is presented for finite-temperature many-body perturbation expansions of the anharmonic vibrational contributions to thermodynamic functions: the free energy, internal energy, and entropy. The theory is diagrammatically size-consistent at any order, as ensured by the linked-diagram theorem proved here, and thus applicable to molecular gases

David H. Brookes, Jakub Otwinowski, Sam Sinai

Fitness functions map large combinatorial spaces of biological sequences to properties of interest. Inferring these multimodal functions from experimental data is a central task in modern protein engineering. Global epistasis models are an effective and physically-grounded class of models for estimating fitness functions from observed data. These models assu

Tobias Holck Colding, William P. Minicozzi

We will show that if a gradient shrinking Ricci soliton has an approximate symmetry on one scale, this symmetry propagates to larger scales. This is an example of the shrinker principle which roughly states that information radiates outwards for shrinking solitons.

Xuan Leng, Jiaming Mao, Yutao Sun

We introduce a generic class of dynamic nonlinear heterogeneous parameter models that incorporate individual and time fixed effects in both the intercept and slope. These models are subject to the incidental parameter problem, in that the limiting distribution of the point estimator is not centered at zero, and that test statistics do not follow their standa

Bartosz Bednarczyk, Daumantas Kojelis, Ian Pratt-Hartmann

We define the adjacent fragment AF of first-order logic, obtained by restricting the sequences of variables occurring as arguments in atomic formulas. The adjacent fragment generalizes (after a routine renaming) two-variable logic as well as the fluted fragment. We show that the adjacent fragment has the finite model property, and that its satisfiability pro

Arthur Amalvy, Vincent Labatut, Richard Dufour

Pre-trained transformer-based models have recently shown great performance when applied to Named Entity Recognition (NER). As the complexity of their self-attention mechanism prevents them from processing long documents at once, these models are usually applied in a sequential fashion. Such an approach unfortunately only incorporates local context and preven

Henrique Bursztyn, Thiago Drummond, Clarice Netto

We introduce Courant 1-derivations, which describe a compatibility between Courant algebroids and linear (1,1)-tensor fields and lead to the notion of Courant-Nijenhuis algebroids. We provide examples of Courant 1-derivations on exact Courant algebroids and show that holomorphic Courant algebroids can be viewed as special types of Courant-Nijenhuis algebroid

Kaixin Ma, Hao Cheng, Yu Zhang, Xiaodong Liu

The retrieval model is an indispensable component for real-world knowledge-intensive tasks, e.g., open-domain question answering (ODQA). As separate retrieval skills are annotated for different datasets, recent work focuses on customized methods, limiting the model transferability and scalability. In this work, we propose a modular retriever where individual

Noah Patton, Kia Rahmani, Meghana Missula, Joydeep Biswas

The goal of programmatic Learning from Demonstration (LfD) is to learn a policy in a programming language that can be used to control a robot's behavior from a set of user demonstrations. This paper presents a new programmatic LfD algorithm that targets long-horizon robot tasks which require synthesizing programs with complex control flow structures, includi

Noureddine Mhadhbi, Sameh Gana, Hamad Khalid Alharbi

In this paper, we present new techniques for solving a large variety of partial differential equations. The proposed method reduces the PDEs to first order differential equations known as classical equations such as Bernoulli, Ricatti and Abel equations. The main idea is based on implementing new techniques by combining variations of parameters with characte

Param Ahir, Hiteishi M. Diwanji

Visual question answering (VQA) usesimage processing algorithms to process the image and natural language processing methods to understand and answer the question. VQA is helpful to a visually impaired person, can be used for the security surveillance system and online chatbots that learn from the web. It uses NLP methods to learn the semantic of the questio

R. M. L. Nascimento, Claudio J. DaSilva, L. S. Ferreira, A. A. Caparica

In this work, we study and evaluate the impact of a periodic spin-lattice coupling in an Ising-like system on a 2D triangular lattice. Our proposed simple Hamiltonian considers this additional interaction as an effect of preferential phonon propagation direction augmented by the symmetry ofthe underline lattice. The simplified analytical description of this

Elizaveta Savchenko, Ariel Rosenfeld, Svetlana Bunimovich-Mendrazitsky

Timely pre- and post-diagnosis check-ups are critical for cancer patients, across all cancer types, as these often lead to better outcomes. Several socio-demographic properties have been identified as strongly connected with both cancer's clinical dynamics and (indirectly) with different individual check-up behaviors. Unfortunately, existing check-up policie

Yifeng Shi, Marc Niethammer

Multimodal learning has mainly focused on learning large models on, and fusing feature representations from, different modalities for better performances on downstream tasks. In this work, we take a detour from this trend and study the intrinsic nature of multimodal data by asking the following questions: 1) Can we learn more structured latent representation

Milivoje M. Kostic

This comprehensive treatise is written for the special occasion of the author's 70th birthday. It presents his lifelong endeavors and reflections with original reasoning and re-interpretations of the most critical and misleading issues in thermodynamics; since now, we have the advantage to look at the historical developments more comprehensively and objectiv

Yuhang Yao, Syed A. Jafar

The $\Sigma$-QMAC problem is introduced, involving $S$ servers, $K$ classical ($\mathbb{F}_d$) data streams, and $T$ independent quantum systems. Data stream ${\sf W}_k, k\in[K]$ is replicated at a subset of servers $\mathcal{W}(k)\subset[S]$, and quantum system $\mathcal{Q}_t, t\in[T]$ is distributed among a subset of servers $\mathcal{E}(t)\subset[S]$ such

Sorin G. Gal

In this paper we obtain several extension properties for monotone and sublinear operators. The results obtained generalize those known for positive and linear operators.

Paul Großkopf, Joost Vercruysse

We show that under mild conditions on the monoidal base category $\mathcal V$, the category ${\sf VHopf}$ of Hopf $\mathcal V$-categories is locally presentable and deduce the existence of free and cofree Hopf categories. We also provide an explicit description of the free and cofree Hopf categories over a semi-Hopf category. One of the conditions on the bas

Chungjae Lee, Kevin Dalmeijer, Pascal Van Hentenryck, Peibo Zhang

Autonomous trucks are expected to fundamentally transform the freight transportation industry. In particular, Autonomous Transfer Hub Networks (ATHNs), which combine autonomous trucks on middle miles with human-driven trucks on the first and last miles, are seen as the most likely deployment pathway for this technology. This paper presents a framework to opt

Sunia Tanweer, Firas A. Khasawneh, Elizabeth Munch, Joshua R. Tempelman

Changes in the parameters of dynamical systems can cause the state of the system to shift between different qualitative regimes. These shifts, known as bifurcations, are critical to study as they can indicate when the system is about to undergo harmful changes in its behavior. In stochastic dynamical systems, there is particular interest in P-type (phenomeno

Pablo G. Tello, Sauro Succi, Donato Bini, Stuart Kauffman

Based on a previous ansatz by Zel'dovich for the gravitational energy of virtual particle-antiparticle pairs, supplemented with the Holographic Principle, we estimate the vacuum energy in a fairly reasonable agreement with the experimental values of the Cosmological Constant. We further highlight a connection between Wheeler's quantum foam and graviton conde

Bingsheng Yao, Prithviraj Sen, Lucian Popa, James Hendler

Human-annotated labels and explanations are critical for training explainable NLP models. However, unlike human-annotated labels whose quality is easier to calibrate (e.g., with a majority vote), human-crafted free-form explanations can be quite subjective. Before blindly using them as ground truth to train ML models, a vital question needs to be asked: How

Boling Yang, Liyuan Zheng, Lillian J. Ratliff, Byron Boots

Autocurricular training is an important sub-area of multi-agent reinforcement learning~(MARL) that allows multiple agents to learn emergent skills in an unsupervised co-evolving scheme. The robotics community has experimented autocurricular training with physically grounded problems, such as robust control and interactive manipulation tasks. However, the asy

Dariusz Kosz

The aim of this note is twofold. Firstly, we prove an abstract version of the Calder\'on transference principle for inequalities of admissible type in the general commutative multilinear and multiparameter setting. Such an operation does not increase the constants in the transferred inequalities. Secondly, we use the last information to study a certain dicho

Christopher P. J. O'Connor, Sebastian Wieczorek, Andreas Amann

We introduce a formalism to efficiently calculate lasing modes and optical power flow in multi-section lasers with open boundaries. The formalism is underpinned by a projection of the complex-valued electric field and its spatial derivative onto a suitably extended complex $\mathcal{Z}$-plane, to reduce the order of the problem and simplify analysis. In a si

Yulian Vysochanskii, Vitalii Liubachko, Konstantin Glukhov, Ruslan Yevych

Using DFT-based molecular dynamics simulation of Cu$^+$ cations flipping and In$^{3+}$ cations displacive dynamics, we clarify the dipole ordering of CuInP$_2$S$_6$ ferrielectrics through the second order Jahn-Teller effect which determines the double-well potential for copper cations as well as three-well potential for indium cations inside the structural l

Mitchell T. Dennis, Jeremy Sakstein

We present a novel method for incorporating the effects of stellar modeling uncertainties into constraints on the axion-electron coupling constant found using the observed calibration of the tip of the red giant branch (TRGB) I band magnitude $M_I$.~We simulate grids of models with varying initial stellar mass, helium abundance, metallicity, and axion-electr

Lechao Cheng, Zerun Liu, Jingxuan He, Chaowei Fang

Weakly supervised semantic segmentation (WSSS) has recently attracted considerable attention because it requires fewer annotations than fully supervised approaches, making it especially promising for large-scale image segmentation tasks. Although many vision transformer-based methods leverage self-attention affinity matrices to refine Class Activation Maps (

Zhixin Pan, Prabhat Mishra

Machine learning (ML) is successful in achieving human-level artificial intelligence in various fields. However, it lacks the ability to explain an outcome due to its black-box nature. While recent efforts on explainable AI (XAI) has received significant attention, most of the existing solutions are not applicable in real-time systems since they map interpre

Jinyang Li, Binyuan Hui, Ge Qu, Jiaxi Yang

Text-to-SQL parsing, which aims at converting natural language instructions into executable SQLs, has gained increasing attention in recent years. In particular, Codex and ChatGPT have shown impressive results in this task. However, most of the prevalent benchmarks, i.e., Spider, and WikiSQL, focus on database schema with few rows of database contents leavin

Jakob Bæk Tejs Houen, Mikkel Thorup

The \emph{Sparse Johnson-Lindenstrauss Transform} of Kane and Nelson (SODA 2012) provides a linear dimensionality-reducing map $A \in \mathbb{R}^{m \times u}$ in $\ell_2$ that preserves distances up to distortion of $1 + \varepsilon$ with probability $1 - \delta$, where $m = O(\varepsilon^{-2} \log 1/\delta)$ and each column of $A$ has $O(\varepsilon m)$ non

Chao Duan, Rui Wang

Protein aggregation via liquid-liquid phase separation (LLPS) is ubiquitous in nature and intimately connects to many human diseases. Although it is widely known that the addition of salt has crucial impacts on the LLPS of protein, full understanding of the salt effect remains an outstanding challenge. Here, we develop a molecular theory which systematically

Ludger O. Suarez-Burgoa

The saltbox-roof parametric probability distribution is a special case of the triangular distribution, where only one side is truncated. Here it is presented as a single and independent distribution, where the explicit equations are defined for its probability density--, the cumulative distribution--, and the inverse of the cumulative distribution (quantile-

Delia Garijo, Andrew Goodall, Lluís Vena

We extend the notion of graph homomorphism to cellularly embedded graphs (maps) by designing operations on vertices and edges that respect the surface topology; we thus obtain the first definition of map homomorphism that preserves both the combinatorial structure (as a graph homomorphism) and the topological structure of the surface (in particular, orientab

Leo van Iersel, Mark Jones, Esther Julien, Yukihiro Murakami

Phylogenetic networks are used to represent the evolutionary history of species. Recently, the new class of orchard networks was introduced, which were later shown to be interpretable as trees with additional horizontal arcs. This makes the network class ideal for capturing evolutionary histories that involve horizontal gene transfers. Here, we study the min

Muhammed Korkmaz, T. Metin Sezgin

Instance segmentation is a form of image detection which has a range of applications, such as object refinement, medical image analysis, and image/video editing, all of which demand a high degree of accuracy. However, this precision is often beyond the reach of what even state-of-the-art, fully automated instance segmentation algorithms can deliver. The perf

Thea H. Faridani, Smadar Naoz, Gongjie Li, Nicholas Inzunza

Short and ultra-short planets are a peculiar type of exoplanets with periods as short as a few days or less. Although it is challenging to detect them, already several are observed with many additional candidates. If these planets have formation pathways to their longer period counterparts, they are predicted to reside in multi-planet systems. Thus, gravitat

Friedrich Menhorn, Gianluca Geraci, D. Thomas Seidl, Youssef M. Marzouk

Optimization is a key tool for scientific and engineering applications, however, in the presence of models affected by uncertainty, the optimization formulation needs to be extended to consider statistics of the quantity of interest. Optimization under uncertainty (OUU) deals with this endeavor and requires uncertainty quantification analyses at several desi

Akitaka Ariga, Reuven Balkin, Iftah Galon, Enrique Kajomovitz

Only two types of Standard Model particles are able to propagate the $480\,$meters separating the ATLAS interaction point and FASER: neutrinos and muons. Furthermore, muons are copiously produced in proton collisions. We propose to use FASER$\nu$ as a muon fixed target experiment in order to search for new bosonic degrees of freedom coupled predominantly to

Yun Tang, Anna Y. Sun, Hirofumi Inaguma, Xinyue Chen

Transducer and Attention based Encoder-Decoder (AED) are two widely used frameworks for speech-to-text tasks. They are designed for different purposes and each has its own benefits and drawbacks for speech-to-text tasks. In order to leverage strengths of both modeling methods, we propose a solution by combining Transducer and Attention based Encoder-Decoder

Daniel Lundstrom, Meisam Razaviyayn

Deep learning has revolutionized many areas of machine learning, from computer vision to natural language processing, but these high-performance models are generally "black box." Explaining such models would improve transparency and trust in AI-powered decision making and is necessary for understanding other practical needs such as robustness and fairness. A

Michael A Kouritzin, Stephen Styles, Beatrice-Helen Vritsiou

Training a neural network (NN) typically relies on some type of curve-following method, such as gradient descent (GD) (and stochastic gradient descent (SGD)), ADADELTA, ADAM or limited memory algorithms. Convergence for these algorithms usually relies on having access to a large quantity of observations in order to achieve a high level of accuracy and, with

Nicholas Konz, Haoyu Dong, Maciej A. Mazurowski

Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a difficult task due to natural tumor rarity, breast tissue variability, and high resolution. Given the scarcity of abnormal images and the abundance of normal images for this problem, an anomaly detection/localization approach could be well-suited. However, most anomaly localization research

Desik Rengarajan, Nitin Ragothaman, Dileep Kalathil, Srinivas Shakkottai

We consider the problem of federated offline reinforcement learning (RL), a scenario under which distributed learning agents must collaboratively learn a high-quality control policy only using small pre-collected datasets generated according to different unknown behavior policies. Na\"{i}vely combining a standard offline RL approach with a standard federated

Bastián Espinoza

An idea that became unavoidable to study zero entropy symbolic dynamics is that the dynamical properties of a system induce in it a combinatorial structure. An old problem addressing this intuition is finding a structure theorem for linear-growth complexity subshifts using the S-adic formalism. It is known as the S-adic conjecture and motivated several influ

Leonid Bunimovich, Roberta Shapiro

We prove that the billiard claimed to be a possible counterexample to the Birkhoff-Poritsky conjecture is actually not a counterexample. We also show that for a billiard in a table obtained by the string construction over any convex polygon, this polygon is a core of the billiard dynamics.

Víctor Hernández-Santamaría, Alberto Peña-García

The shadow limit is a versatile tool used to study the reduction of reaction-diffusion systems into simpler PDE-ODE models by letting one of the diffusion coefficients tend to infinity. This reduction has been used to understand different qualitative properties and their interplay between the original model and its reduced version. The aim of this work is to

Shuvankar Gupta, Sudip Chakraborty, Santanu Pakhira, Anis Biswas

The realization of spin-glass (S-G) state in Heusler alloys is very rare despite the presence of inherent structural and elemental disorder in those compounds. Although a few half and full Heusler alloys are known to exhibit S-G state, there is hardly any manifestation of the same in cases of quaternary Heusler compounds. Here we report the observation of S-

Michael V. Arnold, Peter Sheridan Dodds, Christopher M. Danforth

Well curated, large-scale corpora of social media posts containing broad public opinion offer an alternative data source to complement traditional surveys. While surveys are effective at collecting representative samples and are capable of achieving high accuracy, they can be both expensive to run and lag public opinion by days or weeks. Both of these drawba

Brendan Hertel, S. Reza Ahmadzadeh

Several methods exist for teaching robots, with one of the most prominent being Learning from Demonstration (LfD). Many LfD representations can be formulated as constrained optimization problems. We propose a novel convex formulation of the LfD problem represented as elastic maps, which models reproductions as a series of connected springs. Relying on the pr

Anant Khandelwal

Existing Question Answering (QA) systems limited by the capability of answering questions from unseen domain or any out-of-domain distributions making them less reliable for deployment to real scenarios. Most importantly all the existing QA domain adaptation methods are either based on generating synthetic data or pseudo labeling the target domain data. The

Ramit Dey, Luís Felipe Longo Micchi, Suvodip Mukherjee, Niayesh Afshordi

The astrophysical stochastic gravitational wave background (SGWB) originates from numerous faint sub-threshold gravitational wave (GW) signals arising from the coalescing binary compact objects. This background is expected to be discovered from the current (or next-generation) network of GW detectors by cross-correlating the signal between multiple pairs of

Breandan Considine, Nicholas Albion, Xujie Si

This paper presents Idiolect, an open source (https://github.com/OpenASR/idiolect) IDE plugin for voice coding and a novel approach to building bots that allows for users to define custom commands on-the-fly. Unlike traditional chatbots, Idiolect does not pretend to be an omniscient virtual assistant but rather a reconfigurable voice programming system that

Xuan Long Do, Bowei Zou, Shafiq Joty, Anh Tai Tran

Conversational Question Generation (CQG) is a critical task for machines to assist humans in fulfilling their information needs through conversations. The task is generally cast into two different settings: answer-aware and answer-unaware. While the former facilitates the models by exposing the expected answer, the latter is more realistic and receiving grow

Shuvankar Gupta, Sudip Chakraborty, Vidha Bhasin, Santanu Pakhira

The pursuit of efficient spin-polarization in quaternary Heusler alloys with the general formula $XX'YZ$ (where X, $X'$, and Y are transition metals and Z is a p-block element), has been a subject of significant scientific interest. While previous studies shows that isoelectronic substitution of 4d element in place of 3d element in quaternary Heusler alloy,

Peijun Li, Yuliang Wang

We propose a scheme for imaging periodic surfaces using a superlens. By employing an inverse scattering model and the transformed field expansion method, we derive an approximate reconstruction formula for the surface profile, assuming small amplitude. This formula suggests that unlimited resolution can be achieved for the linearized inverse problem with per

Jeremy Sakstein, Ippocratis D. Saltas

It has been hypothesized that dark matter is comprised of ultra-light bosons whose collective phenomena can be described as a scalar field undergoing coherent oscillations. Examples include axion and fuzzy dark matter models. In this ultra-light dark matter scenario, the harmonic variation in the field's energy-momentum tensor sources an oscillating componen

E. Karagoz, N. Alan, S. Bilir, S. Ak

We perform extensive spectroscopy of the supernova remnant N63A in the Large Magellanic Cloud, using $\sim 43$ ks {\it Chandra} archival data. By analysing the spectra of the entire remnant, we determine the abundance distributions for O, Ne, Mg, Si, and Fe. We detect evidence of enhanced O and possibly Ne and Mg in some of the central regions which might in

Vitaly V. Chaban

The concentration of carbon dioxide (CO2) in the Earth atmosphere is linked to the acute problem of global warming. For the first time, we herein introduce trialkylsulfonium aprotic ionic liquids (ILs) as a group of seemingly highly capacitive CO2 scavengers. We advocate the viability of the new sorbents by the reaction profiles recorded by means of hybrid d

I. Bartalucci, S. Molendi, E. Rasia, G. W. Pratt

We investigate the statistical properties and the origin of the scatter within the spatially resolved surface brightness profiles of the CHEX-MATE sample, formed by 118 galaxy clusters selected via the SZ effect. These objects have been drawn from the Planck SZ catalogue and cover a wide range of masses, M$_{500}=[2-15] \times 10^{14} $M$_{\odot}$, and redsh

Andreas Crivellin, Matthew Kirk, Anil Thapa

The $SU(2)_L$ triplet scalar with hypercharge $Y=0$ predicts a positive definite shift in the $W$ mass, w.r.t.~the Standard Model prediction, if it acquires a vacuum expectation value. As this new field cannot couple directly to SM fermions (on its own), it has no significant impact on other low-energy precision observables and is weakly constrained by colli

Tiina Liimets, Michaela Kraus, Lydia Cidale, Sergey Karpov

Z Canis Majoris is a fascinating early-type binary with a Herbig Be primary and a FU Orionis-type secondary. Both of the stars exhibit sub-arcsecond jet-like ejecta. In addition, the primary is associated with the extended jet as well as with the large-scale outflow. In this study, we investigate further the nature of the large-scale outflow, which has not b

Alekzander Kosakowski, Warren R. Brown, Mukremin Kilic, Thomas Kupfer

We present the results from our ongoing spectroscopic survey targeting low mass white dwarf binaries, focusing on the southern sky. We used a Gaia DR2 and eDR3 based selection and identified 28 new binaries, including 19 new extremely low mass white dwarfs, one short period, likely eclipsing, DABZ, and two potential LISA binaries. We present orbital and atmo

Bianca Neureiter, Jens Thomas, Antti Rantala, Thorsten Naab

With its cored surface brightness profile, the elliptical galaxy NGC 5419 appears as a typical high-mass early-type galaxy (ETG). However, the galaxy hosts two distinct nuclei in its center. We use high-signal MUSE (Multi-Unit Spectroscopic Explorer) spectral observations and novel triaxial dynamical orbit models to reveal a surprisingly isotropic central or

Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen

We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles. The network captures independent factors of variation in the density profiles within a low-dimensional representation, which we physically interpret using mutual information. Without any prior knowledge of the halos' evolution, the network

Gino Isidori, Zachary Polonsky, Arianna Tinari

We present an updated Standard Model (SM) estimate of the inclusive $b\to s\bar{\ell}\ell$ rate at high dilepton invariant mass ($q^2\geq 15~{\rm GeV}^2$). We show that this estimate is in good agreement with the result obtained summing the SM predictions for the leading one-body modes ($K$ and $K^*$) and the subleading non-resonant $K\pi$ channel (for which

Uri Zvi, Denis R. Candido, Adam Weiss, Aidan R. Jones

Diamond nanocrystals can harbor spin qubit sensors capable of probing the physical properties of biological systems with nanoscale spatial resolution. These diamond nanosensors can readily be delivered into intact cells and even living organisms. However, applications beyond current proof-of-principle experiments require a substantial increase in sensitivity

David Prelogović, Andrei Mesinger

Observations of the cosmic 21-cm power spectrum (PS) are starting to enable precision Bayesian inference of galaxy properties and physical cosmology, during the first billion years of our Universe. Here we investigate the impact of common approximations about the likelihood used in such inferences, including: (i) assuming a Gaussian functional form; (ii) est

Javier Román, R. Michael Rich, Niusha Ahvazi, Laura Sales

The study of dynamically cold stellar streams reveals information about the gravitational potential where they reside and provides important constraints on dark matter properties. However, their intrinsic faintness makes detection beyond Local environments highly challenging. Here we report the detection of an extremely faint stellar stream ($\mu_{g,max}=$ 2

Chengshu Li, Victor L. Quito, Dirk Schuricht, Pedro L. S. Lopes

We investigate the physical properties of $G_2$-symmetric integrable chains with local degrees of freedom in the fundamental representation; given the typical connection between integrability and critical points, we test the model's properties against a hypothesis of conformal-invariant long-distance behavior. Leveraging an embedding between the $G_2$ except

Griffin Hosseinzadeh, David J. Sand, Sumit K. Sarbadhicary, Stuart D. Ryder

We present very early photometric and spectroscopic observations of the Type Ia supernova (SN Ia) 2023bee, starting about 8 hr after the explosion, which reveal a strong excess in the optical and nearest UV (U and UVW1) bands during the first several days of explosion. This data set allows us to probe the nature of the binary companion of the exploding white

Utkarsh Giri, Moritz Münchmeyer, Kendrick M. Smith

We implement a novel formalism to constrain primordial non-Gaussianity of the local type from the large-scale modulation of the small-scale power spectrum. Our approach combines information about primordial non-Gaussianity contained in the squeezed bispectrum and the collapsed trispectrum of large-scale structure together in a computationally amenable and co

Martin Hentschinski, Dmitri E. Kharzeev, Krzysztof Kutak, Zhoudunming Tu

It has been proposed that at small Bjorken $x$, or equivalently at high energy, hadrons represent maximally entangled states of quarks and gluons. This conjecture is in accord with experimental data from the electron-proton collider HERA at the smallest accessible $x$. In this Letter, we propose to study the onset of the maximal entanglement inside the proto

George Stoica, Daniel Bolya, Jakob Bjorner, Pratik Ramesh

Typical deep visual recognition models are capable of performing the one task they were trained on. In this paper, we tackle the extremely difficult problem of combining distinct models with different initializations, each solving a separate task, into one multi-task model without any additional training. Prior work in model merging permutes one model to the

Basile Van Hoorick, Pavel Tokmakov, Simon Stent, Jie Li

Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems. In this paper, we introduce $\textbf{TCOW}$, a new benchmark and model for visual tracking through heavy occlusion and containment. We set up a task where the goal is to, given a video sequence, segment both the projected extent

Ruihan Gao, Wenzhen Yuan, Jun-Yan Zhu

Deep generative models have various content creation applications such as graphic design, e-commerce, and virtual Try-on. However, current works mainly focus on synthesizing realistic visual outputs, often ignoring other sensory modalities, such as touch, which limits physical interaction with users. In this work, we leverage deep generative models to create

Kazunori Asakura, Kiyoshi Hayashida, Tomoki Kawabata, Yoneyama Tomokage

We propose a novel x-ray imaging system, Multi-Image X-ray Interferometer Module (MIXIM), with which a very high angular resolution can be achieved even with a small system size. MIXIM is composed of equally-spaced multiple slits and an x-ray detector, and its angular resolution is inversely proportional to the distance between them. Here we report our evalu

Jun-Kun Chen, Jipeng Lyu, Yu-Xiong Wang

This paper proposes NeuralEditor that enables neural radiance fields (NeRFs) natively editable for general shape editing tasks. Despite their impressive results on novel-view synthesis, it remains a fundamental challenge for NeRFs to edit the shape of the scene. Our key insight is to exploit the explicit point cloud representation as the underlying structure

Renrui Zhang, Zhengkai Jiang, Ziyu Guo, Shilin Yan

Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models. Despite the generality, customizing SAM for specific visual concepts without man-powered prompting is under explored, e.g., automatically segmenting your pet dog in different images. In this pa

Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang

Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback (RLHF) to align the output of large language models (LLMs) with human intentions, ensuring they are helpful, ethical, and reliable. However, this dependence can significantly constrain the true

Louis Hainaut, Dan Petersen

We show that a certain locus inside the moduli space $M_g$ of hyperbolic surfaces, given by surfaces with "sufficiently many" short geodesics, is a classifying space of the handlebody mapping class group. A consequence of the construction is that the top weight cohomology of $M_g$, studied by Chan-Galatius-Payne, maps injectively into the cohomology of the h

Peng-Shuai Wang

We propose octree-based transformers, named OctFormer, for 3D point cloud learning. OctFormer can not only serve as a general and effective backbone for 3D point cloud segmentation and object detection but also have linear complexity and is scalable for large-scale point clouds. The key challenge in applying transformers to point clouds is reducing the quadr

Yuchen Wang, David A. Mazziotti

Electronic excited states of molecules are central to many physical and chemical processes, and yet they are typically more difficult to compute than ground states. In this paper we leverage the advantages of quantum computers to develop an algorithm for the highly accurate calculation of excited states. We solve a contracted Schr\"odinger equation (CSE) --

Connor Z. Lin, Koki Nagano, Jan Kautz, Eric R. Chan

There is a growing demand for the accessible creation of high-quality 3D avatars that are animatable and customizable. Although 3D morphable models provide intuitive control for editing and animation, and robustness for single-view face reconstruction, they cannot easily capture geometric and appearance details. Methods based on neural implicit representatio

Tiger Yu-Yang Hsiao, Abdurro'uf, Dan Coe, Rebecca L. Larson

We present JWST/NIRSpec prism spectroscopy of MACS0647-JD, the triply-lensed $z \sim 11$ candidate discovered in HST imaging and spatially resolved by JWST imaging into two components A and B. Spectroscopy of component A yields a spectroscopic redshift $z=10.17$ based on 7 detected emission lines: CIII] $\lambda\lambda$1907,1909, [OII] $\lambda$3727, [NeIII]

Hagen Muenkler, Hubert Misztela, Michal Pikusa, Marwin Segler

Many contemporary generative models of molecules are variational auto-encoders of molecular graphs. One term in their training loss pertains to reconstructing the input, yet reconstruction capabilities of state-of-the-art models have not yet been thoroughly compared on a large and chemically diverse dataset. In this work, we show that when several state-of-t

An-Chieh Cheng, Xueting Li, Sifei Liu, Xiaolong Wang

Textures are a vital aspect of creating visually appealing and realistic 3D models. In this paper, we study the problem of generating high-fidelity texture given shapes of 3D assets, which has been relatively less explored compared with generic 3D shape modeling. Our goal is to facilitate a controllable texture generation process, such that one texture code

Zijie J. Wang, David Munechika, Seongmin Lee, Duen Horng Chau

Computational notebooks, such as Jupyter Notebook, have become data scientists' de facto programming environments. Many visualization researchers and practitioners have developed interactive visualization tools that support notebooks, yet little is known about the appropriate design of these tools. To address this critical research gap, we investigate the de

Sattwik Ghosal, Ranjan Maitra

The envelope of an elliptical Gaussian complex vector, or equivalently, the amplitude or norm of a bivariate normal random vector has application in many weather and signal processing contexts. We explicitly characterize its distribution in the general case through its probability density, cumulative distribution and moment generating function. Moments and l

Aditya Prakash, Matthew Chang, Matthew Jin, Ruisen Tu

Prior works for reconstructing hand-held objects from a single image train models on images paired with 3D shapes. Such data is challenging to gather in the real world at scale. Consequently, these approaches do not generalize well when presented with novel objects in in-the-wild settings. While 3D supervision is a major bottleneck, there is an abundance of

Ziming Liu, Eric Gan, Max Tegmark

We introduce Brain-Inspired Modular Training (BIMT), a method for making neural networks more modular and interpretable. Inspired by brains, BIMT embeds neurons in a geometric space and augments the loss function with a cost proportional to the length of each neuron connection. We demonstrate that BIMT discovers useful modular neural networks for many simple

Qimiao Si, Nigel E. Hussey

The advent of iron-based superconductors in 2008 came as a complete surprise to the condensed matter community. Now 15 years later, they are beginning to impart some of their new-found wisdom on a slew of emerging superconductors that boast similar traits.

Shengcao Cao, Dhiraj Joshi, Liang-Yan Gui, Yu-Xiong Wang

Object detectors often suffer from the domain gap between training (source domain) and real-world applications (target domain). Mean-teacher self-training is a powerful paradigm in unsupervised domain adaptation for object detection, but it struggles with low-quality pseudo-labels. In this work, we identify the intriguing alignment and synergy between mean-t

Jeremy Taylor

We construct the universal monodromic big tilting sheaf on base affine space and calculate its endomorphisms. By formal completion, we recover Soergel's pro-unipotent Endomorphismensatz with arbitrary field coefficients. We give a Soergel bimodules description of the universal monodromic Hecke category and deduce a conjecture of Eberhardt that uncompletes Ko

Pasquale Di Bari

A solution to the problem of the origin of matter in the universe can be reasonably searched within extensions of the standard model that also explain neutrino masses and mixing. Models embedding the minimal seesaw mechanism can explain the observed matter-antimatter asymmetry of the universe via leptogenesis and dark matter via active-sterile neutrino mixin

Trithep Devakul, Patrick J. Ledwith, Li-Qiao Xia, Aviram Uri

We propose helical trilayer graphene (HTG), a helical structure featuring identical rotation angles $\theta\approx 1.5^\circ$ between three consecutive layers of graphene, as a unique and experimentally accessible platform for realizing exotic correlated topological states of matter. While nominally forming a supermoir\'e (or moir\'e-of-moir\'e) structure, w