May 2023 arXiv papers — page 59

Haoju Leng, Ruining Deng, Zuhayr Asad, R. Michael Womick

Deep-learning techniques have been used widely to alleviate the labour-intensive and time-consuming manual annotation required for pixel-level tissue characterization. Our previous study introduced an efficient single dynamic network - Omni-Seg - that achieved multi-class multi-scale pathological segmentation with less computational complexity. However, the

Andreia Chapouto, Justin Forlano

We consider the real-valued defocusing modified Korteweg-de Vries equation (mKdV) on the circle. Based on the complete integrability of mKdV, Killip-Vi\c{s}an-Zhang (2018) discovered a conserved quantity which they used to prove low regularity a priori bounds for solutions. It has been an open question if this conserved quantity can be used to define invaria

Simeng Sun, Yang Liu, Shuohang Wang, Chenguang Zhu

Strategies such as chain-of-thought prompting improve the performance of large language models (LLMs) on complex reasoning tasks by decomposing input examples into intermediate steps. However, it remains unclear how to apply such methods to reason over long input documents, in which both the decomposition and the output of each intermediate step are non-triv

Aida Wofford, Andrés Sixtos, Stephane Charlot, Gustavo Bruzual

Super star cluster (SSC) A1 (3.1E5 Msun) in NGC 3125 has one of the strongest (EW = 4.6 +/- 0.5 Ang) broad (FWHM = 1131 +\- 40 km/s) He II 1640 emission lines in the nearby Universe and constitutes an important template for interpreting observations of extreme He II emitters out to redshifts of z = 2-3. We use Cosmic Origins Spectrograph (COS) observations o

Yi Hu, Chaoran Zhang, Edward Andert, Harshul Singh

Careful placement of a computational application within a target device cluster is critical for achieving low application completion time. The problem is challenging due to its NP-hardness and combinatorial nature. In recent years, learning-based approaches have been proposed to learn a placement policy that can be applied to unseen applications, motivated b

Yifan Qin, Zheyu Yan, Dailin Gan, Jun Xia

Compute-in-memory accelerators built upon non-volatile memory devices excel in energy efficiency and latency when performing deep neural network (DNN) inference, thanks to their in-situ data processing capability. However, the stochastic nature and intrinsic variations of non-volatile memory devices often result in performance degradation during DNN inferenc

Margarite L. LaBorde

This thesis aims to establish notions of symmetry for quantum states and channels as well as describe algorithms to test for these properties on quantum computers. Ideally, the work will serve as a self-contained overview of the subject. We begin by establishing the necessary mathematical background. We show how to generate a notion of symmetry from a discre

Klaudiusz Czudek

Fix an irrational number $\alpha$ and a real function $\mathfrak{p}$ on the circle with $0<\mathfrak{p}<1$. If a particle is placed at a point $x\in \mathbb R/\mathbb Z$, then in the next step it jumps to $x+\alpha$ with probability $\mathfrak{p}(x)$ and to $x-\alpha$ with probability $1-\mathfrak{p}(x)$. Sinai and Kaloshin proved that if $\mathfrak{p}$ is s

Níckolas de Aguiar Alves

Quantum field theory in curved spacetime is perhaps the most reliable framework in which one can investigate quantum effects in the presence of strong gravitational fields. Nevertheless, it is often studied by means of perturbative treatments. In this thesis, we aim at using the functional renormalization group -- a nonperturbative realization of the renorma

Vidushi Adlakha, Eric Kuo

Recent critiques of Physics Education Research (PER) studies have revoiced the critical issues when drawing causal inferences from observational data where no intervention is present. In response to a call for a "causal reasoning primer", this paper discusses some of the fundamental issues underlying statistical causal inference. In reviewing these issues, w

Ryan M. Lau, Jason Wang, Matthew J. Hankins, Thayne Currie

Wolf-Rayet (WR) 140 is the archetypal periodic dust-forming colliding-wind binary that hosts a carbon-rich WR (WC) star and an O-star companion with an orbital period of 7.93 years and an orbital eccentricity of 0.9. Throughout the past several decades, multiple dust-formation episodes from WR 140 have been observed that are linked to the binary orbit and oc

Tiziano Labruna, Sofia Brenna, Andrea Zaninello, Bernardo Magnini

Large pre-trained language models have exhibited unprecedented capabilities in producing high-quality text via prompting techniques. This fact introduces new possibilities for data collection and annotation, particularly in situations where such data is scarce, complex to gather, expensive, or even sensitive. In this paper, we explore the potential of these

Yuxin Ren, Qipeng Guo, Zhijing Jin, Shauli Ravfogel

Transformer models bring propelling advances in various NLP tasks, thus inducing lots of interpretability research on the learned representations of the models. However, we raise a fundamental question regarding the reliability of the representations. Specifically, we investigate whether transformers learn essentially isomorphic representation spaces, or tho

Lucas M. Sassi, Sathvik Ajay Iyengar, Anand B. Puthirath, Yuefei Huang

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) remain a topic of immense interest. Specifically, given their low operational switching costs, they find many niche applications in new computing architectures with the promise of continued miniaturization. However, challenges lie in Back End of Line (BEOL) integration temperature and time complia

Micah Musser, Andrew Lohn, James X. Dempsey, Jonathan Spring

In July 2022, the Center for Security and Emerging Technology (CSET) at Georgetown University and the Program on Geopolitics, Technology, and Governance at the Stanford Cyber Policy Center convened a workshop of experts to examine the relationship between vulnerabilities in artificial intelligence systems and more traditional types of software vulnerabilitie

Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Javad Hosseini

Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI), necessary for applied tasks like question answering and summarization. We present a series of behavioral studies on several LLM families (LLaMA, GPT-3.5, and PaLM) which probe their behavior using controlled experiments. We establish two biases originating from pretra

Andrey Palaev, Rustam A. Lukmanov, Adil Khan

Controlled data generation with GANs is desirable but challenging due to the nonlinearity and high dimensionality of their latent spaces. In this work, we explore image manipulations learned by GANSpace, a state-of-the-art method based on PCA. Through quantitative and qualitative assessments we show: (a) GANSpace produces a wide range of high-quality image m

Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani

Offline reinforcement learning (RL) allows agents to learn effective, return-maximizing policies from a static dataset. Three popular algorithms for offline RL are Conservative Q-Learning (CQL), Behavior Cloning (BC), and Decision Transformer (DT), from the class of Q-Learning, Imitation Learning, and Sequence Modeling respectively. A key open question is: w

Yinghao Li, Colin Lockard, Prashant Shiralkar, Chao Zhang

Recommending a diversity of product types (PTs) is important for a good shopping experience when customers are looking for products around their high-level shopping interests (SIs) such as hiking. However, the SI-PT connection is typically absent in e-commerce product catalogs and expensive to construct manually due to the volume of potential SIs, which prev

Hou Pong Chan, Qi Zeng, Heng Ji

Existing factual consistency evaluation approaches for text summarization provide binary predictions and limited insights into the weakness of summarization systems. Therefore, we propose the task of fine-grained inconsistency detection, the goal of which is to predict the fine-grained types of factual errors in a summary. Motivated by how humans inspect fac

Yuting Wu, Qiwen Wang, Ziyu Wang, Xinxin Wang

The need for deep neural network (DNN) models with higher performance and better functionality leads to the proliferation of very large models. Model training, however, requires intensive computation time and energy. Memristor-based compute-in-memory (CIM) modules can perform vector-matrix multiplication (VMM) in situ and in parallel, and have shown great pr

Vamsikrishna Chemudupati, Marzieh Tahaei, Heitor Guimaraes, Arthur Pimentel

Large self-supervised pre-trained speech models have achieved remarkable success across various speech-processing tasks. The self-supervised training of these models leads to universal speech representations that can be used for different downstream tasks, ranging from automatic speech recognition (ASR) to speaker identification. Recently, Whisper, a transfo

Nicolás Matte Bon, Volodymyr Nekrashevych, Tianyi Zheng

Conformal dimension is a fundamental invariant of metric spaces, particularly suited to the study of self-similar spaces, such as spaces with an expanding self-covering (e.g. Julia sets of complex rational functions). The dynamics of these systems are encoded by the associated iterated monodromy groups, which are examples of contracting self-similar groups.

Shengwen Gan

We prove a conjecture of H\'era on the dimension of unions of $k$-planes. Let $0<k \le d<n$ be integers, and $\beta\in[0,k+1)$. If $\mathcal{V}\subset A(k,n)$, with $\text{dim}(\mathcal{V})=(k+1)(d-k)+\beta$, then $\text{dim}(\bigcup_{V\in\mathcal{V}}V)\ge d+\min\{1,\beta\}$. The proof combines a recent idea of Zahl and the Brascamp-Lieb inequality.

Yirui Liu, Xinghao Qiao, Yulong Pei, Liying Wang

This paper introduces the Deep Functional Factor Model (DF2M), a Bayesian nonparametric model designed for analysis of high-dimensional functional time series. DF2M is built upon the Indian Buffet Process and the multi-task Gaussian Process, incorporating a deep kernel function that captures non-Markovian and nonlinear temporal dynamics. Unlike many black-bo

Agustina Czenky, William Gvozdjak, Julia Plavnik

We prove that any odd-dimensional modular category of rank at most 23 is pointed. We also show that an odd-dimensional modular category of rank 25 is either pointed, perfect, or equivalent to $\operatorname{Rep}(D^\omega(\mathbb Z_7\rtimes \mathbb Z_3))$. Finally, we give partial classification results for modular categories of rank up to 73.

Eric Ruzomberka, Yongkyu Jang, David J. Love, H. Vincent Poor

We consider point-to-point communication over $q$-ary adversarial channels with partial noiseless feedback. In this setting, a sender Alice transmits $n$ symbols from a $q$-ary alphabet over a noisy forward channel to a receiver Bob, while Bob sends feedback to Alice over a noiseless reverse channel. In the forward channel, an adversary can inject both symbo

Philippe Laban, Wojciech Kryściński, Divyansh Agarwal, Alexander R. Fabbri

With the recent appearance of LLMs in practical settings, having methods that can effectively detect factual inconsistencies is crucial to reduce the propagation of misinformation and improve trust in model outputs. When testing on existing factual consistency benchmarks, we find that a few large language models (LLMs) perform competitively on classification

Rebecca G. Martin

Superorbital periods are observed in the optical light curves of many Be/X-ray binaries yet their origin has remained somewhat elusive. We suggest that precession of the spin axis of the Be star can drive superorbital periods, particularly for short orbital period binaries. We consider the short orbital period ($P_{\rm orb}=16.6\,\rm day$) and highly eccentr

Frédéric Odermatt, Béni Egressy, Roger Wattenhofer

This paper presents a plug-and-play approach for translation with terminology constraints. Terminology constraints are an important aspect of many modern translation pipelines. In both specialized domains and newly emerging domains (such as the COVID-19 pandemic), accurate translation of technical terms is crucial. Recent approaches often train models to cop

Christian Borgs, Jennifer Chayes, Christian Ikeokwu, Ellen Vitercik

On social networks, algorithmic personalization drives users into filter bubbles where they rarely see content that deviates from their interests. We present a model for content curation and personalization that avoids filter bubbles, along with algorithmic guarantees and nearly matching lower bounds. In our model, the platform interacts with $n$ users over

Jakub Macina, Nico Daheim, Sankalan Pal Chowdhury, Tanmay Sinha

While automatic dialogue tutors hold great potential in making education personalized and more accessible, research on such systems has been hampered by a lack of sufficiently large and high-quality datasets. Collecting such datasets remains challenging, as recording tutoring sessions raises privacy concerns and crowdsourcing leads to insufficient data quali

Kexin Huang, Ying Jin, Emmanuel Candès, Jure Leskovec

Graph Neural Networks (GNNs) are powerful machine learning prediction models on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their reliable deployment in settings where the cost of errors is significant. We propose conformalized GNN (CF-GNN), extending conformal prediction (CP) to graph-based models for guaranteed uncert

Muhammad Umar Salman, Asif Hanif, Shady Shehata, Preslav Nakov

Propaganda is a form of communication intended to influence the opinions and the mindset of the public to promote a particular agenda. With the rise of social media, propaganda has spread rapidly, leading to the need for automatic propaganda detection systems. Most work on propaganda detection has focused on high-resource languages, such as English, and litt

Huda Khayrallah, Zuhaib Akhtar, Edward Cohen, Jyothir S

We release MMSMR, a Massively Multi-System MultiReference dataset to enable future work on metrics and evaluation for dialog. Automatic metrics for dialogue evaluation should be robust proxies for human judgments; however, the verification of robustness is currently far from satisfactory. To quantify the robustness correlation and understand what is necessar

Christophe De Beule, E. J. Mele

Artificial crystals such as moir\'e superlattices can have a real-space periodicity much larger than the underlying atomic scale. This facilitates the presence of Bloch oscillations in the presence of a static electric field. We demonstrate that the optical response of such a system, when dressed with a static field, becomes resonant at the frequencies of Bl

Fanjie Kong, Shuai Yuan, Weituo Hao, Ricardo Henao

We address the challenge of generating fair and unbiased image retrieval results given neutral textual queries (with no explicit gender or race connotations), while maintaining the utility (performance) of the underlying vision-language (VL) model. Previous methods aim to disentangle learned representations of images and text queries from gender and racial c

Mohammed Alqadhi, Ali Alkinoon, Saeed Salem, David Mohaisen

This paper examines free content websites (FCWs) and premium content websites (PCWs) in different countries, comparing them to general websites. The focus is on the distribution of malicious websites and their correlation with the national cyber security index (NCSI), which measures a country's cyber security maturity and its ability to deter the hosting of

Sushant Saryal, Deepak Dhar

We study a system of equal-sized circular discs each with an asymmetrically placed pivot at a fixed distance from the center. The pivots are fixed at the vertices of a regular triangular lattice. The discs can rotate freely about the pivots, with the constraint that no discs can overlap with each other. Our Monte Carlo simulations show that the one-point pro

Chong Wang, Xiu Gu, Shu Chen, Yu-xi Liu

Localized edge modes provide a compact channel for transferring an excitation through an extended lattice, but the relation between the microscopic chain and the few levels that actually govern the transfer is often left implicit. We develop this boundary-subspace description for a superconducting-qubit realization of the Rice-Mele model in the single-excita

Alex Shtoff, Elie Abboud, Rotem Stram, Oren Somekh

Factorization machine (FM) variants are widely used for large scale real-time content recommendation systems, since they offer an excellent balance between model accuracy and low computational costs for training and inference. These systems are trained on tabular data with both numerical and categorical columns. Incorporating numerical columns poses a challe

Alexander Kapitanov, Karina Kvanchiani, Alexander Nagaev, Elizaveta Petrova

One of the main challenges of the sign language recognition task is the difficulty of collecting a suitable dataset due to the gap between hard-of-hearing and hearing societies. In addition, the sign language in each country differs significantly, which obliges the creation of new data for each of them. This paper presents the Russian Sign Language (RSL) vid

Barry C Sanders

Electromagnetically induced transparency and Autler-Townes splitting are two distinct yet related effects. These phenomena are relevant to quantum technologies, including quantum memory, quantum switching, and quantum transduction. Here, the similarities and differences between these phenomena along historical and conceptual lines are discussed and their rea

Chen Chen, Zhicheng Liu

We present a state-of-the-art report on visualization corpora in automated chart analysis research. We survey 56 papers that created or used a visualization corpus as the input of their research techniques or systems. Based on a multi-level task taxonomy that identifies the goal, method, and outputs of automated chart analysis, we examine the property space

A. A. Khartov

We study the class $\boldsymbol{Q}$ of distribution functions $F$ that have the property of rational-infinite divisibility: there exist some infinitely divisible distribution functions $F_1$ and $F_2$ such that $F_1=F*F_2$. The class $\boldsymbol{Q}$ is a wide natural extension of the fundamental class of infinitely divisible distribution functions. We are i

A. Tsantiri, A. Palmisano-Kyle, A. Spyrou, P. Mohr

The total cross section of the $^{82}$Kr(p,$\gamma$)$^{83}$Rb reaction was measured for the first time at effective center-of-mass energies between 2.4 and 3.0 MeV, within the relevant Gamow window for the astrophysical $\gamma$ process. The experiment took place at the National Superconducting Cyclotron Laboratory at Michigan State University using the ReA

Yihao Xue, Ali Payani, Yu Yang, Baharan Mirzasoleiman

Pretrained machine learning models need to be adapted to distribution shifts when deployed in new target environments. When obtaining labeled data from the target distribution is expensive, few-shot adaptation with only a few examples from the target distribution becomes essential. In this work, we propose MixPro, a lightweight and highly data-efficient appr

Francesco Pecora, Sergio Servidio, Yan Yang, William H. Matthaeus

A novel multispacecraft technique applied to Magnetospheric Multiscale (MMS) mission data collected in the Earth's magnetosheath enables evaluation of the energy cascade rate solving the full Yaglom's equation in a turbulent space plasma. The method differs from existing approaches in that (i) it is inherently three-dimensional; (ii) it provides a statistica

Ahsan Ullah, Balamurugan Balasubramanian, Bibek Tiwari, Bharat Giri

Analytical calculations and micromagnetic simulations are used to determine the Berry curvature and topological Hall effect (THE) due to conduction electrons in small ferromagnetic particles. Our focus is on small particles of nonellipsoidal shapes, where noncoplanar spin structures yield a nonzero topological Hall signal quantified by the skyrmion number Q.

Alexander S. Kechris, Michael S. Wolman

Nadkarni's Theorem asserts that for a countable Borel equivalence relation (CBER) exactly one of the following holds: (1) It has an invariant Borel probability measure or (2) it admits a Borel compression, i.e., a Borel injection that maps each equivalence class to a proper subset of it. We prove in this paper an effective version of Nadkarni's Theorem, whic

Oleksii Tsepa, Bohdan Naida, Anna Goldenberg, Bo Wang

Drug synergy, characterized by the amplified combined effect of multiple drugs, is critically important for optimizing therapeutic outcomes. Limited data on drug synergy, arising from the vast number of possible drug combinations and testing costs, motivate the need for predictive methods. In this work, we introduce CongFu, a novel Conditional Graph Fusion L

Srinivas Sridharan, Taekyung Heo, Louis Feng, Zhaodong Wang

Benchmarking and co-design are essential for driving optimizations and innovation around ML models, ML software, and next-generation hardware. Full workload benchmarks, e.g. MLPerf, play an essential role in enabling fair comparison across different software and hardware stacks especially once systems are fully designed and deployed. However, the pace of AI

Dan Mickelsen, Herve M. Carruzzo, Susan N. Coppersmith, Clare C. Yu

Silicon quantum dot qubits show great promise but suffer from charge noise with a 1/f^\alpha spectrum, where f is frequency and \alpha \lesssim 1. It has recently been proposed that 1/f^\alpha noise spectra can emerge from a few thermally activated two-level fluctuators in the presence of sub-bath temperature fluctuations associated with a two-dimensional el

O. Abla, M. J. Neves

The non-commutative electrodynamics based on the canonical Poisson gauge theory is studied in this paper. For a pure spatial non-commutativity, we investigate the plane wave solutions in the presence of a constant and uniform magnetic background field for the classical electrodynamics in canonical Poisson gauge theory. We obtain the properties of the medium

Dominik Werner Wolf, Markus Ulrich, Alexander Braun

Windscreen optical quality is an important aspect of any advanced driver assistance system, and also for future autonomous driving, as today at least some cameras of the sensor suite are situated behind the windscreen. Automotive mass production processes require measurement systems that characterize the optical quality of the windscreens in a meaningful way

Jakob Gabriel, Joachim Deutscher

In this paper the robust cooperative output regulation problem for multi-agent systems (MAS) with general heterodirectional hyperbolic PIDE-ODE agents is considered. This setup also covers networks of ODEs with arbitrarily long input and output delays. The output of the agents can be defined at all boundaries, in-domain and may depend on the ODE state, while

A. D. Bermúdez Manjarres

We study the concepts of adiabatic driving and geometric phases of classical integrable systems under the Koopman-von Neumann formalism. In close relation to what happens to a quantum state, a classical Koopman-von Neumann eigenstate will acquire a geometric phase factor $exp\left\{ i\Phi\right\} $ after a closed variation of the parameters $\lambda$ in its

Vladislav D. Kochev, Seidali S. Seidov, Pavel D. Grigoriev

Most high-$T_c$ superconductors are spatially inhomogeneous. Usually, this heterogeneity originates from the interplay of various types of electronic ordering. It affects various superconducting properties, such as the transition temperature, the magnetic upper critical field, the critical current, etc. In this paper, we analyze the parameters of spatial pha

M. C. Baglio, F. Coti Zelati, S. Campana, G. Busquet

Transitional millisecond pulsars are an emerging class of sources that link low-mass X-ray binaries to millisecond radio pulsars in binary systems. These pulsars alternate between a radio pulsar state and an active low-luminosity X-ray disc state. During the active state, these sources exhibit two distinct emission modes (high and low) that alternate unpredi

Gavin Ball, Jesse Madnick

Given a CMC surface in $R^3$, its traceless second fundamental form can be viewed as a holomorphic section called the Hopf differential. By analogy, we show that for an associative submanifold of a 7-manifold $M^7$ with $G_2$-structure, its traceless second fundamental form can be viewed as a twisted spinor. Moreover, if $M$ is $R^7$, $T^7$, or $S^7$ with th

Shashank Sonkar, Richard G. Baraniuk

We explore whether Large Language Models (LLMs) are capable of logical reasoning with distorted facts, which we call Deduction under Perturbed Evidence (DUPE). DUPE presents a unique challenge to LLMs since they typically rely on their parameters, which encode mostly accurate information, to reason and make inferences. However, in DUPE, LLMs must reason over

Y. Samuel Wang, Mladen Kolar, Mathias Drton

Causal discovery procedures aim to deduce causal relationships among variables in a multivariate dataset. While various methods have been proposed for estimating a single causal model or a single equivalence class of models, less attention has been given to quantifying uncertainty in causal discovery in terms of confidence statements. A primary challenge in

Daniel W. Boutros, Simon Markfelder, Edriss S. Titi

We develop a convex integration scheme for constructing nonunique weak solutions to the hydrostatic Euler equations (also known as the inviscid primitive equations of oceanic and atmospheric dynamics) in both two and three dimensions. We also develop such a scheme for the construction of nonunique weak solutions to the three-dimensional viscous primitive equ

Peter Schiansky, Julia Kalb, Esther Sztatecsny, Marie-Christine Roehsner

Digital payments have replaced physical banknotes in many aspects of our daily lives. Similarly to banknotes, they should be easy to use, unique, tamper-resistant and untraceable, but additionally withstand digital attackers and data breaches. Current technology substitutes customers' sensitive data by randomized tokens, and secures the payment's uniqueness

Heon Lee

This paper develops a dynamic monetary model to study the (in)stability of the fractional reserve banking system. The model shows that the fractional reserve banking system can endanger stability in that equilibrium is more prone to exhibit endogenous cyclic, chaotic, and stochastic dynamics under lower reserve requirements, although it can increase consumpt

Alexander Scarlatos, Andrew Lan

Recent developments in large pre-trained language models have enabled unprecedented performance on a variety of downstream tasks. Achieving best performance with these models often leverages in-context learning, where a model performs a (possibly new) task given one or more examples. However, recent work has shown that the choice of examples can have a large

L. Cruz Rodriguez, T. Rook, B. B. Augstein, A. S. Maxwell

We construct two strong-field path integral methods with full Coulomb distortion, in which the quantum pathways are mimicked by interfering electron orbits: the rate-based CQSFA (R-CQSFA) and the hybrid forward-boundary CQSFA (H-CQSFA). The methods have the same starting point as the standard Coulomb quantum-orbit strong-field approximation (CQSFA), but thei

Jacob Thebault-Spieker, Sukrit Venkatagiri, Naomi Mine, Kurt Luther

In recent years, social media companies have grappled with defining and enforcing content moderation policies surrounding political content on their platforms, due in part to concerns about political bias, disinformation, and polarization. These policies have taken many forms, including disallowing political advertising, limiting the reach of political topic

Livio Baldini Soares, Daniel Gillick, Jeremy R. Cole, Tom Kwiatkowski

Neural document rerankers are extremely effective in terms of accuracy. However, the best models require dedicated hardware for serving, which is costly and often not feasible. To avoid this serving-time requirement, we present a method of capturing up to 86% of the gains of a Transformer cross-attention model with a lexicalized scoring function that only re

Thomas Hertog, Oliver Janssen, Joel Karlsson

We show that the Kontsevich-Segal (KS) criterion, applied to the complex saddles that specify the semiclassical no-boundary wave function, acts as a selection mechanism on inflationary scalar field potentials. Completing the observable phase of slow-roll inflation with a no-boundary origin, the KS criterion effectively bounds the tensor-to-scalar ratio of co

Andrew R. Cameron, Kate L. Fenwick, Sandra W. L. Cheng, Sacha Schwarz

Recent experimental progress in quantum optics has enabled measurement of single photons on ultrafast timescales, beyond the resolution limit of single photon detectors. The energy-time degree of freedom has emerged as a promising avenue for quantum technologies, as entanglement between the frequency and temporal properties of two photons can be fully explor

Zhiheng Xi, Senjie Jin, Yuhao Zhou, Rui Zheng

To enhance the multi-step reasoning capabilities of large language models, researchers have extensively explored prompting methods, notably the Chain-of-Thought (CoT) method which explicitly elicits human-like rationales. However, they have inadvertently overlooked the potential of enhancing model reasoning performance by formulating higher-quality problems.

Arnab Ganguly, Tobias Sutter

This paper proposes a statistically optimal approach for learning a function value using a confidence interval in a wide range of models, including general non-parametric estimation of an expected loss described as a stochastic programming problem or various SDE models. More precisely, we develop a systematic construction of highly accurate confidence interv

Elisa Maggio

Gravitational waves provide a unique opportunity to test gravity in the dynamical and nonlinear regime. We present a parametrized test of general relativity (GR) that introduces generic deviations to the plunge, merger and ringdown stages of binary-black-hole coalescences. The novel feature of the model is that it can capture signatures of beyond-GR physics

F. Stoppa, R. Ruiz de Austri, P. Vreeswijk, S. Bhattacharyya

Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, giv

Xinyi Liu, Jinning Li, Dachun Sun, Ruijie Wang

The proliferation of political memes in modern information campaigns calls for efficient solutions for image classification by ideological affiliation. While significant advances have recently been made on text classification in modern natural language processing literature, understanding the political insinuation in imagery is less developed due to the hard

Junyu Mao, Stuart E. Middleton, Mahesan Niranjan

Prompt-based models have gathered a lot of attention from researchers due to their remarkable advancements in the fields of zero-shot and few-shot learning. Developing an effective prompt template plays a critical role. However, prior studies have mainly focused on prompt vocabulary searching or embedding initialization within a predefined template with the

Sky CH-Wang, Arkadiy Saakyan, Oliver Li, Zhou Yu

Designing systems that can reason across cultures requires that they are grounded in the norms of the contexts in which they operate. However, current research on developing computational models of social norms has primarily focused on American society. Here, we propose a novel approach to discover and compare descriptive social norms across Chinese and Amer

M. Patel, B. P. Gompertz, P. T. O'Brien, G. P. Lamb

GRB 201015A is a peculiarly low luminosity, spectrally soft gamma-ray burst (GRB), with $T_{\rm 90} = 9.8 \pm 3.5$ s (time interval of detection of 90\% of photons from the GRB), and an associated supernova (likely to be type Ic or Ic-BL). GRB 201015A has an isotropic energy $E_{\gamma,\rm iso} = 1.75 ^{+0.60} _{-0.53} \times 10^{50}$ erg, and photon index $

Xiang Zhang, Yu Gu, Huan Yan, Yantong Wang

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras; however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring syste

Nghia T. Le, Alan Ritter

Recent work on extending coreference resolution across domains and languages relies on annotated data in both the target domain and language. At the same time, pre-trained large language models (LMs) have been reported to exhibit strong zero- and few-shot learning abilities across a wide range of NLP tasks. However, prior work mostly studied this ability usi

Alison M. Etheridge, Thomas G. Kurtz, Ian Letter, Peter L. Ralph

We introduce a broad class of spatial models to describe how spatially heterogeneous populations live, die, and reproduce. Individuals are represented by points of a point measure, whose birth and death rates can depend both on spatial position and local population density, defined via the convolution of the point measure with a nonnegative kernel. We pass t

Jakob Kaltwasser, Joschka Seip, Erik Fitzke, Maximilian Tippmann

We demonstrate a method to reduce the number of single-photon detectors (SPDs) required in multi-party quantum key distribution (QKD) networks by a factor of two by using detector time multiplexing (DTM). We implement the DTM scheme for an entanglement-based time-bin protocol and compare QKD results with and without DTM in our QKD network with four users. Wh

Jadie Adams, Shireen Elhabian

We present Point2SSM, a novel unsupervised learning approach for constructing correspondence-based statistical shape models (SSMs) directly from raw point clouds. SSM is crucial in clinical research, enabling population-level analysis of morphological variation in bones and organs. Traditional methods of SSM construction have limitations, including the requi

Arijit Khan

Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were constructed to store large-scale, real-world facts as (subject, predicate, object) triples -- that can also be modeled as a graph, where a node (a subject or an object) represents an entity with attributes, and a directed edge (a predicate) is a relationship between two entities.

Sicong Huang, Nico Cappelluti, Massimiliano Galeazzi, Anjali Gupta

We studied the spectral signature of different components of the Diffuse X-ray Background (DXB), including Local Hot Bubble (LHB), Solar Wind Charge Exchange (SWCX), Galactic Halo, and typically unresolved point sources (galaxies and AGN), in the direction of the Chandra Deep Field South (CDFS) using the 4 Ms XMM-Newton survey and Chandra 4 Ms Source Catalog

Jing-Cheng Pang, Pengyuan Wang, Kaiyuan Li, Xiong-Hui Chen

Large Language Models (LLMs) have exhibited remarkable performance across various natural language processing (NLP) tasks. However, fine-tuning these models often necessitates substantial supervision, which can be expensive and time-consuming to obtain. This paper introduces a novel unsupervised method called LanguageModel Self-Improvement by Reinforcement L

Jindřich Libovický

We study how multilingual sentence representations capture European countries and occupations and how this differs across European languages. We prompt the models with templated sentences that we machine-translate into 12 European languages and analyze the most prominent dimensions in the embeddings.Our analysis reveals that the most prominent feature in the

Konstantin Dobler, Gerard de Melo

Using model weights pretrained on a high-resource language as a warm start can reduce the need for data and compute to obtain high-quality language models for other, especially low-resource, languages. However, if we want to use a new tokenizer specialized for the target language, we cannot transfer the source model's embedding matrix. In this paper, we prop

Zihao Fu, Meiru Zhang, Zaiqiao Meng, Yannan Shen

Infectious disease outbreaks continue to pose a significant threat to human health and well-being. To improve disease surveillance and understanding of disease spread, several surveillance systems have been developed to monitor daily news alerts and social media. However, existing systems lack thorough epidemiological analysis in relation to corresponding al

Fumiya Okazaki

We characterize weakly harmonic maps with respect to non-local Dirichlet forms by Markov processes and martingales. In particular, we can obtain discontinuous martingales on Riemannian manifolds from the image of symmetric stable processes under fractional harmonic maps in a weak sense. Based on this characterization, we also consider the continuity of weakl

Lars Vilhuber

Transparency and reproducibility are often seen in opposition to privacy and confidentiality. Data that need to be kept confidential are seen as an impediment to reproducibility, and privacy would seem to inhibit transparency. I bring a more nuanced view to the discussion, and show, using examples from over 1,000 reproducibility assessments, that confidentia

Serge Gratton, Valentin Mercier, Elisa Riccietti, Philippe L. Toint

Multi-level methods are widely used for the solution of large-scale problems, because of their computational advantages and exploitation of the complementarity between the involved sub-problems. After a re-interpretation of multi-level methods from a block-coordinate point of view, we propose a multi-level algorithm for the solution of nonlinear optimization

Alvio Renzini

The mere existence of multiple stellar generations in Milky Way globular clusters indicates that each generation was unable to stop star formation, that instead persisted unimpeded for several million years. This evidence argues for an extended stage of star formation within a forming globular cluster, during which stellar feedback was substantially ineffect

Juan Flores Torres

In this work, we focus on describing the space of bi-invariant metrics in a Lie group up to isometry. I.e, that is, metrics invariant under both left and right translations. We show that $\mathfrak{BI}$, the moduli space of bi-invariant metrics, is an orbifold. Moreover we give an explicit description of this orbifold, and of $\mathfrak{EBI}$, the space of b

Maria Giovanna Mora

In this paper we review some recent results on nonlocal interaction problems. The focus is on interaction kernels that are anisotropic variants of the classical Coulomb kernel. In other words, while preserving the same singularity at zero of the Coulomb kernel, they present preferred directions of interaction. For kernels of this kind and general confinement

Jorge Antezana, Sheldy Ombrosi

In this work we develop a weight theory in the setting of hyperbolic spaces. Our starting point is a variant of the well-known endpoint Fefferman-Stein inequality for the centered Hardy-Littlewood maximal function. This inequality generalizes, in the hyperbolic setting, the weak $(1,1)$ estimates obtained by Str\"omberg in "Weak type L1 estimates for maximal

Yanran Wang, Jonghyuk Baek, Yichun Tang, Jing Du

This work presents an approach for automating the discretization and approximation procedures in constructing digital representations of composites from Micro-CT images featuring intricate microstructures. The proposed method is guided by the Support Vector Machine (SVM) classification, offering an effective approach for discretizing microstructural images.

Spenser Talkington, Eugene J. Mele

We report calculations of terahertz ellipticities in large-angle, 21.79$^\circ$ and 38.21$^\circ$, commensurate twisted bilayer graphene, and predict values as high as 1.5 millidegrees in the terahertz region for this non-magnetic material. This terahertz circular dichroism exhibits a magnitude comparable to that of chiral materials in the visible region. At

Xuanyu Zhang, Bingbing Li, Qing Yang

Generative chat models, such as ChatGPT and GPT-4, have revolutionized natural language generation (NLG) by incorporating instructions and human feedback to achieve significant performance improvements. However, the lack of standardized evaluation benchmarks for chat models, particularly for Chinese and domain-specific models, hinders their assessment and pr