May 2023 arXiv papers — page 102

Benjamin Thérien, Chengjie Huang, Adrian Chow, Krzysztof Czarnecki

Object re-identification (ReID) from images plays a critical role in application domains of image retrieval (surveillance, retail analytics, etc.) and multi-object tracking (autonomous driving, robotics, etc.). However, systems that additionally or exclusively perceive the world from depth sensors are becoming more commonplace without any corresponding metho

Konstantinos Bougiatiotis, Georgios Paliouras

Most phenomena related to biomedical tasks are inherently complex, and in many cases, are expressed as signals on biomedical Knowledge Graphs (KGs). In this work, we introduce the use of a new representation framework, the Prime Adjacency Matrix (PAM) for biomedical KGs, which allows for very efficient network analysis. PAM utilizes prime numbers to enable r

Clayton W. Kosonocky, Aaron L. Feller, Claus O. Wilke, Andrew D. Ellington

Chemical similarity searches are widely used in-silico methods for identifying new drug-like molecules. These methods have historically relied on structure-based comparisons to compute molecular similarity. Here, we use a chemical language model to create a vector-based chemical search. We extend implementations by creating a prompt engineering strategy that

Pablo M. Poggi, Manuel H. Muñoz-Arias

We study the competing effects of collective generalized measurements and interaction-induced scrambling in the dynamics of an ensemble of spin-1/2 particles at the level of quantum trajectories. This setup can be considered as analogous to the one leading to measurement-induced transitions in quantum circuits. We show that the interplay between collective u

Jinyuan Wu

In pixelized detectors, reducing power consumption in the front end ASIC chips becomes a crucial demand. Optimization based on mature pre-amplifier schemes today is unlikely to bring sufficient improvements. A new CMOS front-end gain stage topology with very low power consumption called regenerative current mirror is developed to fulfill the demand. The circ

Gunhee Cho, Jihun Yum

This paper explores the Bergman geometry of bounded domains $\Omega$ in $\mathbb{C}^n$ through the lens of information geometry by introducing a mapping $\Phi: \Omega \rightarrow \mathcal{P}(\Omega)$, where $\mathcal{P}(\Omega)$ denotes a space of probability measures on $\Omega$. A result by J. Burbea and C. Rao establishes that the pullback of the Fisher i

F. A. Muller

The notorious `measurement problem' has been roving around quantum mechanics for nearly a century since its inception, and has given rise to a variety of `interpretations' of quantum mechanics, which are meant to evade it. We argue that no less than six problems need to be distinguished, and that several of them classify as different types of problems. One o

Sergio Garcia-Vega, Andreas G. F. Hoepner, Joeri Rogelj, Frank Schiemann

The Paris Agreement aims to reach net zero greenhouse gas (GHG) emissions in the second half of the 21st century, and the Oil & Gas sector plays a key role in achieving this transition. Understanding progress in emission reductions in the private sector relies on the disclosure of corporate climate-related data, and the Carbon Disclosure Project (CDP) is con

Xiang-Rui Ni, Zhe Zheng, Jia-Rui Lin, Zhen-Zhong Hu

Among different phases of the life cycle of a building or facility, design is of the utmost importance to ensure safety, efficiency and sustainability of the building or facility. How to control and improve design quality and efficiency has been explored for years, and more studies emerged with the popularization of Building Information Modelling (BIM). Howe

Shadi Iskander, Kira Radinsky, Yonatan Belinkov

Natural language processing models tend to learn and encode social biases present in the data. One popular approach for addressing such biases is to eliminate encoded information from the model's representations. However, current methods are restricted to removing only linearly encoded information. In this work, we propose Iterative Gradient-Based Projection

Marta Garnelo, Wojciech Marian Czarnecki

Attention-based models have been a key element of many recent breakthroughs in deep learning. Two key components of Attention are the structure of its input (which consists of keys, values and queries) and the computations by which these three are combined. In this paper we explore the space of models that share said input structure but are not restricted to

Moharram Aghapournahr, Leif Melkersson

Let $R$ be a commutative Noetherian ring, $\fa$ an ideal of $R$, $M$ an arbitrary $R$-module and $N$ a finite $R$-module. We prove that \cite[Theorem 2.1]{Mel} and \cite[Proposition 3.3 (i)$\Leftrightarrow$(ii)]{B1} are true for any Serre subcategory of $R$-modules. We also prove a characterization theorem for $\lc_{\fa}^{i}(M)$ and $\lc_{\fa}^{i}(N,M)$ to b

Yizhi Liu, Weiguang Wang, Guodong Gordon Gao, Ritu Agarwal

Electronic health records (EHRs) serve as an essential data source for the envisioned artificial intelligence (AI)-driven transformation in healthcare. However, clinician biases reflected in EHR notes can lead to AI models inheriting and amplifying these biases, perpetuating health disparities. This study investigates the impact of stigmatizing language (SL)

Ling-Jun Kong, Jingfeng Zhang, Furong Zhang, Xiangdong Zhang

After more than 70 years of development, holography has become an essential tool of modern optics in many applications. In fact, for various applications of different kinds of holographic techniques, stability and antijamming ability are very important. Here, optical topological structures are introduced into holographic technology, and an entirely new conce

Gabriel Coutinho, Emanuel Juliano, Thomás Jung Spier

We prove that the only trees that admit perfect state transfer according to the adjacency matrix model are $P_2$ and $P_3$. This answers a question first asked by Godsil in 2012 and proves a conjecture by Coutinho and Liu from 2015.

Chenyang Shi, Hanxiao Liu, Jing Jin, Wenzhuo Li

Video frame interpolation aims to generate high-quality intermediate frames from boundary frames and increase frame rate. While existing linear, symmetric and nonlinear models are used to bridge the gap from the lack of inter-frame motion, they cannot reconstruct real motions. Event cameras, however, are ideal for capturing inter-frame dynamics with their ex

Longyue Wang, Siyou Liu, Mingzhou Xu, Linfeng Song

Zero pronouns (ZPs) are frequently omitted in pro-drop languages (e.g. Chinese, Hungarian, and Hindi), but should be recalled in non-pro-drop languages (e.g. English). This phenomenon has been studied extensively in machine translation (MT), as it poses a significant challenge for MT systems due to the difficulty in determining the correct antecedent for the

Anuradha Welivita, Pearl Pu

AI-driven chatbots have become an emerging solution to address psychological distress. Due to the lack of psychotherapeutic data, researchers use dialogues scraped from online peer support forums to train them. But since the responses in such platforms are not given by professionals, they contain both conforming and non-conforming responses. In this work, we

Furong Zhang, Lu He, Huizhen Zhang, Ling-Jun Kong

Topological photonics has been developed for more than ten years. It has been proved that the combination of topology and photons is very beneficial to the design of robust optical devices against some disturbances. However, most of the work for robust optical logic devices stays at the theoretical level. There are very few topologically-protected logic devi

Jyothis Chandran, Mathieu Remazeilles, R. B. Barreiro

Taking advantage of the reduced levels of noise and systematics in the data of the latest Planck release (PR4, also known as NPIPE), we construct a new all-sky Compton-$y$ parameter map (hereafter, $y$-map) of the thermal Sunyaev-Zeldovich (SZ) effect from the Planck PR4 data. A tailored Needlet Internal Linear Combination (NILC) pipeline, first validated on

Constantin Waubert de Puiseau, Hasan Tercan, Tobias Meisen

Solving job shop scheduling problems (JSSPs) with a fixed strategy, such as a priority dispatching rule, may yield satisfactory results for several problem instances but, nevertheless, insufficient results for others. From this single-strategy perspective finding a near optimal solution to a specific JSSP varies in difficulty even if the machine setup remain

Zhuoyuan Mao, Haiyue Song, Raj Dabre, Chenhui Chu

The language-independency of encoded representations within multilingual neural machine translation (MNMT) models is crucial for their generalization ability on zero-shot translation. Neural interlingua representations have been shown as an effective method for achieving this. However, fixed-length neural interlingua representations introduced in previous wo

Alexei Ilyin, Ari Laptev, Timon Weinmann

In this short note we prove Lieb--Thirring inequalities on manifolds with negative constant curvature. The discrete spectrum appears below the continuous spectrum $(d-1)^2/4, \infty)$, where $d$ is the dimension of the hyperbolic space. As an application we obtain a P\'olya type inequality with not a sharp constant. An example of a 2D domain is given for whi

Weixuan Zhang, Haiteng Wang, Houjun Sun, Xiangdong Zhang

Inverse Anderson transitions, where the flat-band localization is destroyed by disorder, have been wildly investigated in quantum and classical systems in the presence of Abelian gauge fields. Here, we report the first investigation on inverse Anderson transitions in the system with non-Abelian gauge fields. It is found that pseudospin-dependent localized an

Ting Li, Chengchun Shi, Zhaohua Lu, Yi Li

Many modern tech companies, such as Google, Uber, and Didi, utilize online experiments (also known as A/B testing) to evaluate new policies against existing ones. While most studies concentrate on average treatment effects, situations with skewed and heavy-tailed outcome distributions may benefit from alternative criteria, such as quantiles. However, assessi

Valentina Parravicini, Thomas-C. Jagau

When a vacancy is created in an inner-valence orbital of a dimer of atoms or molecules, the resulting species can undergo interatomic/intermolecular Coulombic decay (ICD): the hole is filled through a relaxation process that leads to a doubly ionized cluster with two positively charged atoms or molecules. Since they are subject to electronic decay, inner-val

Hailan Zhang, Gongjin Song

Lung cancer(LC) is a type of malignant neoplasm that originates in the bronchial mucosa or glands.As a clinically common nodule,solitary pulmonary nodules(SPNs) have a significantly higher probability of malignancy when they are larger than 8 mm in diameter.But there is also a risk of lung cancer when the diameter is less than 8mm,the purpose of this study w

Christos Kolomvakis, Arnaud Vandaele, Nicolas Gillis

Boolean matrix factorization (BMF) approximates a given binary input matrix as the product of two smaller binary factors. As opposed to binary matrix factorization which uses standard arithmetic, BMF uses the Boolean OR and Boolean AND operations to perform matrix products, which leads to lower reconstruction errors. BMF is an NP-hard problem. In this paper,

Yucong Gao

Backscatter WiFi provides a novel solution to IoT device's energy consumption. Different from other backscatter WiFi solutions, MOXcatter works with multiple spatial streams, making it appliable in 802.11n and beyond. In this paper, we present a method to solve the problem of phase ambiguity in MOXcatter due to the deviation of packet offset measurement. The

Ashutosh Dwibedi, Cho Win Aung, Jayanta Dey, Sabyasachi Ghosh

Rotating quarks and hadronic systems, produced in peripheral heavy ion collisions, can experience Coriolis force and other forces due to rotational motion. Considering only the effect of Coriolis force, we have calculated the electrical conductivity for non-relativistic rotating matter using the Relaxation Time Approximation based Boltzmann transport equatio

Guido Cupani, Giorgio Calderone, Stefano Cristiani, Francesco Guarneri

The analysis of absorption features along the line of sight to distant sources is an invaluable tool for observational cosmology, giving a direct insight into the physical and chemical state of the inter/circumgalactic medium. Such endeavour entails the accessibility of bright QSOs as background beacons, and the availability of software tools to extract the

Sichao Li, Rong Wang, Quanling Deng, Amanda Barnard

Interactions among features are central to understanding the behavior of machine learning models. Recent research has made significant strides in detecting and quantifying feature interactions in single predictive models. However, we argue that the feature interactions extracted from a single pre-specified model may not be trustworthy since: a well-trained p

Ameni Dhouib, Braham Mezghrani, Giusy Finocchiaro, Rémi Le Borgne

Functional polymers, such as poly(ethylene glycol) (PEG) terminated with a single phosphonic acid, hereafter PEGik-Ph are often applied to coat metal oxide surfaces during post synthesis steps, but are not sufficient to stabilize sub-10 nm particles in protein-rich biofluids. The instability is attributed to the weak binding affinity of post-grafted phosphon

Minqi Lin, Jian Fang, Xi Deng, Xiaojun Gu

Supersonic lean premixed hydrogen/air combustion stabilised by a cavity-flame holder within a model scramjet, characterized by a Mach 1.5 inflow at 1000 K and 50 kPa, is investigated via direct numerical simulation. By separately implementing wall-bounded turbulent and laminar inlet conditions, this work analysis various physical processes of flame stabiliza

Joseph A. Thas, Koen Thas

This is the second part of our study of epimorphisms with source a thick generalized $m$-gon and target a thin generalized $m$-gon. We classify the case $m = 8$ when the polygons are finite (in the first part [15] we handled the cases $m = 3, 4$ and $6$). Then we show that the infinite case is very different, and construct examples which strongly differ from

Giulio Ciraolo, Filomena Pacella, Camilla Chiara Polvara

We consider semilinear elliptic equations with mixed boundary conditions in spherical sectors inside a cone. The aim of the paper is to show that a radial symmetry result of Gidas-Ni-Nirenberg type for positive solutions does not hold in general nonconvex cones. This symmetry breaking result is achieved by studying the Morse index of radial positive solution

Anna Bonaldi, Philippa Hartley, Tommaso Ronconi, Gianfranco De Zotti

In this paper we extend the Tiered Radio Extragalactic Continuum Simulation (T-RECS) to include HI emission. The HI T-RECS model is based on the most recent HI mass function estimates, combined with prescriptions to convert HI mass to total integrated HI flux. It further models source size, morphology and kinematics, including rotational velocity and HI line

Svetlana Cherlin, Theophile Bigirumurame, Michael J Grayling, Jérémie Nsengimana

Introduction: Even in effectively conducted randomised trials, the probability of a successful study remains relatively low. With recent advances in the next-generation sequencing technologies, there is a rapidly growing number of high-dimensional data, including genetic, molecular and phenotypic information, that have improved our understanding of driver ge

Koen Thas

In this conceptual paper, we discuss quantum formalisms which do not use the famous Axiom of Choice. We also consider the fundamental problem which addresses the (in)correctness of having the complex numbers as the base field for Hilbert spaces in the K{\o}benhavn interpretation of quantum theory, and propose a new approach to this problem (based on the Lefs

Yang Deng, Wenxuan Zhang, Yifei Yuan, Wai Lam

Unlike empathetic dialogues, the system in emotional support conversations (ESC) is expected to not only convey empathy for comforting the help-seeker, but also proactively assist in exploring and addressing their problems during the conversation. In this work, we study the problem of mixed-initiative ESC where the user and system can both take the initiativ

Tom Jurgenson, Aviv Tamar

Recently, a simple yet effective algorithm -- goal-conditioned supervised-learning (GCSL) -- was proposed to tackle goal-conditioned reinforcement-learning. GCSL is based on the principle of hindsight learning: by observing states visited in previously executed trajectories and treating them as attained goals, GCSL learns the corresponding actions via superv

Daniele Gambetta, Giovanni Mauro, Luca Pappalardo

Since the development of the original Schelling model of urban segregation, several enhancements have been proposed, but none have considered the impact of mobility constraints on model dynamics. Recent studies have shown that human mobility follows specific patterns, such as a preference for short distances and dense locations. This paper proposes a segrega

Xiaocui Yang, Shi Feng, Daling Wang, Sun Qi

We have witnessed the rapid proliferation of multimodal data on numerous social media platforms. Conventional studies typically require massive labeled data to train models for Multimodal Aspect-Based Sentiment Analysis (MABSA). However, collecting and annotating fine-grained multimodal data for MABSA is tough. To alleviate the above issue, we perform three

Marco Oesting, Albert Rapp

In this paper, we consider a simple estimator for tail dependence coefficients of a max-stable time series and show its asymptotic normality under a mild condition. The novelty of our result is that this condition does not involve mixing properties that are common in the literature. More importantly, our condition is linked to the transition between long and

Emil Carlsson, Devdatt Dubhashi

In this work we introduce a structured signaling game, an extension of the classical signaling game with a similarity structure between meanings in the context, along with a variant of the Rational Speech Act (RSA) framework which we call structured-RSA (sRSA) for pragmatic reasoning in structured domains. We explore the behavior of the sRSA in the domain of

Elena Gazzarrini, Enrique Garcia, Domenic Gosein, Alba Vendrell Moya

The Virtual Research Environment is an analysis platform developed at CERN serving the needs of scientific communities involved in European Projects. Its scope is to facilitate the development of end-to-end physics workflows, providing researchers with access to an infrastructure and to the digital content necessary to produce and preserve a scientific resul

Yingda Yin, Jiangran Lyu, Yang Wang, Haoran Liu

Estimating the 3DoF rotation from a single RGB image is an important yet challenging problem. As a popular approach, probabilistic rotation modeling additionally carries prediction uncertainty information, compared to single-prediction rotation regression. For modeling probabilistic distribution over SO(3), it is natural to use Gaussian-like Bingham distribu

Jiageng Wu, Xian Wu, Zhaopeng Qiu, Minghui Li

$\textbf{Objectives}$: Large Language Models (LLMs) such as ChatGPT and Med-PaLM have excelled in various medical question-answering tasks. However, these English-centric models encounter challenges in non-English clinical settings, primarily due to limited clinical knowledge in respective languages, a consequence of imbalanced training corpora. We systemati

Shunsuke Maeda, Yusuke Kaneko, Hideaki Muramatsu, Yukihiro Murakami

Phylogenetic networks are used to represent the evolutionary history of species. They are versatile when compared to traditional phylogenetic trees, as they capture more complex evolutionary events such as hybridization and horizontal gene transfer. Distance-based methods such as the Neighbor-Net algorithm are widely used to compute phylogenetic networks fro

Yubao Zhang, Xin Chen, Sumei Gong, Haojie Chen

Wind power is becoming an increasingly important source of renewable energy worldwide. However, wind farm power control faces significant challenges due to the high system complexity inherent in these farms. A novel communication-based multi-agent deep reinforcement learning large-scale wind farm multivariate control is proposed to handle this challenge and

Alon Jacovi, Avi Caciularu, Omer Goldman, Yoav Goldberg

Data contamination has become prevalent and challenging with the rise of models pretrained on large automatically-crawled corpora. For closed models, the training data becomes a trade secret, and even for open models, it is not trivial to detect contamination. Strategies such as leaderboards with hidden answers, or using test data which is guaranteed to be u

Roland Guttenberg

We consider the new extension of population protocols with unordered data and show that the corresponding well-specification problem and therefore also other verification problems are undecidable.

Akhil Vakayil, Roshan Joseph

In this work, we propose a novel framework for large-scale Gaussian process (GP) modeling. Contrary to the global, and local approximations proposed in the literature to address the computational bottleneck with exact GP modeling, we employ a combined global-local approach in building the approximation. Our framework uses a subset-of-data approach where the

Francisco Eiras, Adel Bibi, Rudy Bunel, Krishnamurthy Dj Dvijotham

Recent work provides promising evidence that Physics-Informed Neural Networks (PINN) can efficiently solve partial differential equations (PDE). However, previous works have failed to provide guarantees on the worst-case residual error of a PINN across the spatio-temporal domain - a measure akin to the tolerance of numerical solvers - focusing instead on poi

Mo Yu, Jiangnan Li, Shunyu Yao, Wenjie Pang

Comprehending characters' personalities is a crucial aspect of story reading. As readers engage with a story, their understanding of a character evolves based on new events and information; and multiple fine-grained aspects of personalities can be perceived. This leads to a natural problem of situated and fine-grained personality understanding. The problem h

Dar Arava, Ido Tal

In this paper we show a polar coding scheme for the deletion channel with a probability of error that decays roughly like $2^{-\sqrt{\Lambda}}$, where $\Lambda$ is the length of the codeword. That is, the same decay rate as that of seminal polar codes for memoryless channels. This is stronger than prior art in which the square root is replaced by a cube root

Emil Carlsson, Devdatt Dubhashi, Terry Regier

It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern. We consider efficiency as instantiated in the Information Bottleneck (IB) principle, and a model of cultural evolution that combines iterated learning and communication. We show that this model,

Comfort Mintah, David W. Kribs, Michael Nathanson, Rajesh Pereira

We investigate a graph-theoretic approach to the problem of distinguishing quantum product states in the fundamental quantum communication framework called local operations and classical communication (LOCC). We identify chordality as the key graph structure that drives distinguishability in one-way LOCC, and we derive a one-way LOCC characterization for cho

Oriol Serra, Lluís Vena

In \cite[Serra, Vena, Extremal families for the Kruskal-Katona theorem]{sv21}, the authors have shown a characterization of the extremal families for the Kruskal-Katona Theorem. We further develop some of the arguments given in \cite{sv21} and give additional properties of these extremal families. F\"uredi-Griggs/M\"ors theorem from 1986/85 \cite{furgri86,mo

Jan Pennekamp, Anastasiia Belova, Thomas Bergs, Matthias Bodenbenner

The Internet of Production (IoP) leverages concepts such as digital shadows, data lakes, and a World Wide Lab (WWL) to advance today's production. Consequently, it requires a technical infrastructure that can support the agile deployment of these concepts and corresponding high-level applications, which, e.g., demand the processing of massive data in motion

Phung Van Dong, Duong Van Loi

The $SU(3)_L\otimes U(1)_X$ symmetry actually studied is directly broken to the electroweak symmetry $SU(2)_L\otimes U(1)_Y$ by a Higgs triplet, predicting a relevant new physics at TeV scale. This work argues, by contrast, that the higher weak isospin $SU(3)_L$ might be broken at a high energy scale, much beyond $1$ TeV, by a Higgs octet to an intermediate

Fanqi Wan, Weizhou Shen, Ke Yang, Xiaojun Quan

Retrieving proper domain knowledge from an external database lies at the heart of end-to-end task-oriented dialog systems to generate informative responses. Most existing systems blend knowledge retrieval with response generation and optimize them with direct supervision from reference responses, leading to suboptimal retrieval performance when the knowledge

Diogo Arsénio, Haroune Houamed

We are interested in the stability analysis of two-dimensional incompressible inviscid fluids. Specifically, we revisit a recent result on the stability of Yudovich's solutions to the incompressible Euler equations in $L^\infty([0,T];H^1)$ by providing a new approach to its proof based on the idea of compactness extrapolation and by extending it to the whole

Şengül Kuru, Javier Negro, Sergio Salamanca

The purpose of this work is to present a method based on the factorizations used in one dimensional quantum mechanics in order to find the symmetries of quantum and classical superintegrable systems in higher dimensions. We apply this procedure to the harmonic oscillator and Kepler-Coulomb systems to show the differences with other more standard approaches.

Yuqi Jiang, Chune Zhang, Jiao Liu

Currently, image-denoising methods based on deep learning cannot adequately reconcile contextual semantic information and spatial details. To take these information optimizations into consideration, in this paper, we propose a Context-Space Progressive Collaborative Network (CS-PCN) for image denoising. CS-PCN is a multi-stage hierarchical architecture compo

Tobias Lojewski, Denis Golez, Katharina Ollefs, Loïc Le Guyader

Photo-doped states in strongly correlated charge transfer insulators are characterized by $d$-$d$ and $d$-$p$ interactions and the resulting intertwined dynamics of charge excitations and local multiplets. Here we use femtosecond x-ray absorption spectroscopy in combination with dynamical mean-field theory to disentangle these contributions in NiO. Upon reso

Snehasish Bhattacharjee

In recent times, astounding observations of both over- and under-luminous type Ia supernovae have emerged. These peculiar observations hint not only at surpassing the Chandrasekhar limit but may also suggest potential modifications in the physical attributes of their progenitors, such as their cooling rate. This, in turn, can influence their temporal assessm

Daowan Peng, Wei Wei, Xian-Ling Mao, Yuanyuan Fu

Generalization beyond in-domain experience to out-of-distribution data is of paramount significance in the AI domain. Of late, state-of-the-art Visual Question Answering (VQA) models have shown impressive performance on in-domain data, partially due to the language priors bias which, however, hinders the generalization ability in practice. This paper attempt

Yao Fu, Hao Peng, Tushar Khot, Mirella Lapata

We study whether multiple large language models (LLMs) can autonomously improve each other in a negotiation game by playing, reflecting, and criticizing. We are interested in this question because if LLMs were able to improve each other, it would imply the possibility of creating strong AI agents with minimal human intervention. We ask two LLMs to negotiate

Nicolas Moreno-Gomez, Athanasios G. Athanassiadis, Albert T. Poortinga, Peer Fischer

The benefits of ultrasound are its ease-of-use and its ability to precisely deliver energy in opaque and complex media. However, most materials responsive to ultrasound show a weak response, requiring the use of high powers, which are associated with undesirable streaming, cavitation, or temperature rise. These effects hinder response control and may even ca

Andreas Bluhm, Ángela Capel, Paul Gondolf, Antonio Pérez-Hernández

In this article, we generalize a proof technique by Alicki, Fannes and Winter and introduce a method to prove continuity bounds for entropic quantities derived from different quantum relative entropies. For the Umegaki relative entropy, we mostly recover known almost optimal bounds, whereas, for the Belavkin-Staszewski relative entropy, our bounds are new. F

CRAB collaboration, G. Soum-Sidikov, H. Abele, J. Burkhart

The emission of $\gamma$-rays after a neutron capture in a cryogenic detector can generate mono-energetic nuclear recoils in the sub-keV regime, of direct interest for the calibration of Dark Matter and Coherent Elastic Neutrino Nucleus Scattering experiments. Here we show that accurate predictions of the nuclear recoil spectra induced by neutron captures re

Hao Zhao

Bumblebee is an innovative backscatter system that leverages productive Bluetooth Low Energy (BLE) carriers to generate ZigBee transmissions. It improves upon previous systems like FreeRider and Interscatter by eliminating restrictions on exciters and receivers, resulting in reduced deployment costs and increased throughput. To enable long-distance signal tr

You-Cheng Chou, Leo Herr, Y. -P. Lee

We prove genus $g$ invariants in quantum $K$-theory are determined by genus zero invariants of a smooth stack in the spirit of K.~Costello's result in Gromov--Witten theory.

Tanise Ceron, Dmitry Nikolaev, Sebastian Padó

Automatic extraction of party (dis)similarities from texts such as party election manifestos or parliamentary speeches plays an increasing role in computational political science. However, existing approaches are fundamentally limited to targeting only global party (dis)-similarity: they condense the relationship between a pair of parties into a single figur

Bohan Zeng, Shanglin Li, Xuhui Liu, Sicheng Gao

Brain signal visualization has emerged as an active research area, serving as a critical interface between the human visual system and computer vision models. Although diffusion models have shown promise in analyzing functional magnetic resonance imaging (fMRI) data, including reconstructing high-quality images consistent with original visual stimuli, their

Hushnud Hushnud, Kalyan Dey

Strange particles are produced only during high-energy collisions and carry important information regarding collision dynamics. Recent results by the ALICE Collaboration on strangeness enhancement in high-multiplicity p+p collisions have highlighted the importance of the rope hadronization mechanism in high-energy nucleon-nucleon collisions. With the help of

Wei Feng, Lvwei Wang, Zaiyun Lin, Yanhao Zhu

Generative models for molecules based on sequential line notation (e.g. SMILES) or graph representation have attracted an increasing interest in the field of structure-based drug design, but they struggle to capture important 3D spatial interactions and often produce undesirable molecular structures. To address these challenges, we introduce Lingo3DMol, a po

Hyoung Suk Park, Chang Min Hyun, Sang-Hwy Lee, Jin Keun Seo

This paper presents a fully automatic registration method of dental cone-beam computed tomography (CBCT) and face scan data. It can be used for a digital platform of 3D jaw-teeth-face models in a variety of applications, including 3D digital treatment planning and orthognathic surgery. Difficulties in accurately merging facial scans and CBCT images are due t

Hoai An Le Thi, Thi Tuyet Trinh Nguyen

In secure group communications, users of a group share a common group key to prevent eavesdropping and protect the exchange content. A key server distributes the group key as well as performs group rekeying whenever the membership changes dynamically. Instead of rekeying after each join or leave request, we use batch rekeying to alleviate the out-of-sync pro

Andrea Goldwurm, Aleksandra Gros

Coded mask instruments have been used in high-energy astronomy for the last forty years now and designs for future hard X-ray/low gamma-ray telescopes are still based on this technique when they need to reach moderate angular resolutions over large field of views, particularly for observations dedicated to the, now flourishing, field of time domain astrophys

Wenjuan Sun, Stephan Chretien, Ander Biguri, Manuchehr Soleimani

Due to the merit of establishing volumetric data, X-ray computed tomography (XCT) is increasingly used as a non-destructive evaluation technique in the quality control of advanced manufactured parts with complex non-line-of-sight features. However, the cost of measurement time and data storage hampers the adoption of the technique in production lines. Commer

Angela Montanaro, Kyu Hyung Park, Francesca Fassioli, Francesca Giusti

In organic bulk heterojunction materials, charge delocalization has been proposed to play a vital role in the generation of free carriers by reducing the Coulomb attraction via an interfacial charge transfer exciton (CTX). Pump-push-probe (PPP) experiments produced evidence that the excess energy given by a push pulse enhances delocalization, thereby increas

Yifan Wang, Linlin Zhong

Physics-informed neural network (PINN) has been a prevalent framework for solving PDEs since proposed. By incorporating the physical information into the neural network through loss functions, it can predict solutions to PDEs in an unsupervised manner. However, the design of the neural network structure basically relies on prior knowledge and experience, whi

Zhenxing Zhang, Lambert Schomaker

The goal of a speech-to-image transform is to produce a photo-realistic picture directly from a speech signal. Recently, various studies have focused on this task and have achieved promising performance. However, current speech-to-image approaches are based on a stacked modular framework that suffers from three vital issues: 1) Training separate networks is

Ulrich Berger, Monika Seisenberger, Dieter Spreen, Hideki Tsuiki

Working in a semi-constructive logical system that supports the extraction of concurrent programs, we extract a program inverting non-singular real valued matrices from a constructive proof based on Gaussian elimination. Concurrency is used for efficient pivoting, that is, for finding an entry that is apart from zero in a non-null vector of real numbers.

Omer Belhasin, Yaniv Romano, Daniel Freedman, Ehud Rivlin

Uncertainty quantification for inverse problems in imaging has drawn much attention lately. Existing approaches towards this task define uncertainty regions based on probable values per pixel, while ignoring spatial correlations within the image, resulting in an exaggerated volume of uncertainty. In this paper, we propose PUQ (Principal Uncertainty Quantific

B. Kettle, C. Colgan, E. Los, E. Gerstmayr

Laser-driven compact particle accelerators can provide ultrashort pulses of broadband X-rays, well suited for undertaking X-ray absorption spectroscopy measurements on a femtosecond timescale. Here the Extended X-ray Absorption Fine Structure (EXAFS) features of the K-edge of a copper sample have been observed over a 250 eV window in a single shot using a la

Swapnil Fadte, Edna Vaz, Atul Kr. Ojha, Ramdas Karmali

Konkani is a highly nasalised language which makes it unique among Indo-Aryan languages. This work investigates the acoustic-phonetic properties of Konkani oral and nasal vowels. For this study, speech samples from six speakers (3 male and 3 female) were collected. A total of 74 unique sentences were used as a part of the recording script, 37 each for oral a

Hamed Behzadi-Khormouji, José Oramas

With the continue development of Convolutional Neural Networks (CNNs), there is a growing concern regarding representations that they encode internally. Analyzing these internal representations is referred to as model interpretation. While the task of model explanation, justifying the predictions of such models, has been studied extensively; the task of mode

Alvin Heng, Harold Soh

The recent proliferation of large-scale text-to-image models has led to growing concerns that such models may be misused to generate harmful, misleading, and inappropriate content. Motivated by this issue, we derive a technique inspired by continual learning to selectively forget concepts in pretrained deep generative models. Our method, dubbed Selective Amn

Van-Phuc Bui, Thinh Q. Dinh, Israel Leyva-Mayorga, Shashi Raj Pandey

The amount of data generated by Earth observation satellites can be enormous, which poses a great challenge to the satellite-to-ground connections with limited rate. This paper considers problem of efficient downlink communication of multi-spectral satellite images for Earth observation using change detection. The proposed method for image processing consist

Andrea Lampis, Eugenio Lomurno, Matteo Matteucci

Acquiring and annotating suitable datasets for training deep learning models is challenging. This often results in tedious and time-consuming efforts that can hinder research progress. However, generative models have emerged as a promising solution for generating synthetic datasets that can replace or augment real-world data. Despite this, the effectiveness

Giulio Masetti

In this paper a new conjecture equivalent to Collatz conjecture is presented. In particural, showing that (all) the solution(s) of newly introduced iterative functional equation(s) have a given property is equivalent to prove Collatz conjecture.

Daniel Kvak, Anna Chromcová, Petra Ovesná, Jakub Dandár

In this study, we developed a deep-learning-based automatic detection algorithm (DLAD, Carebot AI CXR) to detect and localize seven specific radiological findings (atelectasis (ATE), consolidation (CON), pleural effusion (EFF), pulmonary lesion (LES), subcutaneous emphysema (SCE), cardiomegaly (CMG), pneumothorax (PNO)) on chest X-rays (CXR). We collected 95

Sidra Aleem, Mayug Maniparambil, Suzanne Little, Noel O'Connor

Chest X-rays have been widely used for COVID-19 screening; however, 3D computed tomography (CT) is a more effective modality. We present our findings on COVID-19 severity prediction from chest CT scans using the STOIC dataset. We developed an ensemble deep learning based model that incorporates multiple neural networks to improve predictions. To address data

Ryota Kawasumi, Koujin Takeda

We study the problem of hyperparameter tuning in sparse matrix factorization under Bayesian framework. In the prior work, an analytical solution of sparse matrix factorization with Laplace prior was obtained by variational Bayes method under several approximations. Based on this solution, we propose a novel numerical method of hyperparameter tuning by evalua

Francesco Vissani

In this essay, I discuss an interdisciplinary science that is just blossoming: that of geo-neutrinos. I begin with a couple of episodes from the history of thought, showing the deep roots of Earth science and its many connections with microphysics. I then recall the stage of full maturity reached in the knowledge of neutrinos, which allows one to argue the f

Vito Barbara, Massimo Guarascio, Nicola Leone, Giuseppe Manco

Artificial Intelligence plays a main role in supporting and improving smart manufacturing and Industry 4.0, by enabling the automation of different types of tasks manually performed by domain experts. In particular, assessing the compliance of a product with the relative schematic is a time-consuming and prone-to-error process. In this paper, we address this

Alicia María Centurión-Fajardo, Alberto Lastra, Anier Soria-Lorente

A dual watermarking scheme based on Sobolev type orthogonal moments, Charlier and Meixner, is proposed based on different discrete measures. The existing relation through the connection formulas allows to provide with structure and recurrence relations, together with two difference equations satisfied by such families. Weighted polynomials derived from them