October 2023 arXiv papers — page 82

Thanakorn Thaminkaew, Piyawat Lertvittayakumjorn, Peerapon Vateekul

Prompt-based learning has shown its effectiveness in few-shot text classification. One important factor in its success is a verbalizer, which translates output from a language model into a predicted class. Notably, the simplest and widely acknowledged verbalizer employs manual labels to represent the classes. However, manual selection does not guarantee the

Denis Vida, Peter G. Brown, Margaret Campbell-Brown, Auriane Egal

18 mm-sized Orionid meteoroids were captured in 2019 and 2020 by the Canadian Automated Observatory's mirror tracking system. Meteor position measurements were made to an accuracy of $\sim1$ m and the meteors were tracked to a limiting magnitude of about $+7.5$ at the faintest point. The trajectory estimation shows the intrinsic physical dispersion of the Or

Bipin Indurkhya

Telling lies and faking emotions is quite common in human-human interactions: though there are risks, in many situations such behaviours provide social benefits. In recent years, there have been many social robots and chatbots that fake emotions or behave deceptively with their users. In this paper, I present a few examples of such robots and chatbots, and a

Han Zhou, Xingchen Wan, Ivan Vulić, Anna Korhonen

Prompt-based learning has been an effective paradigm for large pretrained language models (LLM), enabling few-shot or even zero-shot learning. Black-box prompt search has received growing interest recently for its distinctive properties of gradient-free optimization, proven particularly useful and powerful for model-as-a-service usage. However, the discrete

Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji

With the development of large language models (LLMs), striking a balance between the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmlessness presents a significant challenge during LLM training. To address this issue, we propose Safe Reinforcement Learning from Hu

Felix Ocker, Jörg Deigmöller, Julian Eggert

Service robots need common-sense knowledge to help humans in everyday situations as it enables them to understand the context of their actions. However, approaches that use ontologies face a challenge because common-sense knowledge is often implicit, i.e., it is obvious to humans but not explicitly stated. This paper investigates if Large Language Models (LL

Angsuman Das, Arnab Mandal

In this article, we introduce the study of a class of finite groups $G$ which admits a subgroup which intersects all non-trivial subgroups of $G$. We also explore a subclass of it consisting of all groups $G$ in which the prime order elements commute. In particular, we discuss the relationship between these class of groups with other known classes of finite

Benjamin Antieau, Achim Krause, Thomas Nikolaus

We develop prismatic and syntomic cohomology relative to a $\delta$-ring. This simultaneously generalizes Bhatt and Scholze's absolute and relative prismatic cohomology and shows that the latter, which was defined relative to a prism, is in fact independent of the prism structure and only depends on the underlying $\delta$-ring. We give several possible defi

Joshua Butke, Noriaki Hashimoto, Ichiro Takeuchi, Hiroaki Miyoshi

Whole-slide image analysis via the means of computational pathology often relies on processing tessellated gigapixel images with only slide-level labels available. Applying multiple instance learning-based methods or transformer models is computationally expensive as, for each image, all instances have to be processed simultaneously. The MLP-Mixer is an unde

Wensheng Lin, Yuna Yan, Lixin Li, Zhu Han

This letter proposes a novel anti-interference technique, semantic interference cancellation (SemantIC), for enhancing information quality towards the sixth-generation (6G) wireless networks. SemantIC only requires the receiver to concatenate the channel decoder with a semantic auto-encoder. This constructs a turbo loop which iteratively and alternately elim

Anne-Kathrin Schmuck, K. S. Thejaswini, Irmak Sağlam, Satya Prakash Nayak

This paper considers the problem of solving infinite two-player games over finite graphs under various classes of progress assumptions motivated by applications in cyber-physical system (CPS) design. Formally, we consider a game graph G, a temporal specification $\Phi$ and a temporal assumption $\psi$, where both are given as linear temporal logic (LTL) form

Alexander Arimond, Mauro Molteni, Dominik Jany, Zornitsa Manolova

We propose the application of Transformer-based language models for classifying entity legal forms from raw legal entity names. Specifically, we employ various BERT variants and compare their performance against multiple traditional baselines. Our evaluation encompasses a substantial subset of freely available Legal Entity Identifier (LEI) data, comprising o

Wanli Sun, Zehai Tu, Anton Ragni

Recently there has been a lot of interest in non-autoregressive (non-AR) models for speech synthesis, such as FastSpeech 2 and diffusion models. Unlike AR models, these models do not have autoregressive dependencies among outputs which makes inference efficient. This paper expands the range of available non-AR models with another member called energy-based m

Sofia Canevarolo, Nora Elisa Chisari

Next generation gravitational waves (GWs) observatories are expected to measure GW signals with unprecedented sensitivity, opening new, independent avenues to learn about our Universe. The distance-redshift relation is a fulcrum for cosmology and can be tested with GWs emitted by merging binaries of compact objects, called standard sirens, thanks to the fact

Colin Riba, Solal Stern

We devise a version of Linear Temporal Logic (LTL) on a denotational domain of streams. We investigate this logic in terms of domain theory, (point-free) topology and geometric logic. This yields the first steps toward an extension of the "Domain Theory in Logical Form" paradigm to temporal liveness properties. We show that the negation-free formulae of LTL

Inge S. Helland

Quantum decision theory is introduced here, and new basis for this theory is proposed. It is first based upon the author's general arguments for the Hilbert space formalism in quantum theory, next on arguments for the Born rule, that is, the basis for calculating quantum probabilities. A basic notion behind the quantum theory foundation is that of theoretica

Lida Zhu, Shuichi Makita, Junya Tamaoki, Yiqiang Zhu

Polarization-sensitive optical coherence tomography (PS-OCT) is a promising biomedical imaging tool for differentiation of various tissue properties. However, the presence of multiple-scattering (MS) signals can degrade the quantitative polarization measurement accuracy. We demonstrate a method to reduce MS signals and increase the measurement accuracy of Jo

Richard S. J. Tol

The social cost of carbon is the damage avoided by slightly reducing carbon dioxide emissions. It is a measure of the desired intensity of climate policy. The social cost of carbon is highly uncertain because of the long and complex cause-effect chain, and because it quantifies and aggregates impacts over a long period of time, affecting all people in a wide

F. Castellani, N. Clerc, E. Pointecouteau, Y. M. Bahé

Context: Observations of the hot gas in distant clusters of galaxies, though challenging, are key to understand the role of intense galaxy activity, super-massive black hole feedback and chemical enrichment in the process of massive halos assembly. Aims: We assess the feasibility to retrieve, using X-ray hyperspectral data only, the thermodymamical hot gas p

Kecen Li, Chen Gong, Zhixiang Li, Yuzhong Zhao

Differential Privacy (DP) image data synthesis, which leverages the DP technique to generate synthetic data to replace the sensitive data, allowing organizations to share and utilize synthetic images without privacy concerns. Previous methods incorporate the advanced techniques of generative models and pre-training on a public dataset to produce exceptional

Bishal Ghosh, Sandipan Mohanta, Manas Kulkarni, Bijay Kumar Agarwalla

We investigate open quantum dynamics for a one-dimensional incommensurate Aubry-Andr\'{e}-Harper lattice chain, a part of which is initially filled with electrons and is further connected to dephasing probes at the filled lattice sites. This setup is akin to a step-initial configuration where the non-zero part of the step is subjected to dephasing. We invest

Sivaraman Balakrishnan, Edward Kennedy, Larry Wasserman

In causal inference, the joint law of a set of counterfactual random variables is generally not identified. We show that a conservative version of the joint law - corresponding to the smallest treatment effect - is identified. Finding this law uses recent results from optimal transport theory. Under this conservative law we can bound causal effects and we ma

Liyun Cao, Sheng Wan, Badreddine Assouar

The optical and acoustic metagratings have addressed the limitations of low-efficiency wave manipulation and high-complexity fabrication of metamaterials and metasurfaces. In this research, we introduce the concept of elastic metagrating and present the theoretical and experimental demonstration of locally resonant elastic metagrating (LREM). Remarkably, the

Yuanduo Hong, Jue Wang, Weichao Sun, Huihui Pan

In the wake of Masked Image Modeling (MIM), a diverse range of plain, non-hierarchical Vision Transformer (ViT) models have been pre-trained with extensive datasets, offering new paradigms and significant potential for semantic segmentation. Current state-of-the-art systems incorporate numerous inductive biases and employ cumbersome decoders. Building upon t

P. Peñil, J. R. Westernacher-Schneider, M. Ajello, A. Domínguez

Blazars display variable emission across the entire electromagnetic spectrum, with timescales that can range from a few minutes to several years. Our recent work has shown that a sample of five blazars exhibit hints of periodicity with a global significance $\gtrsim2\,\sigma$ at $\gamma$-ray energies, in the range of 0.1~GeV$<$E$<$800~GeV. In this work, we s

Xunzhu Tang, Zhenghan Chen, Saad Ezzini, Haoye Tian

In recent years, patch representation learning has emerged as a necessary research direction for exploiting the capabilities of machine learning in software generation. These representations have driven significant performance enhancements across a variety of tasks involving code changes. While the progress is undeniable, a common limitation among existing m

Hongyuan Zhang, Xuelong Li

Spectral clustering and its extensions usually consist of two steps: (1) constructing a graph and computing the relaxed solution; (2) discretizing relaxed solutions. Although the former has been extensively investigated, the discretization techniques are mainly heuristic methods, e.g., k-means, spectral rotation. Unfortunately, the goal of the existing metho

Hao Sun, John Hewitt

The Backpack is a Transformer alternative shown to improve interpretability in English language modeling by decomposing predictions into a weighted sum of token sense components. However, Backpacks' reliance on token-defined meaning raises questions as to their potential for languages other than English, a language for which subword tokenization provides a r

Chengze Duan

Let G be a connected reductive group over an algebraically closed field and W be its Weyl group. Steinberg constructed a transversal slice of the regular unipotent orbit in G. This construction was generalized to n was generalized to unipotent orbits associated to any elliptic conjugacy classes of W by He and Lusztig via good minimal length elements. In this

Elias Wölfer, Gerrit Budde, Thorsten Kleine

The carbonaceous Bencubbin-like (CB), high-metal (CH), and Renazzo-like (CR) chondrites are metal-rich chondrites that have been suggested to be genetically linked and are sometimes grouped together as the CR chondrite clan. Of these, the CB and CH chondrites are thought to have formed in an impact-generated vapor-melt plume from material that may be isotopi

Bernhard Böhmler, Karin Erdmann, Viktória Klász, Rene Marczinzik

Let $KG$ be a group algebra with $G$ a finite group and $K$ a field and $M$ an indecomposable $KG$-module. We pose the question, whether $Ext_{KG}^1(M,M) \neq 0$ implies that $Ext_{KG}^i(M,M) \neq 0$ for all $i \geq 1$. We give a positive answer in several important special cases such as for periodic groups and give a positive answer also for all Nakayama al

Lingjun Liu, Shu Wang, Lingda Xu

This paper investigates the decay rates of the contact wave in one-dimensional Navier-Stokes equations. We study two cases of perturbations, with and without zero mass condition, i.e., the integration of initial perturbations is zero and non-zero, respectively. For the case without zero mass condition, we obtain the optimal decay rate $(1+t)^{-\frac{1}{2}}$

Zilong Zhao, Robert Birke, Lydia Chen

Tabular data synthesis is crucial for addressing privacy and security concerns in industries reliant on tabular data. While recent advancements adopt large language models (LLMs) for realistic tabular data generation, their long training times and limited reusability hinder practical applications. In this paper, we propose Tabula, a tabular data synthesizer

S. Valder, A. Gibson-Foster, E. Falk, S. J. M. Peeters

Located 2 km underground in SNOLAB, Sudbury, Canada, SNO+ is a large scale liquid scintillator experiment that primarily aims to search for neutrinoless double beta decay. Whilst SNO+ has light and radioactive calibration sources external to the inner volume, an internally deployed optical source is necessary for the full characterization of the detector mod

Petter Hofverberg, Francoise Bezerra, Marine Ruffenach, Joêl Hérault

A 60 MeV proton beam-line has been developed in Nice, France, in collaboration with the Centre National d'Etudes Spatiales (CNES). Experimental results are presented here to validate the beam-line for radiation hardness testing.

Federico Fontana

The main goal of this work is to improve the efficiency of training binary neural networks, which are low latency and low energy networks. The main contribution of this work is the proposal of two solutions comprised of topology changes and strategy training that allow the network to achieve near the state-of-the-art performance and efficient training. The t

Kyriakos Flouris, Ender Konukoglu

Manifold learning flows are a class of generative modelling techniques that assume a low-dimensional manifold description of the data. The embedding of such a manifold into the high-dimensional space of the data is achieved via learnable invertible transformations. Therefore, once the manifold is properly aligned via a reconstruction loss, the probability de

David Huber, Felix Birkelbach, René Hofmann

To combat climate change, we need to reduce emissions from the transport sector. Synthetic fuels are a long-term solution for aviation, maritime and heavy machinery. Large-scale use requires cost-effectiveness, efficient production and resilience to price changes. In this case study, we simultaneously optimize the cell voltage of the solid oxide electrolysis

Sören Schweers, David F. Locher, Gunter M. Schütz, Philipp Maass

We show that domain walls separating coexisting extremal current phases in driven diffusive systems exhibit complex stochastic dynamics, with a subdiffusive temporal growth of position fluctuations due to long-range anticorrelated current fluctuations and a weak pinning at long times. This weak pinning manifests itself in a saturated width of the domain wall

Wenceslao Arroyo-Machado, Enrique Herrera-Viedma, Daniel Torres-Salinas

Twitter bots are a controversial element of the platform, and their negative impact is well known. In the field of scientific communication, they have been perceived in a more positive light, and the accounts that serve as feeds alerting about scientific publications are quite common. However, despite being aware of the presence of bots in the dissemination

Mathias Sonnleitner, Mario Ullrich

This survey is concerned with the power of random information for approximation in the (deterministic) worst-case setting, with special emphasis on information consisting of functionals selected independently and identically distributed (iid) at random on a class of admissible information functionals. We present a general result based on a weighted least squ

Justin Kin Jun Hew, Kenneth Duru, Stephen Roberts, Christopher Zoppou

We present an energy/entropy stable and high order accurate finite difference (FD) method for solving the nonlinear (rotating) shallow water equations (SWEs) in vector invariant form using the newly developed dual-pairing and dispersion-relation preserving summation by parts (SBP) FD operators. We derive new well-posed boundary conditions (BCs) for the SWE i

Luo-bin Lin, Fu-quan Chen, Shang-shun Lin

A new mechanical model on noncircular shallow tunnelling considering initial stress field is proposed in this paper by constraining far-field ground surface to eliminate displacement singularity at infinity, and the originally unbalanced tunnel excavation problem in existing solutions is turned to an equilibrium one of mixed boundaries. By applying analytic

Aravinda Reddy PN, K. Sreenivasa Rao, Raghavendra Ramachandra, Pabitra mitra

We present a novel face swapping method using the progressively growing structure of a pre-trained StyleGAN. Previous methods use different encoder decoder structures, embedding integration networks to produce high-quality results, but their quality suffers from entangled representation. We disentangle semantics by deriving identity and attribute features se

Vadim Gorin, Karl Liechty

We study the behavior of configurations in the symmetric six-vertex model with $a,b,c$ weights in the $n\times n$ square with Domain Wall Boundary Conditions as $n\to\infty$. We prove that when $\Delta=\frac{a^2+b^2-c^2}{2ab}<1$, configurations near the boundary have fluctuations of order $n^{1/2}$ and are asymptotically described by the GUE-corners process

Emmanuel Fricain, Andreas Hartmann, William T. Ross, Dan Timotin

This paper contains sharp bounds on the coefficients of the polynomials $R$ and $S$ which solve the classical one variable B\'{e}zout identity $A R + B S = 1$, where $A$ and $B$ are polynomials with no common zeros. The bounds are expressed in terms of the separation of the zeros of $A$ and $B$. Our proof involves contour integral representations of these co

Yiming Wang, Qian Huang, Bin Tang, Huashan Sun

Recently, learned video compression has achieved exciting performance. Following the traditional hybrid prediction coding framework, most learned methods generally adopt the motion estimation motion compensation (MEMC) method to remove inter-frame redundancy. However, inaccurate motion vector (MV) usually lead to the distortion of reconstructed frame. In add

Aida Koch, Alix Petit, Christian Schmidt, Adrain Vdberg

Given the importance of the claim, we want to start by exposing the following consideration: this claim comes out more than a year after the article "Practical applications of Set Shaping Theory in Huffman coding" which reports the program that carried out an experiment of data compression in which the coding limit NH0(S) of a single sequence was questioned.

Viktor Wase, Otte Marthin, Albin Fredriksson, Anton Finnson

Background: Proton arc therapy is an emerging radiation therapy technique where either the gantry or the patient continuously rotates during the irradiation treatment. One of the perceived advantages of proton arc therapy is the reduced treatment time, but it is still unclear exactly how long these treatment times will be, given that no machine capable of it

Daniel Kaiser, Siddharth Patwardhan, Minsuk Kim, Filippo Radicchi

Multiplex networks are collections of networks with identical nodes but distinct layers of edges. They are genuine representations for a large variety of real systems whose elements interact in multiple fashions or flavors. However, multiplex networks are not always simple to observe in the real world; often, only partial information on the layer structure o

Matteo Frosi, Mirko Usuelli, Matteo Matteucci

Radar odometry estimation has emerged as a critical technique in the field of autonomous navigation, providing robust and reliable motion estimation under various environmental conditions. Despite its potential, the complex nature of radar signals and the inherent challenges associated with processing these signals have limited the widespread adoption of thi

Eliezer Batista, William Hautekiet, Paolo Saracco, Joost Vercruysse

Making the first steps towards a classification of simple partial comodules, we give a general construction for partial comodules of a Hopf algebra \(H\) using central idempotents in right coideal subalgebras and show that any \(1\)-dimensional partial comodule is of that form. We conjecture that in fact all finite-dimensional simple partial \(H\)-comodules

Johann-Mattis List, Nathan W. Hill, Robert Forkel, Frederic Blum

Despite the inherently fuzzy nature of reconstructions in historical linguistics, most scholars do not represent their uncertainty when proposing proto-forms. With the increasing success of recently proposed approaches to automating certain aspects of the traditional comparative method, the formal representation of proto-forms has also improved. This formali

Bujiao Wu, Dax Enshan Koh

Efficiently estimating fermionic Hamiltonian expectation values is vital for simulating various physical systems. Classical shadow (CS) algorithms offer a solution by reducing the number of quantum state copies needed, but noise in quantum devices poses challenges. We propose an error-mitigated CS algorithm assuming gate-independent, time-stationary, and Mar

Rui-Bo Jin, Zi-Qi Zeng, Dan Xu, Chen-Zhi Yuan

Franson interference can be used to test the nonlocal features of energy-time entanglement and has become a standard in quantum physics. However, most of the previous Franson interference experiments were demonstrated in the time domain, and the spectral properties of Franson interference have not been fully explored. Here, we theoretically and experimentall

Grażyna Mirkowska, Andrzej Salwicki

The Collatz hypothesis is a theorem of the algorithmic theory of natural numbers. We prove the (algorithmic) formula that expresses the halting property of Collatz algorithm. The observation that Collatz&#39;s theorem cannot be proved in any elementary number theory completes the main result.

Yuanxing Xu, Yuting Wei, Bin Wu

The surge in video and social media content underscores the need for a deeper understanding of multimedia data. Most of the existing mature video understanding techniques perform well with short formats and content that requires only shallow understanding, but do not perform well with long format videos that require deep understanding and reasoning. Deep Vid

Arup Mondal, Ruthu Hulikal Rooparaghunath, Debayan Gupta

A Time-lock puzzle (TLP) sends information into the future: a predetermined number of sequential computations must occur (i.e., a predetermined amount of time must pass) to retrieve the information, regardless of parallelization. Buoyed by the excitement around secure decentralized applications and cryptocurrencies, the last decade has witnessed numerous con

Debora Impera, Michele Rimoldi

We prove an abstract structure theorem for weighted manifolds supporting a weighted $f$-Poincar\'e inequality and whose ends satisfy a suitable non-integrability condition. We then study how our arguments can be used to obtain full topological control on two important classes of hypersurfaces of the Euclidean space, namely translators and self-expanders for

Li Luo, Zheming Xu

We develop an invariant theory of quasi-split $\imath$quantum groups $\mathbf{U}_n^\imath$ of type AIII on a tensor space associated to $\imath$Howe dualities. The first and second fundamental theorems for $\mathbf{U}_n^\imath$-invariants are derived.

T. Z. Esirkepov, S. V. Bulanov

As predicted by A. Einstein [Ann. Phys. (Leipzig) 17, 891 (1905)], the electromagnetic wave reflected at a moving mirror is frequency-upshifted and intensified as high as the mirror velocity is close to the speed of light in vacuum. However, at this limit the mirror reflectivity vanishes, because the higher the wave frequency the more transparent matter is.

Bhaskar Arya, Tirthankar Roy Choudhury, Aseem Paranjape, Prakash Gaikwad

Observations of the Lyman-$\alpha$ forest in distant quasar spectra with upcoming surveys are expected to provide significantly larger and higher-quality datasets. To interpret these datasets, it is imperative to develop efficient simulations. One such approach is based on the assumption that baryonic densities in the intergalactic medium (IGM) follow a logn

David O. Tiede, Carlos Romero-Pérez, Katherine A. Koch, K. Burak Ucer

Quantum dot (QD) solids are being widely exploited as a solution-processable technology to develop photovoltaic, light-emission, and photo-detection devices. Charge transport in these materials is the result of a compromise between confinement at the individual QD level and electronic coupling among the different nanocrystals in the ensemble. While this is c

Samudrajit Thapa, Daniel Zaretzky, Ron Vatash, Grzegorz Gradziuk

In the absence of directional motion it is often hard to recognize athermal fluctuations. Probability currents provide such a measure in terms of the rate at which they enclose area in the reduced phase space. We measure this area enclosing rate for trapped colloidal particles, where only one particle is driven. By combining experiment, theory, and simulatio

T. S. Ramazanov S. K. Kodanova, M. K. Issanova, B. Z. Kenzhegulov

The effect of an ionic core on the temperature relaxation in dense hot plasma of beryllium is studied using the pseudpotential model by Gericke et al [Phys. Rev. E 2010, 81, 065401(R)]. Employing the screened version of the ion pseudpotential [by Ramazanov et al, Phys. Plasmas 2021, 28 (9), 092702], we computed the quantum transport cross-section for the ele

Ashok Vardhan Makkuva, Marco Bondaschi, Thijs Vogels, Martin Jaggi

Data-parallel SGD is the de facto algorithm for distributed optimization, especially for large scale machine learning. Despite its merits, communication bottleneck is one of its persistent issues. Most compression schemes to alleviate this either assume noiseless communication links, or fail to achieve good performance on practical tasks. In this paper, we c

Carles Roch I Carceller, Jonatan Bohr Brask

Many protocols and tasks in quantum information science rely inherently on the fundamental notion of contextuality to provide advantages over their classical counterparts, and contextuality represents one of the main differences between quantum and classical physics. In this work we present a witness for preparation contextuality inspired by optimal two-stat

Hongbing Huang, Zhonglu Lin, Wei Zheng, Jinhu Zhang

It is well known that tuna fish in the ocean can dynamically morph their median fins to achieve optimal hydrodynamic performance, e.g. linear acceleration and maneuverability. In this study, based on the previous studies about the median fin's hydrodynamic effects focusing on tethered conditions, we continue to explore the hydrodynamic function of tuna morph

Naoto Shiraishi

The role of the Wasserstein distance in the thermodynamic speed limit inequalities for Markov jump processes is investigated. We elucidate the nature of the Wasserstein distance in the thermodynamic speed limit inequality from three different perspectives with resolving three remaining problems. In the first part, we derive a unified speed limit inequality f

Yaohua Liu, Jiaxin Gao, Xianghao Jiao, Zhu Liu

Adversarial Training (AT), pivotal in fortifying the robustness of deep learning models, is extensively adopted in practical applications. However, prevailing AT methods, relying on direct iterative updates for target model's defense, frequently encounter obstacles such as unstable training and catastrophic overfitting. In this context, our work illuminates

Aniruddha Deshmukh, Ashisha Kumar

In this article, we discuss the equality of two inner products on a vector space. Particularly, we look at some geometric properties that are given to a vector space by an inner product namely, length and angle, and we ask under what conditions on these geometric properties do two inner products coincide. Some parts of this article can be found as exercise p

Daniel Lengyel, Panos Parpas, Nikolas Kantas, Nicholas R. Jennings

The simplex gradient, a popular numerical differentiation method due to its flexibility, lacks a principled method by which to construct the sample set, specifically the location of function evaluations. Such evaluations, especially from real-world systems, are often noisy and expensive to obtain, making it essential that each evaluation is carefully chosen

Ed Mackay, Philip Jonathan

We present a new framework for modelling multivariate extremes, based on an angular-radial representation of the probability density function. Under this representation, the problem of modelling multivariate extremes is transformed to that of modelling an angular density and the tail of the radial variable, conditional on angle. Motivated by univariate theor

Ji Zhou, Haide Wang, Liangchuan Li, Weiping Liu

Point-to-multi-point (PtMP) optical networks become the main solutions for network-edge applications such as passive optical networks and radio access networks. Entropy-loading digital subcarrier multiplexing (DSCM) is the core technology to achieve low latency and approach high capacity for flexible PtMP optical networks. However, the high peak-to-average p

Jun Liu, Jiantao Zhou, Haiwei Wu, Weiwei Sun

Online Social Networks (OSNs) have blossomed into prevailing transmission channels for images in the modern era. Adversarial examples (AEs) deliberately designed to mislead deep neural networks (DNNs) are found to be fragile against the inevitable lossy operations conducted by OSNs. As a result, the AEs would lose their attack capabilities after being transm

Jun Liu, Jiantao Zhou, Jinyu Tian, Weiwei Sun

With the increasing prevalence of cloud computing platforms, ensuring data privacy during the cloud-based image related services such as classification has become crucial. In this study, we propose a novel privacypreserving image classification scheme that enables the direct application of classifiers trained in the plaintext domain to classify encrypted ima

Goda Klumbyte, Hannah Piehl, Claude Draude

This paper presents a series of feminist epistemological concepts as tools for developing critical, more accountable, and contextualised approaches to machine learning systems design. Namely, we suggest that the methods of situated knowledges or situating, figurations or figuring, diffraction or diffracting, and critical fabulation or speculation can be prod

Ruthu Hulikal Rooparaghunath, T. S. Harikrishnan, Debayan Gupta

The average user has between 90-130 online accounts, and around $3 \times 10^{11}$ passwords are in use this year. Most people are terrible at remembering "random" passwords, so they reuse or create similar passwords using a combination of predictable words, numbers, and symbols. Previous password-generation or management protocols have imposed so large a co

Zhihong Chen, Zilei Wang, Yixin Zhang

Source-free object detection (SFOD) aims to adapt a source-trained detector to an unlabeled target domain without access to the labeled source data. Current SFOD methods utilize a threshold-based pseudo-label approach in the adaptation phase, which is typically limited to high-confidence pseudo-labels and results in a loss of information. To address this iss

Yuming Fu, Xue-Bing Wu, Yifan Li, Yuxuan Pang

A complete and pure sample of quasars with accurate redshifts is crucial for quasar studies and cosmology. In this paper, we present CatNorth, an improved Gaia DR3 quasar candidate catalog with more than 1.5 million sources in the 3$\pi$ sky built with data from Gaia, Pan-STARRS1, and CatWISE2020. The XGBoost algorithm is used to reclassify the original Gaia

Alan Haynes

In this paper we prove that all irrational numbers from totally real cubic number fields are well approximable by rationals (i.e. the partial quotients in the continued fraction expansion of such a number are unbounded). This settles the long standing open question of whether or not well approximable algebraic numbers exist. Our proof uses a number theoretic

Mario Kahlhofer, Patrick Kern, Sören Henning, Stefan Rass

Researchers and engineers are increasingly adopting cloud-native technologies for application development and performance evaluation. While this has improved the reproducibility of benchmarks in the cloud, the complexity of cloud-native environments makes it difficult to run benchmarks reliably. Cloud-native applications are often instrumented or altered at

Pete Austin, Sougata Bose, Patrick Totzke

Temporal graphs are a popular modelling mechanism for dynamic complex systems that extend ordinary graphs with discrete time. Simply put, time progresses one unit per step and the availability of edges can change with time. We consider the complexity of solving $\omega$-regular games played on temporal graphs where the edge availability is ultimately periodi

Francesco Calvanese, Camille N. Lambert, Philippe Nghe, Francesco Zamponi

Generative probabilistic models emerge as a new paradigm in data-driven, evolution-informed design of biomolecular sequences. This paper introduces a novel approach, called Edge Activation Direct Coupling Analysis (eaDCA), tailored to the characteristics of RNA sequences, with a strong emphasis on simplicity, efficiency, and interpretability. eaDCA explicitl

J. Escandón-Monardes, D. Uzcátegui, M. Rivera-Tapia, S. P. Walborn

We propose an estimation procedure for $d$-dimensional unitary transformations. For $d>2$, the unitary transformations close to the identity are estimated saturating the quantum Cram\'er-Rao bound. For $d=2$, the estimation of all unitary transformations is also optimal with some prior information. We show through numerical simulations that, even in the abse

Maarten V. de Hoop, Matti Lassas, Jinpeng Lu, Lauri Oksanen

We consider the inverse fault friction problem of determining the friction coefficient in the Tresca friction model, which can be formulated as an inverse problem for differential inequalities. We show that the measurements of elastic waves during a rupture uniquely determine the friction coefficient at the rupture surface with explicit stability estimates.

Yan-Fang Li, Dongkyu Lim, Feng Qi

In the paper, by virtue of the famous formula of Fa\`a di Bruno, with the aid of several identities of partial Bell polynomials, by means of a formula for derivatives of the ratio of two differentiable functions, and with availability of other techniques, the authors establish closed-form formulas in terms of the Bernoulli numbers and the second kind Stirlin

Thorsten Ruprechter, Manoel Horta Ribeiro, Robert West, Denis Helic

Wikipedia, the Web's largest encyclopedia, frequently faces content disputes or malicious users seeking to subvert its integrity. Administrators can mitigate such disruptions by enforcing "page protection" that selectively limits contributions to specific articles to help prevent the degradation of content. However, this practice contradicts one of Wikipedia

Hang Xie, Nan Zhang, Yuteng Ma, Xin Chen

Recently orbital Hall current has attracted attention as an alternative method to switch the magnetization of ferromagnets. Here we present our findings on electrical switching of antiferromagnetic state in Mn3Sn/Cr, where despite the much smaller spin Hall angle of Cr, the switching current density is comparable to heavy metal based heterostructures. On the

Amélie Gheerbrant, Leonid Libkin, Alexandra Rogova, Cristina Sirangelo

To answer database queries over incomplete data the gold standard is finding certain answers: those that are true regardless of how incomplete data is interpreted. Such answers can be found efficiently for conjunctive queries and their unions, even in the presence of constraints. With negation added, the problem becomes intractable however. We concentrate on

Arup Mondal, Ruthu Hulikal Rooparaghunath, Debayan Gupta

Buoyed by the excitement around secure decentralized applications, the last few decades have seen numerous constructions of distributed randomness beacons (DRB) along with use cases; however, a secure DRB (in many variations) remains an open problem. We further note that it is natural to want some kind of reward for participants who spend time and energy eva

Thalles Silva, Adín Ramírez Rivera

We present Consistent Assignment of Views over Random Partitions (CARP), a self-supervised clustering method for representation learning of visual features. CARP learns prototypes in an end-to-end online fashion using gradient descent without additional non-differentiable modules to solve the cluster assignment problem. CARP optimizes a new pretext task base

Adrien Weihs, Jalal Fadili, Matthew Thorpe

Higher-order regularization problem formulations are popular frameworks used in machine learning, inverse problems and image/signal processing. In this paper, we consider the computational problem of finding the minimizer of the Sobolev $\mathrm{W}^{1,p}$ semi-norm with a data-fidelity term. We propose a discretization procedure and prove convergence rates b

Atharva Sehgal, Arya Grayeli, Jennifer J. Sun, Swarat Chaudhuri

We introduce Cosmos, a framework for object-centric world modeling that is designed for compositional generalization (CompGen), i.e., high performance on unseen input scenes obtained through the composition of known visual "atoms." The central insight behind Cosmos is the use of a novel form of neurosymbolic grounding. Specifically, the framework introduces

Anusha M. Krishnan, Michael Wiemeler

We show that closed, simply connected, positively curved 10-manifolds with effective, isometric actions of $3$-dimensional tori are homotopy spheres or homotopy complex projective spaces.

Lucas Maison, Hélion du Mas des Bourboux, Thomas Courtat

Compressing neural networks is a key step when deploying models for real-time or embedded applications. Factorizing the model's matrices using low-rank approximations is a promising method for achieving compression. While it is possible to set the rank before training, this approach is neither flexible nor optimal. In this work, we propose a post-training ra

Viviane Pons

I present an overview of the research I have conducted for the past ten years in algebraic, bijective, enumerative, and geometric combinatorics. The two main objects I have studied are the permutahedron and the associahedron as well as the two partial orders they are related to: the weak order on permutations and the Tamari lattice. This document contains a

Yizhou Peng, Songjie Yang, Wanting Lyu, Ya Li

In this letter, a weighted minimum mean square error (WMMSE) empowered integrated sensing and communication (ISAC) system is investigated. One transmitting base station and one receiving wireless access point are considered to serve multiple users a sensing target. Based on the theory of mutual-information (MI), communication MI and sensing MI rate are utili

Guangzhou Chen, Daniel Horsley, Adam Mammoliti

For positive integers $m$ and $n$, the Zarankiewicz number $Z_{2,2}(m,n)$ can be defined as the maximum total degree of a linear hypergraph with $m$ vertices and $n$ edges. Guy determined $Z_{2,2}(m,n)$ for all $n \geq \binom{m}{2}/3+O(m)$. Here, we extend this by determining $Z_{2,2}(m,n)$ for all $n \geq \binom{m}{2}/3$ and, when $m$ is large, for all $n \

Naresh Kshetri, Vasudha, Denisa Hoxha

Technology has advanced dramatically in the previous several years. There are also cyber assaults. Cyberattacks pose a possible danger to information security and the general public. Since data practice and internet consumption rates continue to upswing, cyber awareness has become progressively important. Furthermore, as businesses pace their digital transfo