November 2025 arXiv papers — page 47

Jared N. Lakhani, Etienne Pienaar

This study investigates the limitations of applying Markov Chain Monte Carlo (MCMC) methods to arbitrary objective functions, focusing on a two-block MCMC framework which alternates between Metropolis-Hastings and Gibbs sampling. While such approaches are often considered advantageous for enabling data-driven regularization, we show that their performance cr

Alexander Van Werde

Sufficient conditions for a simple graph to be characterized up to isomorphism given its spectrum and the spectrum of its complement graph are known due to Wang and Xu. This note establishes a related sufficient condition in the presence of loops: if the walk matrix has square-free determinant, then the graph is characterized by its generalized spectrum. The

Mattia Di Mauro

We present a minimal secluded dark-matter (DM) framework based on an extra $U(1)_X$ gauge symmetry. The model contains a Dirac DM particle $\chi$, three heavy neutrinos $N_I$ with masses $M_{N,I}$, and a singlet scalar $R$ that mixes with the Standard Model Higgs doublet $\Phi$ by an angle $\alpha$. A symmetry forbids the $\Phi$-$R$ portal at tree level; the

Barmak Shams Es Haghi

We argue that the benchmark freeze in dark matter (DM) scenario for direct detection experiments, in which a DM candidate interacts with the Standard Model (SM) through an ultralight dark photon, becomes sensitive to the visible sector reheating temperature if it is sufficiently high. At such temperatures, the irreducible ultraviolet (UV) freeze in productio

CHIME Collaboration, Mandana Amiri, Kevin Bandura, Arnab Chakraborty

We present the first detection of the cosmological 21 cm intensity mapping signal in auto-correlation at z ~ 1 with the Canadian Hydrogen Intensity Mapping Experiment (CHIME). Using 94 nights of observation, we have measured the 21 cm auto-power spectrum over a frequency range from 608.2 MHz to 707.8 MHz (z = 1.34 to 1.01) at 0.4 h Mpc^-1 < k < 1.5 h Mpc^-1,

Wim Beenakker, Daniël Mikkers, Anh Vu Phan, Susanne Westhoff

We perform a comprehensive analysis of dark matter-nucleon scattering via the exchange of axion-like particles (ALPs). At first sight, this might appear of little practical use, as non-relativistic scattering through pseudo-scalar interactions is momentum-suppressed and spin-dependent, resulting in scattering rates below any experimental sensitivity. We show

Frank Petriello, Kaan Şimşek

We propose the ``naive" $T$-odd Collins-Soper moments in the Drell-Yan process as probes of previously unexplored directions in the Standard Model Effective Field Theory (SMEFT) parameter space. We show that the moments $A_6$ and $A_7$ in the high invariant mass and transverse momentum region are sensitive to dimension-8 $CP$-odd semi-leptonic four-fermion o

Atul Rathor, Sahanawaj Akhtar, Arijit Haldar

We predict an in-plane, or hidden, Berry curvature (BC) for magnons in electrically insulating quasi-2D magnets and demonstrate that the hidden magnon Berry curvature (HMBC) gives rise to a previously unrecognized form of vertical, out-of-plane, magnon transport. Combining a semiclassical framework with Boltzmann transport theory, we show that the vertical m

Damian Rovara, Lukas Burgholzer, Robert Wille

The Quantum Approximate Optimization Algorithm (QAOA) requires considered optimization problems to be translated into a compatible format. A popular transformation step in this pipeline involves the quadratization of higher-order binary optimization problems, translating them into Quadratic Unconstrained Binary Optimization (QUBO) formulations through the in

Ruihua Fan, Yantao Wu, Yimu Bao, Zhehao Dai

We investigate whether sequential unitary circuits can prepare two-dimensional chiral states, using a correspondence between sequentially prepared states, isometric tensor network states, and one-dimensional quantum channel circuits. We establish two no-go theorems, one for Gaussian fermion systems and one for generic interacting systems. In Gaussian fermion

Sara Manzini, Stanislav Babak

Pulsar Timing Arrays (PTAs) observations will detect gravitational waves (GWs) from the early inspiral phase of supermassive black hole binaries (SMBHBs) with orbital periods of weeks to years. Current PTA analyses generally assume circular binaries; however, dynamical interactions with the surrounding environment can prevent complete circularisation, allowi

Madisyn Brooks, Jonathan R. Trump, Raymond C. Simons, Justin Cole

JWST has revealed an abundance of low-luminosity active galactic nuclei (AGN) at high redshifts ($z > 3$), pushing the limits of black hole (BH) science in the early Universe. Results have claimed that these BHs are significantly more massive than expected from the BH mass-host galaxy stellar mass relation derived from the local Universe. We present a compre

Facundo Rodriguez, Antonio D. Montero-Dorta

Cosmological simulations predict significant secondary dependencies of halo clustering on internal properties and environment. Detecting these subtle signals in observational data remains challenging, with important ramifications for galaxy evolution and cosmology. We probe secondary halo bias in observational survey data, using galaxy groups as dark matter

Chandan Kumar Das, Bhargav Vaidya, Amit Shukla, Giancarlo Mattia

Fast $\gamma$-ray variability in blazars remains a central puzzle in high-energy astrophysics, challenging standard shock acceleration models. Blazars, a subclass of active galactic nuclei (AGN) with jets pointed close to our line of sight, offer a unique view into jet dynamics. Blazar $\gamma$-ray light curves exhibit rapid, high-amplitude flares that point

Chenyu Fang, Wei-Shu Hou, Chung Kao, Mohamed Krab

We investigate the discovery prospects of a charged Higgs boson ($H^\pm$) at the Large Hadron Collider (LHC) via the process $cg\to bH^\pm \to bc\bar{b}$ within the framework of a general two Higgs doublet model (G2HDM). In most two Higgs doublet models, the $H^+ cb$ coupling ($g_{H^+cb}$) is usually suppressed by the CKM matrix element $V_{cb}$. In G2HDM, t

G. Riva, S. Ghizzardi, S. Molendi, M. Balboni

The enrichment history of galaxy clusters and groups remains far from being fully understood. Recent measurements in massive clusters have revealed remarkably flat iron abundance profiles out to the outskirts, suggesting that similar enrichment processes have occurred for all systems. In contrast, abundance profiles in galaxy groups have sometimes been measu

Luigi Barchiesi, Lucia Marchetti, Mattia Vaccari, Cristian Vignali

Understanding black hole-galaxy co-evolution and the role of AGN feedback requires complete AGN samples, including heavily obscured systems. In this work, we present the first UV line-selected ([Nev]3426 and CIV1549) sample of obscured AGN with full X-ray-to-radio coverage, assembled by combining data from the Chandra COSMOS Legacy survey, the COSMOS2020 cat

Juan Pablo Gatica, Callum R. T. Jones

We describe an on-shell, amplitudes-based approach to incorporating radiation absorption effects in the post-Minkowskian scattering of generic, compact, spinning bodies. Classical spinning observables are recovered by extrapolating to large spin, results calculated with finite quantum spin-$s$ particles using the properties of spin universality and Casimir i

Ankita Bera, Sultan Hassan, Robert Feldmann, Romeel Davé

The elevated UV luminosity functions (UVLF) from recent James Webb Space Telescope (JWST) have challenged the viability of existing theoretical models. To address this, we use a semi-analytical framework -- which couples a physically motivated source model derived from radiative-transfer hydrodynamic simulations of reionization with a Markov Chain Monte Carl

Akash Kumar Saha, Abhishek Dubey, Nirmal Raj

Interactions with particle dark matter could brighten old, isolated neutron stars to thermal luminosities detectable at current and next-generation telescopes. We present a novel mechanism for such signals. Non-annihilating (e.g., asymmetric) dark matter capturing in a neutron star could form a small black hole in its core, which could then rapidly evaporate

Steven Abel, Iwo Wasek, Simon Williams

We formulate a continuous-variable quantum computing (CVQC) algorithm to study Berry's phase on photonic quantum computers. We demonstrate that CVQC allows the simulation of charged particles with orbital angular momentum under the influence of an adiabatically changing $\vec{B}$ field. Although formulated entirely in the CVQC setting, our construction uses

Tsung-Cheng Lu, Yu-Jie Liu, Sarang Gopalakrishnan, Yizhi You

We introduce a holographic framework for analyzing the steady states of repeated quantum channels with strong symmetries. Using channel-state duality, we show that the steady state of a $d$-dimensional quantum channel is holographically mapped to the boundary reduced density matrix of a $(d+1)$-dimensional wavefunction generated by a sequential unitary circu

Arthur Niwazuki, Matthew S. Foster

In a significant conceptual revision to the tenfold classification scheme for topological insulators and superconductors, it was recently demonstrated that most three-dimensional (3D) classes are simultaneously "localizable" in two distinct, but intricately connected ways: (1) There is no obstruction to Wannier localization of all bulk eigenstates, and (2) A

Ronan Legin, Connor Stone, Alexandre Adam, Gabriel Missael Barco

We present new state-of-the-art lens models for strong gravitational lensing systems from the Sloan Lens ACS (SLACS) survey, developed within a Bayesian framework that employs high-dimensional (pixellated), data-driven priors for the background source, foreground lens light, and point-spread function (PSF). Unlike conventional methods, our approach delivers

Connor Stone, Ronan Legin, Alexandre Adam, Nikolay Malkin

We introduce a novel framework for upsampled Point Spread Function (PSF) modeling using pixel-level Bayesian inference. Accurate PSF characterization is critical for precision measurements in many fields including: weak lensing, astrometry, and photometry. Our method defines the posterior distribution of the pixelized PSF model through the combination of an

Danilo Chiaramello, Nicolò Cibrario, Jacob Lange, Koustav Chandra

Gravitational waves from binary black hole (BBH) mergers allow us to test general relativity in the strong-field, high-curvature regime. However, existing gravitational wave-based tests have so far assumed non-eccentric signal sources, limiting their applicability to more general astrophysical scenarios. In this work, we present pTEOBResumS, a new parametriz

J. Kurpas, A. M. Pires, A. D. Schwope, B. Li

We report on follow-up observations with XMM-Newton, the FORS2 instrument at the ESO-VLT, and FAST, aiming to characterise the nature of five thermally emitting isolated neutron star (INS) candidates recently discovered from searches in the footprint of the Spectrum Roentgen Gamma (SRG)/eROSITA All-sky Survey. We find that the X-ray spectra are predominantly

Zhi-Qiang Gao, Chunxiao Liu, Joel E. Moore

Nonabelian topological orders host exotic anyons central to quantum computing, yet established realizations rely on case-by-case constructions that are often conceptually involved. In this work, we present a systematic construction of nonabelian dihedral quantum double phases based on a continuous $O(2)$ gauge field. We first formulate a topological $S[O(2)\

Gavin Wang, Jerry Xuan, Darío Picos, Zhoujian Zhang

CD-35 2722 B is an L dwarf companion to the nearby, $\sim 50-200$ Myr old M1 dwarf CD-35 2722 A. We present a detailed analysis of both objects using high-resolution ($R \sim 35,000$) $K$ band spectroscopy from the Keck Planet Imager and Characterizer (KPIC) combined with archival photometry. With a mass of $30^{+5}_{-4} M_{\mathrm{Jup}}$ (planet-to-host mas

Rachana, M. Vivek, Yue Shen

Narrow-line Seyfert 1 galaxies (NLSy1s) are a subclass of active galactic nuclei (AGNs), commonly associated with rapidly accreting, relatively low-mass black holes ($10^6$ - $10^8 M_\odot$) hosted in spiral galaxies. Although typically considered to have high Eddington ratios, recent observations, particularly of $\gamma$-ray-emitting NLSy1s, have raised qu

Ian Harris, Yonatan Kahn

Gravitational wave interferometers have studied compact object mergers and solidified our understanding of strong gravity. Their increasing precision raises the possibility of detecting new physics, especially in a neutron star binary system that may contain hidden-sector particles. In particular, a new vector force between binary constituents, giving rise t

Jesus Fuentes, Cynthia Keeler, William Munizzi, Jason Pollack

The monogamy of mutual information (MMI) is a quantum entropy inequality that enforces the non-positivity of tripartite information. We investigate the failure of MMI in graph states as a forbidden-subgraph phenomenon, conjecturing that every MMI-violating graph state is local-Clifford equivalent to one whose graph contains a four-star subgraph. We construct

Jingzhi Bao, Hongze Chen, Lingting Zhu, Chenyu Liu

Physically-based rendering (PBR) provides a principled standard for realistic material-lighting interactions in computer graphics. Despite recent advances in generating PBR textures, existing methods fail to address two fundamental challenges: 1) materials decomposition from image prompts under limited illumination cues, and 2) seamless and view-consistent t

Qiang Wang, Xinyuan Gao, Yuhang He, Jizhou Han

Existing Video Detailed Captioning (VDC) methods predominantly rely on costly human annotations or distillation from powerful proprietary models, creating a dependency on external supervision. In this paper, we propose VDC-Agent, an autonomous self-evolving framework that empowers a single Multimodal Large Language Model (MLLM) to generate and refine high-qu

Zechuan Zhang, Zhenyuan Chen, Zongxin Yang, Yi Yang

Large-scale video diffusion models show strong world simulation and temporal reasoning abilities, but their use as zero-shot image editors remains underexplored. We introduce IF-Edit, a tuning-free framework that repurposes pretrained image-to-video diffusion models for instruction-driven image editing. IF-Edit addresses three key challenges: prompt misalign

Yasin Esfandiari, Stefan Bauer, Sebastian U. Stich, Andrea Dittadi

Diffusion models for image generation often exhibit a trade-off between perceptual sample quality and data likelihood: training objectives emphasizing high-noise denoising steps yield realistic images but poor likelihoods, whereas likelihood-oriented training overweights low-noise steps and harms visual fidelity. We introduce a simple plug-and-play sampling

Felix M. Mayor, Wenyan Guan, Erik Szakiel, Amir H. Safavi-Naeini

Unlike the rigid, high-volume automation found in industry, academic research requires process flexibility that has historically relied on variable manual operations. This hinders the fabrication of advanced, complex devices. We propose to address this gap by automating these low-volume, high-stakes tasks using a robotic arm to improve process control and co

Jacob Lin, Edward Gryspeerdt, Ronald Clark

There has been great progress in improving numerical weather prediction and climate models using machine learning. However, most global models act at a kilometer-scale, making it challenging to model individual clouds and factors such as extreme precipitation, wind gusts, turbulence, and surface irradiance. Therefore, there is a need to move towards higher-r

Dingkang Liang, Cheng Zhang, Xiaopeng Xu, Jianzhong Ju

Task scheduling is critical for embodied AI, enabling agents to follow natural language instructions and execute actions efficiently in 3D physical worlds. However, existing datasets often simplify task planning by ignoring operations research (OR) knowledge and 3D spatial grounding. In this work, we propose Operations Research knowledge-based 3D Grounded Ta

Tsubasa Sugeno, Wen Yin

It is known that Yang-Mills theories, especially in the large-$N$ limit, exhibit a $\theta$-vacuum structure with a multi-branched vacuum energy. In this work, we demonstrate that this multi-branch structure can play a crucial role in axion cosmology when the axion acquires its mass from the Yang-Mills sector, even when that sector is never reheated by the i

Shangyuan Tong, Nanye Ma, Saining Xie, Tommi Jaakkola

State-of-the-art flow models achieve remarkable quality but require slow, iterative sampling. To accelerate this, flow maps can be distilled from pre-trained teachers, a procedure that conventionally requires sampling from an external dataset. We argue that this data-dependency introduces a fundamental risk of Teacher-Data Mismatch, as a static dataset may p

Jayanaka L. Dantanarayana, Savini Kashmira, Thakee Nathees, Zichen Zhang

AI-Integrated programming is emerging as a foundational paradigm for building intelligent systems with large language models (LLMs). Recent approaches such as Meaning Typed Programming (MTP) automate prompt generation by leveraging the semantics already present in code. However, many real-world applications depend on contextual cues, developer intent, and do

Yun Zhou, Yaoting Wang, Guangquan Jie, Jinyu Liu

SAM3D has garnered widespread attention for its strong 3D object reconstruction capabilities. However, a key limitation remains: SAM3D cannot reconstruct specific objects referred to by textual descriptions, a capability that is essential for practical applications such as 3D editing, game development, and virtual environments. To address this gap, we introd

Tianrun Chen, Runlong Cao, Xinda Yu, Lanyun Zhu

The rapid rise of large-scale foundation models has reshaped the landscape of image segmentation, with models such as Segment Anything achieving unprecedented versatility across diverse vision tasks. However, previous generations-including SAM and its successor-still struggle with fine-grained, low-level segmentation challenges such as camouflaged object det

Rihab Ben Belgacem, Mohamed Majdoub

We investigate the Cauchy problem for a semilinear spatio--temporal fractional diffusion equation with a time-dependent forcing term: \[ \partial_t^\alpha u + (-\Delta)^{\mathsf{s}} u = |u|^p + t^{\sigma}\,\mathbf{w}(x), \quad (t,x) \in (0,\infty) \times \mathbb{R}^N, \] where $\alpha,\mathsf{s}\in (0,1)$, $\sigma > -\alpha$, and $\mathbf{w}$ is a given cont

Nicklas Hansen, Hao Su, Xiaolong Wang

General-purpose control demands agents that act across many tasks and embodiments, yet research on reinforcement learning (RL) for continuous control remains dominated by single-task or offline regimes, reinforcing a view that online RL does not scale. Inspired by the foundation model recipe (large-scale pretraining followed by light RL) we ask whether a sin

Bruno Jacob, Khushbu Agarwal, Marcel Baer, Peter Rice

We present Genie-CAT, a tool-augmented large-language-model (LLM) system designed to accelerate scientific hypothesis generation in protein design. Using metalloproteins (e.g., ferredoxins) as a case study, Genie-CAT integrates four capabilities -- literature-grounded reasoning through retrieval-augmented generation (RAG), structural parsing of Protein Data

David Jiahao Fu, Aryan Gupta, Aaron Councilman, David Grove

Recent advancements in large language models (LLMs) have shown very impressive capabilities in code generation across many programming languages. However, even state-of-the-art LLMs generate programs that contains syntactic errors and fail to complete the given tasks, especially for low-resource programming languages (LRPLs). In addition, high training cost

Amy K. Strong, Ali Kashani, Claus Danielson, Leila Bridgeman

Positive invariant (PI) sets are essential for ensuring safety, i.e. constraint adherence, of dynamical systems. With the increasing availability of sampled data from complex (and often unmodeled) systems, it is advantageous to leverage these data sets for PI set synthesis. This paper uses data driven geometric conditions of invariance to synthesize PI sets

Susobhan Chattopadhyay, Amol Dighe

Neutrinos can acquire "refractive masses" as a consequence of their interactions with ultralight dark matter (DM). We explore a model with two additional sterile neutrinos and an ultralight scalar field which acts as DM and interacts with all five neutrinos. We show that the effective $5 \times 5$ Hamiltonian for neutrino propagation can be diagonalized by a

S. Buson, M. De Toma, S. Larsson, C. C. Cheung

We present the characterization of macrolensing properties of the gravitationally lensed system PKS 1830-211, utilizing data from the Fermi Large Area Telescope. While at gamma-rays we can not spatially resolve the lensed images, a macrolensing-induced time pattern is expected in the blazar's lightcurve, resulting from the delay between variable gamma-ray co

James Y. Huang, Sheng Zhang, Qianchu Liu, Guanghui Qin

Large Language Models (LLMs) have demonstrated remarkable capabilities in challenging, knowledge-intensive reasoning tasks. However, extending LLMs to perceive and reason over a new modality (e.g., vision), often requires costly development of large-scale vision language models (VLMs) with LLMs as backbones. Smaller VLMs are more efficient and adaptable but

Jeff Calder

Here, we give a self-contained and elementary proof of a minimax theorem due to Fan in a simplified setting that can be taught in an advanced undergraduate course. Our proof follows Nikaido's argument with some simplifications.

Josef Soucek, Michael Petrov, Michal Gulka, Emilie Bourgeois

We report on a mathematical model of the photoelectric response of NV colour centres in diamond, that can be employed for sensing and quantum science information applications. Although the model applies to NV centre in diamond, it can be applied with small modifications to other semiconducting solid state qubits. In our model, we include the drift and collec

Hadi Cheraghi, Ali G. Moghaddam, Teemu Ojanen

We propose a general connection between entanglement-entropy scaling laws and the linear response functions of particle-conserving fermionic systems in their ground state. Specifically, we show that the response to perturbations coupled to the particle number within a finite region exhibits the same size scaling as the entanglement entropy of that region. We

Max Nendel

In this paper, we study an approximation scheme for L\'evy processes with drift in terms of a representation that is akin to the celebrated Mehler formula for L\'evy-Ornstein-Uhlenbeck processes. The approximation scheme is based on a variant of the Chernoff product formula on the space of bounded continuous functions. In a first step, we provide sufficient

Zhaolong Su, Wang Lu, Hao Chen, Sharon Li

Unified Multimodal Models (UMMs) have shown impressive performance in both understanding and generation with a single architecture. However, UMMs still exhibit a fundamental inconsistency: understanding favors compact embeddings, whereas generation favors reconstruction-rich representations. This structural trade-off produces misaligned decision boundaries,

Jeroen Hekking, Adeel A. Khan, David Rydh

We develop an analogue of the deformation to the normal cone in the context of derived algebraic geometry. This provides any given morphism of derived stacks with a degeneration to the zero section of its normal bundle (i.e., its 1-shifted relative tangent bundle). The construction is realized via the derived Weil restriction along the zero section of the af

Katya Scheinberg, Miaolan Xie

We consider an unconstrained continuous optimization problem where, in each iteration, gradient estimates may be arbitrarily corrupted with a probability greater than 1/2. Additionally, function value estimates may exhibit heavy-tailed noise. This setting captures challenging scenarios where both gradient and function value estimates can be unreliable, makin

Udari Madhushani Sehwag, Shayan Shabihi, Alex McAvoy, Vikash Sehwag

Recent advances in Large Language Models (LLMs) have sparked concerns over their potential to acquire and misuse dangerous or high-risk capabilities, posing frontier risks. Current safety evaluations primarily test for what a model \textit{can} do - its capabilities - without assessing what it $\textit{would}$ do if endowed with high-risk capabilities. This

Olivier de Gaay Fortman, Ananth N. Shankar

We prove the existence of abelian varieties over $\overline{\mathbb Q(t)}$ with no power isogenous to a Jacobian. Moreover, given a positive integer $N$, we prove the existence of abelian varieties over $\overline{\mathbb Q(t)}$ with maximal monodromy such that the $n$th power is not isogenous to a Jacobian for $n \leq N$. We make use of an Arakelov inequali

Shreyashi Sinha, Shantanu Pathak, Saswata Bhattacharya, Sujit Manna

We present a controlled mechanical exfoliation technique for bulk 3D topological insulators that yields atomically clean ultrathin flakes, enabling quantum well states (QWS) of Dirac electrons to be clearly resolved. Achieving reliable fabrication of pristine, high-quality two-dimensional layers suitable for atomic-scale spectroscopy remains a central experi

Arnab Seth, Fay Borhani, Itamar Kimchi

We study the spin-1/2 Kitaev honeycomb gapless spin liquid in the presence of Stone-Wales-type local lattice defects with odd-sided plaquettes. While the clean Kitaev model has no finite-temperature phase transitions, we find that introducing a finite defect density $n_d\approx 10^{-4}$--$10^{-2}$ produces a true phase transition with a sizeable $T_c \approx

S. T. Petcov, A. V. Titov

We update the analysis of the viability of the lepton mixing patterns originating from $A_4$, $S_4$ and $A_5$ discrete flavour symmetries and leading to predictions for the solar neutrino mixing angle, $\theta_{12}$. We perform a statistical analysis using as an input (i) the results of the latest global fit to neutrino oscillation data, and (ii) the first J

Johan Samsing, Lorenz Zwick, Pankaj Saini, János Takátsy

The observation of multiple images from a strongly lensed gravitational wave (GW) source provides the observer with a stereoscopic view of the source. This allows for a measure of its relative proper motion by comparing the induced GW Doppler shifts between the different images. In addition, if the GW source is in a dynamical environment it will be subject t

Rebecca Lee, Alexander Coulter, Greg J. Siegle, Scott A. Bruce

The power spectrum of biomedical time series provides important indirect measurements of physiological processes underlying health and biological functions. However, simultaneously characterizing power spectra for multiple time series remains challenging due to extra spectral variability and varying time series lengths. We propose a method for hierarchical B

Dereck Piche, Mohammed Muqeeth, Milad Aghajohari, Juan Duque

As agentic AI becomes more widespread, agents with distinct and possibly conflicting goals will interact in complex ways. These multi-agent interactions pose a fundamental challenge, particularly in social dilemmas, where agents' individual incentives can undermine collective welfare. While reinforcement learning (RL) has been effective for aligning large la

Zikai Shen, Zonghao Chen, Dimitri Meunier, Ingo Steinwart

We study the problem of nonparametric instrumental variable regression with observed covariates, which we refer to as NPIV-O. Compared with standard nonparametric instrumental variable regression (NPIV), the additional observed covariates facilitate causal identification and enables heterogeneous causal effect estimation. However, the presence of observed co

Jorge Ortigoso-Narro, Jose A. Belloch, Maximo Morales-Cespedes, Maximo Cobos

The use of planar and concentric circular microphone arrays in beamforming has gained attention due to their ability to optimize both azimuth and elevation angles, making them ideal for spatial audio tasks like sound source localization and noise suppression. Unlike linear arrays, which restrict steering to a single axis, 2D arrays offer dual-axis optimizati

Aswinkumar Varathakumaran, Nirmala Paramanandham

Provident vehicle detection has a lot of scope in the detection of vehicle during night time. The extraction of features other than the headlamps of vehicles allows us to detect oncoming vehicles before they appear directly on the camera. However, it faces multiple issues especially in the field of night vision, where a lot of noise caused due to weather con

Gongfan Fang, Xinyin Ma, Xinchao Wang

Large-scale video generative models have recently demonstrated strong visual capabilities, enabling the prediction of future frames that adhere to the logical and physical cues in the current observation. In this work, we investigate whether such capabilities can be harnessed for controllable image-to-video generation by interpreting visual signals embedded

Elena Cordero, Gianluca Giacchi, Luigi Rodino

We study the Wigner kernel and the Gabor matrix associated with the propagators of a broad class of linear evolution equations, including the complex heat, wave, and Hermite equations. Within the framework of time-frequency analysis, we derive explicit expressions for the Wigner kernels of Fourier multipliers and establish quantitative decay estimates for th

Ilias Diakonikolas, Daniel M. Kane, Sihan Liu, Thanasis Pittas

This work studies information-computation gaps for statistical problems. A common approach for providing evidence of such gaps is to show sample complexity lower bounds (that are stronger than the information-theoretic optimum) against natural models of computation. A popular such model in the literature is the family of low-degree polynomial tests. While th

Jan de Leeuw

We present R and C implementations for metric (ratio) and non-metric (ordinal) versions of Elastic MDS, the multidimensional scaling technique proposed by McGee (1966). The R and C versions are compared for speed, with the C version anywhere from 15 to 100 times as fast as the R version.

Jorge Ortigoso-Narro, Jose A. Belloch, Adrian Amor-Martin, Sandra Roger

Advances in object tracking and acoustic beamforming are driving new capabilities in surveillance, human-computer interaction, and robotics. This work presents an embedded system that integrates deep learning-based tracking with beamforming to achieve precise sound source localization and directional audio capture in dynamic environments. The approach combin

Endre Takacs, Hunter Staiger, Steven A. Blundell, Naoki Kimura

The nuclear charge radius is a fundamental observable that encodes key aspects of nuclear structure, deformation, and pairing. Isotonic (constant neutron number) systematics in the deformed rare-earth region have long suggested that odd-$Z$ nuclei are more compact than their even-$Z$ neighbors - except for Lu, whose recommended radius appeared anomalously la

Utkarsh Mali, Reed Essick

The nature of the compact objects within the supposed "lower mass gap" remains uncertain. Observations of GW190814 and GW230529 highlight the challenges gravitational waves face in distinguishing neutron stars from black holes. Interpreting these systems is especially difficult because classifications depend simultaneously on measurement noise, compact binar

Bruno Valeixo Bento, Miquel Salicrú Herberg

Massive bosonic fields can trigger superradiant instabilities in rotating astrophysical black holes leading to gaps in their mass-spin distribution. For spin-2 fields, the instability timescale is orders of magnitude shorter than for any other superradiant mode, thereby yielding much stronger constraints. We consider a tower of ultra-light spin-2 fields aris

Gabriel Berzunza Ojeda, Harlan Connor

Consider a supercritical Crump-Mode-Jagers process $(\mathcal{Z}_{t}^{\varphi})_{t \geq 0}$ counted with a random characteristic $\varphi$ that depends on an individual's life and their descendant process up to a fixed generation. Under second moment assumptions, we establish a central limit theorem for $\mathcal{Z}_{t}^{\varphi}$ as $t \rightarrow \infty$.

Rudy Morel, Francesco Pio Ramunno, Jeff Shen, Alberto Bietti

Conditional diffusion models provide a natural framework for probabilistic prediction of dynamical systems and have been successfully applied to fluid dynamics and weather prediction. However, in many settings, the available information at a given time represents only a small fraction of what is needed to predict future states, either due to measurement unce

Mutlu Cukurova, Wannapon Suraworachet, Qi Zhou, Sahan Bulathwela

Generative artificial intelligence (GenAI) is increasingly used in education, posing significant challenges for teachers adapting to these changes. GenAI offers unprecedented opportunities for accessibility, scalability and productivity in educational tasks. However, the automation of teaching tasks through GenAI raises concerns about reduced teacher agency,

Chinmay Tripurwar, Utkarsh Maurya, Dishant

Model pruning is a widely adopted technique to reduce the computational complexity and memory footprint of Deep Neural Networks (DNNs). However, global unstructured pruning often leads to significant degradation in accuracy, typically necessitating fine-tuning on the original training dataset to recover performance. In privacy-sensitive domains such as healt

Lucas G. Rabelo, Igor C. Almeida, Eduardo Miranda, Vladimir Dobrosavljević

We propose a minimal model to capture the anomalous low-temperature thermodynamics of doped semiconductors, such as Si:P, across the metal-insulator transition. We consider pairs of local magnetic moments coupled to a highly disordered, non-interacting electronic bath that undergoes a metal-insulator transition with increasing doping. Using a large-$\mathcal

A. Duviryak

It has been shown by Yu.~Yaremko [Elect. J. Theor. Phys. {\bf 9}, 153 (2012)] within the classial electrodynamics that the hypothetical massless charged particle must generate an infinitely strong radiation reaction, thus not an external force can accelerate this particle. Here the version the Staruszkiewicz model is presented to describe the relativistic sy

Marzieh Forough, Ja A Jeong, Karen R. Strung

In this paper we show that the Cuntz--Pimsner algebras associated to minimal homeomorphisms twisted by line bundles, along with their orbit-breaking subalgebras, are $\mathcal{Z}$-stable whenever the underlying dynamical system has the small boundary property. This entails that this class is classified by the Elliott invariant. Furthermore, we show that the

Sergey A. Melikhov

The present note contains a new proof of Y. Hashizume's 1958 theorem that every non-split link in $S^3$ admits a unique factorization into prime links. While the new proof does not go far beyond standard techniques, it is considerably shorter than the original proof and avoids most of its case exhaustion. We apply this proof to obtain a string link version (

R. Abbasi, M. Ackermann, J. Adams, S. K. Agarwalla

Dark matter is approximately five times more abundant than baryonic matter in the universe, but its physical nature continues to elude physicists. One potential candidate for dark matter is a weakly-interacting massive particle (WIMP), which is predicted by various extensions to the Standard Model (SM) of particle physics. After becoming gravitationally boun

Catherine Meusburger, Vincentas Mulevicius, Fiona Torzewska

We use Gay and Kirby's description of 4-manifolds in terms of trisections and trisection diagrams to define a new 4-manifold invariant. The algebraic data are an indecomposable finite semisimple bimodule category over a pair of spherical fusion categories, equipped with a bimodule trace, and a pivotal functor from another spherical fusion category into the s

Dimitrios E. Diamantis, Dimitris K. Iakovidis

Gastrointestinal (GI) imaging via Wireless Capsule Endoscopy (WCE) generates a large number of images requiring manual screening. Deep learning-based Clinical Decision Support (CDS) systems can assist screening, yet their performance relies on the existence of large, diverse, training medical datasets. However, the scarcity of such data, due to privacy const

A. B. Németh

It is proved that the linearity of metric projections on subspaces and the convexity of the polars of the convex cones in the uniformly convex and uniformly smooth Banach space are equivalent, and both of them is equivalent with the fact that the space is an inner product space.

Chifeng Shen, Yuejiao Fu, Michael Chen, Xiaoping Shi

Statistical depth, which measures the center-outward rank of a given sample with respect to its underlying distribution, has become a popular and powerful tool in nonparametric inference. In this paper, we investigate the use of statistical depth in multivariate two-sample problems. We propose a new depth-based nonparametric two-sample test, which has the Ch

Maroun Ayli, Youssef Bakouny, Tushar Sharma, Nader Jalloul

Enterprise software companies maintain thousands of user interface screens across products and versions, creating critical challenges for design consistency, pattern discovery, and compliance check. Existing approaches rely on visual similarity or text semantics, lacking explicit modeling of structural properties fundamental to user interface (UI) compositio

Abhay Goyal, Navin Kumar, Kimberly DiMeola, Rafael Trujillo

Chronic pain (CP) and opioid use disorder (OUD) are common and interrelated chronic medical conditions. Currently, there is a paucity of evidence-based integrated treatments for CP and OUD among individuals receiving medication for opioid use disorder (MOUD). Wearable devices have the potential to monitor complex patient information and inform treatment deve

Srishti Gupta, Yashasvee Taiwade

Denoising Diffusion Probabilistic Models (DDPMs) have established a new state-of-the-art in generative image synthesis, yet their deployment is hindered by significant computational overhead during inference, often requiring up to 1,000 iterative steps. This study presents a rigorous comparative analysis of DDPMs against the emerging Flow Matching (Rectified

Chandrasekhar Gokavarapu, D. Madhusudhana Rao

This paper introduces a new class of error-correcting codes constructed from the ideal lattices of finite commutative ternary Gamma-semirings (TGS). Unlike classical linear or ring-linear codes, which rely on binary operations, TGS codes arise from the intrinsic ternary operation $[x,y,z]$ and the op-plus order that governs coordinatewise absorption. The fun

Mamoon Aamir, Mariyam Sattar, Naveed Ur Rehman Junejo, Aqsa Zafar Abbasi

Given the increasing need for large aperture antennas in space missions, the difficulty of fitting such structures into small launch vehicles has prompted the design of deployable antenna systems. The thesis introduces a new Triple Scissors Deployable Truss Mechanism (TSDTM) for space antenna missions. The new mechanism is to be stowed during launch and effi

Maria Thoma, Michalis A. Savelonas, Dimitris K. Iakovidis

Ischemic stroke is a time-critical medical emergency where rapid diagnosis is essential for improving patient outcomes. Non-contrast computed tomography (NCCT) serves as the frontline imaging tool, yet it often fails to reveal the subtle ischemic changes present in the early, hyperacute phase. This limitation can delay crucial interventions. To address this

Chifeng Shen, Yuejiao Fu, Xiaoping Shi, Michael Chen

Temporal point processes (TPPs) model the timing of discrete events along a timeline and are widely used in fields such as neuroscience and fi- nance. Statistical depth functions are powerful tools for analyzing centrality and ranking in multivariate and functional data, yet existing depth notions for TPPs remain limited. In this paper, we propose a novel pr

Yuansi Chen

We prove that under the heat semigroup $(P_\tau)$ on the Boolean hypercube, any nonnegative function exhibits a uniform tail bound that is better than Markov's inequality. Specifically, for any $\tau > 0$, $n \geq 1$, $\eta > e^3$, and $f: \{-1,1\}^n \to \mathbb{R}_+$ with $\int f d\mu > 0$, we have \begin{align*} \mathbb{P}_{X \sim \mu}\left( P_\tau f(X) >

Leon J. Goertz, Paul Wedrich

Topological quantum field theories (TQFTs) are symmetric monoidal functors out of cobordism categories. In dimension two, oriented TQFTs are famously classified by commutative Frobenius algebras. In the unoriented setting, the classification requires additional data: an involution and a value assigned to the M\"obius strip. In this work, we describe an inter