Tootfinder

Maximal intrinsic randomness of noisy quantum measurements
Fionnuala Curran, Morteza Moradi, Gabriel Senno, Magdalena Stobinska, Antonio Ac\'in
https://arxiv.org/abs/2506.22294

Maximal intrinsic randomness of noisy quantum measurements
Quantum physics exhibits an intrinsic and private form of randomness with no classical counterpart. Any setup for quantum randomness generation involves measurements acting on quantum states. In this work, we consider the following question: Given a quantum measurement, how much randomness can be generated from it? In real life, measurements are noisy and thus contain an additional, extrinsic form of randomness due to ignorance. This extrinsic randomness is not private since, in an adversarial …

Fuzzing: Randomness? Reasoning! Efficient Directed Fuzzing via Large Language Models
Xiaotao Feng, Xiaogang Zhu, Kun Hu, Jincheng Wang, Yingjie Cao, Guang Gong, Jianfeng Pan
https://arxiv.org/abs/2507.22065

Fuzzing: Randomness? Reasoning! Efficient Directed Fuzzing via Large Language Models
Fuzzing is highly effective in detecting bugs due to the key contribution of randomness. However, randomness significantly reduces the efficiency of fuzzing, causing it to cost days or weeks to expose bugs. Even though directed fuzzing reduces randomness by guiding fuzzing towards target buggy locations, the dilemma of randomness still challenges directed fuzzers. Two critical components, which are seeds and mutators, contain randomness and are closely tied to the conditions required for trigge…

Energy minima and ordering in ferromagnets with quenched randomness
D. A. Garanin
https://arxiv.org/abs/2506.02292 https://arxiv.org/…

Energy minima and ordering in ferromagnets with quenched randomness
Energy minimization at T=0 and Monte Carlo simulations at T>0 have been performed for 2D and 3D random-field and random-anisotropy systems of up to 100 million classical spins. The main finding is that 3D random-anisotropy systems magnetically order on lowering temperature, contrary to the theoretical predictions based on the Imry-Ma argument. If random-anisotropy is stronger than the exchange, which can be the case in sintered materials, the system still orders but the magnetization is strongl…

New Tests of Randomness for Circular Data
Shriya Gehlot, Arnab Kumar Laha
https://arxiv.org/abs/2506.23522 https://arxiv.org/pdf/2506…

New Tests of Randomness for Circular Data
Randomness or mutual independence is an important underlying assumption for most widely used statistical methods for circular data. Verifying this assumption is essential to ensure the validity and reliability of the resulting inferences. In this paper, we introduce two tests for assessing the randomness assumption in circular statistics, based on random circular arc graphs (RCAGs). We define and analyze RCAGs in detail, showing that their key properties depend solely on the i.i.d. nature of th…

Soliton Synchronization with Randomness: Rogue Waves and Universality
Manuela Girotti, Tamara Grava, Robert Jenkins, Guido Mazzuca, Ken McLaughlin, Maxim Yattselev
https://arxiv.org/abs/2507.01253

Soliton Synchronization with Randomness: Rogue Waves and Universality
We consider an $N$-soliton solution of the focusing nonlinear Schrödinger equations. We give conditions for the synchronous collision of these $N$ solitons. When the solitons velocities are well separated and the solitons have equal amplitude, we show that the local wave profile at the collision point scales as the $\operatorname{sinc}(x)$ function. We show that this behaviour persists when the amplitudes of the solitons are i.i.d. sub-exponential random variables. Namely the central collision…

Reusing Samples in Variance Reduction
Yujia Jin, Ishani Karmarkar, Aaron Sidford, Jiayi Wang
https://arxiv.org/abs/2509.02526 https://arxiv.org/pdf/2509.02…

Reusing Samples in Variance Reduction
We provide a general framework to improve trade-offs between the number of full batch and sample queries used to solve structured optimization problems. Our results apply to a broad class of randomized optimization algorithms that iteratively solve sub-problems to high accuracy. We show that such algorithms can be modified to reuse the randomness used to query the input across sub-problems. Consequently, we improve the trade-off between the number of gradient (full batch) and individual functio…

Time Series Analysis of DECAL Sensor Noise for the Generation of Truly Random Numbers
Dimos Aslanis, Alex Kehagias, Ioannis Kopsalis, Ioannis Theodonis
https://arxiv.org/abs/2509.02203

Time Series Analysis of DECAL Sensor Noise for the Generation of Truly Random Numbers
We explore here the stochastic behavior of the DECAL sensor's noise output, and we evaluate its potential application as a true random number generator (TRNG) using time series analysis. The main objectives are twofold: first, to characterize the intrinsic noise properties of the DECAL sensor in the absence of external stimuli, and second, to determine the feasibility of employing the sensor as a source of randomness. The collected sensor data are examined through statistical and time series me…

Gender Differences in Healthcare Utilisation - Evidence from Unexpected Adverse Health Shocks
Nadja van 't Hoff, Giovanni Mellace, Seetha Menon
https://arxiv.org/abs/2509.01310

Gender Differences in Healthcare Utilisation - Evidence from Unexpected Adverse Health Shocks
This paper is the first to provide causal evidence of gender differences in healthcare utilisation to better understand the male-female health-survival paradox, where women live longer but experience worse health outcomes. Using rich Danish administrative healthcare data, we apply a staggered difference-in-differences approach that exploits the randomness in treatment timing to estimate the causal impact of adverse health shocks, such as non-fatal heart attacks or strokes, on healthcare use. Ou…

Statistical Invisibility of a Physical Attack on QRNGs After Randomness Extraction
Yifan Chen, Dong Wang, Yibo Zhao, Liang Cheng, Yi Zhang, Yang Zhang
https://arxiv.org/abs/2508.21498

Statistical Invisibility of a Physical Attack on QRNGs After Randomness Extraction
Current prevailing designs of quantum random number generators (QRNGs) designs typically employ post-processing techniques to distill raw random data, followed by statistical verification with suites like NIST SP 800-22. This paper demonstrates that this widely adopted practice harbors a critical flaw. We show that the powerful extraction process can create a false sense of security by perfectly concealing physical-layer attacks, rendering the subsequent statistical tests blind to a compromised…

Weighted Support Points from Random Measures: An Interpretable Alternative for Generative Modeling
Peiqi Zhao, Carlos E. Rodr\'iguez, Rams\'es H. Mena, Stephen G. Walker
https://arxiv.org/abs/2508.21255

Weighted Support Points from Random Measures: An Interpretable Alternative for Generative Modeling
Support points summarize a large dataset through a smaller set of representative points that can be used for data operations, such as Monte Carlo integration, without requiring access to the full dataset. In this sense, support points offer a compact yet informative representation of the original data. We build on this idea to introduce a generative modeling framework based on random weighted support points, where the randomness arises from a weighting scheme inspired by the Dirichlet process a…

Evaluating Randomness Assumption: A Novel Graph Theoretic Approach
Shriya Gehlot, Arnab Kumar Laha
https://arxiv.org/abs/2506.21157 https://

Evaluating Randomness Assumption: A Novel Graph Theoretic Approach
Randomness or mutual independence is a fundamental assumption forming the basis of statistical inference across disciplines such as economics, finance, and management. Consequently, validating this assumption is essential for the reliable application of statistical methods. However, verifying randomness remains a challenge, as existing tests in the literature are often restricted to detecting specific types of data dependencies. In this paper, we propose a novel graph-theoretic approach to test…

Relationship between spin-glass three-dimensional (3D) Ising model and traveling salesman problems
Zhidong Zhang
https://arxiv.org/abs/2507.01914 https://

Relationship between spin-glass three-dimensional (3D) Ising model and traveling salesman problems
In this work, the relationship between a spin-glass three-dimensional (3D) Ising model with the lattice size N = mnl and the traveling salesman problem (TSP) in a 3D lattice is studied. In particular, the mathematical structures of the two systems are investigated in details. In both the hard problems, the nontrivial topological structures, the non-planarity graphs, the nonlocalities and/or the long-range spin entanglements exist, while randomness presents, which make the computation very compl…

Non-randomness of Japan megaquakes implied by stress recovery and accumulation
K. Z. Nanjo, T. Hori, D. Iwata
https://arxiv.org/abs/2507.21478 https://arxi…

Non-randomness of Japan megaquakes implied by stress recovery and accumulation
Monitoring stress recovery and accumulation associated with megaquakes helps to assess their recurrence. Previous studies proposed a high likelihood of imminent recurrence for the 2011 Tohoku and the 17th-century Hokkaido megaquakes belonging to the magnitude-9 class, although their current stress state remains uncertain. Here we compare the occurrence of small earthquakes relative to larger ones, using b-values, showing high b-values in the source area of the Tohoku earthquake, indicating low …

An efficient construction of Raz's two-source randomness extractor with improved parameters
Cameron Foreman, Lewis Wooltorton, Kevin Milner, Florian J. Curchod
https://arxiv.org/abs/2506.15547

An efficient construction of Raz's two-source randomness extractor with improved parameters
Randomness extractors are algorithms that distill weak random sources into near-perfect random numbers. Two-source extractors enable this distillation process by combining two independent weak random sources. Raz's extractor (STOC '05) was the first to achieve this in a setting where one source has linear min-entropy (i.e., proportional to its length), while the other has only logarithmic min-entropy in its length. However, Raz's original construction is impractical due to a polynomial computat…

Decentralized online stochastic generalized Nash Equilibrium seeking for multi-cluster games: A Byzantine-resilient algorithm
Bingqian Liu, Guanghui Wen, Liyuan Chen, Yiguang Hong
https://arxiv.org/abs/2507.22357

Decentralized online stochastic generalized Nash Equilibrium seeking for multi-cluster games: A Byzantine-resilient algorithm
This paper addresses the challenge of solving the generalized Nash Equilibrium seeking problem for decentralized stochastic online multi-cluster games amidst Byzantine agents. During the game process, each honest agent is influenced by both randomness and malicious information propagated by Byzantine agents. Additionally, none of the agents have prior knowledge about the number and identities of Byzantine agents. Furthermore, the stochastic local cost function and coupled global constraint func…

Universal relation between residual resistivity and A coefficient in correlated metals
Anna Yu. Efimova, Yohei Saito, Atsushi Kawamoto, Martin Dressel, Louk Rademaker, Andrej Pustogow
https://arxiv.org/abs/2508.21759

Universal relation between residual resistivity and A coefficient in correlated metals
The effects of strong electronic correlations and disorder are crucial for emergent phenomena such as unconventional superconductivity, metal-insulator transitions, and quantum criticality. While both are omnipresent in real materials, their individual impacts on charge transport remain elusive. To disentangle their respective roles, we have independently varied the degree of randomness and the strength of electronic correlations -- by chemical substitution and physical pressure, respectively -…

Integration of quantum random number generators with post-quantum cryptography algorithms
Paula Alonso Blanco, Luis Trigo Vidarte, Marc Romeu Casas, Jos\'e Ram\'on Mart\'inez Saavedra, Fernando de la Iglesia, Jordi Mur-Petit, Valerio Pruneri
https://arxiv.org/abs/2507.00658

Integration of quantum random number generators with post-quantum cryptography algorithms
As quantum technologies advance, the security of popular cryptographic protocols becomes more threatened by the capabilities of Cryptographically Relevant Quantum Computers (CRQCs). In this scenario, Post-Quantum Cryptography (PQC) has become a potential solution to prolong the life of existing Public Key Infrastructure (PKI) systems. However, PQC protocols depend on high-quality randomness for key generation and encapsulation procedures, with the quality of the entropy source potentially havin…

Symmetric Private Information Retrieval (SPIR) on Graph-Based Replicated Systems
Shreya Meel, Sennur Ulukus
https://arxiv.org/abs/2507.17736 https://arxiv.…

Symmetric Private Information Retrieval (SPIR) on Graph-Based Replicated Systems
We introduce the problem of symmetric private information retrieval (SPIR) on replicated databases modeled by a simple graph. In this model, each vertex corresponds to a server, and a message is replicated on two servers if and only if there is an edge between them. We consider the setting where the server-side common randomness necessary to accomplish SPIR is also replicated at the servers according to the graph, and we call this as message-specific common randomness. In this setting, we estab…

Locally Differentially Private Multi-Sensor Fusion Estimation With System Intrinsic Randomness
Xinhao Yan, Bo Chen, Hailong Huang
https://arxiv.org/abs/2508.15175 https://

Locally Differentially Private Multi-Sensor Fusion Estimation With System Intrinsic Randomness
This paper focuses on the privacy-preserving multi-sensor fusion estimation (MSFE) problem with differential privacy considerations. Most existing research efforts are directed towards the exploration of traditional differential privacy, also referred to as centralized differential privacy (CDP). It is important to note that CDP is tailored to protect the privacy of statistical data at fusion center such as averages and sums rather than individual data at sensors, which renders it inappropriate…

An inverse random diffraction grating problem for the Helmholtz equation
Zhiqi Sun, Yiwen Lin
https://arxiv.org/abs/2507.19744 https://arxiv.org/pdf/2507.1…

An inverse random diffraction grating problem for the Helmholtz equation
This paper investigates the inverse scattering problem of time-harmonic plane waves incident on a perfectly reflecting random periodic structure. To simulate random perturbations arising from manufacturing defects and surface wear in real-world grating profiles, we propose a stochastic surface modeling framework motivated by the discretization of the Wiener process. Our approach introduces randomness at discrete nodes and then applies linear interpolation to construct the surface, marking a nov…

»Forscher entwickeln ersten fälschungssicheren Zufallszahlengenerator:
[…] In einem 40-tägigen Test generierte das Forscherteam 7454 Mal #Zufallszahlen und stellte fest, dass in 7434 Fällen eine wirklich zufällige Zahl erzeugt wurde - eine Erfolgsrate von 99,7 Prozent. […]«
Pures #Random i…

Forscher entwickeln ersten fälschungssicheren Zufallszahlengenerator
Forscher haben ein System zur Erzeugung wirklich zufälliger Zahlen entwickelt. Das "Colorado University Randomness Beacon" nutzt Quantenmechanik und Blockchain-Technologie, um vollständig manipulationssichere Zufallszahlen zu erzeugen.

Confidence Optimization for Probabilistic Encoding
Pengjiu Xia, Yidian Huang, Wenchao Wei, Yuwen Tan
https://arxiv.org/abs/2507.16881 https://

Confidence Optimization for Probabilistic Encoding
Probabilistic encoding introduces Gaussian noise into neural networks, enabling a smooth transition from deterministic to uncertain states and enhancing generalization ability. However, the randomness of Gaussian noise distorts point-based distance measurements in classification tasks. To mitigate this issue, we propose a confidence optimization probabilistic encoding (CPE) method that improves distance reliability and enhances representation learning. Specifically, we refine probabilistic enco…

Studying propagating turbulent structures in the near wake of a sphere using Hilbert proper orthogonal decomposition
Shaun Davey, Callum Atkinson, Julio Soria
https://arxiv.org/abs/2507.19167

Studying propagating turbulent structures in the near wake of a sphere using Hilbert proper orthogonal decomposition
Turbulent flows, despite their apparent randomness, exhibit coherent structures that underpin their dynamics. Proper orthogonal decomposition (POD) has been widely used to extract these structures from experimental data. While periodic features like vortex shedding can be identified using POD mode pairs when periodicity dominates the flow, detecting such structures in complex flows is more challenging. The Hilbert proper orthogonal decomposition (HPOD) addresses this by applying POD to the anal…

A Variant Of Chaitin's Omega function
Yuxuan Li, Shuheng Zhang, Xiaoyan Zhang, Xuanheng Zhao
https://arxiv.org/abs/2508.16892 https://arxiv.org/pdf/250…

A Variant Of Chaitin's Omega function
We investigate the continuous function $f$ defined by $$x\mapsto \sum_{σ\le_L x }2^{-K(σ)}$$ as a variant of Chaitin's Omega from the perspective of analysis, computability, and algorithmic randomness. Among other results, we obtain that: (i) $f$ is differentiable precisely at density random points; (ii) $f(x)$ is $x$-random if and only if $x$ is weakly low for $K$ (low for $Ω$); (iii) the range of $f$ is a null, nowhere dense, perfect $Π^0_1(\emptyset')$ class with Hausdorff dimension $1$;…

mmKey: Channel-Aware Beam Shaping for Reliable Key Generation in mmWave Wireless Networks
Poorya Mollahosseini, Yasaman Ghasempour
https://arxiv.org/abs/2508.19010 https://

mmKey: Channel-Aware Beam Shaping for Reliable Key Generation in mmWave Wireless Networks
Physical-layer key generation (PLKG) has emerged as a promising technique to secure next-generation wireless networks by exploiting the inherent properties of the wireless channel. However, PLKG faces fundamental challenges in the millimeter wave (mmWave) regime due to channel sparsity, higher phase noise, and higher path loss, which undermine both the randomness and reciprocity required for secure key generation. In this paper, we present mmKey, a novel PLKG framework that capitalizes on the a…

Seeing through randomness with topological light
Cade Peters, Kelsey Everts, Tatjana Kleine, Pedro Ornelas, Andrew Forbes
https://arxiv.org/abs/2508.12305 https://

Seeing through randomness with topological light
Transmitting structured light robustly through complex random media is crucial in many applications, from sensing to communication. Unfortunately, the spatial structure of light is distorted in such media due to refractive index inhomogeneities that cause multiple scattering, requiring mitigating strategies such as iterative optimisation and adaptive optics. Here, we use topological light to see through random media without the need for any corrective measures. Using skyrmions as our optical to…

UniSegDiff: Boosting Unified Lesion Segmentation via a Staged Diffusion Model
Yilong Hu, Shijie Chang, Lihe Zhang, Feng Tian, Weibing Sun, Huchuan Lu
https://arxiv.org/abs/2507.18362

UniSegDiff: Boosting Unified Lesion Segmentation via a Staged Diffusion Model
The Diffusion Probabilistic Model (DPM) has demonstrated remarkable performance across a variety of generative tasks. The inherent randomness in diffusion models helps address issues such as blurring at the edges of medical images and labels, positioning Diffusion Probabilistic Models (DPMs) as a promising approach for lesion segmentation. However, we find that the current training and inference strategies of diffusion models result in an uneven distribution of attention across different timest…

Replaced article(s) found for gr-qc. https://arxiv.org/list/gr-qc/new
[1/2]:
- Assumptions of Randomness in Cosmology Models
Leonid A. Levin

Heavy-tailed open quantum systems reveal long-lived and ultrasensitive coherence
Sunkyu Yu, Xianji Piao, Namkyoo Park
https://arxiv.org/abs/2506.22717 http…

Heavy-tailed open quantum systems reveal long-lived and ultrasensitive coherence
Understanding random open quantum systems is critical for characterizing the performance of large-scale quantum devices and exploring macroscopic quantum phenomena. Various features in these systems, including spectral distributions, gap scaling, and decoherence, have been examined by modelling randomness under the central limit theorem. Here, we investigate random open quantum systems beyond the central limit theorem, focusing on heavy-tailed system-environment interactions. By extending the G…

To Zip Through the Cost Analysis of Probabilistic Programs
Matthias Hetzenberger, Georg Moser, Florian Zuleger
https://arxiv.org/abs/2508.14249 https://arx…

To Zip Through the Cost Analysis of Probabilistic Programs
Probabilistic programming and the formal analysis of probabilistic algorithms are active areas of research, driven by the widespread use of randomness to improve performance. While functional correctness has seen substantial progress, automated reasoning about expected runtime remains comparatively limited. In this work, we address this challenge by introducing a refinement-typed probability monad in Liquid Haskell. Our monad enables automated reasoning about expected values and costs by encodi…

Bimodal Dynamics of the Artificial Limit Order Book Stock Exchange with Autonomous Traders
Matej Steinbacher, Mitja Steinbacher, Matjaz Steinbacher
https://arxiv.org/abs/2508.17837

Bimodal Dynamics of the Artificial Limit Order Book Stock Exchange with Autonomous Traders
This paper explores the bifurcative dynamics of an artificial stock market exchange (ASME) with endogenous, myopic traders interacting through a limit order book (LOB). We showed that agent-based price dynamics possess intrinsic bistability, which is not a result of randomness but an emergent property of micro-level trading rules, where even identical initial conditions lead to qualitatively different long-run price equilibria: a deterministic zero-price state and a persistent positive-price eq…

ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings
Rehan Raza, Guanjin Wang, Kevin Wong, Hamid Laga, Marco Fisichella
https://arxiv.org/abs/2508.13672

ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings
Explainable Artificial Intelligence (XAI) methods, such as Local Interpretable Model-Agnostic Explanations (LIME), have advanced the interpretability of black-box machine learning models by approximating their behavior locally using interpretable surrogate models. However, LIME's inherent randomness in perturbation and sampling can lead to locality and instability issues, especially in scenarios with limited training data. In such cases, data scarcity can result in the generation of unrealistic…

Rate-Distortion-Perception Trade-off with Strong Realism Constraints: Role of Side Information and Common Randomness
Yassine Hamdi, Aaron B. Wagner, Deniz G\"und\"uz
https://arxiv.org/abs/2507.14825

Rate-Distortion-Perception Trade-off with Strong Realism Constraints: Role of Side Information and Common Randomness
In image compression, with recent advances in generative modeling, existence of a trade-off between the rate and perceptual quality has been brought to light, where the perceptual quality is measured by the closeness of the output and source distributions. We consider the compression of a memoryless source sequence $X^n=(X_1, \ldots, X_n)$ in the presence of memoryless side information $Z^n=(Z_1, \ldots, Z_n),$ originally studied by Wyner and Ziv, but elucidate the impact of a strong perfect re…

On the dynamical evolution of randomness Part B: Geometrisation and the origin of convergence in LLN
Allen Lobo
https://arxiv.org/abs/2506.14804 https://…

On the dynamical evolution of randomness Part B: Geometrisation and the origin of convergence in LLN
In classical probability theory, the convergence of empirical frequencies to theoretical probabilities: as captured by the Law of Large Numbers (LLN): is treated as axiomatic and emergent from statistical assumptions such as independence and identical distribution. In this work, a novel dynamical framework is constructed in which convergence arises as a consequence of structured evolution in outcome space, rather than a statistical postulate. Through this formalism, statistical convergence is d…

Formal proof of the approximation of a random phenomenon by a chaotic phenomenon
Mohamed El Ouafi, Hajar Ahalli, Abderrahim Aslimani, Kaoutar Lamrini Uahabi
https://arxiv.org/abs/2507.08209

Formal proof of the approximation of a random phenomenon by a chaotic phenomenon
This article examines the subtle relationship between chaos and randomness, two concepts that, although they refer to seemingly unpredictable phenomenon, are based on fundamentally different principles. Chaos manifests in deterministic systems where small variations in initial conditions lead to unpredictable long-term behaviors, while randomness pertains to intrinsically probabilistic processes, characterized by fundamental uncertainty. Although these phenomena are based on distinct mechanisms…

Relatively hyperbolic groups, Grothendieck pairs, and uncountable profinite ambiguity among fibre products
Martin R. Bridson, Alan W. Reid
https://arxiv.org/abs/2507.15009

Relatively hyperbolic groups, Grothendieck pairs, and uncountable profinite ambiguity among fibre products
These notes expand upon our lectures on {\em profinite rigidity} at the international colloquium on randomness, geometry and dynamics, organised by TIFR Mumbai at IISER Pune in January 2024. We are interested in the extent to which groups that arise in hyperbolic geometry and 3-manifold topology are determined by their finite quotients. The main theme of these notes is the radical extent to which rigidity is lost when one passes from consideration of groups with hyperbolic features to considera…

A stochastic model for the diffusion of competing opinions with trend-following, opposition, and indifference
Manuel Gonz\'alez-Navarrete
https://arxiv.org/abs/2506.18313

A stochastic model for the diffusion of competing opinions with trend-following, opposition, and indifference
We study a stochastic model for the diffusion of competing opinions in a population composed of three types of agents: trend-followers, opposers, and indifferent individuals. The decision dynamics are driven by reinforcement mechanisms, modulated by a latent trend process, allowing us to capture realistic features such as amplification, resistance, and randomness in opinion formation. We derive explicit formulas for the finite-time moments of the opinion count vector and establish a set of stro…

Existence and Uniqueness of Solutions to Nonlinear Diffusion with Memory in Random Media
Yixian Chen
https://arxiv.org/abs/2507.16659 https://

Existence and Uniqueness of Solutions to Nonlinear Diffusion with Memory in Random Media
This paper studies a nonlinear diffusion equation with memory and spatial randomness: $$u_t=\nabla\cdot \big( D(x,w)\cdot\int_0^t K(t-s) \nabla\cdotΦ(u(x,s))ds \big)+f(x,t)$$ where $K$ is memory Kernel and $D(x,w)$ is bounded random coefficient. Under monotonicity and growth conditions on $Φ$, the existence and uniqueness of weak solution is established. The analysis employs Orthogonal approximation, energy estimates, and monotone operator theory. The convolution structure is handled within v…

ALEA IACTA EST: A Declarative Domain-Specific Language for Manually Performable Random Experiments
Baltasar Tranc\'on y Widemann, Markus Lepper
https://arxiv.org/abs/2506.11794

ALEA IACTA EST: A Declarative Domain-Specific Language for Manually Performable Random Experiments
Random experiments that are simple and clear enough to be performed by human agents feature prominently in the teaching of elementary stochastics as well as in games. We present Alea, a domain-specific language for the specification of random experiments. Alea code can either be analyzed statically to obtain and inspect probability distributions of outcomes, or be executed with a source pseudo-randomness for simulation or as a game assistant. The language is intended for ease of use by non-expe…

Stochastic synaptic dynamics under learning
Jakob Stubenrauch, Naomi Auer, Richard Kempter, Benjamin Lindner
https://arxiv.org/abs/2508.13846 https://arxiv…

Stochastic synaptic dynamics under learning
Learning is based on synaptic plasticity, which drives and is driven by neural activity. Because pre- and postsynaptic spiking activity is shaped by randomness, the synaptic weights follow a stochastic process, requiring a probabilistic framework to capture the noisy synaptic dynamics. We consider a paradigmatic supervised learning example: a presynaptic neural population impinging in a sequence of episodes on a recurrent network of integrate-and-fire neurons through synapses undergoing spike-t…

Optimal Feedback Control in Social Networks in a McKean-Vlasov-Friedkin-Johnsen System
Paramahansa Pramanik
https://arxiv.org/abs/2508.17138 https://arxiv.…

Optimal Feedback Control in Social Networks in a McKean-Vlasov-Friedkin-Johnsen System
This paper presents a comprehensive analytical formulation for deriving a closed-form optimal strategy for agents operating within a social network, modeled through a McKean-Vlasov stochastic differential equation (SDE). Each agent aims to minimize a personal dynamic cost functional that accounts for deviations from the collective opinions of others, their own past beliefs, and is influenced by randomness and inherent opinion rigidity, often described as stubbornness. To tackle this, we develop…

Leakage-Resilient Extractors against Number-on-Forehead Protocols
Eshan Chattopadhyay, Jesse Goodman
https://arxiv.org/abs/2506.12595 https://

Leakage-Resilient Extractors against Number-on-Forehead Protocols
Given a sequence of $N$ independent sources $\mathbf{X}_1,\mathbf{X}_2,\dots,\mathbf{X}_N\sim\{0,1\}^n$, how many of them must be good (i.e., contain some min-entropy) in order to extract a uniformly random string? This question was first raised by Chattopadhyay, Goodman, Goyal and Li (STOC '20), motivated by applications in cryptography, distributed computing, and the unreliable nature of real-world sources of randomness. In their paper, they showed how to construct explicit low-error extracto…

Certified randomness from quantum speed limits
Caroline L. Jones, Albert Aloy, Gerard Higgins, Markus P. Mueller
https://arxiv.org/abs/2506.14526 https://

Certified randomness from quantum speed limits
Quantum speed limits are usually regarded as fundamental restrictions, constraining the amount of computation that can be achieved within some given time and energy. Complementary to this intuition, here we show that these limitations are also of operational value: they enable the secure generation of certified randomness. We consider a prepare-and-measure scenario with some (experimentally determined or promised) upper bound on the energy uncertainty of the average prepared quantum state, but …

Spintronic Bayesian Hardware Driven by Stochastic Magnetic Domain Wall Dynamics
Tianyi Wang, Bingqian Dai, Kin Wong, Yaochen Li, Yang Cheng, Qingyuan Shu, Haoran He, Puyang Huang, Hanshen Huang, Kang L. Wang
https://arxiv.org/abs/2507.17193

Spintronic Bayesian Hardware Driven by Stochastic Magnetic Domain Wall Dynamics
As artificial intelligence (AI) advances into diverse applications, ensuring reliability of AI models is increasingly critical. Conventional neural networks offer strong predictive capabilities but produce deterministic outputs without inherent uncertainty estimation, limiting their reliability in safety-critical domains. Probabilistic neural networks (PNNs), which introduce randomness, have emerged as a powerful approach for enabling intrinsic uncertainty quantification. However, traditional C…

Efficient and Robust Semantic Image Communication via Stable Cascade
Bilal Khalid, Pedro Freire, Sergei K. Turitsyn, Jaroslaw E. Prilepsky
https://arxiv.org/abs/2507.17416

Efficient and Robust Semantic Image Communication via Stable Cascade
Diffusion Model (DM) based Semantic Image Communication (SIC) systems face significant challenges, such as slow inference speed and generation randomness, that limit their reliability and practicality. To overcome these issues, we propose a novel SIC framework inspired by Stable Cascade, where extremely compact latent image embeddings are used as conditioning to the diffusion process. Our approach drastically reduces the data transmission overhead, compressing the transmitted embedding to just …

Topology induced modifications in the critical behavior of the Yaldram Khan catalytic reaction model
Paulo F. Gomes, Henrique A. Fernandes, Roberto da Silva
https://arxiv.org/abs/2506.04485

Topology induced modifications in the critical behavior of the Yaldram Khan catalytic reaction model
In this work, we investigated how the use of complex networks as catalytic surfaces can affect the phase diagram of the Yaldram-Khan model, as well as how the order of the phase transitions present in the seminal work behaves when the randomness is added to the model. The study was conducted by taking into consideration two well-known random networks, the Erdos-Renyi network (ERN), with its long-range randomness, and the random geometric graph (RGG), with its spatially constrained randomness. W…

Mxplainer: Explain and Learn Insights by Imitating Mahjong Agents
Lingfeng Li, Yunlong Lu, Yongyi Wang, Qifan Zheng, Wenxin Li
https://arxiv.org/abs/2506.14246

Mxplainer: Explain and Learn Insights by Imitating Mahjong Agents
People need to internalize the skills of AI agents to improve their own capabilities. Our paper focuses on Mahjong, a multiplayer game involving imperfect information and requiring effective long-term decision-making amidst randomness and hidden information. Through the efforts of AI researchers, several impressive Mahjong AI agents have already achieved performance levels comparable to those of professional human players; however, these agents are often treated as black boxes from which few in…

Ising versus infinite randomness criticality in arrays of Rydberg atoms trapped with non-perfect tweezers
Jose Soto-Garcia, Natalia Chepiga
https://arxiv.org/abs/2506.11985

Ising versus infinite randomness criticality in arrays of Rydberg atoms trapped with non-perfect tweezers
Chains of Rydberg atoms have emerged as an amazing platform for simulating quantum physics in low dimensions. This remarkable success is due to the versatility of lattice geometries achieved by trapping neutral atoms with optical tweezers. On a given lattice, the competition between the repulsive van der Waals potential and the detuning of the laser frequency brings the atoms to highly excited Rydberg states, leading to a variety of exotic phases and quantum phase transitions. Experiments on th…

Silentflow: Leveraging Trusted Execution for Resource-Limited MPC via Hardware-Algorithm Co-design
Zhuoran Li, Hanieh Totonchi Asl, Ebrahim Nouri, Yifei Cai, Danella Zhao
https://arxiv.org/abs/2508.13357

Silentflow: Leveraging Trusted Execution for Resource-Limited MPC via Hardware-Algorithm Co-design
Secure Multi-Party Computation (MPC) offers a practical foundation for privacy-preserving machine learning at the edge, with MPC commonly employed to support nonlinear operations. These MPC protocols fundamentally rely on Oblivious Transfer (OT), particularly Correlated OT (COT), to generate correlated randomness essential for secure computation. Although COT generation is efficient in conventional two-party settings with resource-rich participants, it becomes a critical bottleneck in real-worl…

This https://arxiv.org/abs/2412.08693 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Random Circuits in the Black Hole Interior
In this paper, we present a quantitative holographic relation between a microscopic measure of randomness and the geometric length of the wormhole in the black hole interior. To this end, we perturb an AdS black hole with Brownian semiclassical sources, implementing the continuous version of a random quantum circuit for the black hole. We use the random circuit to prepare ensembles of states of the black hole whose semiclassical duals contain Einstein-Rosen (ER) caterpillars: long cylindrical w…

Oblivious Deletion Codes
Roni Con, Ray Li
https://arxiv.org/abs/2506.18878 https://arxiv.org/pdf/2506.18878

Oblivious Deletion Codes
We construct deletion error-correcting codes in the oblivious model, where errors are adversarial but oblivious to the encoder's randomness. Oblivious errors bridge the gap between the adversarial and random error models, and are motivated by applications like DNA storage, where the noise is caused by hard-to-model physical phenomena, but not by an adversary. (1) (Explicit oblivious) We construct $t$ oblivious deletion codes, with redundancy $\sim 2t\log n$, matching the existential bound for…

NeuroPDE: A Neuromorphic PDE Solver Based on Spintronic and Ferroelectric Devices
Siqing Fu, Lizhou Wu, Tiejun Li, Chunyuan Zhang, Sheng Ma, Jianmin Zhang, Yuhan Tang, Jixuan Tang
https://arxiv.org/abs/2507.04677

NeuroPDE: A Neuromorphic PDE Solver Based on Spintronic and Ferroelectric Devices
In recent years, new methods for solving partial differential equations (PDEs) such as Monte Carlo random walk methods have gained considerable attention. However, due to the lack of hardware-intrinsic randomness in the conventional von Neumann architecture, the performance of PDE solvers is limited. In this paper, we introduce NeuroPDE, a hardware design for neuromorphic PDE solvers that utilizes emerging spintronic and ferroelectric devices. NeuroPDE incorporates spin neurons that are capable…

Modulator-free, self-testing quantum random number generator
Ana Bl\'azquez-Co\'ido, Fadri Gr\"unenfelder, Anthony Martin, Raphael Houlmann, Hugo Zbinden, Davide Rusca
https://arxiv.org/abs/2507.12346

Modulator-free, self-testing quantum random number generator
Quantum random number generators (QRNGs) use the inherent unpredictability of quantum mechanics to generate true randomness, as opposed to classical random number generators. However, ensuring the authenticity of this randomness still requires robust verification. Self-testing QRNGs address this need by enabling the validation of the randomness produced based on the observed data from the experiment while requiring few assumptions. In this work, we present a practical, self-testing QRNG designe…

Fiducial Matching: Differentially Private Inference for Categorical Data
Ogonnaya Michael Romanus, Younes Boulaguiem, Roberto Molinari
https://arxiv.org/abs/2507.11762

Fiducial Matching: Differentially Private Inference for Categorical Data
The task of statistical inference, which includes the building of confidence intervals and tests for parameters and effects of interest to a researcher, is still an open area of investigation in a differentially private (DP) setting. Indeed, in addition to the randomness due to data sampling, DP delivers another source of randomness consisting of the noise added to protect an individual's data from being disclosed to a potential attacker. As a result of this convolution of noises, in many cases…

Replaced article(s) found for cs.DS. https://arxiv.org/list/cs.DS/new
[1/1]:
- Efficient Online Random Sampling via Randomness Recycling
Thomas L. Draper, Feras A. Saad

Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Network
Chen Chen, Lijin Wang, Yanzhao Cao, Xupeng Cheng
https://arxiv.org/abs/2507.14467

Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Network
In this paper we propose a novel neural network model for learning stochastic Hamiltonian systems (SHSs) from observational data, termed the stochastic generating function neural network (SGFNN). SGFNN preserves symplectic structure of the underlying stochastic Hamiltonian system and produces symplectic predictions. Our model utilizes the autoencoder framework to identify the randomness of the latent system by the encoder network, and detects the stochastic generating function of the system thr…

How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction
Jun Chen, Hong Chen, Yonghua Yu, Yiming Ying
https://arxiv.org/abs/2507.11161

How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction
In recent years, contrastive learning has achieved state-of-the-art performance in the territory of self-supervised representation learning. Many previous works have attempted to provide the theoretical understanding underlying the success of contrastive learning. Almost all of them rely on a default assumption, i.e., the label consistency assumption, which may not hold in practice (the probability of failure is called labeling error) due to the strength and randomness of common augmentation st…

A control theoretical approach to gene regulation raises quantitative constraints for dynamic homeostasis in stochastic gene expression
Guilherme Giovanini, Cyro von Zuben de Valega Negr\~ao, Ammar Alsinai, Alexandre Ferreira Ramos
https://arxiv.org/abs/2508.09038

A control theoretical approach to gene regulation raises quantitative constraints for dynamic homeostasis in stochastic gene expression
Cell phenotype dynamic homeostasis contrasts with the inherent randomness of intracellular reactions. Although feedback control of master regulatory genes (MRG) is a key strategy for maintaining gene network expression ranges limited, understanding the quantitative constraints and corresponding mechanisms enabling such a dynamic stability under noise remains elusive. Here we model MRG expression as a stochastic process and downstream genes as sensors which response conditionally induce MRG acti…

Discrete-Time LQ Stochastic Two Person Nonzero Sum Difference Games With Random Coefficients:~Closed-Loop Nash Equilibrium
Qingxin Meng, Yiwei Wu
https://arxiv.org/abs/2507.16412 …

Discrete-Time LQ Stochastic Two Person Nonzero Sum Difference Games With Random Coefficients:~Closed-Loop Nash Equilibrium
This paper investigates closed-loop Nash equilibria for discrete-time linear-quadratic (LQ) stochastic nonzero-sum difference games with random coefficients. Unlike existing works, we consider randomness in both state dynamics and cost functionals, leading to a complex structure of fully coupled cross-coupled stochastic Riccati equations (CCREs). The key contributions lie in characterizing the equilibrium via state-feedback strategies derived by decoupling stochastic Hamiltonian systems governe…

On the statistical nature of Betti numbers and Euler characteristic of smooth random fields
Pravabati Chingangbam
https://arxiv.org/abs/2507.06255 https://…

On the statistical nature of Betti numbers and Euler characteristic of smooth random fields
We represent excursion sets of smooth random fields as unions of a topological basis consisting of a sequence of simply and multiply connected compact subsets of the underlying manifold. The associated coefficients, which are non-negative discrete random variables, reflect the randomness of the field. Betti numbers of the excursion sets can be expressed as summations over the coefficients, and the Euler characteristic and the sum of Betti numbers can also be expressed as their (alternating) sum…

This https://arxiv.org/abs/2502.13978 has been replaced.
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On the dynamical evolution of randomness Part A: Random experiments as dynamical systems
In this work, Bernoulli's Law of Large Numbers, also known as the Golden theorem, has been extended to study the relations between empirical probability and empirical randomness of an otherwise random experiment. Using the example of a coin toss and a dice role, some interesting results are drawn. Analytically and using numerical computations, empirical randomness of each outcome has been shown to increase by \textit{ chance}, which itself depends on the growth rate of empirical probabilities. …

A c-theorem for the effective central charge in the R=1 replica limit, and applications to systems with measurement-induced randomness
Rushikesh A. Patil, Andreas W. W. Ludwig
https://arxiv.org/abs/2507.07959

A c-theorem for the effective central charge in the R=1 replica limit, and applications to systems with measurement-induced randomness
We present a general theorem demonstrating non-perturbatively the decrease of the "effective central charge" $c_{\text{eff}}=(d c/dR)|_{R=1}$ under renormalization group (RG) flow in the $R\rightarrow1$ replica limit of a $R$-copy $2D$ conformal field theory (CFT) action $S_{*}$ perturbed by a replica interaction of the form $$-\mathbb{S}=-\sum_{a=1}^{R}S_{*}^{(a)}+Δ\int d^2 x \sum_{\substack{a,b=1\\ a\neq b}}^{R}φ^{(a)}(x)φ^{(b)}(x).$$ Here $φ$ is a scaling field belonging to the CFT …

This https://arxiv.org/abs/2505.01874 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Towards Trustworthy Federated Learning with Untrusted Participants
Resilience against malicious participants and data privacy are essential for trustworthy federated learning, yet achieving both with good utility typically requires the strong assumption of a trusted central server. This paper shows that a significantly weaker assumption suffices: each pair of participants shares a randomness seed unknown to others. In a setting where malicious participants may collude with an untrusted server, we propose CafCor, an algorithm that integrates robust gradient agg…

Dimers with layered disorder
Quentin Moulard (TU Wien), Fabio Toninelli (TU Wien)
https://arxiv.org/abs/2507.11964 https://arxiv.org/…

Dimers with layered disorder
We study the dimer model on the square grid, with quenched random edge weights. Randomness is chosen to have a layered structure, similar to that of the celebrated McCoy-Wu disordered Ising model. Disorder has a highly non-trivial effect and it produces an essential singularity of the free energy, with $e^{-\sqrt{\rm distance}}$ decay of dimer-dimer correlations, at a point of the ``liquid'' (or ``massless'') phase where the homogeneous dimer model has instead a real analytic free energy and co…

Improving Deep Learning Framework Testing with Model-Level Metamorphic Testing
Yanzhou Mu, Juan Zhai, Chunrong Fang, Xiang Chen, Zhixiang Cao, Peiran Yang, Kexin Zhao, An Guo, Zhenyu Chen
https://arxiv.org/abs/2507.04354

Improving Deep Learning Framework Testing with Model-Level Metamorphic Testing
Deep learning (DL) frameworks are essential to DL-based software systems, and framework bugs may lead to substantial disasters, thus requiring effective testing. Researchers adopt DL models or single interfaces as test inputs and analyze their execution results to detect bugs. However, floating-point errors, inherent randomness, and the complexity of test inputs make it challenging to analyze execution results effectively, leading to existing methods suffering from a lack of suitable test oracl…

Mathematical proof of errors in Capasso's excess noise factor formula for an n-step staircase multiplier
Ankitha E Bangera
https://arxiv.org/abs/2506.09649

Mathematical proof of errors in Capasso's excess noise factor formula for an n-step staircase multiplier
Solid-state devices such as multistep staircase avalanche photodiodes (APDs) are analogues to the photomultiplier tubes and are considered as a cascade-amplifier. The major source of internal noise in these APDs is due to the randomness in their stepwise impact ionization. Recent literature on staircase APDs by research groups such as Campbell and co-workers have reported the theoretical estimates of total excess noise factors using Capasso's excess noise factor formula. This formula is based o…

Win Probabilities, Hand Sizes, and Game Duration Analysis in the Bhikar-Sawkar Card Game
Mihir Durve
https://arxiv.org/abs/2506.03680 https://

Win Probabilities, Hand Sizes, and Game Duration Analysis in the Bhikar-Sawkar Card Game
We present a Monte Carlo simulation study of the Bhikar-Sawkar card game, a non-deterministic game structurally similar to the classic Beggar-My-Neighbour, which is fully deterministic. Although both games share a common setup, key differences in their rules, particularly the reshuffling of cards after each won hand in Bhikar-Sawkar, introduce stochasticity and significantly increase the space of possible game evolutions. This inherent randomness raises a range of interesting statistical questi…

Generalized and Unified Equivalences between Hardness and Pseudoentropy
Lunjia Hu, Salil Vadhan
https://arxiv.org/abs/2507.05972 https://

Generalized and Unified Equivalences between Hardness and Pseudoentropy
Pseudoentropy characterizations provide a quantitatively precise demonstration of the close relationship between computational hardness and computational randomness. We prove a unified pseudoentropy characterization that generalizes and strengthens previous results for both uniform and non-uniform models of computation. Our characterization holds for a general family of entropy notions that encompasses the common notions of Shannon entropy and min entropy as special cases. Moreover, we show tha…

Finite-size security analysis for quantum protocols: A Python framework using the Entropy Accumulation Theorem with graphical interface
Piotr Mironowicz, Mohamed Bourennane
https://arxiv.org/abs/2506.18888

Finite-size security analysis for quantum protocols: A Python framework using the Entropy Accumulation Theorem with graphical interface
We present a comprehensive software framework for the finite-size security analysis of quantum random number generation (QRNG) and quantum key distribution (QKD) protocols, based on the Entropy Accumulation Theorem (EAT). Our framework includes both a Python API and an intuitive graphical user interface (GUI), designed to support protocol designers and experimentalists in certifying randomness and key rates under realistic, finite-resource conditions. At its core, the framework automates the co…

A low-rank solver for the Stokes-Darcy model with random hydraulic conductivity and Beavers-Joseph condition
Yujun Zhu, Yulan Ning, Zhipeng Yang, Xiaoming He, Ju Ming
https://arxiv.org/abs/2508.05328

A low-rank solver for the Stokes-Darcy model with random hydraulic conductivity and Beavers-Joseph condition
This paper proposes, analyzes, and demonstrates an efficient low-rank solver for the stochastic Stokes-Darcy interface model with a random hydraulic conductivity both in the porous media domain and on the interface. We consider three interface conditions with randomness, including the Beavers-Joseph interface condition with the random hydraulic conductivity, on the interface between the free flow and the porous media flow. Our solver employs a novel generalized low-rank approximation of the lar…

This https://arxiv.org/abs/2505.24603 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches
Gaussian sketching, which consists of pre-multiplying the data with a random Gaussian matrix, is a widely used technique for multiple problems in data science and machine learning, with applications spanning computationally efficient optimization, coded computing, and federated learning. This operation also provides differential privacy guarantees due to its inherent randomness. In this work, we revisit this operation through the lens of Renyi Differential Privacy (RDP), providing a refined pri…

A Review of Modern Stochastic Modeling: SDE/SPDE Numerics, Data-Driven Identification, and Generative Methods with Applications in Biology and Epidemiology
Yassine Sabbar
https://arxiv.org/abs/2508.11004

A Review of Modern Stochastic Modeling: SDE/SPDE Numerics, Data-Driven Identification, and Generative Methods with Applications in Biology and Epidemiology
This review maps developments in stochastic modeling, highlighting non-standard approaches and their applications to biology and epidemiology. It brings together four strands: (1) core models for systems that evolve with randomness; (2) learning key parts of those models directly from data; (3) methods that can generate realistic synthetic data in continuous time; and (4) numerical techniques that keep simulations stable, accurate, and faithful over long runs. The objective is practical: help r…

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective
Zhiyi Dong, Zixuan Liu, Yongyi Mao
https://arxiv.org/abs/2507.06552

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective
This paper studies the hardness of unsupervised domain adaptation (UDA) under covariate shift. We model the uncertainty that the learner faces by a distribution $π$ in the ground-truth triples $(p, q, f)$ -- which we call a UDA class -- where $(p, q)$ is the source -- target distribution pair and $f$ is the classifier. We define the performance of a learner as the overall target domain risk, averaged over the randomness of the ground-truth triple. This formulation couples the source distributi…

Analysis of Reaction-Diffusion Predator-Prey System under Random Switching
Nguyen H. Du, Nhu N. Nguyen
https://arxiv.org/abs/2507.06491 https://arxiv.org/pdf/2507.06491 https://arxiv.org/html/2507.06491
arXiv:2507.06491v1 Announce Type: new
Abstract: This paper investigates the long-term dynamics of a reaction-diffusion predator-prey system subject to random environmental fluctuations modeled by Markovian switching. The model is formulated as a hybrid system of partial differential equations (PDEs), where the switching between different ecological regimes captures the randomness in environmental conditions. We derive a critical threshold parameter that determines whether the predator species will eventually go extinct or persist. We further characterize the system's asymptotic behavior by providing a detailed pathwise description of the omega-limit set of solutions. This analysis reveals how the effects of random switching shape the distribution and long-term coexistence of the species. Numerical simulations are provided to validate and illustrate the theoretical findings, highlighting transitions between different dynamical regimes. To the best of our knowledge, this is the first work that rigorously analyzes a spatially diffusive predator-prey model under Markovian switching, thereby bridging the gap between spatial ecology and stochastic hybrid PDE systems.
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The effects of initial conditions on the accuracy of mean-field approximations of Markov processes on large random graphs
Pierfrancesco Dionigi, D\'aniel Keliger
https://arxiv.org/abs/2506.12872

The effects of initial conditions on the accuracy of mean-field approximations of Markov processes on large random graphs
We study the evolution of a general class of stochastic processes (containing, e.g. SIS and SIR models) on large random networks, focusing on a particular general class of random graph models. To approximate the expected dynamics of these processes, we employ a mean-field technique known as the N-Intertwined Mean-Field Approximation (NIMFA). Our primary goal is to quantify the impact of the randomness in the network topology on the accuracy of such approximations. While classical work by Kurtz …

On Differential Privacy for Adaptively Solving Search Problems via Sketching
Shiyuan Feng, Ying Feng, George Z. Li, Zhao Song, David P. Woodruff, Lichen Zhang
https://arxiv.org/abs/2506.05503

On Differential Privacy for Adaptively Solving Search Problems via Sketching
Recently differential privacy has been used for a number of streaming, data structure, and dynamic graph problems as a means of hiding the internal randomness of the data structure, so that multiple possibly adaptive queries can be made without sacrificing the correctness of the responses. Although these works use differential privacy to show that for some problems it is possible to tolerate $T$ queries using $\widetilde{O}(\sqrt{T})$ copies of a data structure, such results only apply to numer…

What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness
Yang Cai, Alkis Kalavasis, Katerina Mamali, Anay Mehrotra, Manolis Zampetakis
https://arxiv.org/abs/2506.04194

What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness
Most of the widely used estimators of the average treatment effect (ATE) in causal inference rely on the assumptions of unconfoundedness and overlap. Unconfoundedness requires that the observed covariates account for all correlations between the outcome and treatment. Overlap requires the existence of randomness in treatment decisions for all individuals. Nevertheless, many types of studies frequently violate unconfoundedness or overlap, for instance, observational studies with deterministic tr…

\nu-QSSEP: A toy model for entanglement spreading in stochastic diffusive quantum systems
Vincenzo Alba
https://arxiv.org/abs/2507.11674 https://

ν-QSSEP: A toy model for entanglement spreading in stochastic diffusive quantum systems
We investigate out-of-equilibrium entanglement dynamics in a generalization of the so-called $QSSEP$ model, which is a free-fermion chain with stochastic in space and time hopping amplitudes. In our setup, the noisy amplitudes are spatially-modulated satisfying a $ν$-site translation invariance but retaining their randomness in time. For each noise realization, the dynamics preserves Gaussianity, which allows to obtain noise-averaged entanglement-related quantities. The statistics of the stead…

A theory of vectorial probability for quantum correlations
Sheng Feng
https://arxiv.org/abs/2506.12101 https://arxiv.org/pdf/2506.121…

A theory of vectorial probability for quantum correlations
Randomness is a ubiquitous phenomenon that is practically accompanied by physical events described by probability theory. However, probability by definition in the theory is a nonnegative scalar quantity. Here, we propose the concept of vectorial probability, quantified as a vector with interesting but hidden geometry, and develop a theory to describe random events with correlations dictated by this geometry. Based on this theory, we construct a local model that is able to reproduce the predict…

This https://arxiv.org/abs/2410.07984 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Large Deviation Analysis for the Reverse Shannon Theorem
Channel simulation is to simulate a noisy channel using noiseless channels with unlimited shared randomness. This can be interpreted as the reverse problem to Shannon's noisy coding theorem. In contrast to previous works, our approach employs Rényi divergence (with the parameter $α\in(0,\infty)$) to measure the level of approximation. Specifically, we obtain the reverse Shannon theorem under the Rényi divergence, which characterizes the Rényi simulation rate, the minimum communication cost …

Wiener chaos expansion for stochastic Maxwell equations driven by Wiener process
Lihai Ji, Kuan Xue, Liying Zhang
https://arxiv.org/abs/2508.01271 https://…

Wiener chaos expansion for stochastic Maxwell equations driven by Wiener process
A novel and efficient algorithm based on the Wiener chaos expansion is proposed for the stochastic Maxwell equations driven by Wiener process. The proposed algorithm can reduce the original stochastic system to the deterministic case and separate the randomness in the computation. Therefore, it can yield a significant improvement of efficiency and lead to less computational errors compared to the Monte Carlo method, since the statistics of the solution can be solved directly without repeating o…

This https://arxiv.org/abs/2309.02073 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates
Completely randomized experiment is the gold standard for causal inference. When the covariate information for each experimental candidate is available, one typical way is to include them in covariate adjustments for more accurate treatment effect estimation. In this paper, we investigate this problem under the randomization-based framework, i.e., that the covariates and potential outcomes of all experimental candidates are assumed as deterministic quantities and the randomness comes solely fro…

Central Limit Theorems for Sample Average Approximations in Stochastic Optimal Control
Johannes Milz, Alexander Shapiro
https://arxiv.org/abs/2508.01942 https://

Central Limit Theorems for Sample Average Approximations in Stochastic Optimal Control
We establish central limit theorems for the Sample Average Approximation (SAA) method in discrete-time, finite-horizon Stochastic Optimal Control. Using the dynamic programming principle and backward induction, we characterize the limiting distributions of the SAA value functions. The asymptotic variance at each stage decomposes into two components: a current-stage variance arising from immediate randomness, and a propagated future variance accumulated from subsequent stages. This decomposition…

AL-SPCE -- Reliability analysis for nondeterministic models using stochastic polynomial chaos expansions and active learning
A. Pires, M. Moustapha, S. Marelli, B. Sudret
https://arxiv.org/abs/2507.04553

AL-SPCE -- Reliability analysis for nondeterministic models using stochastic polynomial chaos expansions and active learning
Reliability analysis typically relies on deterministic simulators, which yield repeatable outputs for identical inputs. However, many real-world systems display intrinsic randomness, requiring stochastic simulators whose outputs are random variables. This inherent variability must be accounted for in reliability analysis. While Monte Carlo methods can handle this, their high computational cost is often prohibitive. To address this, stochastic emulators have emerged as efficient surrogate models…

High-Performance Heterodyne Receiver for Quantum Information Processing in a Laser Written Integrated Photonic Platform
Andrea Peri, Giulio Gualandi, Tommaso Bertapelle, Mattia Sabatini, Giacomo Corrielli, Yoann Pi\'etri, Davide Giacomo Marangon, Giuseppe Vallone, Paolo Villoresi, Roberto Osellame, Marco Avesani
https://arxi…

High-Performance Heterodyne Receiver for Quantum Information Processing in a Laser Written Integrated Photonic Platform
Continuous-Variable Quantum Key Distribution (CV-QKD) and Quantum Random Number Generation (CV-QRNG) are critical technologies for secure communication and high-speed randomness generation, exploiting shot-noise-limited coherent detection for their operation. Integrated photonic solutions are key to advancing these protocols, as they enable compact, scalable, and efficient system implementations. In this work, we introduce Femtosecond Laser Micromachining (FLM) on borosilicate glass as a novel …

The Spurious Factor Dilemma: Robust Inference in Heavy-Tailed Elliptical Factor Models
Jiang Hu, Jiahui Xie, Yangchun Zhang, Wang Zhou
https://arxiv.org/abs/2506.05116

The Spurious Factor Dilemma: Robust Inference in Heavy-Tailed Elliptical Factor Models
Factor models are essential tools for analyzing high-dimensional data, particularly in economics and finance. However, standard methods for determining the number of factors often overestimate the true number when data exhibit heavy-tailed randomness, misinterpreting noise-induced outliers as genuine factors. This paper addresses this challenge within the framework of Elliptical Factor Models (EFM), which accommodate both heavy tails and potential non-linear dependencies common in real-world da…

Unitary designs in nearly optimal depth
Laura Cui, Thomas Schuster, Fernando Brandao, Hsin-Yuan Huang
https://arxiv.org/abs/2507.06216 https://

Unitary designs in nearly optimal depth
We construct $\varepsilon$-approximate unitary $k$-designs on $n$ qubits in circuit depth $O(\log k \log \log n k / \varepsilon)$. The depth is exponentially improved over all known results in all three parameters $n$, $k$, $\varepsilon$. We further show that each dependence is optimal up to exponentially smaller factors. Our construction uses $\tilde{O}(nk)$ ancilla qubits and ${O}(nk)$ bits of randomness, which are also optimal up to $\log(n k)$ factors. An alternative construction achieves a…