Tootfinder

A Letter to My Friends, Family, and Online Communities
#MarxismLeninism #Anarchism

[2025-06-09 Mon (UTC), 10 new articles found for cs.LO Logic in Computer Science]
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Enter, Exit, Page Fault, Leak: Testing Isolation Boundaries for Microarchitectural Leaks
Oleksii Oleksenko, Flavien Solt, C\'edric Fournet, Jana Hofmann, Boris K\"opf, Stavros Volos
https://arxiv.org/abs/2507.06039

Enter, Exit, Page Fault, Leak: Testing Isolation Boundaries for Microarchitectural Leaks
CPUs provide isolation mechanisms like virtualization and privilege levels to protect software. Yet these focus on architectural isolation while typically overlooking microarchitectural side channels, exemplified by Meltdown and Foreshadow. Software must therefore supplement architectural defenses with ad-hoc microarchitectural patches, which are constantly evolving as new attacks emerge and defenses are proposed. Such reactive approach makes ensuring complete isolation a daunting task, and lea…

Just learned about NYC Local Law 144 which requires bias audits for hiring decisions
https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page
(From younger, more principled brother; happy birthday dkg!)
I am really interes…

[2025-06-09 Mon (UTC), 21 new articles found for cs.RO Robotics]
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[2025-06-09 Mon (UTC), 9 new articles found for cs.CY Computers and Society]
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Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
Anish Dhir, Cristiana Diaconu, Valentinian Mihai Lungu, James Requeima, Richard E. Turner, Mark van der Wilk
https://arxiv.org/abs/2507.05526

Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
In scientific domains -- from biology to the social sciences -- many questions boil down to \textit{What effect will we observe if we intervene on a particular variable?} If the causal relationships (e.g.~a causal graph) are known, it is possible to estimate the intervention distributions. In the absence of this domain knowledge, the causal structure must be discovered from the available observational data. However, observational data are often compatible with multiple causal graphs, making met…

[2025-06-09 Mon (UTC), 9 new articles found for stat.ME Methodology]
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[2025-06-09 Mon (UTC), 7 new articles found for eess.IV Image and Video Processing]
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[2025-06-09 Mon (UTC), 6 new articles found for physics.flu-dyn Fluid Dynamics]
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Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Towards Exception Safety Code Generation with Intermediate Representation Agents Framework
Xuanming Zhang, Yuxuan Chen, Yuan Yuan, Minlie Huang

TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation
Muhammad Sohail Danish, Muhammad Akhtar Munir, Syed Roshaan Ali Shah, Muhammad Haris Khan, Rao Muhammad Anwer, Jorma Laaksonen, Fahad Shahbaz Khan, Salman Khan
https://arxiv.org/abs/2506.06281

TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation
Modern Earth observation (EO) increasingly leverages deep learning to harness the scale and diversity of satellite imagery across sensors and regions. While recent foundation models have demonstrated promising generalization across EO tasks, many remain limited by the scale, geographical coverage, and spectral diversity of their training data, factors critical for learning globally transferable representations. In this work, we introduce TerraFM, a scalable self-supervised learning model that l…

Coherent and ideal actions in ideally exact categories
Manuel Mancini, Giuseppe Metere, Federica Piazza
https://arxiv.org/abs/2507.06124 https://

Coherent and ideal actions in ideally exact categories
In the context of ideally exact categories, we introduce the notions of internal coherent action and internal ideal action that generalize different aspects of unital actions of rings and algebras. We prove that every ideal action is coherent, and that the converse statement holds in some relevant ideally exact contexts. Furthermore, a connection with G. Janelidze's notion of semidirect product in ideally exact categories is analyzed.

Found this going through my 8 year old iPad (image is from 2013 by @…)
What a difference 12 years makes, huhn?

[2025-06-09 Mon (UTC), 8 new articles found for eess.SP Signal Processing]
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[2025-06-09 Mon (UTC), 6 new articles found for math.DG Differential Geometry]
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[2025-06-09 Mon (UTC), 12 new articles found for math.CO Combinatorics]
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Design Tradeoffs in Photonically Linked Qubit Networks
Ely Novakoski, Jungsang Kim
https://arxiv.org/abs/2506.06268 https://arxiv.org…

Design Tradeoffs in Photonically Linked Qubit Networks
Quantum networking can be realized by distributing pairs of entangled qubits between remote quantum processing nodes. Devoted communication qubits within each node can naturally interface with photons which bus quantum information between nodes. With the introduction of CQED to enhance interactions between communication qubits and photons, advanced protocols capable of achieving high entanglement distribution rates with high fidelity become feasible. In this paper, we consider two such protocol…

Cooperative Mapping, Localization, and Beam Management via Multi-Modal SLAM in ISAC Systems
Hang Que, Jie Yang, Tao Du, Shuqiang Xia, Chao-Kai Wen, Shi Jin
https://arxiv.org/abs/2507.05718

Cooperative Mapping, Localization, and Beam Management via Multi-Modal SLAM in ISAC Systems
Simultaneous localization and mapping (SLAM) plays a critical role in integrated sensing and communication (ISAC) systems for sixth-generation (6G) millimeter-wave (mmWave) networks, enabling environmental awareness and precise user equipment (UE) positioning. While cooperative multi-user SLAM has demonstrated potential in leveraging distributed sensing, its application within multi-modal ISAC systems remains limited, particularly in terms of theoretical modeling and communication-layer integra…

lolol

[2025-06-09 Mon (UTC), 4 new articles found for cs.DC Distributed, Parallel, and Cluster Computing]
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Hybrid Diffusion Policies with Projective Geometric Algebra for Efficient Robot Manipulation Learning
Xiatao Sun, Yuxuan Wang, Shuo Yang, Yinxing Chen, Daniel Rakita
https://arxiv.org/abs/2507.05695

Hybrid Diffusion Policies with Projective Geometric Algebra for Efficient Robot Manipulation Learning
Diffusion policies have become increasingly popular in robot learning due to their reliable convergence in motion generation tasks. At a high level, these policies learn to transform noisy action trajectories into effective ones, conditioned on observations. However, each time such a model is trained in a robotics context, the network must relearn fundamental spatial representations and operations, such as translations and rotations, from scratch in order to ground itself and operate effectivel…

CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations
Xiaohu Li, Yunfeng Ning, Zepeng Bao, Mayi Xu, Jianhao Chen, Tieyun Qian
https://arxiv.org/abs/2507.06043

CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations
Security alignment enables the Large Language Model (LLM) to gain the protection against malicious queries, but various jailbreak attack methods reveal the vulnerability of this security mechanism. Previous studies have isolated LLM jailbreak attacks and defenses. We analyze the security protection mechanism of the LLM, and propose a framework that combines attack and defense. Our method is based on the linearly separable property of LLM intermediate layer embedding, as well as the essence of j…

Deep Learning of Continuous and Structured Policies for Aggregated Heterogeneous Treatment Effects
Jennifer Y. Zhang, Shuyang Du, Will Y. Zou
https://arxiv.org/abs/2507.05511

Deep Learning of Continuous and Structured Policies for Aggregated Heterogeneous Treatment Effects
As estimation of Heterogeneous Treatment Effect (HTE) is increasingly adopted across a wide range of scientific and industrial applications, the treatment action space can naturally expand, from a binary treatment variable to a structured treatment policy. This policy may include several policy factors such as a continuous treatment intensity variable, or discrete treatment assignments. From first principles, we derive the formulation for incorporating multiple treatment policy variables into t…

CoMemo: LVLMs Need Image Context with Image Memory
Shi Liu, Weijie Su, Xizhou Zhu, Wenhai Wang, Jifeng Dai
https://arxiv.org/abs/2506.06279 https://…

CoMemo: LVLMs Need Image Context with Image Memory
Recent advancements in Large Vision-Language Models built upon Large Language Models have established aligning visual features with LLM representations as the dominant paradigm. However, inherited LLM architectural designs introduce suboptimal characteristics for multimodal processing. First, LVLMs exhibit a bimodal distribution in attention allocation, leading to the progressive neglect of middle visual content as context expands. Second, conventional positional encoding schemes fail to preser…

The familial nature of enrichment over virtual double categories
Soichiro Fujii, Stephen Lack
https://arxiv.org/abs/2507.05529 https://

The familial nature of enrichment over virtual double categories
Originally enriched categories were defined over a monoidal category, but it was gradually realized that important examples can only be included when one enriches over more general structures such as bicategories and virtual double categories. We show that, as well as allowing more examples, working over virtual double categories also gives better formal properties. We study the 2-functor sending a virtual double category to the 2-category of categories enriched over it. We show that this is a …

[2025-06-09 Mon (UTC), 11 new articles found for cs.SE Software Engineering]
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Swap Kripke models for deontic LFIs
Mahan Vaz, Marcelo E. Coniglio
https://arxiv.org/abs/2506.06181 https://arxiv.org/pdf/2506.06181

Swap Kripke models for deontic LFIs
We present a construction of nondeterministic semantics for some deontic logics based on the class of paraconsistent logics known as Logics of Formal Inconsistency (LFIs), for the first time combining swap structures and Kripke models through the novel notion of swap Kripe models. We start by making use of Nmatrices to characterize systems based on LFIs that do not satisfy axiom (cl), while turning to RNmatrices when the latter is considered in the underlying LFIs. This paper also presents, for…

Beyond the Projection Postulate and Back: Quantum Theories with Generalised State-Update Rules
Vincenzo Fiorentino, Stefan Weigert
https://arxiv.org/abs/2506.06207

Beyond the Projection Postulate and Back: Quantum Theories with Generalised State-Update Rules
Are there consistent and physically reasonable alternatives to the projection postulate? Does it have unique properties compared to acceptable alternatives? We answer these questions by systematically investigating hypothetical state-update rules for quantum systems that nature could have chosen over the Lüders rule. Among other basic properties, any prospective rule must define unique post-measurement states and not allow for superluminal signalling. Particular attention will be paid to consi…

Integrating Diffusion-based Multi-task Learning with Online Reinforcement Learning for Robust Quadruped Robot Control
Xinyao Qin, Xiaoteng Ma, Yang Qi, Qihan Liu, Chuanyi Xue, Ning Gui, Qinyu Dong, Jun Yang, Bin Liang
https://arxiv.org/abs/2507.05674

The Impact of Event Data Partitioning on Privacy-aware Process Discovery
Jungeun Lim, Stephan A. Fahrenkrog-Petersen, Xixi Lu, Jan Mendling, Minseok Song
https://arxiv.org/abs/2507.06008

The Impact of Event Data Partitioning on Privacy-aware Process Discovery
Information systems support the execution of business processes. The event logs of these executions generally contain sensitive information about customers, patients, and employees. The corresponding privacy challenges can be addressed by anonymizing the event logs while still retaining utility for process discovery. However, trading off utility and privacy is difficult: the higher the complexity of event log, the higher the loss of utility by anonymization. In this work, we propose a pipeline …

Heterogeneous Causal Learning for Optimizing Aggregated Functions in User Growth
Shuyang Du, Jennifer Zhang, Will Y. Zou
https://arxiv.org/abs/2507.05510 h…

Heterogeneous Causal Learning for Optimizing Aggregated Functions in User Growth
User growth is a major strategy for consumer internet companies. To optimize costly marketing campaigns and maximize user engagement, we propose a novel treatment effect optimization methodology to enhance user growth marketing. By leveraging deep learning, our algorithm learns from past experiments to optimize user selection and reward allocation, maximizing campaign impact while minimizing costs. Unlike traditional prediction methods, our model directly models uplifts in key business metrics.…

ExAct: A Video-Language Benchmark for Expert Action Analysis
Han Yi, Yulu Pan, Feihong He, Xinyu Liu, Benjamin Zhang, Oluwatumininu Oguntola, Gedas Bertasius
https://arxiv.org/abs/2506.06277

ExAct: A Video-Language Benchmark for Expert Action Analysis
We present ExAct, a new video-language benchmark for expert-level understanding of skilled physical human activities. Our new benchmark contains 3521 expert-curated video question-answer pairs spanning 11 physical activities in 6 domains: Sports, Bike Repair, Cooking, Health, Music, and Dance. ExAct requires the correct answer to be selected from five carefully designed candidate options, thus necessitating a nuanced, fine-grained, expert-level understanding of physical human skills. Evaluating…

Monitorability for the Modal mu-Calculus over Systems with Data: From Practice to Theory
Luca Aceto, Antonis Achilleos, Duncan Paul Attard, L\'eo Exibard, Adrian Francalanza, Anna Ing\'olfsd\'ottir, Karoliina Lehtinen
https://arxiv.org/abs/2506.06172

Monitorability for the Modal mu-Calculus over Systems with Data: From Practice to Theory
Runtime verification, also known as runtime monitoring, consists of checking whether a system satisfies a given specification by observing the trace it produces during its execution. It is used as a lightweight verification technique to complement or substitute costlier methods such as model-checking. In the regular setting, Hennessy-Milner logic with recursion, a variant of the modal mu-calculus, provides a versatile formalism for expressing linear- and branching-time specifications of the c…

Metainformation in Quantum Guessing Games
Teiko Heinosaari, Hanwool Lee
https://arxiv.org/abs/2506.06197 https://arxiv.org/pdf/2506.0…

Metainformation in Quantum Guessing Games
Quantum guessing games offer a structured approach to analyzing quantum information processing, where information is encoded in quantum states and extracted through measurement. An additional aspect of this framework is the influence of partial knowledge about the input on the optimal measurement strategies. This kind of side information can significantly influence the guessing strategy and earlier work has shown that the timing of such side information, whether revealed before or after the mea…

Where Do We Look When We Teach? Analyzing Human Gaze Behavior Across Demonstration Devices in Robot Imitation Learning
Yutaro Ishida, Takamitsu Matsubara, Takayuki Kanai, Kazuhiro Shintani, Hiroshi Bito
https://arxiv.org/abs/2506.05808

Where Do We Look When We Teach? Analyzing Human Gaze Behavior Across Demonstration Devices in Robot Imitation Learning
Imitation learning for acquiring generalizable policies often requires a large volume of demonstration data, making the process significantly costly. One promising strategy to address this challenge is to leverage the cognitive and decision-making skills of human demonstrators with strong generalization capability, particularly by extracting task-relevant cues from their gaze behavior. However, imitation learning typically involves humans collecting data using demonstration devices that emulate…

Post-Processing in Local Differential Privacy: An Extensive Evaluation and Benchmark Platform
Alireza Khodaie, Berkay Kemal Balioglu, Mehmet Emre Gursoy
https://arxiv.org/abs/2507.05875

Post-Processing in Local Differential Privacy: An Extensive Evaluation and Benchmark Platform
Local differential privacy (LDP) has recently gained prominence as a powerful paradigm for collecting and analyzing sensitive data from users' devices. However, the inherent perturbation added by LDP protocols reduces the utility of the collected data. To mitigate this issue, several post-processing (PP) methods have been developed. Yet, the comparative performance of PP methods under diverse settings remains underexplored. In this paper, we present an extensive benchmark comprising 6 popular L…

Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning
Ze'ev Zukerman, Bassel Hamoud, Kfir Y. Levy
https://arxiv.org/abs/2507.05508

Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning
Distributed learning methods have gained substantial momentum in recent years, with communication overhead often emerging as a critical bottleneck. Gradient compression techniques alleviate communication costs but involve an inherent trade-off between the empirical efficiency of biased compressors and the theoretical guarantees of unbiased compressors. In this work, we introduce a novel Multilevel Monte Carlo (MLMC) compression scheme that leverages biased compressors to construct statistically…

Scalable Counting of Minimal Trap Spaces and Fixed Points in Boolean Networks
Mohimenul Kabir, Van-Giang Trinh, Samuel Pastva, Kuldeep S Meel
https://arxiv.org/abs/2506.06013

Scalable Counting of Minimal Trap Spaces and Fixed Points in Boolean Networks
Boolean Networks (BNs) serve as a fundamental modeling framework for capturing complex dynamical systems across various domains, including systems biology, computational logic, and artificial intelligence. A crucial property of BNs is the presence of trap spaces -- subspaces of the state space that, once entered, cannot be exited. Minimal trap spaces, in particular, play a significant role in analyzing the long-term behavior of BNs, making their efficient enumeration and counting essential. The…

Polarization properties of photon Bose-Einstein condensates
Sven Enns, Julian Schulz, Kirankumar Karkihalli Umesh, Frank Vewinger, Georg von Freymann
https://arxiv.org/abs/2506.06141

Polarization properties of photon Bose-Einstein condensates
The first experimental realization of a photon Bose-Einstein condensate was demonstrated more than a decade ago. However, the polarization of the condensate has not been fully understood and measured in this weakly driven-dissipative system. In this letter, we experimentally investigate the polarization of thermal and condensed light depending on the power and polarization of the pump beam. With full control over the polarization of the pump, it is possible to create arbitrary states on the sur…

LDP$^3$: An Extensible and Multi-Threaded Toolkit for Local Differential Privacy Protocols and Post-Processing Methods
Berkay Kemal Balioglu, Alireza Khodaie, Mehmet Emre Gursoy
https://arxiv.org/abs/2507.05872

LDP$^3$: An Extensible and Multi-Threaded Toolkit for Local Differential Privacy Protocols and Post-Processing Methods
Local differential privacy (LDP) has become a prominent notion for privacy-preserving data collection. While numerous LDP protocols and post-processing (PP) methods have been developed, selecting an optimal combination under different privacy budgets and datasets remains a challenge. Moreover, the lack of a comprehensive and extensible LDP benchmarking toolkit raises difficulties in evaluating new protocols and PP methods. To address these concerns, this paper presents LDP$^3$ (pronounced LDP-C…

Dynamic Campus Origin-Destination Mobility Prediction using Graph Convolutional Neural Network on WiFi Logs
Godwin Badu-Marfo, Bilal Farooq
https://arxiv.org/abs/2507.05507

Dynamic Campus Origin-Destination Mobility Prediction using Graph Convolutional Neural Network on WiFi Logs
We present an integrated graph-based neural networks architecture for predicting campus buildings occupancy and inter-buildings movement at dynamic temporal resolution that learns traffic flow patterns from Wi-Fi logs combined with the usage schedules within the buildings. The relative traffic flows are directly estimated from the WiFi data without assuming the occupant behaviour or preferences while maintaining individual privacy. We formulate the problem as a data-driven graph structure repre…

Paraconsistent Relations as a Variant of Kleene Algebras
Juliana Cunha, Alexandre Madeira, Lu\'is S. Barbosa
https://arxiv.org/abs/2506.05840 https://

Paraconsistent Relations as a Variant of Kleene Algebras
Kleene algebras (KA) and Kleene algebras with tests (KAT) provide an algebraic framework to capture the behavior of conventional programming constructs. This paper explores a broader understanding of these structures, in order to enable the expression of programs and tests yielding vague or inconsistent outcomes. Within this context, we introduce the concept of a paraconsistent Kleene Algebra with tests (PKAT), capable of capturing vague and contradictory computations. Finally, to establish the…

A low-loss telecom-band nanofiber cavity for interfacing Yb atomic qubits
Seitaro Horikawa, Shinya Kato, Ryotaro Inoue, Takao Aoki, Akihisa Goban, Hideki Konishi
https://arxiv.org/abs/2506.06123

A low-loss telecom-band nanofiber cavity for interfacing Yb atomic qubits
We demonstrate the fabrication of an optical nanofiber cavity designed for efficient interface with ytterbium (Yb) atoms at telecom-wavelength transitions. Replacing the conventional hydrogen-oxygen flame with a deuterium-oxygen flame in the heat-and-pull method suppresses hydroxyl-induced absorption losses and enables low-loss nanofiber production with minimal modifications to the existing fabrication system. Using this technique, we fabricate a nanofiber cavity at 1389 nm that exhibits an int…

Fast and Accurate Collision Probability Estimation for Autonomous Vehicles using Adaptive Sigma-Point Sampling
Charles Champagne Cossette, Taylor Scott Clawson, Andrew Feit
https://arxiv.org/abs/2507.06149

Fast and Accurate Collision Probability Estimation for Autonomous Vehicles using Adaptive Sigma-Point Sampling
A novel algorithm is presented for the estimation of collision probabilities between dynamic objects with uncertain trajectories, where the trajectories are given as a sequence of poses with Gaussian distributions. We propose an adaptive sigma-point sampling scheme, which ultimately produces a fast, simple algorithm capable of estimating the collision probability with a median error of 3.5%, and a median runtime of 0.21ms, when measured on an Intel Xeon Gold 6226R Processor. Importantly, the al…

Automated Reasoning for Vulnerability Management by Design
Avi Shaked, Nan Messe
https://arxiv.org/abs/2507.05794 https://arxiv.org/p…

Automated Reasoning for Vulnerability Management by Design
For securing systems, it is essential to manage their vulnerability posture and design appropriate security controls. Vulnerability management allows to proactively address vulnerabilities by incorporating pertinent security controls into systems designs. Current vulnerability management approaches do not support systematic reasoning about the vulnerability postures of systems designs. To effectively manage vulnerabilities and design security controls, we propose a formally grounded automated r…

Explainable Hierarchical Deep Learning Neural Networks (Ex-HiDeNN)
Reza T. Batley, Chanwook Park, Wing Kam Liu, Sourav Saha
https://arxiv.org/abs/2507.05498

Explainable Hierarchical Deep Learning Neural Networks (Ex-HiDeNN)
Data-driven science and computation have advanced immensely to construct complex functional relationships using trainable parameters. However, efficiently discovering interpretable and accurate closed-form expressions from complex dataset remains a challenge. The article presents a novel approach called Explainable Hierarchical Deep Learning Neural Networks or Ex-HiDeNN that uses an accurate, frugal, fast, separable, and scalable neural architecture with symbolic regression to discover closed-f…

Towards an Analysis of Proofs in Arithmetic
Alexander Leitsch, Anela Loli\'c, Stella Mahler
https://arxiv.org/abs/2506.05837 https://

Towards an Analysis of Proofs in Arithmetic
Inductive proofs can be represented as proof schemata, i.e. as parameterized sequences of proofs defined in a primitive recursive way. Applications of proof schemata can be found in the area of automated proof analysis where the schemata admit (schematic) cut-elimination and the construction of Herbrand systems. This work focuses on the expressivity of proof schemata. We show that proof schemata can simulate primitive recursive arithmetic. The translation of proofs in arithmetic to proof schema…

Optimal absorption and emission of itinerant fields into a spin ensemble memory
Linda Greggio, Tristan Lorriaux, Alexandru Petrescu, Mazyar Mirrahimi, Audrey Bienfait
https://arxiv.org/abs/2506.06107

Optimal absorption and emission of itinerant fields into a spin ensemble memory
Quantum memories integrated in a modular quantum processing architecture can rationalize the resources required for quantum computation. This work focuses on spin-based quantum memories, where itinerant electromagnetic fields are stored in large ensembles of effective two-level systems, such as atomic or solid-state spin ensembles, embedded in a cavity. Using a mean-field framework, we model the ensemble as an effective spin communication channel and develop a cascaded quantum model to describe…

Asynchronous Event Error-Minimizing Noise for Safeguarding Event Dataset
Ruofei Wang, Peiqi Duan, Boxin Shi, Renjie Wan
https://arxiv.org/abs/2507.05728 ht…

Asynchronous Event Error-Minimizing Noise for Safeguarding Event Dataset
With more event datasets being released online, safeguarding the event dataset against unauthorized usage has become a serious concern for data owners. Unlearnable Examples are proposed to prevent the unauthorized exploitation of image datasets. However, it's unclear how to create unlearnable asynchronous event streams to prevent event misuse. In this work, we propose the first unlearnable event stream generation method to prevent unauthorized training from event datasets. A new form of asynchr…

Navigating Sparse Molecular Data with Stein Diffusion Guidance
Van Khoa Nguyen, Lionel Blond\'e, Alexandros Kalousis
https://arxiv.org/abs/2507.05482 h…

Navigating Sparse Molecular Data with Stein Diffusion Guidance
Stochastic optimal control (SOC) has recently emerged as a principled framework for fine-tuning diffusion models. However, its dependence on computationally intensive simulations makes it impractical for fast sampling. In parallel, a class of training-free approaches has been developed that guides diffusion models using off-the-shelf classifiers on predicted clean samples, bypassing the need to train classifiers on noisy data. These methods can be interpreted as approximate SOC schemes, using T…

Learning-Augmented Model-Based Multi-Robot Planning for Time-Critical Search and Inspection Under Uncertainty
Abhish Khanal, Joseph Prince Mathew, Cameron Nowzari, Gregory J. Stein
https://arxiv.org/abs/2507.06129

Learning-Augmented Model-Based Multi-Robot Planning for Time-Critical Search and Inspection Under Uncertainty
In disaster response or surveillance operations, quickly identifying areas needing urgent attention is critical, but deploying response teams to every location is inefficient or often impossible. Effective performance in this domain requires coordinating a multi-robot inspection team to prioritize inspecting locations more likely to need immediate response, while also minimizing travel time. This is particularly challenging because robots must directly observe the locations to determine which o…

Nominal Equational Rewriting and Narrowing
Mauricio Ayala-Rinc\'on (University of Bras\'ilia, Brazil), Maribel Fern\'andez (King's College London, UK), Daniele Nantes-Sobrinho (University of Bras\'ilia, Brazil,Imperial College London, UK), Daniella Santaguida (University of Bras\'ilia, Brazil)
https://arx…

Nominal Equational Rewriting and Narrowing
Narrowing is a well-known technique that adds to term rewriting mechanisms the required power to search for solutions to equational problems. Rewriting and narrowing are well-studied in first-order term languages, but several problems remain to be investigated when dealing with languages with binders using nominal techniques. Applications in programming languages and theorem proving require reasoning modulo alpha-equivalence considering structural congruences generated by equational axioms, suc…

Polyadic encryption
Steven Duplij, Qiang Guo
https://arxiv.org/abs/2507.05683 https://arxiv.org/pdf/2507.05683

Polyadic encryption
A novel original procedure of encryption/decryption based on the polyadic algebraic structures and on signal processing methods is proposed. First, we use signals with integer amplitudes to send information. Then we use polyadic techniques to transfer the plaintext into series of special integers. The receiver restores the plaintext using special rules and systems of equations.

Dynamic Regret Reduces to Kernelized Static Regret
Andrew Jacobsen, Alessandro Rudi, Francesco Orabona, Nicolo Cesa-Bianchi
https://arxiv.org/abs/2507.05478

Dynamic Regret Reduces to Kernelized Static Regret
We study dynamic regret in online convex optimization, where the objective is to achieve low cumulative loss relative to an arbitrary benchmark sequence. By observing that competing with an arbitrary sequence of comparators $u_{1},\ldots,u_{T}$ in $\mathcal{W}\subseteq\mathbb{R}^{d}$ is equivalent to competing with a fixed comparator function $u:[1,T]\to \mathcal{W}$, we frame dynamic regret minimization as a static regret problem in a function space. By carefully constructing a suitable functi…

Optimizing entanglement distribution via noisy quantum channels
Piotr Masajada, Marco Fellous-Asiani, Alexander Streltsov
https://arxiv.org/abs/2506.06089 …

Optimizing entanglement distribution via noisy quantum channels
Entanglement distribution is a crucial problem in quantum information science, owing to the essential role that entanglement plays in enabling advanced quantum protocols, including quantum teleportation and quantum cryptography. We investigate strategies for distributing quantum entanglement between two distant parties through noisy quantum channels. Specifically, we compare two configurations: one where the entanglement source is placed at the midpoint of the communication line, and another wh…

SCCRUB: Surface Cleaning Compliant Robot Utilizing Bristles
Jakub F. Kowalewski, Keeyon Hajjafar, Alyssa Ugent, Jeffrey Ian Lipton
https://arxiv.org/abs/2507.06053

SCCRUB: Surface Cleaning Compliant Robot Utilizing Bristles
Scrubbing surfaces is a physically demanding and time-intensive task. Removing adhered contamination requires substantial friction generated through pressure and torque or high lateral forces. Rigid robotic manipulators, while capable of exerting these forces, are usually confined to structured environments isolated from humans due to safety risks. In contrast, soft robot arms can safely work around humans and adapt to environmental uncertainty, but typically struggle to transmit the continuous…

Regional, Lattice and Logical Representations of Neural Networks
Sandro Preto (Federal University of ABC, Brazil), Marcelo Finger (University of Sao Paulo, Brazil)
https://arxiv.org/abs/2506.05834

Regional, Lattice and Logical Representations of Neural Networks
A possible path to the interpretability of neural networks is to (approximately) represent them in the regional format of piecewise linear functions, where regions of inputs are associated to linear functions computing the network outputs. We present an algorithm for the translation of feedforward neural networks with ReLU activation functions in hidden layers and truncated identity activation functions in the output layer. We also empirically investigate the complexity of regional representati…

TuneShield: Mitigating Toxicity in Conversational AI while Fine-tuning on Untrusted Data
Aravind Cheruvu, Shravya Kanchi, Sifat Muhammad Abdullah, Nicholas Kong, Daphne Yao, Murtuza Jadliwala, Bimal Viswanath
https://arxiv.org/abs/2507.05660

TuneShield: Mitigating Toxicity in Conversational AI while Fine-tuning on Untrusted Data
Recent advances in foundation models, such as LLMs, have revolutionized conversational AI. Chatbots are increasingly being developed by customizing LLMs on specific conversational datasets. However, mitigating toxicity during this customization, especially when dealing with untrusted training data, remains a significant challenge. To address this, we introduce TuneShield, a defense framework designed to mitigate toxicity during chatbot fine-tuning while preserving conversational quality. TuneSh…

Epistemically-guided forward-backward exploration
N\'uria Armengol Urp\'i, Marin Vlastelica, Georg Martius, Stelian Coros
https://arxiv.org/abs/2507.05477

Epistemically-guided forward-backward exploration
Zero-shot reinforcement learning is necessary for extracting optimal policies in absence of concrete rewards for fast adaptation to future problem settings. Forward-backward representations (FB) have emerged as a promising method for learning optimal policies in absence of rewards via a factorization of the policy occupancy measure. However, up until now, FB and many similar zero-shot reinforcement learning algorithms have been decoupled from the exploration problem, generally relying on other …

Beating joint quantum estimation limits with stepwise multiparameter metrology
Chiranjib Mukhopadhyay, Abolfazl Bayat, Victor Montenegro, Matteo G. A. Paris
https://arxiv.org/abs/2506.06075

Beating joint quantum estimation limits with stepwise multiparameter metrology
Conventional multiparameter quantum sensing relies on joint estimation, but this approach faces two key limitations: theoretical bounds may be unattainable due to measurement incompatibility, and sensing may fail due to parameter interdependencies. We propose stepwise estimation and identify regimes where it outperforms joint estimation. For multiple quantum sensors, this scheme achieves far lower error bounds than joint estimation. With many-body probes, stepwise sensing retains a quantum-enha…

Robust Speech-Workload Estimation for Intelligent Human-Robot Systems
Julian Fortune, Julie A. Adams, Jamison Heard
https://arxiv.org/abs/2507.05985 https:…

Robust Speech-Workload Estimation for Intelligent Human-Robot Systems
Demanding task environments (e.g., supervising a remotely piloted aircraft) require performing tasks quickly and accurately; however, periods of low and high operator workload can decrease task performance. Intelligent modulation of the system's demands and interaction modality in response to changes in operator workload state may increase performance by avoiding undesirable workload states. This system requires real-time estimation of each workload component (i.e., cognitive, physical, visual,…

Fuzzy Lattice-based Description Logic
Yiwen Ding, Krishna Manoorkar
https://arxiv.org/abs/2506.05833 https://arxiv.org/pdf/2506.05833…

Fuzzy Lattice-based Description Logic
Recently, description logic LE-ALC was introduced for reasoning in the semantic environment of enriched formal contexts, and a polynomial-time tableaux algorithm was developed to check the consistency of knowledge bases with acyclic TBoxes. In this work, we introduce a fuzzy generalization of LE-ALC called LE-FALC which provides a description logic counterpart of many-valued normal non-distributive logic a.k.a. many-valued LE-logic. This description logic can be used to represent and reason a…

DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning
Kaixiang Zhao, Joseph Yousry Attalla, Qian Lou, Yushun Dong
https://arxiv.org/abs/2507.05649

DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph-based learning tasks. However, enabling privacy-preserving GNNs in encrypted domains, such as under Fully Homomorphic Encryption (FHE), typically incurs substantial computational overhead, rendering real-time and privacy-preserving inference impractical. In this work, we propose DESIGN (EncrypteD GNN Inference via sErver-Side Input Graph pruNing), a novel framework for efficient encrypted GNN inference. DES…

The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation
Alexander Xiong, Xuandong Zhao, Aneesh Pappu, Dawn Song
https://arxiv.org/abs/2507.05578

The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation
Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet they also exhibit memorization of their training data. This phenomenon raises critical questions about model behavior, privacy risks, and the boundary between learning and memorization. Addressing these concerns, this paper synthesizes recent studies and investigates the landscape of memorization, the factors influencing it, and methods for its detection and mitigation. We explore key driver…

Anomalous flow in correlated quantum systems: No-go result and multiple-charge scenario
Rui Guan, Junjie Liu
https://arxiv.org/abs/2506.05995 https://

Anomalous flow in correlated quantum systems: No-go result and multiple-charge scenario
Correlated quantum systems can exhibit thermodynamic behaviors that defy classical expectations, with anomalous energy flow (AEF) against temperature gradients serving as a paradigmatic example. While AEF has been shown to arise from the consumption of initial quantum correlations, little is known about whether AEF can occur without correlation depletion, or if analogous anomalous transport exists for conserved charges beyond energy. Here, we develop a general global-local thermodynamic approac…

AURA-CVC: Autonomous Ultrasound-guided Robotic Assistance for Central Venous Catheterization
Deepak Raina, Lidia Al-Zogbi, Brian Teixeira, Vivek Singh, Ankur Kapoor, Thorsten Fleiter, Muyinatu A. Lediju Bell, Vinciya Pandian, Axel Krieger
https://arxiv.org/abs/2507.05979

AURA-CVC: Autonomous Ultrasound-guided Robotic Assistance for Central Venous Catheterization
Purpose: Central venous catheterization (CVC) is a critical medical procedure for vascular access, hemodynamic monitoring, and life-saving interventions. Its success remains challenging due to the need for continuous ultrasound-guided visualization of a target vessel and approaching needle, which is further complicated by anatomical variability and operator dependency. Errors in needle placement can lead to life-threatening complications. While robotic systems offer a potential solution, achiev…

Properties of UTxO Ledgers and Programs Implemented on Them
Polina Vinogradova (Input Output Global), Alexey Sorokin (Input Output Global)
https://arxiv.org/abs/2506.05832

Properties of UTxO Ledgers and Programs Implemented on Them
Trace-based properties are the gold standard for program behaviour analysis. One of the domains of application of this type of analysis is cryptocurrency ledgers, both for the purpose of analyzing the behaviour of the ledger itself, and any user-defined programs called by it, known as smart contracts. The (extended) UTxO ledger model is a kind of ledger model where all smart contract code is stateless, and additional work must be done to model stateful programs. We formalize the application of …

How Not to Detect Prompt Injections with an LLM
Sarthak Choudhary, Divyam Anshumaan, Nils Palumbo, Somesh Jha
https://arxiv.org/abs/2507.05630 https://

How Not to Detect Prompt Injections with an LLM
LLM-integrated applications and agents are vulnerable to prompt injection attacks, in which adversaries embed malicious instructions within seemingly benign user inputs to manipulate the LLM's intended behavior. Recent defenses based on $\textit{known-answer detection}$ (KAD) have achieved near-perfect performance by using an LLM to classify inputs as clean or contaminated. In this work, we formally characterize the KAD framework and uncover a structural vulnerability in its design that invalid…

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster
Linda Lu, Ayush Sekhari, Karthik Sridharan
https://arxiv.org/abs/2506.06073 https://

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster
Machine unlearning addresses the problem of updating a machine learning model/system trained on a dataset $S$ so that the influence of a set of deletion requests $U \subseteq S$ on the unlearned model is minimized. The gold standard definition of unlearning demands that the updated model, after deletion, be nearly identical to the model obtained by retraining. This definition is designed for a worst-case attacker (one who can recover not only the unlearned model but also the remaining data samp…

An optical frequency shifter based on continuous-wave pump fields
Anica Hamer, Frank Vewinger, Michael H. Frosz, Simon Stellmer
https://arxiv.org/abs/2506.05989

An optical frequency shifter based on continuous-wave pump fields
Practical implementations of quantum information networks require frequency conversion of individual photons. Approaches based on a molecular gas as the nonlinear medium cover a wide range of the optical spectrum and promise high efficiency at negligible background. We present polarization-preserving frequency conversion in a hydrogen-loaded hollow core fiber using continuous-wave pump fields. We demonstrate conversion efficiency at the level of a few per mille, discuss various limitations and …

FineGrasp: Towards Robust Grasping for Delicate Objects
Yun Du, Mengao Zhao, Tianwei Lin, Yiwei Jin, Chaodong Huang, Zhizhong Su
https://arxiv.org/abs/2507.05978

FineGrasp: Towards Robust Grasping for Delicate Objects
Recent advancements in robotic grasping have led to its integration as a core module in many manipulation systems. For instance, language-driven semantic segmentation enables the grasping of any designated object or object part. However, existing methods often struggle to generate feasible grasp poses for small objects or delicate components, potentially causing the entire pipeline to fail. To address this issue, we propose a novel grasping method, FineGrasp, which introduces improvements in th…

Model Checking as Program Verification by Abstract Interpretation (Extended Version)
Paolo Baldan, Roberto Bruni, Francesco Ranzato, Diletta Rigo
https://arxiv.org/abs/2506.05525 …

Model Checking as Program Verification by Abstract Interpretation (Extended Version)
Abstract interpretation offers a powerful toolset for static analysis, tackling precision, complexity and state-explosion issues. In the literature, state partitioning abstractions based on (bi)simulation and property-preserving state relations have been successfully applied to abstract model checking. Here, we pursue a different track in which model checking is seen as an instance of program verification. To this purpose, we introduce a suitable language-called MOKA (for MOdel checking as abst…

DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective
Shuo Shao, Yiming Li, Mengren Zheng, Zhiyang Hu, Yukun Chen, Boheng Li, Yu He, Junfeng Guo, Tianwei Zhang, Dacheng Tao, Zhan Qin
https://arxiv.org/abs/2507.05622

DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective
The widespread application of Deep Learning across diverse domains hinges critically on the quality and composition of training datasets. However, the common lack of disclosure regarding their usage raises significant privacy and copyright concerns. Dataset auditing techniques, which aim to determine if a specific dataset was used to train a given suspicious model, provide promising solutions to addressing these transparency gaps. While prior work has developed various auditing methods, their r…

TRUST: Test-time Resource Utilization for Superior Trustworthiness
Haripriya Harikumar, Santu Rana
https://arxiv.org/abs/2506.06048 https://

TRUST: Test-time Resource Utilization for Superior Trustworthiness
Standard uncertainty estimation techniques, such as dropout, often struggle to clearly distinguish reliable predictions from unreliable ones. We attribute this limitation to noisy classifier weights, which, while not impairing overall class-level predictions, render finer-level statistics less informative. To address this, we propose a novel test-time optimization method that accounts for the impact of such noise to produce more reliable confidence estimates. This score defines a monotonic subs…

Quantum Checkers: The Development and Analysis of a Quantum Combinatorial Game
Marien Raat, Luuk van den Nouweland, Matthias M\"uller-Brockhausen, Mike Preuss, Evert van Nieuwenburg
https://arxiv.org/abs/2506.05962

Quantum Checkers: The Development and Analysis of a Quantum Combinatorial Game
This paper develops and analyses a novel quantum combinatorial game: quantum checkers (codenamed Cheqqers). The concepts of superposition, entanglement, measurements and interference from quantum mechanics are integrated into the game of checkers by adding new types of legal moves. The addition of these new rules is done gradually by introducing several levels of `quantumness'. Quantum checkers provides a framework for interpolating between a known and solved classical game and a more complex q…

Comparison of Path Planning Algorithms for Autonomous Vehicle Navigation Using Satellite and Airborne LiDAR Data
Chang Liu, Zhexiong Xue, Tamas Sziranyi
https://arxiv.org/abs/2507.05884

Comparison of Path Planning Algorithms for Autonomous Vehicle Navigation Using Satellite and Airborne LiDAR Data
Autonomous vehicle navigation in unstructured environments, such as forests and mountainous regions, presents significant challenges due to irregular terrain and complex road conditions. This work provides a comparative evaluation of mainstream and well-established path planning algorithms applied to weighted pixel-level road networks derived from high-resolution satellite imagery and airborne LiDAR data. For 2D road-map navigation, where the weights reflect road conditions and terrain difficul…

Diffusion-Based Hierarchical Graph Neural Networks for Simulating Nonlinear Solid Mechanics
Tobias W\"urth, Niklas Freymuth, Gerhard Neumann, Luise K\"arger
https://arxiv.org/abs/2506.06045

Diffusion-Based Hierarchical Graph Neural Networks for Simulating Nonlinear Solid Mechanics
Graph-based learned simulators have emerged as a promising approach for simulating physical systems on unstructured meshes, offering speed and generalization across diverse geometries. However, they often struggle with capturing global phenomena, such as bending or long-range correlations, and suffer from error accumulation over long rollouts due to their reliance on local message passing and direct next-step prediction. We address these limitations by introducing the Rolling Diffusion-Batched …

Ancilla measurement-based Quantum Otto engine using double-pair spin architecture
S R Rathnakaran, Asoka Biswas
https://arxiv.org/abs/2506.05948 https://…

Ancilla measurement-based Quantum Otto engine using double-pair spin architecture
We present a quantum heat engine model utilizing a dual spin-pair architecture, wherein an Otto-like cycle is implemented using a single heat bath. The conventional cold bath is replaced by a measurement protocol, enabling engine operation without the need for a second thermal reservoir. Unlike standard quantum heat engines, our framework employs an ancillary spin pair in a two-dimensional configuration to regulate performance. Operating in finite time, the engine attains finite power, which is…

Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments
Woonsang Kang, Joohyung Lee, Seungjun Kim, Jungchan Cho, Yoonseon Oh
https://arxiv.org/abs/2507.05861

Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments
Grasp pose detection (GPD) is a fundamental capability for robotic autonomy, but its reliance on large, diverse datasets creates significant data privacy and centralization challenges. Federated Learning (FL) offers a privacy-preserving solution, but its application to GPD is hindered by the substantial communication overhead of large models, a key issue for resource-constrained robots. To address this, we propose a novel module-wise FL framework that begins by analyzing the learning dynamics o…

Variational Inference for Quantum HyperNetworks
Luca Nepote, Alix Lh\'eritier, Nicolas Bondoux, Marios Kountouris, Maurizio Filippone
https://arxiv.org/abs/2506.05888

Variational Inference for Quantum HyperNetworks
Binary Neural Networks (BiNNs), which employ single-bit precision weights, have emerged as a promising solution to reduce memory usage and power consumption while maintaining competitive performance in large-scale systems. However, training BiNNs remains a significant challenge due to the limitations of conventional training algorithms. Quantum HyperNetworks offer a novel paradigm for enhancing the optimization of BiNN by leveraging quantum computing. Specifically, a Variational Quantum Algorit…

LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving
Yuhang Zhang, Jiaqi Liu, Chengkai Xu, Peng Hang, Jian Sun
https://arxiv.org/abs/2507.05754

LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving
A principal barrier to large-scale deployment of urban autonomous driving systems lies in the prevalence of complex scenarios and edge cases. Existing systems fail to effectively interpret semantic information within traffic contexts and discern intentions of other participants, consequently generating decisions misaligned with skilled drivers' reasoning patterns. We present LeAD, a dual-rate autonomous driving architecture integrating imitation learning-based end-to-end (E2E) frameworks with l…

Optically accessible high-finesse millimeter-wave resonator for cavity quantum electrodynamics with atom arrays
Tony Zhang, Michelle Wu, Sam R. Cohen, Lin Xin, Debadri Das, Kevin K. S. Multani, Nolan Peard, Anne-Marie Valente-Feliciano, Paul B. Welander, Amir H. Safavi-Naeini, Emilio A. Nanni, Monika Schleier-Smith
https://arxiv…

Optically accessible high-finesse millimeter-wave resonator for cavity quantum electrodynamics with atom arrays
Cavity quantum electrodynamics (QED) is a powerful tool in quantum science, enabling preparation of non-classical states of light and scalable entanglement of many atoms coupled to a single field mode. While the most coherent atom-photon interactions have been achieved using superconducting millimeter-wave cavities coupled to Rydberg atoms, these platforms so far lack the optical access required for trapping and addressing individual atomic qubits. We present a millimeter-wave Fabry-Pérot cavi…

A Learning-based Planning and Control Framework for Inertia Drift Vehicles
Bei Zhou, Zhouheng Li, Lei Xie, Hongye Su, Johannes Betz
https://arxiv.org/abs/2507.05748

A Learning-based Planning and Control Framework for Inertia Drift Vehicles
Inertia drift is a transitional maneuver between two sustained drift stages in opposite directions, which provides valuable insights for navigating consecutive sharp corners for autonomous racing.However, this can be a challenging scenario for the drift controller to handle rapid transitions between opposing sideslip angles while maintaining accurate path tracking. Moreover, accurate drift control depends on a high-fidelity vehicle model to derive drift equilibrium points and predict vehicle st…

Pathfinding Quantum Simulations of Neutrinoless Double-$\beta$ Decay
Ivan A. Chernyshev, Roland C. Farrell, Marc Illa, Martin J. Savage, Andrii Maksymov, Felix Tripier, Miguel Angel Lopez-Ruiz, Andrew Arrasmith, Yvette de Sereville, Aharon Brodutch, Claudio Girotto, Ananth Kaushik, Martin Roetteler
https://arxiv.org/abs/2506.057…

Pathfinding Quantum Simulations of Neutrinoless Double-$β$ Decay
We present results from co-designed quantum simulations of the neutrinoless double-$β$ decay of a simple nucleus in 1+1D quantum chromodynamics using IonQ's Forte-generation trapped-ion quantum computers. Electrons, neutrinos, and up and down quarks are distributed across two lattice sites and mapped to 32 qubits, with an additional 4 qubits used for flag-based error mitigation. A four-fermion interaction is used to implement weak interactions, and lepton-number violation is induced by a neutr…

Simultaneous Triggering and Synchronization of Sensors and Onboard Computers
Morten Nissov, Nikhil Khedekar, Kostas Alexis
https://arxiv.org/abs/2507.05717

Simultaneous Triggering and Synchronization of Sensors and Onboard Computers
High fidelity estimation algorithms for robotics require accurate data. However, timestamping of sensor data is a key issue that rarely receives the attention it deserves. Inaccurate timestamping can be compensated for in post-processing but is imperative for online estimation. Simultaneously, even online mitigation of timing issues can be achieved through a relaxation of the tuning parameters from their otherwise more performative optimal values, but at a detriment to performance. To address t…

Quantum Latin squares with all possible cardinalities
Ying Zhang, Xin Wang, Lijun Ji
https://arxiv.org/abs/2507.05642 https://arxiv.o…

Quantum Latin squares with all possible cardinalities
A quantum Latin square of order $n$ (denoted as QLS$(n)$) is an $n\times n$ array whose entries are unit column vectors from the $n$-dimensional Hilbert space $\mathcal{H}_n$, such that each row and column forms an orthonormal basis. Two unit vectors $|u\rangle, |v\rangle\in \mathcal{H}_n$ are regarded as identical if there exists a real number $θ$ such that $|u\rangle=e^{iθ}|v\rangle$; otherwise, they are considered distinct. The cardinality $c$ of a QLS$(n)$ is the numbe…

DRO-EDL-MPC: Evidential Deep Learning-Based Distributionally Robust Model Predictive Control for Safe Autonomous Driving
Hyeongchan Ham, Heejin Ahn
https://arxiv.org/abs/2507.05710

DRO-EDL-MPC: Evidential Deep Learning-Based Distributionally Robust Model Predictive Control for Safe Autonomous Driving
Safety is a critical concern in motion planning for autonomous vehicles. Modern autonomous vehicles rely on neural network-based perception, but making control decisions based on these inference results poses significant safety risks due to inherent uncertainties. To address this challenge, we present a distributionally robust optimization (DRO) framework that accounts for both aleatoric and epistemic perception uncertainties using evidential deep learning (EDL). Our approach introduces a novel…

Learnable quantum spectral filters for hybrid graph neural networks
Ammar Daskin
https://arxiv.org/abs/2507.05640 https://arxiv.org/p…

Learnable quantum spectral filters for hybrid graph neural networks
In this paper, we describe a parameterized quantum circuit that can be considered as convolutional and pooling layers for graph neural networks. The circuit incorporates the parameterized quantum Fourier circuit where the qubit connections for the controlled gates derived from the Laplacian operator. Specifically, we show that the eigenspace of the Laplacian operator of a graph can be approximated by using QFT based circuit whose connections are determined from the adjacency matrix. For an $N\t…

Stable Tracking-in-the-Loop Control of Cable-Driven Surgical Manipulators under Erroneous Kinematic Chains
Neelay Joglekar, Fei Liu, Florian Richter, Michael C. Yip
https://arxiv.org/abs/2507.05663

Stable Tracking-in-the-Loop Control of Cable-Driven Surgical Manipulators under Erroneous Kinematic Chains
Remote Center of Motion (RCM) robotic manipulators have revolutionized Minimally Invasive Surgery, enabling precise, dexterous surgical manipulation within the patient's body cavity without disturbing the insertion point on the patient. Accurate RCM tool control is vital for incorporating autonomous subtasks like suturing, blood suction, and tumor resection into robotic surgical procedures, reducing surgeon fatigue and improving patient outcomes. However, these cable-driven systems are subject …

Noise-Canceling Quantum Feedback: non-Hermitian Dynamics with Applications to State Preparation and Magic State Distillation
Tathagata Karmakar, Philippe Lewalle, Yipei Zhang, K. Birgitta Whaley
https://arxiv.org/abs/2507.05611

Noise-Canceling Quantum Feedback: non-Hermitian Dynamics with Applications to State Preparation and Magic State Distillation
Time-continuous quantum measurement allows for the tracking of a quantum system in real time via sequences of short, and individually weak, measurement intervals. Such measurements are necessarily invasive, imparting backaction to the system, and allowing the observer to update their state estimate based on stochastic measurement outcomes. Feedback control then involves real-time interventions by an observer, conditioned on the time-continuous measurement signal that they receive. We here consi…

3DGS_LSR:Large_Scale Relocation for Autonomous Driving Based on 3D Gaussian Splatting
Haitao Lu, Haijier Chen, Haoze Liu, Shoujian Zhang, Bo Xu, Ziao Liu
https://arxiv.org/abs/2507.05661

3DGS_LSR:Large_Scale Relocation for Autonomous Driving Based on 3D Gaussian Splatting
In autonomous robotic systems, precise localization is a prerequisite for safe navigation. However, in complex urban environments, GNSS positioning often suffers from signal occlusion and multipath effects, leading to unreliable absolute positioning. Traditional mapping approaches are constrained by storage requirements and computational inefficiency, limiting their applicability to resource-constrained robotic platforms. To address these challenges, we propose 3DGS-LSR: a large-scale relocaliz…

Tensor network algorithm to solve polaron impurity problems
Ruofan Chen, Lei Gu, Chu Guo
https://arxiv.org/abs/2507.05580 https://arx…

Tensor network algorithm to solve polaron impurity problems
The polaron problem is a very old problem in condensed matter physics that dates back to the thirties, but still remain largely unsolved today, especially when electron-electron interaction is taken into consideration. The presence of both electron-electron and electron-phonon interactions in the problem invalidates most existing numerical methods, either computationally too expensive or simply intractable. The continuous time quantum Monte Carlo (CTQMC) methods could tackle this problem, but a…

A Physics-Based Continuum Model for Versatile, Scalable, and Fast Terramechanics Simulation
Huzaifa Unjhawala, Luning Bakke, Harry Zhang, Michael Taylor, Ganesh Arivoli, Radu Serban, Dan Negrut
https://arxiv.org/abs/2507.05643

A Physics-Based Continuum Model for Versatile, Scalable, and Fast Terramechanics Simulation
This paper discusses Chrono's Continuous Representation Model (called herein Chrono

Universal Long-Time Behavior of the Quantum Fisher Information in Dynamical Quantum Phase Transitions
J. Mumford, R. J. Lewis-Swan
https://arxiv.org/abs/2507.05579

Universal Long-Time Behavior of the Quantum Fisher Information in Dynamical Quantum Phase Transitions
We investigate dynamical quantum phase transitions (DQPTs) in quantum systems that possess well-defined classical limits, focusing on the spinor Bose-Einstein condensate and the Lipkin-Meshkov-Glick model. We diagnose the DQPTs with the long-time average of the quantum Fisher information (QFI) showing that it abruptly changes at the transition point. Using mean-field and semiclassical approximations, we demonstrate that the long-time average of the QFI reveals universal behavior that persists a…

DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
Young Hun Kim, Seungyeon Kim, Yonghyeon Lee, Frank Chongwoo Park
https://arxiv.org/abs/2507.05627

DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
Partial-view 3D recognition -- reconstructing 3D geometry and identifying object instances from a few sparse RGB images -- is an exceptionally challenging yet practically essential task, particularly in cluttered, occluded real-world settings where full-view or reliable depth data are often unavailable. Existing methods, whether based on strong symmetry priors or supervised learning on curated datasets, fail to generalize to such scenarios. In this work, we introduce DreamGrasp, a framework tha…

Theory Framework of Multiplexed Photon-Number-Resolving Detectors
Xiaobin Zhao, Hezheng Qin, Hong X. Tang, Linran Fan, Quntao Zhuang
https://arxiv.org/abs/2507.05554

Theory Framework of Multiplexed Photon-Number-Resolving Detectors
Photon counting is a fundamental component in quantum optics and quantum information. However, implementing ideal photon-number-resolving (PNR) detectors remains experimentally challenging. Multiplexed PNR detection offers a scalable and practical alternative by distributing photons across multiple modes and detecting their presence using simple ON-OFF detectors, thereby enabling approximate photon-number resolution. In this work, we establish a theoretical model for such detectors and prove th…

Special-Unitary Parameterization for Trainable Variational Quantum Circuits
Kuan-Cheng Chen, Huan-Hsin Tseng, Samuel Yen-Chi Chen, Chen-Yu Liu, Kin K. Leung
https://arxiv.org/abs/2507.05535

Special-Unitary Parameterization for Trainable Variational Quantum Circuits
We propose SUN-VQC, a variational-circuit architecture whose elementary layers are single exponentials of a symmetry-restricted Lie subgroup, $\mathrm{SU}(2^{k}) \subset \mathrm{SU}(2^{n})$ with $k \ll n$. Confining the evolution to this compact subspace reduces the dynamical Lie-algebra dimension from $\mathcal{O}(4^{n})$ to $\mathcal{O}(4^{k})$, ensuring only polynomial suppression of gradient variance and circumventing barren plateaus that plague hardware-efficient ansätze. Exact, hardware-…

Reservoir-engineered squeezed lasing through the parametric coupling
Yuhang Tian, Yajun Wang, Weijie Wang, Xiaocong Sun, Yuhang Li, Shaoping Shi, Long Tian, Yaohui Zheng
https://arxiv.org/abs/2507.05708

Reservoir-engineered squeezed lasing through the parametric coupling
We report the first experimental demonstration of squeezed lasing in a reservoir-engineered optical parametric oscillator (OPO). The OPO provides a basis of squeezed states and parametric amplification in lasing emission, whose vacuum reservoir is coupled to a squeezed vacuum generated by a second OPO. With a precisely controlled squeezing angle and strong squeezing injection, the parametric interaction in the first OPO is exponentially enhanced. It successfully circumvents the decoherence in t…