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Let's just say this answer surprised me. Happy reading! #writing #writers
I Refuse to Support My Friend’s Crappy Self-Published Novel: Am I the Asshole? ‹ Literary Hub

I Refuse to Support My Friend’s Crappy Self-Published Novel: Am I the Asshole?
That’s right, you know what time it is! We’re back for another wild installment of everyone’s favorite drunken advice column, Am I the Literary Asshole?, the place where your questions and my answe…

From Alerts to Intelligence: A Novel LLM-Aided Framework for Host-based Intrusion Detection
Danyu Sun, Jinghuai Zhang, Jiacen Xu, Yu Zheng, Yuan Tian, Zhou Li
https://arxiv.org/abs/2507.10873

From Alerts to Intelligence: A Novel LLM-Aided Framework for Host-based Intrusion Detection
Host-based intrusion detection system (HIDS) is a key defense component to protect the organizations from advanced threats like Advanced Persistent Threats (APT). By analyzing the fine-grained logs with approaches like data provenance, HIDS has shown successes in capturing sophisticated attack traces. Despite the progresses embarked by the research community and industry, HIDS still frequently encounters backlash from their operators in the deployed environments, due to issues like high false-p…

AGFS-Tractometry: A Novel Atlas-Guided Fine-Scale Tractometry Approach for Enhanced Along-Tract Group Statistical Comparison Using Diffusion MRI Tractography
Ruixi Zheng, Wei Zhang, Yijie Li, Xi Zhu, Zhou Lan, Jarrett Rushmore, Yogesh Rathi, Nikos Makris, Lauren J. O'Donnell, Fan Zhang
https://arxiv.org/abs/2507.10601…

AGFS-Tractometry: A Novel Atlas-Guided Fine-Scale Tractometry Approach for Enhanced Along-Tract Group Statistical Comparison Using Diffusion MRI Tractography
Diffusion MRI (dMRI) tractography is currently the only method for in vivo mapping of the brain's white matter (WM) connections. Tractometry is an advanced tractography analysis technique for along-tract profiling to investigate the morphology and microstructural properties along the fiber tracts. Tractometry has become an essential tool for studying local along-tract differences between different populations (e.g., health vs disease). In this study, we propose a novel atlas-guided fine-scale t…

Discovery of a 10:1 Resonator with a Novel Libration State: #202VN40

GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news
Abdul Haque, Umm e Hani, Ahmad Din, Muhammad Babar, Ali Abbas, Insaf Ullah
https://arxiv.org/abs/2506.11600

GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news
GraphRAG-Causal introduces an innovative framework that combines graph-based retrieval with large language models to enhance causal reasoning in news analysis. Traditional NLP approaches often struggle with identifying complex, implicit causal links, especially in low-data scenarios. Our approach addresses these challenges by transforming annotated news headlines into structured causal knowledge graphs. It then employs a hybrid retrieval system that merges semantic embeddings with graph-based s…

DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images
Haoran Sun, Haoyu Bian, Shaoning Zeng, Yunbo Rao, Xu Xu, Lin Mei, Jianping Gou
https://arxiv.org/abs/2507.08648

DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images
Common knowledge indicates that the process of constructing image datasets usually depends on the time-intensive and inefficient method of manual collection and annotation. Large models offer a solution via data generation. Nonetheless, real-world data are obviously more valuable comparing to artificially intelligence generated data, particularly in constructing image datasets. For this reason, we propose a novel method for auto-constructing datasets from real-world images by a multiagent colla…

Conversational AI as a Catalyst for Informal Learning: An Empirical Large-Scale Study on LLM Use in Everyday Learning
Na{\dj}a Terzimehi\'c, Babette B\"uhler, Enkelejda Kasneci
https://arxiv.org/abs/2506.11789

Conversational AI as a Catalyst for Informal Learning: An Empirical Large-Scale Study on LLM Use in Everyday Learning
Large language models have not only captivated the public imagination but have also sparked a profound rethinking of how we learn. In the third year following the breakthrough launch of ChatGPT, everyday informal learning has been transformed as diverse user groups explore these novel tools. Who is embracing LLMs for self-directed learning, and who remains hesitant? What are their reasons for adoption or avoidance? What learning patterns emerge with this novel technological landscape? We presen…

Bi-Contextuality: A Novel Non-Classical Phenomenon in Bipartite Quantum Systems
Gabriel Ruffolo, Nigel Benjamin Lee Junsheng, Kim Mu Young, Dzmitry Matsukevich, Rafael Rabelo, Dagomir Kaszlikowski, Pawe{\l} Kurzy\'nski
https://arxiv.org/abs/2507.08461

Bi-Contextuality: A Novel Non-Classical Phenomenon in Bipartite Quantum Systems
We present and experimentally demonstrate a novel non-classical phenomenon, bi-contextuality, observed in quantum systems prepared by two independent sources. This discovery plays a key role in the developing framework of network nonlocality, offering a new method for confirming the quantum nature of measurements within a single network node. Bi-contextuality acts as a reversed Bell scenario: while Bell scenarios involve splitting a system for independent measurements, our approach combines sys…

Abstract Sound Fusion with Unconditioned Inversion Model
Jing Liu, EnQi Lian
https://arxiv.org/abs/2506.11811 https://arxiv.org/pdf/2…

Abstract Sound Fusion with Unconditioned Inversion Model
An abstract sound is defined as a sound that does not disclose identifiable real-world sound events to a listener. Sound fusion aims to synthesize an original sound and a reference sound to generate a novel sound that exhibits auditory features beyond mere additive superposition of the sound constituents. To achieve this fusion, we employ inversion techniques that preserve essential features of the original sample while enabling controllable synthesis. We propose novel SDE and ODE inversion mod…

Mgmt: We don't have the resources to make our own LLM. Can we just monetize AI prompts?
Business School Student: Hold my beer 🍺
#blockchain

PromptChain: A Decentralized Web3 Architecture for Managing AI Prompts as Digital Assets
We present PromptChain, a decentralized Web3 architecture that establishes AI prompts as first-class digital assets with verifiable ownership, version control, and monetization capabilities. Current centralized platforms lack mechanisms for proper attribution, quality assurance, or fair compensation for prompt creators. PromptChain addresses these limitations through a novel integration of IPFS for immutable storage, smart contracts for governance, and token incentives for community curation. O…

Enhancing Essay Cohesion Assessment: A Novel Item Response Theory Approach
Bruno Alexandre Rosa, Hil\'ario Oliveira, Luiz Rodrigues, Eduardo Araujo Oliveira, Rafael Ferreira Mello
https://arxiv.org/abs/2507.08487

Enhancing Essay Cohesion Assessment: A Novel Item Response Theory Approach
Essays are considered a valuable mechanism for evaluating learning outcomes in writing. Textual cohesion is an essential characteristic of a text, as it facilitates the establishment of meaning between its parts. Automatically scoring cohesion in essays presents a challenge in the field of educational artificial intelligence. The machine learning algorithms used to evaluate texts generally do not consider the individual characteristics of the instances that comprise the analysed corpus. In this…

CGVQM D: Computer Graphics Video Quality Metric and Dataset
Akshay Jindal, Nabil Sadaka, Manu Mathew Thomas, Anton Sochenov, Anton Kaplanyan
https://arxiv.org/abs/2506.11546

CGVQM+D: Computer Graphics Video Quality Metric and Dataset
While existing video and image quality datasets have extensively studied natural videos and traditional distortions, the perception of synthetic content and modern rendering artifacts remains underexplored. We present a novel video quality dataset focused on distortions introduced by advanced rendering techniques, including neural supersampling, novel-view synthesis, path tracing, neural denoising, frame interpolation, and variable rate shading. Our evaluations show that existing full-reference…

The full formula for the probability of "success" is:
p = {
1/(2^(-n 1)) if n is negative, or
1 - (1/(2^(n 1))) if n is zero or positive
}
(Both branches have the same value when n is 0, so the behavior is smooth around the origin.)
How can we tweak this?
First, we can introduce fixed success and/or failure chances unaffected by level, with this formula only taking effect if those don't apply. For example, you could do 10% failure, 80% by formula, and 10% success to keep things from being too sure either way even when levels are very high or low. On the other hand, this flattening makes the benefit of extra advantage levels even less exciting.
Second, we could allow for gradations of success/failure, and treat the coin pools I used to explain that math like dice pools a bit. An in-between could require linearly more success flips to achieve the next higher grade of success at each grade. For example, simple success on a crit role might mean dealing 1.5x damage, but if you succeed on 2 of your flips, you get 9/4 damage, or on 4 flips 27/8, or on 7 flips 81/16. In this world, stacking crit levels might be a viable build, and just giving up on armor would be super dangerous. In the particular case I was using this for just now, I can't easily do gradations of success (that's the reason I turned to probabilities in the first place) but I think I'd favor this approach when feasible.
The main innovation here over simple dice pools is how to handle situations where the number of dice should be negative. I'm almost certain it's not a truly novel innovation though, and some RPG fan can point out which system already does this (please actually do this, I'm an RPG nerd too at heart).
I'll leave this with one more tweak we could do: what if the number 2 in the probability equation were 3, or 2/3? I think this has a similar effect to just scaling all the modifiers a bit, but the algebra escapes me in this moment and I'm a bit lazy. In any case, reducing the base of the probability exponent should let you get a few more gradations near 50%, which is probably a good thing, since the default goes from 25% straight to 50% and then to 75% with no integer stops in between.

Decidable Reversible Equivalences for Finite Petri Nets
Roberto Gorrieri, Ivan Lanese
https://arxiv.org/abs/2506.11517 https://arxiv.…

Decidable Reversible Equivalences for Finite Petri Nets
In the setting of Petri nets, we prove that {\em causal-net bisimilarity} \cite{G15,Gor22,Gor25a}, which is a refinement of history-preserving bisimilarity \cite{RT88,vGG89,DDM89}, and the novel {\em hereditary} causal-net bisimilarity, which is a refinement of hereditary history-preserving bisimilarity \cite{Bed91,JNW96}, do coincide. This means that causal-net bisimilarity is a {\em reversible behavioral equivalence}, as causal-net bisimilar markings not only are able to match each other's fo…

NavComposer: Composing Language Instructions for Navigation Trajectories through Action-Scene-Object Modularization
Zongtao He, Liuyi Wang, Lu Chen, Chengju Liu, Qijun Chen
https://arxiv.org/abs/2507.10894

NavComposer: Composing Language Instructions for Navigation Trajectories through Action-Scene-Object Modularization
Language-guided navigation is a cornerstone of embodied AI, enabling agents to interpret language instructions and navigate complex environments. However, expert-provided instructions are limited in quantity, while synthesized annotations often lack quality, making them insufficient for large-scale research. To address this, we propose NavComposer, a novel framework for automatically generating high-quality navigation instructions. NavComposer explicitly decomposes semantic entities such as act…

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning
Jihao Li, Keqi Zhu, Guodong Lu, I-Ming Chen, Huixu Dong
https://arxiv.org/abs/2506.11570

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning
We present a novel under-actuated gripper with two 3-joint fingers, which realizes force feedback control by the deep learning technique- Long Short-Term Memory (LSTM) model, without any force sensor. First, a five-linkage mechanism stacked by double four-linkages is designed as a finger to automatically achieve the transformation between parallel and enveloping grasping modes. This enables the creation of a low-cost under-actuated gripper comprising a single actuator and two 3-phalange fingers…

Access graph: a novel graph representation of public transport networks for accessibility analysis
Tina \v{S}filigoj, Aljo\v{s}a Peperko, Oded Cats
https://arxiv.org/abs/2507.08361

Access graph: a novel graph representation of public transport networks for accessibility analysis
Accessibility, defined as travel impedance between spatially dispersed opportunities for activity, is one of the main determinants of public transport (PT) use. In-depth understanding of its properties is crucial for optimal public transport systems planning and design. Although the concept has been around for decades and there is a large body of literature on accessibility operationalisation and measurement, a unified approach is lacking. To this end, we introduce a novel graph representation …

A novel visual data-based diagnostic approach for estimation of regime transition in pool boiling
Pranay Nirapure, Ayushman Singh, Srikanth Rangarajan, Bahgat Sammakia
https://arxiv.org/abs/2506.10832

A novel visual data-based diagnostic approach for estimation of regime transition in pool boiling
This study introduces a novel metric, the Index of Visual Similarity (IVS), to qualitatively characterize boiling heat transfer regimes using only visual data. The IVS is constructed by combining morphological similarity, through SIFT-based feature matching, with physical similarity, via vapor area estimation using Mask R-CNN. High-speed images of pool boiling on two distinct surfaces, polished copper and porous copper foam, are employed to demonstrate the generalizability of the approach. IVS …

PermRust: A Token-based Permission System for Rust
Lukas Gehring, Sebastian Rehms, Florian Tschorsch
https://arxiv.org/abs/2506.11701 https://

PermRust: A Token-based Permission System for Rust
Permission systems which restrict access to system resources are a well-established technology in operating systems, especially for smartphones. However, as such systems are implemented in the operating system they can at most manage access on the process-level. Since moderns software often (re)uses code from third-parties libraries, a permission system for libraries can be desirable to enhance security. In this short-paper, we adapt concepts from capability systems building a novel theoretical…

Cyclic Data Streaming on GPUs for Short Range Stencils Applied to Molecular Dynamics
Martin Rose, Simon Homes, Lukas Ramsperger, Jose Gracia, Christoph Niethammer, Jadran Vrabec
https://arxiv.org/abs/2507.11289

Cyclic Data Streaming on GPUs for Short Range Stencils Applied to Molecular Dynamics
In the quest for highest performance in scientific computing, we present a novel framework that relies on high-bandwidth communication between GPUs in a compute cluster. The framework offers linear scaling of performance for explicit algorithms that is only limited by the size of the dataset and the number of GPUs. Slices of the dataset propagate in a ring of processes (GPUs) from one GPU, where they are processed, to the next, which results in a parallel-in-time parallelization. The user of th…

A Mathematical Optimization Approach to Multisphere Support Vector Data Description
V\'ictor Blanco, Inmaculada Espejo, Ra\'ul P\'aez, Antonio M. Rodr\'iguez-Ch\'ia
https://arxiv.org/abs/2507.11106

A Mathematical Optimization Approach to Multisphere Support Vector Data Description
We present a novel mathematical optimization framework for outlier detection in multimodal datasets, extending Support Vector Data Description approaches. We provide a primal formulation, in the shape of a Mixed Integer Second Order Cone model, that constructs Euclidean hyperspheres to identify anomalous observations. Building on this, we develop a dual model that enables the application of the kernel trick, thus allowing for the detection of outliers within complex, non-linear data structures.…

Secondary Bounded Rationality: A Theory of How Algorithms Reproduce Structural Inequality in AI Hiring
Jia Xiao
https://arxiv.org/abs/2507.09233 https://…

Secondary Bounded Rationality: A Theory of How Algorithms Reproduce Structural Inequality in AI Hiring
AI-driven recruitment systems, while promising efficiency and objectivity, often perpetuate systemic inequalities by encoding cultural and social capital disparities into algorithmic decision making. This article develops and defends a novel theory of secondary bounded rationality, arguing that AI systems, despite their computational power, inherit and amplify human cognitive and structural biases through technical and sociopolitical constraints. Analyzing multimodal recruitment frameworks, we …

A Compact Dynamic Omnidirectional Antenna
Sheng Huang, Jacob R. Randall, Cory Hilton, Jeffrey A. Nanzer
https://arxiv.org/abs/2506.11351 https://

A Compact Dynamic Omnidirectional Antenna
We propose a novel omnidirectional antenna design incorporating directional modulation for secure narrow planar information transmission. The proposed antenna features a compact size and stable omnidirectional radiation performance by employing two tightly spaced, printed meander line monopole antennas, acting as a single radiating element. To achieve a narrow information secure region, the proposed antenna is fed by differential power excitation of two ports with real-time dynamic switching. T…

Can SMEFT discover New Physics?
Martin Hirsch, Luca Mantani, Veronica Sanz
https://arxiv.org/abs/2507.11109 https://arxiv.org/pdf/250…

Can SMEFT discover New Physics?
We present a novel strategy to uncover indirect signs of new physics in collider data using the Standard Model Effective Field Theory (SMEFT) framework, offering notably improved sensitivity compared to traditional global analyses. Our approach leverages genetic algorithms to efficiently navigate the high-dimensional space of operator subsets, identifying deformations that improve agreement with data without relying on prior UV assumptions. This enables the systematic detection of SMEFT scenari…

Near-perfect Clustering Based on Recursive Binary Splitting Using Max-MMD
Sourav Chakrabarty, Anirvan Chakraborty, Shyamal K. De
https://arxiv.org/abs/2507.11158

Near-perfect Clustering Based on Recursive Binary Splitting Using Max-MMD
We develop novel clustering algorithms for functional data when the number of clusters $K$ is unknown and also when it is prefixed. These algorithms are developed based on the Maximum Mean Discrepancy (MMD) measure between two sets of observations. The algorithms recursively use a binary splitting strategy to partition the dataset into two subgroups such that they are maximally separated in terms of an appropriate weighted MMD measure. When $K$ is unknown, the proposed clustering algorithm has …

A Spontaneous Symmetry Breaking Machine -- A Theory for a Novel Type of Spontaneous Symmetry Breaking in a Unique Dissipative System and one Application
Toshiya Sato
https://arxiv.org/abs/2507.11044

A Spontaneous Symmetry Breaking Machine -- A Theory for a Novel Type of Spontaneous Symmetry Breaking in a Unique Dissipative System and one Application
We focus on an interesting dissipative system (DS) found in a photonics system. In this DS, robust causality is created, and as a result, it becomes possible to find behavior that can be understood as spontaneous symmetry breaking (SSB). Furthermore, by arranging such DSs as basic elements and building certain relationships between them through optical interference, it becomes possible to create a unique system that causes complex SSB as a whole. This unique SSB can be understood as having a du…

"Despite good-faith efforts by DSI [dense street imagery] providers to protect individual privacy through blurring faces and license plates, these measures fail to address broader privacy concerns. In this work, we find that increased data density and advancements in artificial intelligence enable harmful group membership inferences from supposedly anonymized data."
via @…

Privacy of Groups in Dense Street Imagery
Spatially and temporally dense street imagery (DSI) datasets have grown unbounded. In 2024, individual companies possessed around 3 trillion unique images of public streets. DSI data streams are only set to grow as companies like Lyft and Waymo use DSI to train autonomous vehicle algorithms and analyze collisions. Academic researchers leverage DSI to explore novel approaches to urban analysis. Despite good-faith efforts by DSI providers to protect individual privacy through blurring faces and l…

On the High-Rate FDPC Codes: Construction, Encoding, and a Generalization
Mohsen Moradi, Sheida Rabeti, Hessam Mahdavifar
https://arxiv.org/abs/2506.11345 …

On the High-Rate FDPC Codes: Construction, Encoding, and a Generalization
Recently introduced Fair-Density Parity-Check (FDPC) codes, targeting high-rate applications, offer superior error-correction performance (ECP) compared to 5G Low-Density Parity-Check (LDPC) codes, given the same number of message-passing decoding iterations. In this paper, we present a novel construction method for FDPC codes, introduce a generalization of these codes, and propose a low-complexity encoding algorithm. Numerical results demonstrate the fast convergence of the message-passing dec…

Confinement in QCD: A Hybrid String Model with Vortex Corrections and Entanglement Entropy
Fidele J. Twagirayezu
https://arxiv.org/abs/2507.10825 https://

Confinement in QCD: A Hybrid String Model with Vortex Corrections and Entanglement Entropy
Confinement in Quantum Chromodynamics (QCD), binding quarks and gluons into hadrons, is characterized by a linear potential and the Wilson loop area law. We develop an analytical framework in $\text{SU(3)}$ gauge theory, proposing a hybrid effective string model that integrates chromoelectric flux tubes with topological corrections from \(\mathbb{Z}_3\) center vortices. Using strong-coupling expansion, we derive the Wilson loop expectation value, incorporating novel logarithmic vortex correctio…

Quantum modified inertia: an application to galaxy rotation curves
Jonathan Gillot
https://arxiv.org/abs/2507.11524 https://arxiv.org…

Quantum modified inertia: an application to galaxy rotation curves
This study explores the field of modified inertia through a novel model involving maximal and minimal acceleration bounds. A principle of dynamics is developed within special relativity and has direct implications in astrophysics, especially for galaxy rotation curves. The presence of a minimal acceleration significantly reduces the amount of dark matter required to account for these curves. The model presented here is however conceptually different from fiduciary Modified Newtonian Dynamics (M…

Doughnut or Mickey Mouse? Detecting Toroidal Structure in Data through Persistent Cup-Length
Ekaterina S. Ivshina, Galit Anikeeva, Ling Zhou
https://arxiv.org/abs/2507.11151

Doughnut or Mickey Mouse? Detecting Toroidal Structure in Data through Persistent Cup-Length
Understanding the structure of high-dimensional data is fundamental to neuroscience and other data-intensive scientific fields. While persistent homology effectively identifies basic topological features such as "holes," it lacks the ability to reliably detect more complex topologies, particularly toroidal structures, despite previous heuristic attempts. To address this limitation, recent work introduced persistent cup-length, a novel topological invariant derived from persistent cohomology. In…

Adaptive determinantal scheduling with fairness in wireless networks
H. P. Keeler, B. B{\l}aszczyszyn
https://arxiv.org/abs/2506.11738 https://

Adaptive determinantal scheduling with fairness in wireless networks
We propose a novel framework for wireless network scheduling with fairness using determinantal (point) processes. Our approach incorporates the repulsive nature of determinantal processes, generalizing traditional Aloha protocols that schedule transmissions independently. We formulate the scheduling problem with an utility function representing fairness. We then recast this formulation as a convex optimization problem over a certain class of determinantal point processes called $L$-ensembles, w…

LASANA: Large-scale Surrogate Modeling for Analog Neuromorphic Architecture Exploration
Jason Ho, James A. Boyle, Linshen Liu, Andreas Gerstlauer
https://arxiv.org/abs/2507.10748 …

LASANA: Large-scale Surrogate Modeling for Analog Neuromorphic Architecture Exploration
Neuromorphic systems using in-memory or event-driven computing are motivated by the need for more energy-efficient processing of artificial intelligence workloads. Emerging neuromorphic architectures aim to combine traditional digital designs with the computational efficiency of analog computing and novel device technologies. A crucial problem in the rapid exploration and co-design of such architectures is the lack of tools for fast and accurate modeling and simulation. Typical mixed-signal des…

Learning the Analytic Geometry of Transformations to Achieve Efficient Computation
Pei-Chun Su, Ronald R. Coifman
https://arxiv.org/abs/2506.11990 https://…

Learning the Analytic Geometry of Transformations to Achieve Efficient Computation
We propose a novel framework for fast integral operations by uncovering hidden geometries in the row and column structures of the underlying operators. This is accomplished through an iterative procedure that constructs adaptive hierarchical partition trees, revealing latent multiscale organization and exposing local low-rank structures within the data. Guided by this geometry, we employ two complementary techniques: (1) the \emph{butterfly algorithm}, which exploits the learned hierarchical lo…

Machine-learning inference of stellar properties using integrated photometric and spectroscopic data
Ilay Kamai, Alex M. Bronstein, Hagai B. Perets
https://arxiv.org/abs/2507.10666

Machine-learning inference of stellar properties using integrated photometric and spectroscopic data
Stellar astrophysics relies on diverse observational modalities-primarily photometric light curves and spectroscopic data-from which fundamental stellar properties are inferred. While machine learning (ML) has advanced analysis within individual modalities, the complementary information encoded across modalities remains largely underexploited. We present DESA (Dual Embedding model for Stellar Astrophysics), a novel multi-modal foundation model that integrates light curves and spectra to learn a…

A Gauge-Theoretic Action Principle for Viscous Incompressible Fluids
Rashmi R. Nayak
https://arxiv.org/abs/2506.11202 https://arxiv.o…

A Gauge-Theoretic Action Principle for Viscous Incompressible Fluids
We propose a novel action principle for two dimensional incompressible fluid dynamics that naturally incorporates both vorticity and viscous dissipation via gauge field couplings. The action features a Chern Simons like term, $ε^{μνρ} A_μ\partial_νA_ρ$, capturing the topological structure of vorticity, alongside a quadratic term $(ε^{μνρ} \partial_νA_ρ)^2$ representing viscous damping. Incompressibility is enforced through a Lagrange multiplier, while coupling to an external potent…

Invariant measures on moduli spaces of twisted holomorphic 1-forms and strata of dilation surfaces
Paul Apisa, Nick Salter
https://arxiv.org/abs/2507.10685

Invariant measures on moduli spaces of twisted holomorphic 1-forms and strata of dilation surfaces
The moduli space of twisted holomorphic 1-forms on Riemann surfaces, equivalently dilation surfaces with scaling, admits a stratification and GL(2,R)-action as in the case of moduli spaces of translation surfaces. We produce an analogue of Masur-Veech measure, i.e. an SL(2,R)-invariant Lebesgue class measure on strata or explicit covers thereof. This relies on a novel computation of cohomology with coefficients for the mapping class group. The computation produces a framed mapping class group i…

Sparse Autoencoders Bridge The Deep Learning Model and The Brain
Ziming Mao, Jia Xu, Zeqi Zheng, Haofang Zheng, Dabing Sheng, Yaochu Jin, Guoyuan Yang
https://arxiv.org/abs/2506.11123

Sparse Autoencoders Bridge The Deep Learning Model and The Brain
We present SAE-BrainMap, a novel framework that directly aligns deep learning visual model representations with voxel-level fMRI responses using sparse autoencoders (SAEs). First, we train layer-wise SAEs on model activations and compute the correlations between SAE unit activations and cortical fMRI signals elicited by the same natural image stimuli with cosine similarity, revealing strong activation correspondence (maximum similarity up to 0.76). Depending on this alignment, we construct a vo…

The short version of my larger soapbox here is that a lot of the hand-wringing and hair-pulling about e-bikes, novel UPS delivery thingers, Those Darned Kids tearing down the sidewalk on scooters, etc. is at its root not about the vehicles, but about infrastructure.
We need vastly more and better infrastructure — physical, legal, and social — for vehicles that are:
- zero-emission (which currently means human- and electric-powered)
- of limited mass (just how much could it harm a pedestrian?)
- traveling in the 10-20mph range

If quasars form from primordial black holes
Jeremy Mould, Adam Batten
https://arxiv.org/abs/2507.11023 https://arxiv.org/pdf/2507.110…

If quasars form from primordial black holes
We explore the consequences of a novel but increasingly well-supported hypothesis that supermassive black holes may have formed from primordial black holes form ed prior to, and rapidly growing in, the radiation-dominated universe. We show that this hypothesis can predict the luminosity of quasars and their luminosity distribution. With reasonable values of the parameters introduced, these predictions are borne out by observations. The model predicts density evolution in accordance with observa…

Dynamic Grid Trading Strategy: From Zero Expectation to Market Outperformance
Kai-Yuan Chen, Kai-Hsin Chen, Jyh-Shing Roger Jang
https://arxiv.org/abs/2506.11921

Dynamic Grid Trading Strategy: From Zero Expectation to Market Outperformance
We propose a profitable trading strategy for the cryptocurrency market based on grid trading. Starting with an analysis of the expected value of the traditional grid strategy, we show that under simple assumptions, its expected return is essentially zero. We then introduce a novel Dynamic Grid-based Trading (DGT) strategy that adapts to market conditions by dynamically resetting grid positions. Our backtesting results using minute-level data from Bitcoin and Ethereum between January 2021 and Ju…

SC*-Regular spaces and some functions
Neeraj Kumar Tomar, Amit Ujlayan, M. C. Sharma
https://arxiv.org/abs/2505.09951 https://arxiv.o…

SC*-Regular spaces and some functions
This paper introduces a novel class of topological spaces, termed SC*-regular spaces, which are defined using SC*-open sets. We explore their fundamental properties and examine their connections with existing regularity concepts, such as regular, almost, softly, weakly, alpha, zeta, and generalized-regular spaces respectively. Furthermore, we examined and define, analyze generalized SC*-closed sets and SC*-generalized closed functions, establishing key properties and preservation theorems. Seve…

Geometric Cosmology models: statistical analysis with observational data
Mat\'ias Leizerovich, Luisa G. Jaime, Susana J. Landau, Gustavo Arciniega
https://arxiv.org/abs/2506.11299

Geometric Cosmology models: statistical analysis with observational data
Although the standard cosmological model is capable of explaining most current observational data, it faces some theoretical and observational issues. This is the main motivation for exploring alternative cosmological models. In this paper, we focus on a novel proposal that consists in adding an infinite tower of higher-order curvature invariants to the usual Einstein-Hilbert action. We obtain the late-time background evolution for three families of models that can be obtained from this proposa…

Let the Tree Decide: FABART A Non-Parametric Factor Model
Sofia Velasco
https://arxiv.org/abs/2506.11551 https://arxiv.org/pdf/2506.1…

Let the Tree Decide: FABART A Non-Parametric Factor Model
This article proposes a novel framework that integrates Bayesian Additive Regression Trees (BART) into a Factor-Augmented Vector Autoregressive (FAVAR) model to forecast macro-financial variables and examine asymmetries in the transmission of oil price shocks. By employing nonparametric techniques for dimension reduction, the model captures complex, nonlinear relationships between observables and latent factors that are often missed by linear approaches. A simulation experiment comparing FABART…

SceneCompleter: Dense 3D Scene Completion for Generative Novel View Synthesis
Weiliang Chen, Jiayi Bi, Yuanhui Huang, Wenzhao Zheng, Yueqi Duan
https://arxiv.org/abs/2506.10981

SceneCompleter: Dense 3D Scene Completion for Generative Novel View Synthesis
Generative models have gained significant attention in novel view synthesis (NVS) by alleviating the reliance on dense multi-view captures. However, existing methods typically fall into a conventional paradigm, where generative models first complete missing areas in 2D, followed by 3D recovery techniques to reconstruct the scene, which often results in overly smooth surfaces and distorted geometry, as generative models struggle to infer 3D structure solely from RGB data. In this paper, we propo…

A novel quantum circuit for the quantum Fourier transform
Juan M. Romero, Emiliano Montoya-Gonz\'alez, Guillermo Cruz, Roberto C. Romero
https://arxiv.org/abs/2507.08699

A novel quantum circuit for the quantum Fourier transform
The Quantum Fourier Transform (QFT) is a fundamental component of many quantum computing algorithms. In this paper, we present an alternative method for factoring this transformation. Inspired by this approach, we introduce a new quantum circuit for implementing the QFT. We show that this circuit is more efficient than the conventional design. Furthermore, using this circuit, we develop an alternative version of the HHL algorithm, which also demonstrates improved performance compared to the sta…

Access Control for Information-Theoretically Secure Key-Document Stores
Yin Li, Sharad Mehrota, Shantanu Sharma, Komal Kumari
https://arxiv.org/abs/2507.10730

Access Control for Information-Theoretically Secure Key-Document Stores
This paper presents a novel key-based access control technique for secure outsourcing key-value stores where values correspond to documents that are indexed and accessed using keys. The proposed approach adopts Shamir's secret-sharing that offers unconditional or information-theoretic security. It supports keyword-based document retrieval while preventing leakage of the data, access rights of users, or the size (\textit{i}.\textit{e}., volume of the output that satisfies a query). The proposed …

MPC-based Coarse-to-Fine Motion Planning for Robotic Object Transportation in Cluttered Environments
Chen Cai, Ernesto Dickel Saraiva, Ya-jun Pan, Steven Liu
https://arxiv.org/abs/2507.11211

MPC-based Coarse-to-Fine Motion Planning for Robotic Object Transportation in Cluttered Environments
This letter presents a novel coarse-to-fine motion planning framework for robotic manipulation in cluttered, unmodeled environments. The system integrates a dual-camera perception setup with a B-spline-based model predictive control (MPC) scheme. Initially, the planner generates feasible global trajectories from partial and uncertain observations. As new visual data are incrementally fused, both the environment model and motion planning are progressively refined. A vision-based cost function pr…

Self : Merging Human and AI for Co-Determined XR Living in the Metaverse
Thammathip Piumsomboon
https://arxiv.org/abs/2507.10967 https://

Self++: Merging Human and AI for Co-Determined XR Living in the Metaverse
This position paper introduces Self++, a novel nine-level framework for co-determined living in the Metaverse, grounded in Self-Determination Theory. Self++ prioritises human flourishing by progressively cultivating competence, autonomy, and relatedness through dynamic human-AI collaboration in extended reality (XR). Unlike technologically deterministic approaches, Self++ emphasises user empowerment by enhancing competency, mitigating cognitive biases and leveraging XR's immersive capabilities.…

Vector Representations of Vessel Trees
James Batten, Michiel Schaap, Matthew Sinclair, Ying Bai, Ben Glocker
https://arxiv.org/abs/2506.11163 https://

Vector Representations of Vessel Trees
We introduce a novel framework for learning vector representations of tree-structured geometric data focusing on 3D vascular networks. Our approach employs two sequentially trained Transformer-based autoencoders. In the first stage, the Vessel Autoencoder captures continuous geometric details of individual vessel segments by learning embeddings from sampled points along each curve. In the second stage, the Vessel Tree Autoencoder encodes the topology of the vascular network as a single vector r…

Dictionary Learning Based Regularization in Quantitative MRI: A Nested Alternating Optimization Framework
Guozhi Dong, Michael Hinterm\"uller, Clemens Sirotenko
https://arxiv.org/abs/2506.11977

Dictionary Learning Based Regularization in Quantitative MRI: A Nested Alternating Optimization Framework
In this article, we propose a novel regularization method for a class of nonlinear inverse problems that is inspired by an application in quantitative magnetic resonance imaging (qMRI). The latter is a special instance of a general dynamical image reconstruction technique, wherein a radio-frequency pulse sequence gives rise to a time-discrete physics-based mathematical model which acts as a side constraint in our inverse problem. To enhance reconstruction quality, we employ dictionary learning …

FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
Arnab Mukherjee, Raju Halder, Joydeep Chandra
https://arxiv.org/abs/2507.11430

FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
Federated Learning (FL) has undergone significant development since its inception in 2016, advancing from basic algorithms to complex methodologies tailored to address diverse challenges and use cases. However, research and benchmarking of novel FL techniques against a plethora of established state-of-the-art solutions remain challenging. To streamline this process, we introduce FLsim, a comprehensive FL simulation framework designed to meet the diverse requirements of FL workflows in the liter…

A Novel Signal Processing Strategy for Short-Range Laser Feedback Interferometry Sensors
Alexander Zimmer, Johannes Meyer, Enkelejda Kasneci
https://arxiv.org/abs/2506.10705

A Novel Signal Processing Strategy for Short-Range Laser Feedback Interferometry Sensors
The rapid evolution of wearable technologies, such as AR glasses, demands compact, energy-efficient sensors capable of high-precision measurements in dynamic environments. Traditional Frequency-Modulated Continuous Wave (FMCW) Laser Feedback Interferometry (LFI) sensors, while promising, falter in applications that feature small distances, high velocities, shallow modulation, and low-power constraints. We propose a novel sensor-processing pipeline that reliably extracts distance and velocity me…

Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information
Xiao Zhan, Juan Carlos Carrillo, William Seymour, Jose Such
https://arxiv.org/abs/2506.11680

Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information
LLM-based Conversational AIs (CAIs), also known as GenAI chatbots, like ChatGPT, are increasingly used across various domains, but they pose privacy risks, as users may disclose personal information during their conversations with CAIs. Recent research has demonstrated that LLM-based CAIs could be used for malicious purposes. However, a novel and particularly concerning type of malicious LLM application remains unexplored: an LLM-based CAI that is deliberately designed to extract personal infor…

Pronunciation Deviation Analysis Through Voice Cloning and Acoustic Comparison
Andrew Valdivia, Yueming Zhang, Hailu Xu, Amir Ghasemkhani, Xin Qin
https://arxiv.org/abs/2507.10985

Pronunciation Deviation Analysis Through Voice Cloning and Acoustic Comparison
This paper presents a novel approach for detecting mispronunciations by analyzing deviations between a user's original speech and their voice-cloned counterpart with corrected pronunciation. We hypothesize that regions with maximal acoustic deviation between the original and cloned utterances indicate potential mispronunciations. Our method leverages recent advances in voice cloning to generate a synthetic version of the user's voice with proper pronunciation, then performs frame-by-frame compa…

Unlocking time-dependent $CP$ violation without signal vertexing at $B$ factories
Mirco Dorigo, Sebastiano Raiz, Diego Tonelli, Radek Zlebcik
https://arxiv.org/abs/2506.11196

Unlocking time-dependent $CP$ violation without signal vertexing at $B$ factories
We present a novel method to measure, without reconstructing the signal decay vertex, time-dependent $CP$ violation in $B^0$ mesons produced in energy-asymmetric electron-positron collisions near the $Υ(4S)$ resonance. The method relies on quantum-entangled $B^0\overline{B}^0$ pair-production making the decay time of the partner meson sensitive to $CP$ violation of the signal. We derive analytical expressions for the relevant time-evolution and study, as a use case, an application to $B^0\toπ…

A Two-step Estimating Approach for Heavy-tailed AR Models with General Non-zero Median Noises
Rui She, Linlin Dai, Shiqing Ling
https://arxiv.org/abs/2506.11509

A Two-step Estimating Approach for Heavy-tailed AR Models with General Non-zero Median Noises
This paper develops a novel two-step estimating procedure for heavy-tailed AR models with non-zero median noises, allowing for time-varying volatility. We first establish the self-weighted quantile regression estimator (SQE) across all quantile levels $τ\in (0,1)$ for the AR parameters $θ_{0}$. We show that the SQE, minus a bias, converges weakly to a Gaussian process uniformly at a rate of $n^{-1/2}$. The bias is zero if and only if $τ$ equals $τ_{0}$, the probability that the noise is l…

On minimal bases in homotopical combinatorics
Katharine Adamyk, Scott Balchin, Miguel Barrero, Steven Scheirer, Noah Wisdom, Valentina Zapata Castro
https://arxiv.org/abs/2506.11159

On minimal bases in homotopical combinatorics
We present a development in the computational suite for the study of $N_\infty$ operads for a finite group $G$. This progress is achieved using the simple yet powerful observation that Rubin's generation algorithm can be interpreted as a closure operator. Leveraging this perspective, we establish the existence of minimal bases for $N_\infty$ operads. By investigating these bases for certain families of groups we are led to introduce and analyze several novel combinatorial invariants for finite …

3D Magnetic Inverse Routine for Single-Segment Magnetic Field Images
J. Senthilnath, Chen Hao, F. C. Wellstood
https://arxiv.org/abs/2507.11293 https://

3D Magnetic Inverse Routine for Single-Segment Magnetic Field Images
In semiconductor packaging, accurately recovering 3D information is crucial for non-destructive testing (NDT) to localize circuit defects. This paper presents a novel approach called the 3D Magnetic Inverse Routine (3D MIR), which leverages Magnetic Field Images (MFI) to retrieve the parameters for the 3D current flow of a single-segment. The 3D MIR integrates a deep learning (DL)-based Convolutional Neural Network (CNN), spatial-physics-based constraints, and optimization techniques. The metho…

Refined stability estimates for mixed problems by exploiting semi norm arguments
Nicolas Gauger, Alexander Linke, Christian Merdon
https://arxiv.org/abs/2506.11566

Refined stability estimates for mixed problems by exploiting semi norm arguments
Refined stability estimates are derived for classical mixed problems. The novel emphasis is on the importance of semi norms on data functionals, inspired by recent progress on pressure-robust discretizations for the incompressible Navier--Stokes equations. In fact, kernels of these semi norms are shown to be connected to physical regimes in applications and are related to some well-known consistency errors in classical discretizations of mixed problems. Consequently, significantly sharper stabi…

A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
Giulio Giacomuzzo, Mohamed Abdelwahab, Marco Cal\`i, Alberto Dalla Libera, Ruggero Carli
https://arxiv.org/abs/2507.11170

A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
In this paper, we propose a novel learning-based robust feedback linearization strategy to ensure precise trajectory tracking for an important family of Lagrangian systems. We assume a nominal knowledge of the dynamics is given but no a-priori bounds on the model mismatch are available. In our approach, the key ingredient is the adoption of a regression framework based on Gaussian Processes (GPR) to estimate the model mismatch. This estimate is added to the outer loop of a classical feedback li…

Because the problem is in the infrastructure, I’d ask my fellow cyclists and urbanists to make sure any fist-shaking about these vehicles is directed toward •improving infrastructure• instead of •policing novel vehicles•.
I can easily imagine a backlash to these vehicles at the city Council meeting turning into rules that make it harder to get better vehicles on the street.
Instead, the reaction I’d like to see to these UPS cargo-bike-adjacent thingers is “Great!! Obviously they’re a problem in these too-narrow bike we’ve stuffed onto road shoulders, so where can we put them?”

Hilbert subspace imprint: a new mechanism for non-thermalization
Hui Yu, Jiangping Hu, Shi-Xin Zhang
https://arxiv.org/abs/2506.11922 https://

Hilbert subspace imprint: a new mechanism for non-thermalization
The search for non-ergodic mechanisms in quantum many-body systems has become a frontier area of research in non-equilibrium physics. In this Letter, we introduce Hilbert subspace imprint (HSI)-a novel mechanism that enables evasion of thermalization and bridges the gap between quantum many-body scars (QMBS) and Hilbert space fragmentation (HSF). HSI manifests when initial states overlap exclusively with a polynomial scaling (with system size) set of eigenstates. We demonstrate this phenomenon …

MalCodeAI: Autonomous Vulnerability Detection and Remediation via Language Agnostic Code Reasoning
Jugal Gajjar, Kamalasankari Subramaniakuppusamy, Noha El Kachach
https://arxiv.org/abs/2507.10898

MalCodeAI: Autonomous Vulnerability Detection and Remediation via Language Agnostic Code Reasoning
The growing complexity of cyber threats and the limitations of traditional vulnerability detection tools necessitate novel approaches for securing software systems. We introduce MalCodeAI, a language-agnostic, multi-stage AI pipeline for autonomous code security analysis and remediation. MalCodeAI combines code decomposition and semantic reasoning using fine-tuned Qwen2.5-Coder-3B-Instruct models, optimized through Low-Rank Adaptation (LoRA) within the MLX framework, and delivers scalable, accu…

REVA: Supporting LLM-Generated Programming Feedback Validation at Scale Through User Attention-based Adaptation
Xiaohang Tang, Sam Wong, Zicheng He, Yalong Yang, Yan Chen
https://arxiv.org/abs/2507.11470

REVA: Supporting LLM-Generated Programming Feedback Validation at Scale Through User Attention-based Adaptation
This paper introduces REVA, a human-AI system that expedites instructor review of voluminous AI-generated programming feedback by sequencing submissions to minimize cognitive context shifts and propagating instructor-driven revisions across semantically similar instances. REVA introduces a novel approach to human-AI collaboration in educational feedback by adaptively learning from instructors' attention in the review and revision process to continuously improve the feedback validation process. …

Solving Integrated Periodic Railway Timetabling with Satisfiability Modulo Theories: A Scalable Approach to Routing and Vehicle Circulation
Florian Fuchs, Bernardo Martin-Iradi, Francesco Corman
https://arxiv.org/abs/2507.11489

Solving Integrated Periodic Railway Timetabling with Satisfiability Modulo Theories: A Scalable Approach to Routing and Vehicle Circulation
This paper introduces a novel approach for jointly solving the periodic Train Timetabling Problem (TTP), train routing, and Vehicle Circulation Problem (VCP) through a unified optimization model. While these planning stages are traditionally addressed sequentially, their interdependencies often lead to suboptimal vehicle usage. We propose the VCR-PESP, an integrated formulation that minimizes fleet size while ensuring feasible and infrastructure-compliant periodic timetables. We present the fir…

CATVis: Context-Aware Thought Visualization
Tariq Mehmood, Hamza Ahmad, Muhammad Haroon Shakeel, Murtaza Taj
https://arxiv.org/abs/2507.11522 https://

CATVis: Context-Aware Thought Visualization
EEG-based brain-computer interfaces (BCIs) have shown promise in various applications, such as motor imagery and cognitive state monitoring. However, decoding visual representations from EEG signals remains a significant challenge due to their complex and noisy nature. We thus propose a novel 5-stage framework for decoding visual representations from EEG signals: (1) an EEG encoder for concept classification, (2) cross-modal alignment of EEG and text embeddings in CLIP feature space, (3) captio…

Fairness-Aware Secure Integrated Sensing and Communications with Fractional Programming
Ali Khandan Boroujeni, Kuranage Roche Rayan Ranasinghe, Giuseppe Thadeu Freitas de Abreu, Stefan K\"opsell, Ghazal Bagheri, Rafael F. Schaefer
https://arxiv.org/abs/2507.11224

Fairness-Aware Secure Integrated Sensing and Communications with Fractional Programming
We propose a novel secure integrated sensing and communications (ISAC) system designed to serve multiple communication users (CUs) and targets. To that end, we formulate an optimization problem that maximizes the secrecy rate under constraints balancing both communication and sensing requirements. To enhance fairness among users, an entropy-regularized fairness metric is introduced within the problem framework. We then propose a solution employing an accelerated quadratic transform (QT) with a …

Hadamard Encoded Row Column Ultrasonic Expansive Scanning (HERCULES) with Bias-Switchable Row-Column Arrays
Darren Olufemi Dahunsi, Randy Palmar, Tyler Henry, Mohammad Rahim Sobhani, Negar Majidi, Joy Wang, Afshin Kashani Ilkhechi, Jeremy Brown, Roger Zemp
https://arxiv.org/abs/2506.11443

Hadamard Encoded Row Column Ultrasonic Expansive Scanning (HERCULES) with Bias-Switchable Row-Column Arrays
Top-Orthogonal-to-Bottom-Electrode (TOBE) arrays, also known as bias-switchable row-column arrays (RCAs), allow for imaging techniques otherwise impossible for non-bias-switachable RCAs. Hadamard Encoded Row Column Ultrasonic Expansive Scanning (HERCULES) is a novel imaging technique that allows for expansive 3D scanning by transmitting plane or cylindrical wavefronts and receiving using Hadamard-Encoded-Read-Out (HERO) to perform beamforming on what is effectively a full 2D synthetic receive a…

Nuclear modification factor within a dynamical approach to the complex entropic index
R. Baptista, L. G. Rocha, J. M. C. Pareja, T. Bhattacharyya, A. Deppman, E. Megias, M. Rybczynski, G. Wilk, Z. Wlodarczyk
https://arxiv.org/abs/2507.11451

Nuclear modification factor within a dynamical approach to the complex entropic index
This work introduces a novel approach to the nuclear deformation factor $R_{AA}$, grounded in the dynamical effects of the Quark-Gluon Plasma on parton momentum. The approach uses the Blast-Wave method combined with Tsallis Statistics, within the Copper-Frye freeze-out framework and, by profiting from appropriate simplifications, it gives analytical expressions that describe the observed $R_{AA}$ for two sets of independent measurements at $\sqrt{s}=2.76$ TeV and $\sqrt{s}=5.02$ TeV. A nonlinea…

Confidence-Based Self-Training for EMG-to-Speech: Leveraging Synthetic EMG for Robust Modeling
Xiaodan Chen, Xiaoxue Gao, Mathias Quoy, Alexandre Pitti, Nancy F. Chen
https://arxiv.org/abs/2506.11862

Confidence-Based Self-Training for EMG-to-Speech: Leveraging Synthetic EMG for Robust Modeling
Voiced Electromyography (EMG)-to-Speech (V-ETS) models reconstruct speech from muscle activity signals, facilitating applications such as neurolaryngologic diagnostics. Despite its potential, the advancement of V-ETS is hindered by a scarcity of paired EMG-speech data. To address this, we propose a novel Confidence-based Multi-Speaker Self-training (CoM2S) approach, along with a newly curated Libri-EMG dataset. This approach leverages synthetic EMG data generated by a pre-trained model, followe…

Entanglement Classification in the Graph States: The generalization to $n$-Qubits States using the Entanglement Matrix
Sameer Sharma
https://arxiv.org/abs/2507.11458

Entanglement Classification in the Graph States: The generalization to $n$-Qubits States using the Entanglement Matrix
Graph states represent a significant class of multi-partite entangled quantum states with applications in quantum error correction, quantum communication, and quantum computation. In this work, we introduce a novel formalism called the Entanglement Matrix for quantifying and classifying entanglement in n-qubit graph states. Leveraging concepts from graph theory and quantum information, we develop a systematic approach to analyze entanglement by identifying primary and secondary midpoints in gra…

Technical Evaluation of a Disruptive Approach in Homomorphic AI
Eric Filiol
https://arxiv.org/abs/2506.11954 https://arxiv.org/pdf/25…

Technical Evaluation of a Disruptive Approach in Homomorphic AI
We present a technical evaluation of a new, disruptive cryptographic approach to data security, known as HbHAI (Hash-based Homomorphic Artificial Intelligence). HbHAI is based on a novel class of key-dependent hash functions that naturally preserve most similarity properties, most AI algorithms rely on. As a main claim, HbHAI makes now possible to analyze and process data in its cryptographically secure form while using existing native AI algorithms without modification, with unprecedented perf…

Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion
Shunpeng Yang, Zhen Fu, Zhefeng Cao, Guo Junde, Patrick Wensing, Wei Zhang, Hua Chen
https://arxiv.org/abs/2506.11470

Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion
Generalizing locomotion policies across diverse legged robots with varying morphologies is a key challenge due to differences in observation/action dimensions and system dynamics. In this work, we propose Multi-Loco, a novel unified framework combining a morphology-agnostic generative diffusion model with a lightweight residual policy optimized via reinforcement learning (RL). The diffusion model captures morphology-invariant locomotion patterns from diverse cross-embodiment datasets, improving…

Enhancing Wildlife Density Estimation: A New Two-Parameter Detection Function for Line Transect Sampling
Midhat M. Edous, Omar M. Eidous
https://arxiv.org/abs/2507.10572

Enhancing Wildlife Density Estimation: A New Two-Parameter Detection Function for Line Transect Sampling
Accurate estimation of wildlife density is vital for effective ecological monitoring, conservation, and management. Line transect sampling, a central technique in distance sampling, relies on selecting an appropriate detection function to model the probability of detecting individuals as a function of their distance from the transect line. In this study, we propose a novel two-parameter detection function that extends the flexibility of traditional models such as the half-normal and exponential…

VGR: Visual Grounded Reasoning
Jiacong Wang, Zijiang Kang, Haochen Wang, Haiyong Jiang, Jiawen Li, Bohong Wu, Ya Wang, Jiao Ran, Xiao Liang, Chao Feng, Jun Xiao
https://arxiv.org/abs/2506.11991

VGR: Visual Grounded Reasoning
In the field of multimodal chain-of-thought (CoT) reasoning, existing approaches predominantly rely on reasoning on pure language space, which inherently suffers from language bias and is largely confined to math or science domains. This narrow focus limits their ability to handle complex visual reasoning tasks that demand comprehensive understanding of image details. To address these limitations, this paper introduces VGR, a novel reasoning multimodal large language model (MLLM) with enhanced …

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Nan Mu, Ruiqi Song, Xiaoning Li, Zhihui Xu, Jingfeng Jiang, Chen Zhao
https://arxiv.org/abs/2506.11454

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Background: Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. Precise segmentation of coronary arteries from invasive coronary angiography (ICA) is critical for effective clinical decision-making. Objective: This study aims to propose a novel deep learning model based on frequency-domain analysis to enhance the accuracy of coronary artery segmentation and stenosis detection in ICA, thereby offering robust support for the stenosis detection and treatment of …

SuperADMM: Solving Quadratic Programs Faster with Dynamic Weighting ADMM
P. C. N. Verheijen, D. Goswami, M. Lazar
https://arxiv.org/abs/2506.11608 https://…

SuperADMM: Solving Quadratic Programs Faster with Dynamic Weighting ADMM
In this paper we develop an accelerated Alternating Direction Method of Multipliers (ADMM) algorithm for solving quadratic programs called superADMM. Unlike standard ADMM QP solvers, superADMM uses a novel dynamic weighting method that penalizes each constraint individually and performs weight updates at every ADMM iteration. We provide a numerical stability analysis, methods for parameter selection and infeasibility detection. The algorithm is implemented in c with efficient linear algebra pac…

Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
Nico Meyer, Christopher Mutschler, Andreas Maier, Daniel D. Scherer
https://arxiv.org/abs/2506.11552

Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
Quantum error correction is crucial for protecting quantum information against decoherence. Traditional codes like the surface code require substantial overhead, making them impractical for near-term, early fault-tolerant devices. We propose a novel objective function for tailoring error correction codes to specific noise structures by maximizing the distinguishability between quantum states after a noise channel, ensuring efficient recovery operations. We formalize this concept with the distin…

Sparse Regression Codes exploit Multi-User Diversity without CSI
V S V Sandeep, Sai Dinesh Kancharana, Arun Pachai Kannu
https://arxiv.org/abs/2507.11383 h…

Sparse Regression Codes exploit Multi-User Diversity without CSI
We study sparse regression codes (SPARC) for multiple access channels with multiple receive antennas, in non-coherent flat fading channels. We propose a novel practical decoder, referred to as maximum likelihood matching pursuit (MLMP), which greedily finds the support of the codewords of users with partial maximum likelihood metrics. As opposed to the conventional successive-cancellation based greedy algorithms, MLMP works as a successive-combining energy detector. We also propose MLMP modific…

Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs
HyeYoung Lee, Muhammad Nadeem, Pavel Tsoi
https://arxiv.org/abs/2507.10622 h…

Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs
The rapid expansion of Internet of Things (IoT) networks has led to a surge in security vulnerabilities, emphasizing the critical need for robust anomaly detection and classification techniques. In this work, we propose a novel approach for identifying anomalies in IoT network traffic by leveraging the Mel-frequency cepstral coefficients (MFCC) and ResNet-18, a deep learning model known for its effectiveness in feature extraction and image-based tasks. Learnable MFCCs enable adaptive spectral f…

Probing Cosmic Neutrino Background through Parametric Fluorescence
Guo-yuan Huang, Shun Zhou
https://arxiv.org/abs/2507.10868 https://

Probing Cosmic Neutrino Background through Parametric Fluorescence
We point out that relic neutrinos from the Big Bang may induce the parametric fluorescence in molecular systems, which offers a novel way to discover cosmic neutrino background. By coherently scattering with molecular energy levels, a massive neutrino can spontaneously ``decay" into a lighter neutrino and an infrared signal photon, i.e., $ν^{}_{i} + M \to ν^{}_{j} + γ^{}_{\rm S} + M$, where the molecular state $M$ remains unchanged after the scattering. Because the amplitudes of different ra…

TRAN-D: 2D Gaussian Splatting-based Sparse-view Transparent Object Depth Reconstruction via Physics Simulation for Scene Update
Jeongyun Kim, Seunghoon Jeong, Giseop Kim, Myung-Hwan Jeon, Eunji Jun, Ayoung Kim
https://arxiv.org/abs/2507.11069

TRAN-D: 2D Gaussian Splatting-based Sparse-view Transparent Object Depth Reconstruction via Physics Simulation for Scene Update
Understanding the 3D geometry of transparent objects from RGB images is challenging due to their inherent physical properties, such as reflection and refraction. To address these difficulties, especially in scenarios with sparse views and dynamic environments, we introduce TRAN-D, a novel 2D Gaussian Splatting-based depth reconstruction method for transparent objects. Our key insight lies in separating transparent objects from the background, enabling focused optimization of Gaussians correspon…

(SimPhon Speech Test): A Data-Driven Method for In Silico Design and Validation of a Phonetically Balanced Speech Test
Stefan Bleeck
https://arxiv.org/abs/2506.11620

(SimPhon Speech Test): A Data-Driven Method for In Silico Design and Validation of a Phonetically Balanced Speech Test
Traditional audiometry often provides an incomplete characterization of the functional impact of hearing loss on speech understanding, particularly for supra-threshold deficits common in presbycusis. This motivates the development of more diagnostically specific speech perception tests. We introduce the Simulated Phoneme Speech Test (SimPhon Speech Test) methodology, a novel, multi-stage computational pipeline for the in silico design and validation of a phonetically balanced minimal-pair speec…

A sequential classification learning for estimating quantile optimal treatment regimes
Junwen Xia, Jingxiao Zhang, Dehan Kong
https://arxiv.org/abs/2507.11255

A sequential classification learning for estimating quantile optimal treatment regimes
Quantile optimal treatment regimes (OTRs) aim to assign treatments that maximize a specified quantile of patients' outcomes. Compared to treatment regimes that target the mean outcomes, quantile OTRs offer fairer regimes when a lower quantile is selected, as it focuses on improving outcomes for individuals who would otherwise experience relatively poor results. In this paper, we propose a novel method for estimating quantile OTRs by reformulating the problem as a sequential classification task.…

HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging
Arefin Ittesafun Abian, Ripon Kumar Debnath, Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Md Rafiqul Islam, Asif Karim, Reem E. Mohamed, Sami Azam
https://arxiv.org/abs/2507.11325

HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging
Accurate liver and tumor segmentation on abdominal CT images is critical for reliable diagnosis and treatment planning, but remains challenging due to complex anatomical structures, variability in tumor appearance, and limited annotated data. To address these issues, we introduce Hyperbolic-convolutions Adaptive-temporal-attention with Neural-representation and Synaptic-plasticity Network (HANS-Net), a novel segmentation framework that synergistically combines hyperbolic convolutions for hierar…

U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
Hongbo Ye, Fenghe Tang, Peiang Zhao, Zhen Huang, Dexin Zhao, Minghao Bian, S. Kevin Zhou
https://arxiv.org/abs/2507.11415

U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
Achieving equity in healthcare accessibility requires lightweight yet high-performance solutions for medical image segmentation, particularly in resource-limited settings. Existing methods like U-Net and its variants often suffer from limited global Effective Receptive Fields (ERFs), hindering their ability to capture long-range dependencies. To address this, we propose U-RWKV, a novel framework leveraging the Recurrent Weighted Key-Value(RWKV) architecture, which achieves efficient long-range …

FacialMotionID: Identifying Users of Mixed Reality Headsets using Abstract Facial Motion Representations
Adriano Castro, Simon Hanisch, Matin Fallahi, Thorsten Strufe
https://arxiv.org/abs/2507.11138

FacialMotionID: Identifying Users of Mixed Reality Headsets using Abstract Facial Motion Representations
Facial motion capture in mixed reality headsets enables real-time avatar animation, allowing users to convey non-verbal cues during virtual interactions. However, as facial motion data constitutes a behavioral biometric, its use raises novel privacy concerns. With mixed reality systems becoming more immersive and widespread, understanding whether face motion data can lead to user identification or inference of sensitive attributes is increasingly important. To address this, we conducted a stu…

Stochastic Entanglement Configuration for Constructive Entanglement Topologies in Quantum Machine Learning with Application to Cardiac MRI
Mehri Mehrnia, Mohammed S. M. Elbaz
https://arxiv.org/abs/2507.11401

Stochastic Entanglement Configuration for Constructive Entanglement Topologies in Quantum Machine Learning with Application to Cardiac MRI
Efficient entanglement strategies are essential for advancing variational quantum circuits (VQCs) for quantum machine learning (QML). However, most current approaches use fixed entanglement topologies that are not adaptive to task requirements, limiting potential gains over classical models. We introduce a novel stochastic entanglement configuration method that systematically generates diverse entanglement topologies to identify a subspace of constructive entanglement configurations, defined as…

ILCL: Inverse Logic-Constraint Learning from Temporally Constrained Demonstrations
Minwoo Cho, Jaehwi Jang, Daehyung Park
https://arxiv.org/abs/2507.11000 …

ILCL: Inverse Logic-Constraint Learning from Temporally Constrained Demonstrations
We aim to solve the problem of temporal-constraint learning from demonstrations to reproduce demonstration-like logic-constrained behaviors. Learning logic constraints is challenging due to the combinatorially large space of possible specifications and the ill-posed nature of non-Markovian constraints. To figure it out, we introduce a novel temporal-constraint learning method, which we call inverse logic-constraint learning (ILCL). Our method frames ICL as a two-player zero-sum game between 1) …

A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction
Haoxuan Qu, Yujun Cai, Hossein Rahmani, Ajay Kumar, Junsong Yuan, Jun Liu
https://arxiv.org/abs/2507.11321

A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction
Recently, Gaussian Splatting (GS) has received a lot of attention in surface reconstruction. However, while 3D objects can be of complex and diverse shapes in the real world, existing GS-based methods only limitedly use a single type of splatting primitive (Gaussian ellipse or Gaussian ellipsoid) to represent object surfaces during their reconstruction. In this paper, we highlight that this can be insufficient for object surfaces to be represented in high quality. Thus, we propose a novel frame…

Deep Learning-based mmWave MIMO Channel Estimation using sub-6 GHz Channel Information: CNN and UNet Approaches
Faruk Pasic, Lukas Eller, Stefan Schwarz, Markus Rupp, Christoph F. Mecklenbr\"auker
https://arxiv.org/abs/2506.11714

Deep Learning-based mmWave MIMO Channel Estimation using sub-6 GHz Channel Information: CNN and UNet Approaches
Future wireless multiple-input multiple-output (MIMO) systems will integrate both sub-6 GHz and millimeter wave (mmWave) frequency bands to meet the growing demands for high data rates. MIMO link establishment typically requires accurate channel estimation, which is particularly challenging at mmWave frequencies due to the low signal-to-noise ratio (SNR). In this paper, we propose two novel deep learning-based methods for estimating mmWave MIMO channels by leveraging out-of-band information fro…

Propensity score weighting across counterfactual worlds: longitudinal effects under positivity violations
Alec McClean, Iv\'an D\'iaz
https://arxiv.org/abs/2507.10774

Propensity score weighting across counterfactual worlds: longitudinal effects under positivity violations
When examining a contrast between two interventions, longitudinal causal inference studies frequently encounter positivity violations when one or both regimes are impossible to observe for some subjects. Existing weighting methods either assume positivity holds or produce effects that conflate interventions' impacts on ultimate outcomes with their effects on intermediate treatments and covariates. We propose a novel class of estimands -- cumulative cross-world weighted effects -- that weights p…

When and Where do Data Poisons Attack Textual Inversion?
Jeremy Styborski, Mingzhi Lyu, Jiayou Lu, Nupur Kapur, Adams Kong
https://arxiv.org/abs/2507.10578

When and Where do Data Poisons Attack Textual Inversion?
Poisoning attacks pose significant challenges to the robustness of diffusion models (DMs). In this paper, we systematically analyze when and where poisoning attacks textual inversion (TI), a widely used personalization technique for DMs. We first introduce Semantic Sensitivity Maps, a novel method for visualizing the influence of poisoning on text embeddings. Second, we identify and experimentally verify that DMs exhibit non-uniform learning behavior across timesteps, focusing on lower-noise sa…

Framework of a multiscale data-driven digital twin of the muscle-skeletal system
Martina Paccini, Simone Cammarasana, Giuseppe Patan\`e
https://arxiv.org/abs/2506.11821

Framework of a multiscale data-driven digital twin of the muscle-skeletal system
Musculoskeletal disorders (MSDs) are a leading cause of disability worldwide, requiring advanced diagnostic and therapeutic tools for personalised assessment and treatment. Effective management of MSDs involves the interaction of heterogeneous data sources, making the Digital Twin (DT) paradigm a valuable option. This paper introduces the Musculoskeletal Digital Twin (MS-DT), a novel framework that integrates multiscale biomechanical data with computational modelling to create a detailed, patie…

Robust Optimal Task Planning to Maximize Battery Life
Jiachen Li, Chu Jian, Feiyang Zhao, Shihao Li, Wei Li, Dongmei Chen
https://arxiv.org/abs/2506.11264 …

Robust Optimal Task Planning to Maximize Battery Life
This paper proposes a control-oriented optimization platform for autonomous mobile robots (AMRs), focusing on extending battery life while ensuring task completion. The requirement of fast AMR task planning while maintaining minimum battery state of charge, thus maximizing the battery life, renders a bilinear optimization problem. McCormick envelop technique is proposed to linearize the bilinear term. A novel planning algorithm with relaxed constraints is also developed to handle parameter unce…

SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies
Nadun Ranawaka Arachchige, Zhenyang Chen, Wonsuhk Jung, Woo Chul Shin, Rohan Bansal, Pierre Barroso, Yu Hang He, Yingyang Celine Lin, Benjamin Joffe, Shreyas Kousik, Danfei Xu
https://arxiv.org/abs/2506.11948

SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies
Offline Imitation Learning (IL) methods such as Behavior Cloning are effective at acquiring complex robotic manipulation skills. However, existing IL-trained policies are confined to executing the task at the same speed as shown in demonstration data. This limits the task throughput of a robotic system, a critical requirement for applications such as industrial automation. In this paper, we introduce and formalize the novel problem of enabling faster-than-demonstration execution of visuomotor p…

KEENHash: Hashing Programs into Function-Aware Embeddings for Large-Scale Binary Code Similarity Analysis
Zhijie Liu, Qiyi Tang, Sen Nie, Shi Wu, Liang Feng Zhang, Yutian Tang
https://arxiv.org/abs/2506.11612

KEENHash: Hashing Programs into Function-Aware Embeddings for Large-Scale Binary Code Similarity Analysis
Binary code similarity analysis (BCSA) is a crucial research area in many fields such as cybersecurity. Specifically, function-level diffing tools are the most widely used in BCSA: they perform function matching one by one for evaluating the similarity between binary programs. However, such methods need a high time complexity, making them unscalable in large-scale scenarios (e.g., 1/n-to-n search). Towards effective and efficient program-level BCSA, we propose KEENHash, a novel hashing approach…

EquiContact: A Hierarchical SE(3) Vision-to-Force Equivariant Policy for Spatially Generalizable Contact-rich Tasks
Joohwan Seo, Arvind Kruthiventy, Soomi Lee, Megan Teng, Xiang Zhang, Seoyeon Choi, Jongeun Choi, Roberto Horowitz
https://arxiv.org/abs/2507.10961

EquiContact: A Hierarchical SE(3) Vision-to-Force Equivariant Policy for Spatially Generalizable Contact-rich Tasks
This paper presents a framework for learning vision-based robotic policies for contact-rich manipulation tasks that generalize spatially across task configurations. We focus on achieving robust spatial generalization of the policy for the peg-in-hole (PiH) task trained from a small number of demonstrations. We propose EquiContact, a hierarchical policy composed of a high-level vision planner (Diffusion Equivariant Descriptor Field, Diff-EDF) and a novel low-level compliant visuomotor policy (Ge…

A Novel Feedforward Youla Parameterization Method for Avoiding Local Minima in Stereo Image Based Visual Servoing Control
Rongfei Li, Francis Assadian
https://arxiv.org/abs/2506.10252

A Novel Feedforward Youla Parameterization Method for Avoiding Local Minima in Stereo Image Based Visual Servoing Control
In robot navigation and manipulation, accurately determining the camera's pose relative to the environment is crucial for effective task execution. In this paper, we systematically prove that this problem corresponds to the Perspective-3-Point (P3P) formulation, where exactly three known 3D points and their corresponding 2D image projections are used to estimate the pose of a stereo camera. In image-based visual servoing (IBVS) control, the system becomes overdetermined, as the 6 degrees of fre…