Tootfinder

Cardiovascular function changes following lung resection: a computational model to compare afterload increase and contractility loss mechanisms
Shiting Huang, Sanjay Pant, Sean McGinty, Richard Good, Ben Shelley, Ankush Aggarwal
https://arxiv.org/abs/2505.01264

Cardiovascular function changes following lung resection: a computational model to compare afterload increase and contractility loss mechanisms
Functional limitation after lung resection surgery has been consistently documented in clinical studies, and right ventricle (RV) dysfunction has been hypothesized as a contributing reason. However, the mechanisms of RV dysfunction after lung resection remain unclear, particularly whether change in afterload or contractility is the main cause. This study is the first to employ a lumped parameter model to simulate the effects of lung resection. The implementation of a computational model allowed…

SATversary: Adversarial Attacks on Satellite Fingerprinting
Joshua Smailes, Sebastian K\"ohler, Simon Birnbach, Martin Strohmeier, Ivan Martinovic
https://arxiv.org/abs/2506.06119

SATversary: Adversarial Attacks on Satellite Fingerprinting
As satellite systems become increasingly vulnerable to physical layer attacks via SDRs, novel countermeasures are being developed to protect critical systems, particularly those lacking cryptographic protection, or those which cannot be upgraded to support modern cryptography. Among these is transmitter fingerprinting, which provides mechanisms by which communication can be authenticated by looking at characteristics of the transmitter, expressed as impairments on the signal. Previous works s…

Oversight Structures for Agentic AI in Public-Sector Organizations
Chris Schmitz, Jonathan Rystr{\o}m, Jan Batzner
https://arxiv.org/abs/2506.04836 https:/…

Oversight Structures for Agentic AI in Public-Sector Organizations
This paper finds that the introduction of agentic AI systems intensifies existing challenges to traditional public sector oversight mechanisms -- which rely on siloed compliance units and episodic approvals rather than continuous, integrated supervision. We identify five governance dimensions essential for responsible agent deployment: cross-departmental implementation, comprehensive evaluation, enhanced security protocols, operational visibility, and systematic auditing. We evaluate the capaci…

Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
Armin Keki\'c, Sergio Hernan Garrido Mejia, Bernhard Sch\"olkopf
https://arxiv.org/abs/2506.04945

Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
Estimating causal effects of joint interventions on multiple variables is crucial in many domains, but obtaining data from such simultaneous interventions can be challenging. Our study explores how to learn joint interventional effects using only observational data and single-variable interventions. We present an identifiability result for this problem, showing that for a class of nonlinear additive outcome mechanisms, joint effects can be inferred without access to joint interventional data. W…

"The RDBMS field is so young, we can actually see it grow through the pages of computer magazines of the 1980s. BYTE Magazine had its first issue dedicated to databases in November 1981, and then another one in October 1984. Dr. Dobb’s Journal did not feature an article about databases until 1984 and did not have many more throughout the decade; actually most of them were authored by Gene Head, and talk about dBASE."

The Elephant In The Room
We have often highlighted in the pages of this magazine the seemingly never-ending sequence of fashion waves hitting the software industry. We have discussed in 2020 the one impacting type systems, and more recently, another one concerning object-oriented programming. What is important in both cases is not the details of the technologies per se, but the social mechanisms at play in both situations. In this edition, we will encounter yet another example of such a clash, this time concerning rela…

Decoupling Local Electrostatic Potential and Temperature-Driven Atomistic Forming Mechanisms in TaOx/HfO2-Based ReRAMs using Reactive Molecular Dynamics Simulations
Simanta Lahkar, Valeria Bragaglia, Behnaz Bagheri, Donato Francesco Falcone, Matteo Galetta, Marilyne Sousa, Aida Todri-Sanial
https://arxiv.org/abs/2505.24468

Decoupling Local Electrostatic Potential and Temperature-Driven Atomistic Forming Mechanisms in TaOx/HfO2-Based ReRAMs using Reactive Molecular Dynamics Simulations
Resistive random access memories (ReRAMs) with a bilayer TaOx/HfO2 stack structure have been shown to possess uniquely promising resistive switching characteristics. However, the key atomistic forming mechanisms and the physical processes that govern the behavior of this kind of device remain to be clarified. Here, we present a detailed analysis of the physical mechanisms underlying its forming at the atomistic level through molecular dynamics (MD) simulations using an extended charge equilibra…

Nematic ordering in active fluids driven by substrate deformations: Mechanisms and patterning regimes
Varun Venkatesh, Amin Doostmohammadi
https://arxiv.org/abs/2505.24639

Nematic ordering in active fluids driven by substrate deformations: Mechanisms and patterning regimes
The interplay between active matter and its environment is central to understanding emergent behavior in biological and synthetic systems. Here, we show that coupling active nematic flows to small-amplitude deformations of a compliant substrate can fundamentally reorganize the system's dynamics. Using a model that combines active nematohydrodynamics with substrate mechanics, we find that contractile active nematics-normally disordered in flat geometries-undergo a sharp transition to long-range …

Diffusion of Responsibility in Collective Decision Making
Pavel Naumov, Jia Tao
https://arxiv.org/abs/2506.07935 https://arxiv.org/pd…

Diffusion of Responsibility in Collective Decision Making
The term "diffusion of responsibility'' refers to situations in which multiple agents share responsibility for an outcome, obscuring individual accountability. This paper examines this frequently undesirable phenomenon in the context of collective decision-making mechanisms. The work shows that if a decision is made by two agents, then the only way to avoid diffusion of responsibility is for one agent to act as a "dictator'', making the decision unilaterally. In scenarios with more than two a…

This https://arxiv.org/abs/2404.09284 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Deriving a GENERIC system from a Hamiltonian system
We reconsider the fundamental problem of coarse-graining infinite-dimensional Hamiltonian dynamics to obtain a macroscopic system which includes dissipative mechanisms. In particular, we study the thermodynamical implications concerning Hamiltonians, energy, and entropy and the induced geometric structures such as Poisson and Onsager brackets (symplectic and dissipative brackets). We start from a general finite-dimensional Hamiltonian system that is coupled linearly to an infinite-dimensional…

This https://arxiv.org/abs/2501.14755 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models
The burgeoning field of foundation models necessitates advanced data processing mechanisms capable of harnessing vast and valuable data with various types used by these models. Nevertheless, the current landscape presents unique challenges that traditional data processing frameworks struggle to handle effectively, particularly in handling the complexity of multimodal data. In response, we present Data-Juicer 2.0, a data processing system backed by 100+ data processing operators spanning text, i…

VChatter: Exploring Generative Conversational Agents for Simulating Exposure Therapy to Reduce Social Anxiety
Han Zhang, KaWing Tsang, Zhenhui Peng
https://arxiv.org/abs/2506.03520

VChatter: Exploring Generative Conversational Agents for Simulating Exposure Therapy to Reduce Social Anxiety
Many people struggle with social anxiety, feeling fear, or even physically uncomfortable in social situations like talking to strangers. Exposure therapy, a clinical method that gradually and repeatedly exposes individuals to the source of their fear and helps them build coping mechanisms, can reduce social anxiety but traditionally requires human therapists' guidance and constructions of situations. In this paper, we developed a multi-agent system VChatter to explore large language models(LLMs…

Reminder: YouTube is a capitalist garbage platform designed to commodify dissent. It allows anti-capitalist voices only to monitor, control, and neutralize them.
This process turns genuine resistance into another product for consumption. The system profits from rebellion while stripping it of its power.
YouTube does not free us. Instead, it keeps us bound within its mechanisms of control, maintaining the very structures we seek to dismantle.

This isn't about balancing security vs. usability. This is both poor security and poor usability. https://scalewithlee.substack.com/p/when-etchsts-breaks-your-substack

When /etc/h*sts Breaks Your Substack Editor: An Adventure in Web Content Filtering
An exploration of web security mechanisms and their unexpected consequences

Cloud-Based Interoperability in Residential Energy Systems
Darren Leniston, David Ryan, Ammar Malik, Jack Jackman, Terence O'Donnell
https://arxiv.org/abs/2506.05076

Cloud-Based Interoperability in Residential Energy Systems
As distributed energy resources (DERs) such as solar PV, batteries and electric vehicles become increasingly prevalent at the edge, maintaining grid stability requires advanced monitoring and control mechanisms. This paper presents a scalable smart grid gateway architecture that enables interoperability between Modbus-based inverters and IEEE 2030.5 cloud-based control systems. The proposed solution leverages Azure cloud services and edge-computing gateway devices to support dynamic configurati…

This https://arxiv.org/abs/2502.17433 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Dynamical phases of short-term memory mechanisms in RNNs
Short-term memory is essential for cognitive processing, yet our understanding of its neural mechanisms remains unclear. Neuroscience has long focused on how sequential activity patterns, where neurons fire one after another within large networks, can explain how information is maintained. While recurrent connections were shown to drive sequential dynamics, a mechanistic understanding of this process still remains unknown. In this work, we introduce two unique mechanisms that can support this f…

This https://arxiv.org/abs/2409.04588 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

PVAC: Package Version Activity Categorizer, Leveraging Semantic Versioning in a Heterogeneous System
Context: Modern open-source software ecosystems, such as those managed by GNU/Linux distributions, are composed of numerous packages developed independently by diverse communities. These ecosystems employ package management tools to facilitate software installation and dependency resolution. However, these tools lack robust mechanisms for systematically evaluating the development activity and versioning dynamics within their heterogeneous software environments. Objective: This research aims to …

Hydrodynamic fluctuations of stochastic charged cellular automata
Takato Yoshimura, \v{Z}iga Krajnik
https://arxiv.org/abs/2506.05247 https://

Hydrodynamic fluctuations of stochastic charged cellular automata
We study charge fluctuations of a family of stochastic charged cellular automata away from the deterministic single-file limit and obtain the exact typical charge probability distributions, known to be anomalous, using hydrodynamics. The cellular automata considered are examples of linearly degenerate systems where two distinct mechanisms of diffusion, namely normal and convective diffusion, coexist. Our formalism, based on macroscopic fluctuation theory, allows us to describe current fluctuati…

An OTFS-based Random Access Scheme for GNSS Independent Operation in NTN
Marius Caus, Musbah Shaat
https://arxiv.org/abs/2506.03852 https://

An OTFS-based Random Access Scheme for GNSS Independent Operation in NTN
This paper investigates the random access procedure for non-terrestrial networks operating without global navigation satellite system (GNSS) support. In such scenarios, positioning uncertainties can reach several kilometers, which directly impacts the open-loop compensation mechanisms employed by the user equipment. To ensure that the resulting time and carrier frequency offsets can be handled by the network, the robustness of the standardized random access signal design and detection scheme mu…

This https://arxiv.org/abs/2504.11632 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Magnetoresistivity in the Antiferromagnetic Hubbard Model
We investigate the magnetotransport properties of the half-filled antiferromagnetic (AF) one-band Hubbard model under an external magnetic field using the single-site dynamical mean-field approximation (DMFT). Particular attention is paid to the mechanisms driving the magnetoresistivity behavior. We analyze the dependence of magnetoresistivity on temperature and the strength of the applied magnetic field, providing insights into the interplay between magnetic fluctuations and transport properti…

This https://arxiv.org/abs/2305.14653 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Conceptualizing educational opportunity hoarding: the emergence of hoarding without hoarders
Social scientists increasingly use the concept of opportunity hoarding to explain the formation of Black-White educational inequalities. However, this concept is often loosely defined, leading to varied interpretations of the inequality-producing mechanisms it captures. To bring clarity to this valuable sociological concept, this theoretical paper, informed by the concept's original definition and existing empirical research, proposes a more precise definition of opportunity hoarding and formal…

Unifying Framework for Amplification Mechanisms: Criticality, Resonance and Non-Normality
Virgile Troude, Didier Sornette
https://arxiv.org/abs/2506.01996 …

Unifying Framework for Amplification Mechanisms: Criticality, Resonance and Non-Normality
We bring together three key amplification mechanisms in linear dynamical systems: spectral criticality, resonance, and non-normality. We disentangle and quantitatively couple these effects through two fundamental parameters: (i) the spectral distance to a conventional bifurcation or to a resonance and (ii) a non-normal index K (or condition number $κ$) that measures the obliqueness of the eigenvectors. Closed-form expressions for the system's response to both Gaussian noise and periodic forcin…

This https://arxiv.org/abs/2309.05901 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss Parameters
In this paper, we study the concurrent composition of interactive mechanisms with adaptively chosen privacy-loss parameters. In this setting, the adversary can interleave queries to existing interactive mechanisms, as well as create new ones. We prove that every valid privacy filter and odometer for noninteractive mechanisms extends to the concurrent composition of interactive mechanisms if privacy loss is measured using $(ε, δ)$-DP, $f$-DP, or Rényi DP of fixed order. Our results offer stro…

Longer Lists Yield Better Matchings
Yuri Faenza, Aapeli Vuorinen
https://arxiv.org/abs/2506.06217 https://arxiv.org/pdf/2506.06217

Longer Lists Yield Better Matchings
Many centralized mechanisms for two-sided matching markets that enjoy strong theoretical properties assume that the planner solicits full information on the preferences of each participating agent. In particular, they expect that participants compile and communicate their complete preference lists over agents from the other side of the market. However, real-world markets are often very large and agents cannot always be expected to even produce a ranking of all options on the other side. It is t…

G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems
Guibin Zhang, Muxin Fu, Guancheng Wan, Miao Yu, Kun Wang, Shuicheng Yan
https://arxiv.org/abs/2506.07398

G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems
Large language model (LLM)-powered multi-agent systems (MAS) have demonstrated cognitive and execution capabilities that far exceed those of single LLM agents, yet their capacity for self-evolution remains hampered by underdeveloped memory architectures. Upon close inspection, we are alarmed to discover that prevailing MAS memory mechanisms (1) are overly simplistic, completely disregarding the nuanced inter-agent collaboration trajectories, and (2) lack cross-trial and agent-specific customiza…

Competing Mechanisms at Vibrated Interfaces of Density-Stratified Fluids
Tianyi Chu, Benjamin Wilfong, Timothy Koehler, Ryan M. McMullen, Spencer H. Bryngelson
https://arxiv.org/abs/2505.23578

Competing Mechanisms at Vibrated Interfaces of Density-Stratified Fluids
Fluid--fluid interfacial instability and subsequent fluid mixing are ubiquitous in nature and engineering. Two hydrodynamic instabilities have long been thought to govern the interface behavior: the pressure gradient-driven long-wavelength Rayleigh--Taylor (RT) instability and resonance-induced short-wavelength Faraday instability. However, neither instability alone can explain the dynamics when both mechanisms act concurrently. Instead, we identify a previously unseen multi-modal instability e…

Properties of slow magneto-acoustic waves observed simultaneously using Hi-C 2.1 and AIA
Suraj K. Tripathy, S. Krishna Prasad, D. Banerjee
https://arxiv.org/abs/2506.06126

Properties of slow magneto-acoustic waves observed simultaneously using Hi-C 2.1 and AIA
Propagating slow magneto-acoustic waves are commonly observed in different coronal structures but are most prominent in active region fan loops. Their rapid damping with damping lengths of the order of a wavelength has been investigated in the past by several authors. Although different physical mechanisms have been proposed, significant discrepancies between the theory and observations remain. Recent high-resolution observations captured simultaneously by two different instruments reveal disti…

The optimization of crop response to climatic stress through modulation of plant stress response mechanisms. Opportunities for biostimulants and plant hormones to meet climate challenges
Jing Li, Giulia Forghieri, Danny Geelen, Patrick du Jardin, Patrick H. Brown
https://arxiv.org/abs/2506.01714

The optimization of crop response to climatic stress through modulation of plant stress response mechanisms. Opportunities for biostimulants and plant hormones to meet climate challenges
Climate change is a major threat to crop potential and is characterized by both long-term shifts in temperature and precipitation patterns as well as increased occurrence of extreme weather events, these extreme weather events are the most immediate and intractable threat to agriculture. Crop resilience in the face of stress depends upon the speed and effectiveness with which plants and cropping systems sense and respond to that stress. A variety of agronomic practices including breeding, exoge…

This https://arxiv.org/abs/2506.00691 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Optimizing Sensory Neurons: Nonlinear Attention Mechanisms for Accelerated Convergence in Permutation-Invariant Neural Networks for Reinforcement Learning
Training reinforcement learning (RL) agents often requires significant computational resources and extended training times. To address this, we build upon the foundation laid by Google Brain's Sensory Neuron, which introduced a novel neural architecture for reinforcement learning tasks that maintained permutation in-variance in the sensory neuron system. While the baseline model demonstrated significant performance improvements over traditional approaches, we identified opportunities to enhance…

Revisiting the Mechanisms of Thermal Transport in Vacancy-Defective Silicon
Xueyan Zhu, Jin Yang, J. Shiomi, Cheng Shao
https://arxiv.org/abs/2506.02436 ht…

Revisiting the Mechanisms of Thermal Transport in Vacancy-Defective Silicon
Understanding heat conduction in defective silicon is crucial for electronics and thermoelectrics. Conventional understanding relies on phonon gas picture, treating defects as scattering centers that reduce phonon lifetimes without altering frequencies and group velocities. We go beyond phonon gas picture by employing Wigner transport equation to investigate heat conduction in vacancy-defected silicon. Our findings reveal that while thermal conduction in pristine silicon stems mainly from parti…

Shapley-Coop: Credit Assignment for Emergent Cooperation in Self-Interested LLM Agents
Yun Hua, Haosheng Chen, Shiqin Wang, Wenhao Li, Xiangfeng Wang, Jun Luo
https://arxiv.org/abs/2506.07388

Shapley-Coop: Credit Assignment for Emergent Cooperation in Self-Interested LLM Agents
Large Language Models (LLMs) show strong collaborative performance in multi-agent systems with predefined roles and workflows. However, in open-ended environments lacking coordination rules, agents tend to act in self-interested ways. The central challenge in achieving coordination lies in credit assignment -- fairly evaluating each agent's contribution and designing pricing mechanisms that align their heterogeneous goals. This problem is critical as LLMs increasingly participate in complex hum…

Olfactory Inertial Odometry: Methodology for Effective Robot Navigation by Scent
Kordel K. France, Ovidiu Daescu
https://arxiv.org/abs/2506.02373 https://

Olfactory Inertial Odometry: Methodology for Effective Robot Navigation by Scent
Olfactory navigation is one of the most primitive mechanisms of exploration used by organisms. Navigation by machine olfaction (artificial smell) is a very difficult task to both simulate and solve. With this work, we define olfactory inertial odometry (OIO), a framework for using inertial kinematics, and fast-sampling olfaction sensors to enable navigation by scent analogous to visual inertial odometry (VIO). We establish how principles from SLAM and VIO can be extrapolated to olfaction to ena…

Learning Recommender Mechanisms for Bayesian Stochastic Games
Bengisu Guresti, Chongjie Zhang, Yevgeniy Vorobeychik
https://arxiv.org/abs/2505.22979 https:…

Learning Recommender Mechanisms for Bayesian Stochastic Games
An important challenge in non-cooperative game theory is coordinating on a single (approximate) equilibrium from many possibilities - a challenge that becomes even more complex when players hold private information. Recommender mechanisms tackle this problem by recommending strategies to players based on their reported type profiles. A key consideration in such mechanisms is to ensure that players are incentivized to participate, report their private information truthfully, and follow the recom…

Goodness-of-fit testing for the stationary density of a size-structured PDE
Van Ha Hoang, Phu Thanh Nguyen, Thanh Mai Pham Ngoc, Vincent Rivoirard, Viet Chi Tran
https://arxiv.org/abs/2506.05103

Goodness-of-fit testing for the stationary density of a size-structured PDE
We consider two division models for structured cell populations, where cells can grow, age and divide. These models have been introduced in the literature under the denomination of `mitosis' and `adder' models. In the recent years, there has been an increasing interest in Biology to understand whether the cells divide equally or not, as this can be related to important mechanisms in cellular aging or recovery. We are therefore interested in testing the null hypothesis $H_0$ where the division o…

This https://arxiv.org/abs/2504.18073 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Modular integration of neural connectomics, dynamics and biomechanics for identification of behavioral sensorimotor pathways in Caenorhabditis elegans
Computational approaches which emulate in-vivo nervous system are needed to investigate mechanisms of the brain to orchestrate behavior. Such approaches must integrate a series of biophysical models encompassing the nervous system, muscles, biomechanics to allow observing the system in its entirety while supporting model variations. Here we develop modWorm: a modular modeling framework for the nematode C. elegans. modWorm allows for construction of a model as an integrated series of configurabl…

This https://arxiv.org/abs/2505.24159 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

A Causation-Based Framework for Pricing and Cost Allocation of Energy, Reserves, and Transmission in Modern Power Systems
The increasing vulnerability of power systems has heightened the need for operating reserves to manage contingencies such as generator outages, line failures, and sudden load variations. Unlike energy costs, driven by consumer demand, operating reserve costs arise from addressing the most critical credible contingencies - prompting the question: how should these costs be allocated through efficient pricing mechanisms? As an alternative to previously reported schemes, this paper presents a new c…

Toward Understanding Bugs in Vector Database Management Systems
Yinglin Xie, Xinyi Hou, Yanjie Zhao, Shenao Wang, Kai Chen, Haoyu Wang
https://arxiv.org/abs/2506.02617

Toward Understanding Bugs in Vector Database Management Systems
Vector database management systems (VDBMSs) play a crucial role in facilitating semantic similarity searches over high-dimensional embeddings from diverse data sources. While VDBMSs are widely used in applications such as recommendation, retrieval-augmented generation (RAG), and multimodal search, their reliability remains underexplored. Traditional database reliability models cannot be directly applied to VDBMSs because of fundamental differences in data representation, query mechanisms, and s…

Parametric Coincidence in the Baryon to Dark Matter Ratio from Affleck-Dine Baryogenesis and UV Freeze-in Dark Matter
Jae Hyeok Chang, Chang Sub Shin, James Unwin
https://arxiv.org/abs/2506.03269

Parametric Coincidence in the Baryon to Dark Matter Ratio from Affleck-Dine Baryogenesis and UV Freeze-in Dark Matter
We highlight that the observed concurrence between the baryon and dark matter relic densities can be explained via a parametric coincidence between two distinct production mechanisms: Affleck-Dine baryogenesis and dark matter UV freeze-in. In the Affleck-Dine mechanism, the baryon asymmetry is naturally proportional to the inflationary reheating temperature $T_{\rm rh}$, which also plays a critical role in setting the relic abundance of UV freeze-in dark matter. Since Affleck-Dine baryogenesis …

Understanding Stability Mechanisms in Single-Atom Alloys with Theory-infused Deep Learning
Yang Huang, Shih-Han Wang, Shuyi Cao, Luke E. K. Achenie, Hongliang Xin
https://arxiv.org/abs/2506.03031

Understanding Stability Mechanisms in Single-Atom Alloys with Theory-infused Deep Learning
We present an interpretable deep learning model that enhances the prediction of cohesive energy in transition metal alloys (TMAs) by incorporating cohesion theory into a graph neural network (GNN) framework. The model not only predicts the total cohesive energy-an indicator of crystal stability-but also disentangles its various contributing factors and underlying physical parameters. The physics insights extracted from the model clarify the stability trends of transition metal surfaces across t…

This https://arxiv.org/abs/2505.06459 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
Physics-Informed Neural Networks (PINNs) have been widely used to obtain solutions to various physical phenomena modeled as Differential Equations. As PINNs are not naturally equipped with mechanisms for Uncertainty Quantification, some work has been done to quantify the different uncertainties that arise when dealing with PINNs. In this paper, we use a two-step procedure to train Bayesian Neural Networks that provide uncertainties over the solutions to differential equation systems provided by…

This https://arxiv.org/abs/2506.04119 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Exploring over 700 massive quiescent galaxies at z = 2-7: Demographics and stellar mass functions
High-redshift ($z>2$) massive quiescent galaxies are crucial tests of early galaxy formation and evolutionary mechanisms through their cosmic number densities and stellar mass functions (SMFs). We explore a sample of 745 massive ($\rm M_*> 10^{9.5}M_\odot$) quiescent galaxies from $z=2-7$ in over 800 arcmin$^2$ of NIRCam imaging from a compilation of public JWST fields (with a total area $>$ 5 $\times$ previous JWST studies). We compute and report their cosmic number densities, stellar mass fun…

This https://arxiv.org/abs/2409.01198 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Direct Kinematics, Inverse Kinematics, and Motion Planning of 1-DoF Rational Linkages
This study presents a set of algorithms that deal with trajectory planning of rational single-loop mechanisms with one degree of freedom (DoF). Benefiting from a dual quaternion representation of a rational motion, a formula for direct (forward) kinematics, a numerical inverse kinematics algorithm, and the generation of a driving-joint trajectory are provided. A novel approach using the Gauss-Newton search for the one-parameter inverse kinematics problem is presented. Additionally, a method for…

This https://arxiv.org/abs/2502.09755 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Jailbreak Attack Initializations as Extractors of Compliance Directions
Safety-aligned LLMs respond to prompts with either compliance or refusal, each corresponding to distinct directions in the model's activation space. Recent works show that initializing attacks via self-transfer from other prompts significantly enhances their performance. However, the underlying mechanisms of these initializations remain unclear, and attacks utilize arbitrary or hand-picked initializations. This work presents that each gradient-based jailbreak attack and subsequent initializatio…

Stochastically Dominant Peer Prediction
Yichi Zhang, Shengwei Xu, David Pennock, Grant Schoenebeck
https://arxiv.org/abs/2506.02259 https://

Stochastically Dominant Peer Prediction
Eliciting reliable human feedback is essential for many machine learning tasks, such as learning from noisy labels and aligning AI systems with human preferences. Peer prediction mechanisms incentivize truthful reporting without ground truth verification by scoring agents based on correlations with peers. Traditional mechanisms, which ensure that truth-telling maximizes the expected scores in equilibrium, can elicit honest information while assuming agents' utilities are linear functions of the…

This https://arxiv.org/abs/2502.06231 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
A major challenge in estimating treatment effects in observational studies is the reliance on untestable conditions such as the assumption of no unmeasured confounding. In this work, we propose an algorithm that can falsify the assumption of no unmeasured confounding in a setting with observational data from multiple heterogeneous sources, which we refer to as environments. Our proposed falsification strategy leverages a key observation that unmeasured confounding can cause observed causal mech…

This https://arxiv.org/abs/2411.07161 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

RoundTable: Investigating Group Decision-Making Mechanism in Multi-Agent Collaboration
Effective group decision-making is critical in Multi-Agent Systems (MAS). Yet, how different mechanisms for reaching consensus impact collaboration quality and efficiency remains understudied. We conduct a systematic study on group decision-making mechanisms in a decentralized setting. Through controlled experiments, we analyze how different voting rules affect decision quality and efficiency in a multi-round collaboration. Results reveal that majority voting often cause inefficient collaborati…

Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
Shenzhi Wang, Le Yu, Chang Gao, Chujie Zheng, Shixuan Liu, Rui Lu, Kai Dang, Xionghui Chen, Jianxin Yang, Zhenru Zhang, Yuqiong Liu, An Yang, Andrew Zhao, Yang Yue, Shiji Song, Bowen Yu, Gao Huang, Junyang Lin
https://arx…

Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful approach to enhancing the reasoning capabilities of Large Language Models (LLMs), while its mechanisms are not yet well understood. In this work, we undertake a pioneering exploration of RLVR through the novel perspective of token entropy patterns, comprehensively analyzing how different tokens influence reasoning performance. By examining token entropy patterns in Chain-of-Thought (CoT) reasoning, we observe that o…

This https://arxiv.org/abs/2505.19014 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Tokenizing Electron Cloud in Protein-Ligand Interaction Learning
The affinity and specificity of protein-molecule binding directly impact functional outcomes, uncovering the mechanisms underlying biological regulation and signal transduction. Most deep-learning-based prediction approaches focus on structures of atoms or fragments. However, quantum chemical properties, such as electronic structures, are the key to unveiling interaction patterns but remain largely underexplored. To bridge this gap, we propose ECBind, a method for tokenizing electron cloud sign…

A Reliable Vertical Federated Learning Framework for Traffic State Estimation with Data Selection and Incentive Mechanisms
Zijun Zhan, Yaxian Dong, Daniel Mawunyo Doe, Yuqing Hu, Shuai Li, Shaohua Cao, Zhu Han
https://arxiv.org/abs/2506.01285

A Reliable Vertical Federated Learning Framework for Traffic State Estimation with Data Selection and Incentive Mechanisms
Vertical Federated Learning (VFL)-based Traffic State Estimation (TSE) offers a promising approach for integrating vertically distributed traffic data from municipal authorities (MA) and mobility providers (MP) while safeguarding privacy. However, given the variations in MPs' data collection capabilities and the potential for MPs to underperform in data provision, we propose a reliable VFL-based TSE framework that ensures model reliability during training and operation. The proposed framework c…

This https://arxiv.org/abs/2504.16571 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

LaSDVS : A Post-Quantum Secure Compact Strong-Designated Verifier Signature
Digital signatures are fundamental cryptographic primitives that ensure the authenticity and integrity of digital communication. However, in scenarios involving sensitive interactions -- such as e-voting or e-cash -- there is a growing need for more controlled signing mechanisms. Strong-Designated Verifier Signature (SDVS) offers such control by allowing the signer to specify and restrict the verifier of a signature. The existing state-of-the-art SDVS are mostly based on number-theoretic hardne…

This https://arxiv.org/abs/2505.21119 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Universal Value-Function Uncertainties
Estimating epistemic uncertainty in value functions is a crucial challenge for many aspects of reinforcement learning (RL), including efficient exploration, safe decision-making, and offline RL. While deep ensembles provide a robust method for quantifying value uncertainty, they come with significant computational overhead. Single-model methods, while computationally favorable, often rely on heuristics and typically require additional propagation mechanisms for myopic uncertainty estimates. In …

This https://arxiv.org/abs/2505.07752 has been replaced.
initial toot: https://mastoxiv.page/@a…

Mapping of Microstructure Transitions during Rapid Alloy Solidification Using Bayesian-Guided Phase-Field Simulations
This study addresses microstructure selection mechanisms in rapid solidification, specifically targeting the transition from cellular/dendritic to planar interface morphologies under conditions relevant to additive manufacturing. We use a phase-field model that quantitatively captures solute trapping, kinetic undercooling, and morphological instabilities across a broad range of growth velocities ($V$) and thermal gradients ($G$), and apply it to a binary Fe-Cr alloy, as a surrogate for 316L sta…

This https://arxiv.org/abs/2502.14847 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Red-Teaming LLM Multi-Agent Systems via Communication Attacks
Large Language Model-based Multi-Agent Systems (LLM-MAS) have revolutionized complex problem-solving capability by enabling sophisticated agent collaboration through message-based communications. While the communication framework is crucial for agent coordination, it also introduces a critical yet unexplored security vulnerability. In this work, we introduce Agent-in-the-Middle (AiTM), a novel attack that exploits the fundamental communication mechanisms in LLM-MAS by intercepting and manipulat…

This https://arxiv.org/abs/2407.14485 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

On Sybil-proof Mechanisms
We show that in the single-parameter mechanism design environment, the only non-wasteful, symmetric, incentive compatible and Sybil-proof direct mechanism is a second price auction with symmetric tie-breaking. Thus, if there is private information, lotteries or other mechanisms that do not always allocate to a highest-value bidder are not Sybil-proof or not incentive compatible. Moreover, we show that our main (im)possibility result extends beyond linear valuations, but not to multi-unit object…

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models
Yifan Hao, Chenlu Ye, Chi Han, Tong Zhang
https://arxiv.org/abs/2506.01919

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models
Transformer based models have shown remarkable capabilities in sequence learning across a wide range of tasks, often performing well on specific task by leveraging input-output examples. Despite their empirical success, a comprehensive theoretical understanding of this phenomenon remains limited. In this work, we investigate the layerwise behavior of Transformers to uncover the mechanisms underlying their multi-task generalization ability. Taking explorations on a typical sequence model, i.e, H…

This https://arxiv.org/abs/2410.02644 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents
Although LLM-based agents, powered by Large Language Models (LLMs), can use external tools and memory mechanisms to solve complex real-world tasks, they may also introduce critical security vulnerabilities. However, the existing literature does not comprehensively evaluate attacks and defenses against LLM-based agents. To address this, we introduce Agent Security Bench (ASB), a comprehensive framework designed to formalize, benchmark, and evaluate the attacks and defenses of LLM-based agents, i…

This https://arxiv.org/abs/2205.09386 has been replaced.
link: https://scholar.google.com/scholar?q=a

On the Distortion of Multi-winner Election Using Single-Candidate Ballots
In this paper, we study the distortion bounds for voting mechanisms in multi-winner elections in general metric spaces. Our study pertains to the case in which each voter only reports her favorite candidate amongst $m$ possible choices. Given that candidates' locations are undisclosed to the mechanism, the mechanism has to form a $w-$winner committee based solely on the number of votes received by candidates. We establish distortion bounds for both truthful and non-truthful mechanisms. Our rese…

This https://arxiv.org/abs/2505.17072 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Safety Alignment Can Be Not Superficial With Explicit Safety Signals
Recent studies on the safety alignment of large language models (LLMs) have revealed that existing approaches often operate superficially, leaving models vulnerable to various adversarial attacks. Despite their significance, these studies generally fail to offer actionable solutions beyond data augmentation for achieving more robust safety mechanisms. This paper identifies a fundamental cause of this superficiality: existing alignment approaches often presume that models can implicitly learn a …