Large-scale GPU traces play a critical role in identifying performance bottlenecks within heterogeneous High-Performance Computing (HPC) architectures. However, the sheer volume and complexity of a single trace of data make performance analysis both computationally expensive and time-consuming. To address this challenge, we present an end-to-end parallel performance analysis framework designed to handle multiple large-scale GPU traces efficiently. Our proposed framework partitions and processes trace data concurrently and employs causal graph methods and parallel coordinating chart to expose performance variability and dependencies across execution flows. Experimental results demonstrate a 67% improvement in terms of scalability, highlighting the effectiveness of our pipeline for analyzing multiple traces independently.

Distributed resource allocation (DRA) is fundamental to modern networked systems, spanning applications from economic dispatch in smart grids to CPU scheduling in data centers. Conventional DRA approaches require reliable communication, yet real-world networks frequently suffer from link failures, packet drops, and communication delays due to environmental conditions, network congestion, and security threats. We introduce a novel resilient DRA algorithm that addresses these critical challenges, and our main contributions are as follows: (1) guaranteed constraint feasibility at all times, ensuring resource-demand balance even during algorithm termination or network disruption; (2) robust convergence despite sector-bound nonlinearities at nodes/links, accommodating practical constraints like quantization and saturation; and (3) optimal performance under merely uniformly-connected networks, eliminating the need for continuous connectivity. Unlike existing approaches that require persistent network connectivity and provide only asymptotic feasibility, our graph-theoretic solution leverages network percolation theory to maintain performance during intermittent disconnections. This makes it particularly valuable for mobile multi-agent systems where nodes frequently move out of communication range. Theoretical analysis and simulations demonstrate that our algorithm converges to optimal solutions despite heterogeneous time delays and substantial link failures, significantly advancing the reliability of distributed resource allocation in practical network environments.

This is the first part of the comprehensive review, focusing on the historical development of Agent-Based Modeling (ABM) and its classic cases. It begins by discussing the development history and design principles of Agent-Based Modeling (ABM), helping readers understand the significant challenges that traditional physical simulation methods face in the social domain. Then, it provides a detailed introduction to foundational models for simulating social systems, including individual models, environmental models, and rule-based models. Finally, it presents classic cases of social simulation, covering three types: thought experiments, mechanism exploration, and parallel optimization.

The objective of designing a control system is to steer a dynamical system with a control signal, guiding it to exhibit the desired behavior. The Hamilton-Jacobi-Bellman (HJB) partial differential equation offers a framework for optimal control system design. However, numerical solutions to this equation are computationally intensive, and analytical solutions are frequently unavailable. Knowledge-guided machine learning methodologies, such as physics-informed neural networks (PINNs), offer new alternative approaches that can alleviate the difficulties of solving the HJB equation numerically. This work presents a multistage ensemble framework to learn the optimal cost-to-go, and subsequently the corresponding optimal control signal, through the HJB equation. Prior PINN-based approaches rely on a stabilizing the HJB enforcement during training. Our framework does not use stabilizer terms and offers a means of controlling the nonlinear system, via either a singular learned control signal or an ensemble control signal policy. Success is demonstrated in closed-loop control, using both ensemble- and singular-control, of a steady-state time-invariant two-state continuous nonlinear system with an infinite time horizon, accounting of noisy, perturbed system states and varying initial conditions.

Abstract-- Fruit harvesting remains predominantly a labor-intensive process, motivating the development of research for robotic grippers. Conventional rigid or vacuum-driven grippers require complex mechanical design or high energy consumption. Current enveloping-based fruit harvesting grippers lack adaptability to fruits of different sizes. This paper introduces a drawstring-inspired, cable-driven soft gripper for versatile and gentle fruit harvesting. The design employs 3D-printed Thermoplastic Polyurethane (TPU) pockets with integrated steel wires that constrict around the fruit when actuated, distributing pressure uniformly to minimize bruising and allow versatility to fruits of varying sizes. The lightweight structure, which requires few components, reduces mechanical complexity and cost compared to other grippers. Actuation is achieved through servo-driven cable control, while motor feedback provides autonomous grip adjustment with tunable grip strength. Experimental validation shows that, for tomatoes within the gripper's effective size range, harvesting was achieved with a 0% immediate damage rate and a bruising rate of less than 9% after five days, reinforcing the gripper's suitability for fruit harvesting. While there is ongoing research and development towards fruit harvesting solutions [1] [2], hand-picking remains the dominant method due to its delicacy for soft fruits [3].

Round-Robin Behavior Cloning (R2BC): Traditional Behavior Cloning (left) requires coordinated and centralized demonstrations, where an expert demonstrates actions near-optimally for all agents. In multi-agent domains, a lone human operator may not be able to provide high-quality demonstrations due to underactuated control and increased cognitive burden. Our method (right), R2BC, removes this restriction by letting the human control one agent at a time while the other agents act via their learned policies. This round-robin process collects realistic demonstrations and iteratively trains cooperative multi-agent behavior. Abstract-- Imitation Learning (IL) is a natural way for humans to teach robots, particularly when high-quality demonstrations are easy to obtain. While IL has been widely applied to single-robot settings, relatively few studies have addressed the extension of these methods to multi-agent systems, especially in settings where a single human must provide demonstrations to a team of collaborating robots. In this paper, we introduce and study Round-Robin Behavior Cloning (R2BC), a method that enables a single human operator to effectively train multi-robot systems through sequential, single-agent demonstrations. Our approach allows the human to teleoperate one agent at a time and incrementally teach multi-agent behavior to the entire system, without requiring demonstrations in the joint multi-agent action space.

The integration of Unmanned Aerial Vehicles (UAVs) into Open Radio Access Networks (O-RAN) enhances communication in disaster management and Search and Rescue (SAR) operations by ensuring connectivity when infrastructure fails. However, SAR scenarios demand stringent security and low-latency communication, as delays or breaches can compromise mission success. While UAVs serve as mobile relays, they introduce challenges in energy consumption and resource management, necessitating intelligent allocation strategies. Existing UAV-assisted O-RAN approaches often overlook the joint optimization of security, latency, and energy efficiency in dynamic environments. This paper proposes a novel Reinforcement Learning (RL)-based framework for dynamic resource allocation in UAV relays, explicitly addressing these trade-offs. Our approach formulates an optimization problem that integrates security-aware resource allocation, latency minimization, and energy efficiency, which is solved using RL. Unlike heuristic or static methods, our framework adapts in real-time to network dynamics, ensuring robust communication. Simulations demonstrate superior performance compared to heuristic baselines, achieving enhanced security and energy efficiency while maintaining ultra-low latency in SAR scenarios.

Knowledge editing techniques promise to implant new factual knowledge into large language models (LLMs). But do LLMs really believe these facts? We develop a framework to measure belief depth and use it to evaluate the success of knowledge editing techniques. We operationalize belief depth as the extent to which implanted knowledge 1) generalizes to related contexts (e.g. Fermi estimates several logical steps removed), 2) is robust to self-scrutiny and direct challenge, and 3) is represented similarly to genuine knowledge (as measured by linear probes). Our evaluations show that simple prompting and mechanistic editing techniques fail to implant knowledge deeply. In contrast, Synthetic Document Finetuning (SDF) - where models are trained on LLM-generated documents consistent with a fact - often succeeds at implanting beliefs that behave similarly to genuine knowledge. However, SDF's success is not universal, as implanted beliefs that contradict basic world knowledge are brittle and representationally distinct from genuine knowledge. Overall, our work introduces measurable criteria for belief depth and enables the rigorous evaluation necessary for deploying knowledge editing in real-world applications.

Abstract--Integrated Satellite-Aerial-Terrestrial Networks (ISATNs) are envisioned as key enablers of 6G, providing global connectivity for applications such as autonomous transportation, Industrial IoT, and disaster response. ISATN-specific control knobs, including carbon-aware handovers, UA V duty-cycling, and renewable-aware edge placement, are exploited to reduce emissions. I. Introduction The rapid evolution of next-generation communication systems is driving the integration of Satellite, Aerial, and Terrestrial Networks (ISATNs) into a unified infrastructure capable of delivering seamless global connectivity. This convergence is critical for enabling emerging applications such as autonomous transportation, Industrial Internet of Things (IIoT), remote healthcare, and disaster response, where reliable, low-latency, and high-capacity communication is essential [ 1 ]. However, the energy consumption associated with operating dense terrestrial base stations, satellite constellations, and aerial platforms introduces significant carbon emissions, posing new challenges for designing energy-efficient and environmentally sustainable integrated networks. As communication networks scale toward 6G and beyond, addressing carbon emissions and energy optimization has become a priority. The increasing reliance on renewable energy sources and fluctuating carbon intensity in power grids demand intelligent orchestration mechanisms capable of balancing Quality of Service (QoS) with environmental impact. S. Javaid is with the College of Electronics and Information Engineering, Tongji University, Shanghai 201804, and the State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai 201210, China N. Saeed is with the Department of Electrical and Communication Engineering, College of Engineering, UAE University, Al-Ain 15551, UAE (e-mail: mr.nasir.saeed@ieee.org).

Visual navigation is a fundamental problem in embodied AI, yet practical deployments demand long-horizon planning capabilities to address multi-objective tasks. A major bottleneck is data scarcity: policies learned from limited data often overfit and fail to generalize OOD. Existing neural network-based agents typically increase architectural complexity that paradoxically become counterproductive in the small-sample regime. This paper introduce NeuRO, a integrated learning-to-optimize framework that tightly couples perception networks with downstream task-level robust optimization. Specifically, NeuRO addresses core difficulties in this integration: (i) it transforms noisy visual predictions under data scarcity into convex uncertainty sets using Partially Input Convex Neural Networks (PICNNs) with conformal calibration, which directly parameterize the optimization constraints; and (ii) it reformulates planning under partial observability as a robust optimization problem, enabling uncertainty-aware policies that transfer across environments. Extensive experiments on both unordered and sequential multi-object navigation tasks demonstrate that NeuRO establishes SoTA performance, particularly in generalization to unseen environments. Our work thus presents a significant advancement for developing robust, generalizable autonomous agents.