The ALICE Collaboration is planning to construct a new detector (ALICE 3) aiming at exploiting the potential of the high-luminosity Large Hadron Collider (LHC). The new detector will allow ALICE to participate in LHC Run 5 scheduled from 2036 to 2041. The muon-identifier subsystem (MID) is part of the ALICE 3 reference detector layout. The MID will consist of a standard magnetic iron absorber ($\approx4$ nuclear interaction lengths) followed by muon chambers. The baseline option for the MID chambers considers plastic scintillation bars equipped with wave-length shifting fibers and readout with silicon photomultipliers. This paper reports on the performance of a MID chamber prototype using 3 GeV/$c$ pion- and muon-enriched beams delivered by the CERN Proton Synchrotron (PS). The prototype was built using extruded plastic scintillator produced by FNAL-NICADD (Fermi National Accelerator Laboratory - Northern Illinois Center for Accelerator and Detector Development). The prototype was experimentally evaluated using varying absorber thicknesses (60, 70, 80, 90, and 100 cm) to assess its performance. The analysis was performed using Machine Learning techniques and the performance was validated with GEANT 4 simulations. Potential improvements in both hardware and data analysis are discussed.
We present an efficient hierarchical motion planning pipeline for differential drive mobile manipulators. Our approach first searches for multiple collisionfree and topologically distinct paths for the mobile base to extract the space in which optimal solutions may exist. Further sampling and optimization are then conducted in parallel to explore feasible whole-body trajectories. For trajectory optimization, we employ polynomial trajectories and arc length-yaw parameterization, enabling efficient handling of the nonholonomic dynamics while ensuring optimality.
Dockless e-scooters, a key micromobility service, have emerged as eco-friendly and flexible urban transport alternatives. These services improve first and last-mile connectivity, reduce congestion and emissions, and complement public transport for short-distance travel. However, effective management of these services depends on accurate demand prediction, which is crucial for optimal fleet distribution and infrastructure planning. While previous studies have focused on analyzing spatial or temporal factors in isolation, this study introduces a framework that integrates spatial, temporal, and network dependencies for improved micromobility demand forecasting. This integration enhances accuracy while providing deeper insights into urban micromobility usage patterns. Our framework improves demand prediction accuracy by 27 to 49% over baseline models, demonstrating its effectiveness in capturing micromobility demand patterns. These findings support data-driven micromobility management, enabling optimized fleet distribution, cost reduction, and sustainable urban planning.
This article focuses on integrating path-planning and control with specializing on the unique needs of robotic unicycles. A unicycle design is presented which is capable of accelerating/breaking and carrying out a variety of maneuvers. The proposed path-planning method segments the path into straight and curved path sections dedicated for accelerating/breaking and turning maneuvers, respectively. The curvature profiles of the curved sections are optimized while considering the control performance and the slipping limits of the wheel. The performance of the proposed integrated approach is demonstrated via numerical simulations.
Complex information needs in real-world search scenarios demand deep reasoning and knowledge synthesis across diverse sources, which traditional retrieval-augmented generation (RAG) pipelines struggle to address effectively. Current reasoning-based approaches suffer from a fundamental limitation: they use a single model to handle both high-level planning and detailed execution, leading to inefficient reasoning and limited scalability. In this paper, we introduce HiRA, a hierarchical framework that separates strategic planning from specialized execution. Our approach decomposes complex search tasks into focused subtasks, assigns each subtask to domain-specific agents equipped with external tools and reasoning capabilities, and coordinates the results through a structured integration mechanism. This separation prevents execution details from disrupting high-level reasoning while enabling the system to leverage specialized expertise for different types of information processing. Experiments on four complex, cross-modal deep search benchmarks demonstrate that HiRA significantly outperforms state-of-the-art RAG and agent-based systems. Our results show improvements in both answer quality and system efficiency, highlighting the effectiveness of decoupled planning and execution for multi-step information seeking tasks. Our code is available at https://github.com/ignorejjj/HiRA.
In this work, we address the challenge of data-efficient exploration in reinforcement learning by examining existing principled, information-theoretic approaches to intrinsic motivation. Specifically, we focus on a class of exploration bonuses that targets epistemic uncertainty rather than the aleatoric noise inherent in the environment. We prove that these bonuses naturally signal epistemic information gains and converge to zero once the agent becomes sufficiently certain about the environment's dynamics and rewards, thereby aligning exploration with genuine knowledge gaps. Our analysis provides formal guarantees for IG-based approaches, which previously lacked theoretical grounding. To enable practical use, we also discuss tractable approximations via sparse variational Gaussian Processes, Deep Kernels and Deep Ensemble models. We then outline a general framework - Predictive Trajectory Sampling with Bayesian Exploration (PTS-BE) - which integrates model-based planning with information-theoretic bonuses to achieve sample-efficient deep exploration. We empirically demonstrate that PTS-BE substantially outperforms other baselines across a variety of environments characterized by sparse rewards and/or purely exploratory tasks.
This research investigates strategies for multi-robot coordination in multi-human environments. It proposes a multi-objective learning-based coordination approach to addressing the problem of path planning, navigation, task scheduling, task allocation, and human-robot interaction in multi-human multi-robot (MHMR) settings.
We present recent progress in our understanding of the physical interaction mechanisms at work in evolved binaries of low-to-intermediate initial mass, which are surrounded by a stable disc of gas and dust. These systems are known as post-asymptotic giant-branch (post-AGB) binaries, but recently, it has been shown that some systems are too low in luminosity and should be considered as post-red-giant branch (post-RGB) instead. While the systems are currently well within their Roche lobe, they still show signs of active ongoing interaction between the different building blocks. We end this contribution with some future research plans.
Low-temperature detectors are a powerful technology for dark matter search, offering excellent energy resolution and low energy thresholds. COSINUS is the only experiment that combines scintillating sodium iodide (NaI) crystals with an additional phonon readout at cryogenic temperatures, using superconducting sensors (remoTES), alongside the conventional scintillation light signal. Via the simultaneous phonon and scintillation light detection, a unique event-by-event particle identification is enabled. This dual-channel approach allows for a model-independent cross-check of the long-standing DAMA/LIBRA signal with a moderate exposure of a few hundred kg d, while completely avoiding key systematic uncertainties inherent to scintillation-only NaI-based searches. COSINUS built and commissioned a dedicated low-background cryogenic facility at the LNGS underground laboratories. Data taking with eight NaI detector modules (COSINUS1$\pi$ Run1) is planned to begin in late 2025.
Trajectory prediction is an essential step in the pipeline of an autonomous vehicle. Inaccurate or inconsistent predictions regarding the movement of agents in its surroundings lead to poorly planned maneuvers and potentially dangerous situations for the end-user. Current state-of-the-art deep-learning-based trajectory prediction models can achieve excellent accuracy on public datasets. However, when used in more complex, interactive scenarios, they often fail to capture important interdependencies between agents, leading to inconsistent predictions among agents in the traffic scene. Inspired by the efficacy of incorporating human preference into large language models, this work fine-tunes trajectory prediction models in multi-agent settings using preference optimization. By taking as input automatically calculated preference rankings among predicted futures in the fine-tuning process, our experiments--using state-of-the-art models on three separate datasets--show that we are able to significantly improve scene consistency while minimally sacrificing trajectory prediction accuracy and without adding any excess computational requirements at inference time.
Path planning algorithms aim to compute a collision-free path, and many works focus on finding the optimal distance path. However, for some applications, a more suitable approach is to balance response time, safety of the paths, and path length. In this context, a skeleton map is a useful tool in graph-based schemes, as it provides an intrinsic representation of free configuration space. However, skeletonization algorithms are very resource-intensive, being primarily oriented towards image processing tasks. We propose an efficient path-planning methodology that finds safe paths within an acceptable processing time. This methodology leverages a Deep Denoising Auto-Encoder (DDAE) based on U-Net architecture to compute a skeletonized version of the navigation map, which we refer to as SkelUnet. The SkelUnet network facilitates exploration of the entire workspace through one-shot sampling (OSS), as opposed to the iterative process used by exact algorithms or the probabilistic sampling process. SkelUnet is trained and tested on a dataset consisting of 12,500 bi-dimensional dungeon maps. The motion planning methodology is evaluated in a simulation environment for an Unmanned Aerial Vehicle (UAV) using 250 previously unseen maps, and assessed with various navigation metrics to quantify the navigability of the computed paths. The results demonstrate that using SkelUnet to construct a roadmap offers significant advantages, such as connecting all regions of free workspace, providing safer paths, and reducing processing times. These characteristics make this method particularly suitable for mobile service robots in structured environments.
Forests are frequently impacted by climate conditions, vegetation density, and intricate terrain and geology, which contribute to natural disasters. Personnel engaged in or supporting rescue operations in such environments rely on robust communication systems to ensure their safety, highlighting the criticality of channel measurements in forest environments. However, according to current research, there is limited research on channel detection and modeling in forest areas in the existing literature. This paper describes the channel measurements campaign of air and ground in the Arxan National Forest Park of Inner Mongolia. It presents measurement results and propagation models for ground-to-ground (G2G) and air-to-ground (A2G) scenarios. The measurement campaign uses orthogonal frequency division multiplexing signals centered at 1.4 GHz for channel sounding. In the G2G measurement, in addition to using omnidirectional antennas to record data, we also use directional antennas to record the arrival angle information of the signal at the receiver. In the A2G measurement, we pre-plan the flight trajectory of the unmanned aerial vehicle so that it can fly at a fixed angle relative to the ground. We present path loss models suitable for G2G and A2G in forest environments based on the analysis of measurement results. The results indicate that the proposed model reduces error margins compared with other path loss models. Furthermore, we derive the multipath model expression specific to forest environments and conduct statistical analysis on key channel parameters e.g., shadow fading factor, root mean square delay spread, and Rician K factor. Our findings reveal that signal propagation obstruction due to tree crowns in A2G communication is more pronounced than tree trunk obstructions in G2G communication. Adjusting the elevation angle between air and ground can enhance communication quality.
Chain-of-thought (CoT) reasoning has enabled transformer-based language models to excel at complex mathematics and multi-step planning. However, in standard decoder-only architectures, these reasoning steps are externalized in natural language, improving interpretability at the cost of efficiency. To capture reasoning that is not easily represented in words, many works have explored recurrent architectures that aim to internalize reasoning in latent space, potentially supporting latent CoT. In this paper, we investigate whether such reasoning structures emerge in Huginn-3.5B, a depth-recurrent Transformer that reuses layers at inference time without increasing parameter count. We examine the model's internal behavior on arithmetic tasks using a suite of probing techniques including the Logit Lens and Coda Lens. Our findings reveal limited evidence of interpretable latent CoT by tracking rank trajectories of final and intermediate result tokens. Furthermore, we uncover significant probing inconsistencies across recurrent blocks, where the interpretability of hidden states depends heavily on both the layer index and the decoding method. Finally, we empirically show that increasing recurrence depth yields only marginal gains and falls well short of models that explicitly externalize reasoning steps. The code is available at https://github.com/wenquanlu/huginn-latent-cot.
Trajectory planning in robotics is understood as generating a sequence of joint configurations that will lead a robotic agent, or its manipulator, from an initial state to the desired final state, thus completing a manipulation task while considering constraints like robot kinematics and the environment. Typically, this is achieved via sampling-based planners, which are computationally intensive. Recent advances demonstrate that trajectory planning can also be performed by supervised sequence learning of trajectories, often requiring only a single or fixed number of passes through a neural architecture, thus ensuring a bounded computation time. Such fully supervised approaches, however, perform imitation learning; they do not learn based on whether the trajectories can successfully reach a goal, but try to reproduce observed trajectories. In our work, we build on this approach and propose a cognitively inspired self-supervised learning scheme based on a recurrent architecture for building a trajectory model. We evaluate the feasibility of the proposed method on a task of kinematic planning for a robotic arm. The results suggest that the model is able to learn to generate trajectories only using given paired forward and inverse kinematics models, and indicate that this novel method could facilitate planning for more complex manipulation tasks requiring adaptive solutions.
Various ``cosmic shorelines" have been proposed to delineate which planets have atmospheres. The fates of individual planet atmospheres may be set by a complex sea of growth and loss processes, driven by unmeasurable environmental factors or unknown historical events. Yet, defining population-level boundaries helps illuminate which processes matter and identify high-priority targets for future atmospheric searches. Here, we provide a statistical framework for inferring the position, shape, and fuzziness of an instellation-based cosmic shoreline, defined in the three-dimensional space of planet escape velocity, planet bolometric flux received, and host star luminosity; explicitly including luminosity partially circumvents the need to estimate host stars' historical X-ray and extreme ultraviolet fluences. Using Solar System and exoplanet atmospheric constraints, under the restrictive assumption that one planar boundary applies across a wide parameter space, we find the critical flux threshold for atmospheres scales with escape velocity with a power-law index of $p=6.08^{+0.69}_{-0.48}$, steeper than the canonical literature slope of $p=4$, and scales with stellar luminosity with a power-law index of $q=1.25^{+0.31}_{-0.22}$, steep enough to disfavor atmospheres on Earth-sized planets out to the habitable zone for stars less luminous than $\log_{10} (L_\star/L_\odot) = -2.22 \pm 0.21$ (roughly spectral type M4.5V). If we relax the assumption that one power law must stretch from the hottest exoplanets to the coolest Solar System worlds, the narrower question of ``Which warm planets have thick CO$_2$ secondary atmospheres?" is still poorly constrained by data but should improve significantly with planned JWST observations.
Grid cells in the medial entorhinal cortex (MEC) are believed to path integrate speed and direction signals to activate at triangular grids of locations in an environment, thus implementing a population code for position. In parallel, place cells in the hippocampus (HC) fire at spatially confined locations, with selectivity tuned not only to allocentric position but also to environmental contexts, such as sensory cues. Although grid and place cells both encode spatial information and support memory for multiple locations, why animals maintain two such representations remains unclear. Noting that place representations seem to have other functional roles in intrinsically motivated tasks such as recalling locations from sensory cues, we propose that animals maintain grid and place representations together to support planning. Specifically, we posit that place cells auto-associate not only sensory information relayed from the MEC but also grid cell patterns, enabling recall of goal location grid patterns from sensory and motivational cues, permitting subsequent planning with only grid representations. We extend a previous theoretical framework for grid-cell-based planning and show that local transition rules can generalize to long-distance path forecasting. We further show that a planning network can sequentially update grid cell states toward the goal. During this process, intermediate grid activity can trigger place cell pattern completion, reconstructing experiences along the planned path. We demonstrate all these effects using a single-layer RNN that simultaneously models the HC-MEC loop and the planning subnetwork. We show that such recurrent mechanisms for grid cell-based planning, with goal recall driven by the place system, make several characteristic, testable predictions.
We introduce RoboBrain 2.0, our latest generation of embodied vision-language foundation models, designed to unify perception, reasoning, and planning for complex embodied tasks in physical environments. It comes in two variants: a lightweight 7B model and a full-scale 32B model, featuring a heterogeneous architecture with a vision encoder and a language model. Despite its compact size, RoboBrain 2.0 achieves strong performance across a wide spectrum of embodied reasoning tasks. On both spatial and temporal benchmarks, the 32B variant achieves leading results, surpassing prior open-source and proprietary models. In particular, it supports key real-world embodied AI capabilities, including spatial understanding (e.g., affordance prediction, spatial referring, trajectory forecasting) and temporal decision-making (e.g., closed-loop interaction, multi-agent long-horizon planning, and scene graph updating). This report details the model architecture, data construction, multi-stage training strategies, infrastructure and practical applications. We hope RoboBrain 2.0 advances embodied AI research and serves as a practical step toward building generalist embodied agents. The code, checkpoint and benchmark are available at https://superrobobrain.github.io.
This study investigates the deterioration of skid resistance and surface macrotexture following preventive maintenance using micro-milling techniques. Field data were collected from 31 asphalt pavement sections located across four climatic zones in Texas, encompassing a variety of surface types, milling depths, operational speeds, and drum configurations. A standardized data collection protocol was followed, with measurements taken before milling, immediately after treatment, and at 3, 6, 12, and 18 months post-treatment. Skid number and Mean Profile Depth (MPD) were used to evaluate surface friction and texture characteristics. The dataset was reformatted into a time-series structure with 930 observations, incorporating contextual variables such as climatic zone, treatment parameters, and baseline surface condition. A comparative modeling framework was applied to predict the deterioration trends of both skid resistance and macrotexture over time. Eight regression models, including linear, tree-based, and ensemble methods, were evaluated alongside a sequence-to-one transformer model. Results show that the transformer model achieved the highest prediction accuracy for skid resistance (R2=0.981), while Random Forest performing best for macrotexture prediction (R2 = 0.838). The findings indicate that the degradation of surface characteristics after preventive maintenance is nonlinear and influenced by a combination of environmental and operational factors. This study demonstrates the effectiveness of data-driven modeling in supporting transportation agencies with pavement performance forecasting and maintenance planning.
Treatment planning uncertainties are typically managed using margin-based or robust optimization. Margin-based methods expand the clinical target volume (CTV) to a planning target volume, generally unsuited for proton therapy. Robust optimization considers worst-case scenarios, but its quality depends on the uncertainty scenario set: excluding extremes reduces robustness, while too many make plans overly conservative. Probabilistic optimization overcomes these limits by modeling a continuous scenario distribution. We propose a novel probabilistic optimization approach that steers plans toward individualized probability levels to control CTV and organs-at-risk (OARs) under- and overdosage. Voxel-wise dose percentiles ($d$) are estimated by expected value ($E$) and standard deviation (SD) as $E[d] \pm \delta \cdot SD[d]$, where $\delta$ is iteratively tuned to match the target percentile given Gaussian-distributed setup (3 mm) and range (3%) uncertainties. The method involves an inner optimization of $E[d] \pm \delta \cdot SD[d]$ for fixed $\delta$, and an outer loop updating $\delta$. Polynomial Chaos Expansion (PCE) provides accurate and efficient dose estimates during optimization. We validated the method on a spherical CTV abutted by an OAR in different directions and a horseshoe-shaped CTV surrounding a cylindrical spine. For spherical cases with similar CTV coverage, $P(D_{2\%} > 30 Gy)$ dropped by 10-15%; for matched OAR dose, $P(D_{98\%} > 57 Gy)$ increased by 67.5-71%. In spinal plans, $P(D_{98\%} > 57 Gy)$ increased by 10-15% while $P(D_{2\%} > 30 Gy)$ dropped 24-28%. Probabilistic and robust optimization times were comparable for spherical (hours) but longer for spinal cases (7.5 - 11.5 h vs. 9 - 20 min). Compared to discrete scenario-based optimization, the probabilistic method offered better OAR sparing or target coverage depending on the set priorities.
PathDB is a Java-based graph database designed for in-memory data loading and querying. By utilizing Regular Path Queries (RPQ) and a closed path algebra, PathDB processes paths through its three main components: the parser, the logical plan, and the physical plan. This modular design allows for targeted optimizations and modifications without impacting overall functionality. Benchmark experiments illustrate PathDB's execution times and flexibility in handling dynamic and complex path queries, compared to baseline methods like Depth-First Search (DFS) and Breadth-First Search (BFS) guided by an automaton, highlighting PathDB optimizations that contribute to its performance. PathDB was also evaluated against leading commercial graph systems, including Neo4j, Memgraph, and K\`uzu. Benchmark experiments demonstrated PathDB competitive execution times and its ability to support a wide range of path query types.
The optimal transport problem with squared Euclidean cost consists in finding a coupling between two input measures that maximizes correlation. Consequently, the optimal coupling is often singular with respect to Lebesgue measure. Regularizing the optimal transport problem with an entropy term yields an approximation called entropic optimal transport. Entropic penalties steer the induced coupling toward a reference measure with desired properties. For instance, when seeking a diffuse coupling, the most popular reference measures are the Lebesgue measure and the product of the two input measures. In this work, we study the case where the reference coupling is not necessarily assumed to be a product. We focus on the Gaussian case as a motivating paradigm, and provide a reduction of this more general optimal transport criterion to a matrix optimization problem. This reduction enables us to provide a complete description of the solution, both in terms of the primal variable and the dual variables. We argue that flexibility in terms of the reference measure can be important in statistical contexts, for instance when one has prior information, when there is uncertainty regarding the measures to be coupled, or to reduce bias when the entropic problem is used to estimate the un-regularized transport problem. In particular, we show in numerical examples that choosing a suitable reference plan allows to reduce the bias caused by the entropic penalty.
Collision-free motion planning in complex outdoor environments relies heavily on perceiving the surroundings through exteroceptive sensors. A widely used approach represents the environment as a voxelized Euclidean distance field, where robots are typically approximated by spheres. However, for large-scale manipulators such as forestry cranes, which feature long and slender links, this conventional spherical approximation becomes inefficient and inaccurate. This work presents a novel collision detection algorithm specifically designed to exploit the elongated structure of such manipulators, significantly enhancing the computational efficiency of motion planning algorithms. Unlike traditional sphere decomposition methods, our approach not only improves computational efficiency but also naturally eliminates the need to fine-tune the approximation accuracy as an additional parameter. We validate the algorithm's effectiveness using real-world LiDAR data from a forestry crane application, as well as simulated environment data.
This paper presents a Riemannian metric-based model to solve the optimal path planning problem on two-dimensional smooth submanifolds in high-dimensional space. Our model is based on constructing a new Riemannian metric on a two-dimensional projection plane, which is induced by the high-dimensional Euclidean metric on two-dimensional smooth submanifold and reflects the environmental information of the robot. The optimal path planning problem in high-dimensional space is therefore transformed into a geometric problem on the two-dimensional plane with new Riemannian metric. Based on the new Riemannian metric, we proposed an incremental algorithm RRT*-R on the projection plane. The experimental results show that the proposed algorithm is suitable for scenarios with uneven fields in multiple dimensions. The proposed algorithm can help the robot to effectively avoid areas with drastic changes in height, ground resistance and other environmental factors. More importantly, the RRT*-R algorithm shows better smoothness and optimization properties compared with the original RRT* algorithm using Euclidean distance in high-dimensional workspace. The length of the entire path by RRT*-R is a good approximation of the theoretical minimum geodesic distance on projection plane.
The X-ray Integral Field Unit is the X-ray imaging spectrometer on-board one of ESA's next large missions, Athena. Athena is set to investigate the theme of the Hot and Energetic Universe, with a launch planned in the late-2030s. Based on a high sensitivity Transition Edge Sensor (TES) detector array operated at very low temperature (50 mK), X-IFU will provide spatially resolved high resolution spectroscopy of the X-ray sky in the 0.2-12 keV energy band, with an energy resolution goal of 4 eV up to 7 keV [3 eV design goal]. This paper presents the current calibration plan of the X-IFU. It provides the requirements applicable to the X-IFU calibration, describes the overall calibration strategy, and details the procedure and sources needed for the ground calibration of each parameter or characteristics of the X-IFU.
This work explores the application of hybrid quantum-classical algorithms to optimize robotic inspection trajectories derived from Computer-Aided Design (CAD) models in industrial settings. By modeling the task as a 3D variant of the Traveling Salesman Problem, incorporating incomplete graphs and open-route constraints, this study evaluates the performance of two D-Wave-based solvers against classical methods such as GUROBI and Google OR-Tools. Results across five real-world cases demonstrate competitive solution quality with significantly reduced computation times, highlighting the potential of quantum approaches in automation under Industry 4.0.
This paper investigates the problem of computing a two-dimensional optimal curvature straight line (CS) shortest path for an unmanned aerial vehicle (UAV) to intercept a moving target, with both the UAV (pursuer) and target travelling at constant speeds. We formulate an optimal control problem that integrates two critical objectives: avoiding static obstacles and successfully intercepting the target. The approach introduces constraints derived from obstacle avoidance and target interception requirements. A geometric framework is developed, along with sufficient conditions for path optimality under the imposed constraints. The problem is initially examined in the presence of a single obstacle and later extended to scenarios involving a finite number of obstacles. Numerical experiments are carried out to evaluate the performance and efficiency of the proposed model using illustrative examples. Finally, we present a realistic case study using actual geographic data, including obstacle placement, target trajectory, and heading angles, to demonstrate the practical applicability and effectiveness of the proposed method in real-world scenarios.
Recent advancements in open-source Visual Language Models (VLMs) such as LLaVA, Qwen-VL, and Llama have catalyzed extensive research on their integration with diverse systems. The internet-scale general knowledge encapsulated within these models presents significant opportunities for enhancing autonomous driving perception, prediction, and planning capabilities. In this paper we propose VLAD, a vision-language autonomous driving model, which integrates a fine-tuned VLM with VAD, a state-of-the-art end-to-end system. We implement a specialized fine-tuning approach using custom question-answer datasets designed specifically to improve the spatial reasoning capabilities of the model. The enhanced VLM generates high-level navigational commands that VAD subsequently processes to guide vehicle operation. Additionally, our system produces interpretable natural language explanations of driving decisions, thereby increasing transparency and trustworthiness of the traditionally black-box end-to-end architecture. Comprehensive evaluation on the real-world nuScenes dataset demonstrates that our integrated system reduces average collision rates by 31.82% compared to baseline methodologies, establishing a new benchmark for VLM-augmented autonomous driving systems.
The planned space-based gravitational wave detector, LISA, will provide a fundamentally new means of studying the orbital alignment of close white dwarf binaries. However, due to the inherent symmetry of their gravitational wave signals, a fourfold degeneracy arises in the transverse projections of their angular momentum vectors. In this paper, we demonstrate that by incorporating timing information from electromagnetic observations, such as radial velocity modulations and light curves, this degeneracy can be reduced to twofold.
Organizations around the world schedule jobs (programs) regularly to perform various tasks dictated by their end users. With the major movement towards using a cloud computing infrastructure, our organization follows a hybrid approach with both cloud and on-prem servers. The objective of this work is to perform capacity planning, i.e., estimate resource requirements, and job scheduling for on-prem grid computing environments. A key contribution of our approach is handling uncertainty in both resource usage and duration of the jobs, a critical aspect in the finance industry where stochastic market conditions significantly influence job characteristics. For capacity planning and scheduling, we simultaneously balance two conflicting objectives: (a) minimize resource usage, and (b) provide high quality-of-service to the end users by completing jobs by their requested deadlines. We propose approximate approaches using deterministic estimators and pair sampling-based constraint programming. Our best approach (pair sampling-based) achieves much lower peak resource usage compared to manual scheduling without compromising on the quality-of-service.
Professional visualization design has become an increasingly important area of inquiry, yet much of the field's discourse remains anchored in researcher-centered contexts. Studies of design practice often focus on individual designers' decisions and reflections, offering limited insight into the collaborative and systemic dimensions of professional work. In this paper, we propose a systems-level reframing of design judgment grounded in the coordination and adaptation that sustain progress amid uncertainty, constraint, and misalignment. Drawing on sustained engagement across multiple empirical studies--including ethnographic observation of design teams and qualitative studies of individual practitioners--we identify recurring episodes in which coherence was preserved not by selecting an optimal option, but by repairing alignment, adjusting plans, and reframing goals. We interpret these dynamics through the lens of Joint Cognitive Systems, which provide tools for analyzing how judgment emerges as a distributed capacity within sociotechnical activity. This perspective surfaces often-invisible work in visualization design and offers researchers a new conceptual vocabulary for studying how design activity is sustained in practice.