Computer Science (arXiv)

Robots exhibit a rich variety of symmetries arising from their mechanical structure and the properties of their tasks. Although many robotics problems exhibit several symmetries simultaneously, existing approaches typically treat them in isolation, failing to exploit their combined potential. This paper introduces cross-space symmetry compositions, a framework for learning robot policies that are jointly equivariant to multiple symmetries across configuration and task spaces. Leveraging the differential-geometric structure of the forward kinematics map, we both descend symmetries from configuration to task space and lift symmetries from task to configuration space, enabling their composition within a unified representation space. We validate our framework on simulated and real-world experiments on a dual-arm robot, demonstrating that jointly leveraging multiple symmetries yields improved generalization.

The advent of 5G virtualized Radio Access Networks (vRANs) brings a new challenge with regards to computer architectures. It requires to select or design computing technologies that provide a sufficient level of performance while maximizing the flexibility and efficiency of the implemented networks. Several solutions addressing this challenge were proposed, relying on general purpose processors as well as hardware accelerators. This work describes our effort to enable an intensive vRAN deployment using the 5G software stack OpenAirInterface on top of these computer architectures. We had to adapt the software stack to leverage the capabilities of hardware and to find how to scale up the vRAN deployment with several vRAN instances sharing a server. We describe in this work our improvements to the stack and their effect on performance. We also share our observations on the behavior of the computer architectures and how they affect our deployment. We finally discuss the limitations of our deployment and further efforts to implement better vRAN deployments.

Liquid biopsy can detect tumor-derived biomarkers such as circulating tumor DNA (ctDNA), but ultra-low-fraction assays remain costly, slow, and difficult to scale. This motivates interest in intravascular in vivo sensing in the context of intrabody nanonetworks, where nanosensors could support local biomarker monitoring. BioFET-based nanosensors are relevant here because they are label-free, highly miniaturizable, and have shown strong ctDNA sensitivity in controlled media. We examine whether this sensitivity still yields reliable ctDNA detection in whole blood using a reduced-order stochastic simulation model that links operating-point selection, Debye-screened charge transduction, stochastic finite-capacity binding, nonspecific adsorption, background fluctuations, and intrinsic electronic noise to blank-threshold detection. Monte Carlo evaluation with physiologically grounded parameters shows that short Debye length and several-nanometer charge-to-channel separation attenuate the current shift, while low-frequency noise and background fluctuations reduce the margin between target-present and blank responses. Under the tested quasi-static charge-gating regime, the simulated current shifts do not reliably exceed the blank-derived threshold at low ctDNA concentrations. The model therefore provides a whole-blood boundary analysis that identifies which interface configurations and operating conditions most strongly limit reliable BioFET-based intravascular ctDNA detection.

This paper introduces a novel, multi-source framework for the relational validation of Large Language Models (LLMs). While existing benchmarks have demonstrated LLMs' proficiency at factual recall, their ability to understand and reproduce the intricate web of relationships that defines a domain's conceptual structure remains largely unexplored. Our three-layer analytical framework provides a scalable and robust methodology for assessing the depth of an LLM's knowledge across diverse academic domains. By comparing LLM-generated knowledge graphs to expert-curated encyclopedias, we reveal a consistent and significant ``relational deficit'': LLMs recognize domain-specific concepts but consistently fail to reproduce their relational structure. Our findings highlight the need for more sophisticated evaluation metrics that go beyond simple accuracy and assess the relational integrity of an LLM's knowledge. We demonstrate that this deficit is highly domain-dependent, with performance varying significantly across ten specialized encyclopedias spanning sociology, political science, philosophy, and other fields. The cases of complete relational failure in the most specialized domains are particularly revealing, suggesting that the LLM's internal knowledge representation is not aligned with the conceptual structures of these fields. This has significant implications for the deployment of LLMs in high-stakes applications that require a deep, nuanced understanding of domain-specific knowledge.

While multi-task learning based automatic radiology report generation (RRG) is widely adopted to ensure clinical consistency, most focus on architectural designs yet remain limited to coarse linear scalarization strategies. These strategies cannot effectively balance the hard constraints of discriminative clinical supervision with the smoothness requirements of report generation. To address these problems, we analyze the failure mechanism of linear scalarization from the perspective of gradient dynamics, utilizing the stochastic differential equation (SDE) framework to characterize it as a "Double Dilemma" of drift term deviation and diffusion term decay. Based on this, we propose a backbone-agnostic optimizer named Conflict-Averse Magnitude-Enhanced Gradient Descent (CAME-Grad). Through conflict-averse direction rectification and magnitude-enhanced energy injection, the algorithm not only ensures geometric validity, but also avoids local optimal solutions. Then, the adaptive gradient fusion mechanism is used to establish a dynamic balance between the theoretical optimal direction and the task-specific inductive bias. Experiments show that as a universal plug-and-play optimizer, CAME-Grad brings substantial and consistent improvements across eight diverse RRG methods, elevating overall clinical efficacy performance by an average of 2.3\% on MIMIC-CXR and 1.9\% on IU X-Ray. Our code is available at https://github.com/vpsg-research/CAME-Grad.

Skills are increasingly used to package agent instructions, workflows, scripts, and reference materials. In enterprise settings, however, skills often need to express more than task guidance: they must make goals, input boundaries, permissions, evidence requirements, output contracts, quality criteria, verification steps, human approval points, and handoff rules inspectable. This paper proposes contractual skills, a GovernSpec-inspired design framework for organizing SKILL.md files as readable task contracts while preserving lightweight skill discovery and progressive loading. The framework clarifies the boundary between contractual skills, GovernSpec YAML contracts, Model Context Protocol surfaces, tool adapters, runtime guardrails, tracing, and evaluation systems. We evaluate the framework with two offline experiments. A text-generation study covers three enterprise skills, fifteen synthetic tasks, four instruction conditions, and eight generation models, yielding 960 outputs and 1680 cross-judge score records. Contractual skills outperform no-skill and minimal-skill baselines on all tested models. Relative to information-rich plain expanded skills, the gains are small and mixed, suggesting that contractual fields mainly improve checkability and maintainability rather than raw generation quality. A tool-calling challenge covers eight models and 192 simulated tool-call records. Skills usually reduce high-risk tool attempts, but model differences remain and runtime tool guardrails are still required. The results suggest that contractual skills are best understood as a governance layer that makes task intent, boundaries, and acceptance criteria explicit, not as a standalone safety mechanism.

The Python robotics ecosystem faces a challenge: while many libraries exist for rigid body transformations, few are both lightweight and mathematically strict. This paper introduces SE3Kit, a lightweight Python library efficient operations on the Special Euclidean Group SE(3) and the Special Orthogonal Group SO(3). Unlike established frameworks that require heavy dependencies (e.g., SpatialMath, PyPose) or general tools that lack robotics-specific features (e.g., SciPy), SE3Kit targets the gap between these extremes. It is designed for embedded deployment, rapid prototyping, and education while providing rigorous mathematical implementation. It provides a pure-Python, NumPy-only implementation of Lie Group operations, without the overhead of deep learning or other visualization software.

Accurately reconstructing full-body poses from sparse head and hand trajectories is a foundational challenge for immersive AR/VR telepresence. Current methods often struggle with error accumulation and unnatural joint coordination, primarily because they treat the human body as a monolithic entity, thereby failing to capture the fine-grained ``atomic intents'' embedded in subtle signal variations and overlooking the inherent structural topology. To bridge this gap, we present AtomicMotion, a framework designed to decouple and re-integrate body dynamics through three core innovations. First, we introduce a logical body partitioning scheme that decomposes the skeleton into five distinct clusters based on functional intent; this ensures that each partition preserves internal joint synergies while isolating local motion primitives. Second, to robustly map sparse inputs to high-dimensional poses, we employ a masked full-body pre-conditioning strategy during training, forcing the model to internalize global skeletal topology and latent kinematic constraints. Finally, addressing the limitations of vanilla spatial attention, which often ignores fixed physiological connectivity, we propose Kinematic Attention. By embedding the classical kinematic tree structure into the attention mechanism, we ensure biological plausibility in the synthesized motions. Extensive evaluations on the AMASS dataset demonstrate that AtomicMotion significantly outperforms existing baselines, yielding higher reconstruction fidelity and superior biomechanical realism.

Parametric human models capture global pose but cannot represent the non-rigid surface dynamics of clothing and soft tissue. Generic scene flow estimates dense motion but breaks down on articulated bodies, where pixel-level supervision is also intractable to acquire. We introduce H-Flow, a dense human scene flow that captures both skeletal kinematics and surface deformation. A unified multi-head transformer estimates flow from monocular video, jointly predicting pose and depth as companion outputs. The challenge lies in the lack of supervision. In place of unattainable labels, we anchor the network in the physics of human motion, encoding geometric, structural, and biomechanical priors as cross-modal training objectives. We further introduce DynAct4D, a high-fidelity synthetic benchmark providing dense flow annotations across diverse subjects, garments, and motions. On standard benchmarks, H-Flow outperforms scene-flow and parametric baselines, and generalizes zero-shot to in-the-wild video. Code, models, and the DynAct4D benchmark will be released upon publication

Digital Image Correlation (DIC) enables dense, time-resolved measurement of surface strain in deforming materials, providing insight into strain localization and failure mechanisms. However, the resulting strain fields are typically explored frame-by-frame through spatial visualizations, making global temporal patterns difficult to discern. We present a visual summarization approach that represents the evolution of high-strain regions as a single Sankey diagram constructed from superlevel sets of the von Mises equivalent strain field. By tracking connected components over time via spatial overlap, the diagram encodes the birth, persistence, merging, and disappearance of strain concentrations. Applied to four tensile test datasets with varying notch geometries, the approach compactly captures differences in deformation regimes and qualitative precursors to failure, complementing traditional spatial strain visualizations with a global temporal overview.

Wasserstein Policy Optimization (WPO) is a recently proposed reinforcement learning algorithm that leverages Wasserstein gradient flows to optimize stochastic policies in continuous action spaces. Despite its empirical success, the theoretical convergence properties of WPO in environments with continuous state and action spaces have yet to be fully established. In this note, we argue that WPO within the framework of entropy-regularised Markov Decision Processes converges linearly. This is done by leveraging recent advances in mean-field analysis for convergence of gradient flows using log-Sobole inequalities. Assuming existence of sufficiently regular solution to the gradient flow equation we demonstrate monotonic energy dissipation along the flow and establish a local log-Sobolev inequality. Ultimately, these properties allow us to argue that the value function should converge linearly to the global optimum.

The detection of previously unseen network attacks remains a major challenge for intrusion detection systems. Although supervised learning methods often perform well on known attack classes, they are limited when new attack types are not represented in the training data. Unsupervised methods are more suitable for detecting zero-day attacks, as they do not require labelled attack samples, but they often suffer from high false positive rates, which limits their real-world usefulness. This paper presents UNAD+, an enhanced framework for unknown network attack detection derived from the previously proposed Unknown Network Attack Detector (UNAD). UNAD+ combines a benign-only unsupervised ensemble with Weighted Majority Voting (WMV), a supervised refinement stage trained on pseudo-labelled detections, and a post hoc explainability layer that provides both local and global explanations. The framework was evaluated on the CICIDS2017 and NSL-KDD benchmark datasets. The results show that UNAD+ improves on the original UNAD framework, achieving F1-scores above 98% across the benchmark datasets while significantly reducing false positives and enhancing transparency and deployment suitability through integrated explainability.

Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning ability of LLMs, but often depends on external supervision from human annotations or gold-standard solutions. Reinforcement learning from internal feedback (RLIF) has recently emerged as a scalable unsupervised alternative, using signals extracted from the model itself. However, existing RLIF methods typically rely on a single internal reward, which can lead to reward hacking, entropy collapse, and degraded reasoning structure. We propose a multi-reward RLIF framework that decomposes the training signal into two complementary components: an answer-level reward based on cluster voting and a completion-level reward based on token-wise self-certainty. To combine these signals robustly, we apply GDPO-based normalization to reduce reward-scale imbalance. We further introduce KL-Cov regularization, which targets low-entropy token distributions responsible for disproportionate entropy reduction, preserving exploration and preventing late-stage collapse. Across mathematical reasoning and code-generation benchmarks, our method improves stability and robustness over prior unsupervised RL approaches, while achieving performance close to supervised RLVR methods. These results show that complementary internal rewards, combined with targeted regularization, can support stable long-horizon reasoning without relying on external ground-truth supervision. Code will be released soon.

Grounding radiology report descriptions to 3D CT volumes is essential for verifiable clinical interpretation, yet remains challenging due to the semantic-spatial gap between free-text narratives and volumetric anatomy. Existing report-assisted and vision-language grounding methods typically rely on phrase-level alignment or dense pixel supervision, resulting in limited lesion-wise correspondence and suboptimal localization accuracy. We propose GLeVE, a graph-guided lesion grounding framework with anatomical prior verification and octree-based autoregressive refinement. GLeVE treats each lesion description as an atomic semantic unit and encodes organ attribution, attributes, and inter-lesion relations through relation-aware graph reasoning to produce discriminative lesion-wise queries. Anatomy-aware proposal generation with region-level verification enforces one-to-one text-lesion alignment, while hierarchical octree refinement progressively improves boundary delineation. Experiments on AbdomenAtlas 3.0 demonstrate consistent gains over classical multimodal foundation models and report-supervised baselines in both segmentation accuracy and lesion-level localization.

Understanding how conceptual knowledge is grounded in bodily experience, and to what extent machine systems can acquire such knowledge without direct sensorimotor experience, are central questions in both cognitive science and embodied artificial intelligence research. Large-scale normative resources are essential for investigating these questions empirically, yet such resources remain sparse for non-Indo-European languages. We present a novel normative database for 3,000 lexicalized concepts in Mandarin Chinese, comprising 11-dimensional sensorimotor ratings and unidimensional embodiment ratings collected from 378 native Mandarin speakers. The ratings demonstrate high reliability and strong cross-norm validity with existing Chinese resources, each of which covers fewer words and a subset of the 11 sensorimotor dimensions. In a validation study, we tested new variables derived from a theoretically motivated metric, Perceptual Strength of Embodiment (PSE) (Huang et al., 2025), together with seven common composite variables, on lexical decision tasks. The results suggest that PSE-Sensorimotor and Minkowski-3 are the strongest composite predictors of lexical decision performance, capturing the facilitatory effects of sensorimotor information on lexical processing. A further exploratory study showed that sensorimotor ratings are substantially recoverable from purely linguistic representations using simple regression models (mean Spearman r = .62 across dimensions), though recovery varied markedly: visual and auditory dimensions yielded higher correspondence than chemosensory ones. Representational similarity analysis further showed that the relational geometry of the sensorimotor space is also partially recoverable (r = .540), consistent with the view that distributional language use encodes aspects of embodied conceptual structure.

Recent LLM-guided evolutionary search methods have shown that iterative program mutation can discover strong algorithms, but they typically optimize each task independently, even when related tasks share reusable structure. We introduce Evolutionary Multi-Task Optimization (EMO) for LLM-guided program discovery, and propose EMO-STA (Shared-Then-Adapt), a two-stage framework that first evolves a shared archive of executable programs across a task family and then adapts selected shared candidates to each target task. Within EMO-STA, we explore multiple adaptation strategies, including warm-starting from the shared archive, adapting the best average shared program, and adapting the shared program that performs best on each target task. Across eight task families spanning continuous optimization, geometric construction, modeling, and algorithmic optimization, EMO-STA improves over matched-compute single-task evolution in most settings, with STA Best-Local providing the strongest in-distribution adaptation and STA Best-Shared yielding robust transfer to unseen tasks. Compute-allocation experiments show that allocating a substantial fraction of the family-level budget to shared evolution is consistently beneficial, with roughly balanced shared and adaptation budgets often being optimal. Beyond compute efficiency, we show that shared evolution can mitigate overfitting in low-evidence settings (e.g. few training data), including ARC tasks and time-series feature engineering, by favoring programs that generalize across all tasks rather than exploiting task-specific brittle artifacts.

Benchmarks are necessary for healthcare evaluation, but are not sufficient for predicting deployment performance. Our position is that the evaluation--deployment gap arises not because of poorly designed benchmarks, but from implicit assumptions about how users interact with models that cannot be surfaced from benchmarks alone. To make this precise, we propose a classification of assumptions into two categories: task, which can be tested from conversation data alone, and outcome, which requires outcome data and behavioral studies for testing. Critically, outcome assumptions depend on human behavior, something that even well-designed benchmarks cannot directly observe. To demonstrate the operationality of this framework, we retrospectively analyze a healthcare RCT as a case study and find that the gap naturally separates into task and outcome gaps of roughly equal size. To address this, we make two contributions: first, we propose BenchmarkCards, an artifact that documents assumptions, and second, we propose staged evaluation, a procedure that systematically tests assumptions and evaluates performance.

Antimicrobial stewardship (AMS) is critical in pediatric intensive care units (PICUs), where diagnostic uncertainty often drives broad-spectrum antibiotic use, increasing antimicrobial resistance and potential long-term harms. Machine learning offers a promising approach for identifying patient-level opportunities for stewardship interventions from electronic health record data, yet prior work has focused largely on adult populations and static tabular representations. We present a systematic benchmarking study of AMS intervention prediction in the PICU across a public dataset and a private institutional cohort. We define four clinically relevant proxy targets for reducing antibiotic exposure: intravenous-to-oral switching, de-escalation, discontinuation, and short-course therapy. Under a unified evaluation framework, we compare tabular, sequence-based, and graph-based temporal models at multiple temporal resolutions. We find that predictive performance is driven primarily by target prevalence and dataset characteristics rather than model complexity. Sequence models improve the precision-recall trade-off over tabular approaches at coarse (24-hour) resolution, while finer temporal modeling provides limited additional benefit. However, these gains come at the cost of poorer calibration, with simpler tabular models yielding more reliable probability estimates. Multi-task learning produces only marginal improvements, suggesting limited shared structure across stewardship targets. Our findings highlight the importance of target design, temporal representation, and calibration in clinical machine learning, and provide practical guidance for developing reliable decision support systems for pediatric AMS.

Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This autonomy poses serious challenges for overseeing and assessing agent behavior. Most current tools are limited, focusing on observability with basic evaluation capabilities or imposing static, hand-crafted error taxonomies that cannot adapt to new domains. To address this gap, we present Agentic CLEAR, an automatic, dynamic, and easy-to-use evaluation framework. It produces textual insights into the agent behavior on three levels of granularity: system, trace, and node. Agentic CLEAR operates above the observability layer, enabling seamless integration and featuring an intuitive UI that makes agent evaluation highly accessible. In our experiments on four benchmarks, seven agentic settings, and tens of thousands of LLM calls, we show that Agentic CLEAR produces high-quality, data-driven, insightful feedback. Our analysis shows strong alignment with human-annotated errors and the ability to predict task success rate.

Gaze following requires both scene understanding and gaze reasoning to localize the gaze target of an in-scene person. Recently, vision foundation models (VFMs) have demonstrated strong performance on this task, enabling simpler architectures while outperforming prior methods. However, we observe a key limitation of VFM-based approaches: while VFMs substantially improve scene understanding, they contribute little to gaze reasoning. As a result, existing methods often rely on semantically salient objects rather than true gaze cues, leading to degraded performance when targets are not salient. To address this, we propose a novel training mechanism to enhance gaze reasoning in VFMs for gaze following. Our method includes: (1) a head-conditioned local LoRA, which enables localized adaptation to preserve scene token learning while improving head token learning for gaze reasoning; and (2) an out-of-cone penalty, which injects gaze cues into head tokens while aligning them with scene tokens. Experiments on the GazeFollow and VAT datasets demonstrate that our method achieves state-of-the-art performance, with particularly strong improvements when gaze targets are not semantically salient. Our findings offer valuable insights for advancing future gaze following research. We will release the code once the paper is accepted.

We study missing-link inference in partially observed networks by systematically comparing dyadic link prediction (LP) with hyperlink prediction (HP) and an estimation-based ERGM comparator. LP serves as the primary baseline, using classical heuristics computed on the observed graph. HP extends this framework by scoring candidate higher-order structures (cliques) via lifted dyadic scores and via the CHEbyshev Spectral HyperlInk pREdictor (CHESHIRE). All methods are evaluated under a common masking protocol that removes dyadic evidence induced by held-out hyperlinks to ensure comparability. Across public email and covert-network datasets, LP remains strong for dyadic recovery, while HP -- particularly CHESHIRE -- provides gains when the inferential target is higher-order group structure. ERGMs offer an interpretable dependence-based complement through conditional tie probabilities. The contribution is a comparative, reproducible evaluation clarifying when LP, HP, and ERGM estimation are most appropriate under network missingness.

Object detection from Unmanned Aerial Vehicles (UAVs) is challenged by severe ego-motion, camera jitter, and large scale variations. While modern detectors perform well on static images, their direct application to UAV video often fails, particularly for small objects in dynamic scenes. Existing motion-based methods either rely on computationally expensive optical flow or use single-interval differencing, which is sensitive to jitter and limited in capturing diverse motion patterns. We propose a vision-only motion-guided detection framework that decouples target motion from camera-induced disturbances. A homography-based Global Motion Compensation (GMC) first aligns adjacent frames. We then introduce a Dual-Interval Motion Extraction strategy that captures both short-term and long-term motion cues. To integrate these cues, a lightweight Motion-Guided Attention (MGA) module enhances feature representations within a Feature Pyramid Network. Experiments on the VisDrone-VID dataset demonstrate consistent improvements over a strong YOLOv8 baseline under severe ego-motion. Ablation studies further confirm the effectiveness of the dual-interval design and the proposed motion-guided attention mechanism.

The advent of cardless artificial intelligence (AI) banking heralds a paradigm shift in the financial landscape, offering users unprecedented security and convenience. This paper outlines a comprehensive framework designed to enhance cybersecurity, introduce auto-generated virtual cards, and mitigate fraud risks within cardless AI banking systems. The framework envisions a future banking architecture that employs AI-powered data cryptography to create secure virtual cards for seamless transactions. By emphasizing secure communication channels, it ensures the integrity of financial activities among banking systems, cardholders, and third-party vendors. AI-based authorization methodologies play a pivotal role in authenticating each transaction while proactively identifying potential fraud, demonstrating the framework's efficacy in fortifying cardless AI banking security. The initial approach, featuring an AI-driven, feature-based banking system, ensures the generation of virtual cards with encrypted data, minimizing information exposure and reducing fraud risks. Integrating a machine learning algorithm adds an additional layer of protection against potential fraudulent activities. In conclusion, the proposed framework establishes a holistic cybersecurity and fraud-mitigation paradigm for cardless AI banking systems. Its implementation empowers financial institutions to address security concerns associated with traditional banking, paving the way for a future banking landscape that is not only fraud-resistant but also secure and convenient for users.

Persuasive dialogue requires reasoning about others' latent mental states, a capability known as Theory of Mind (ToM). However, due to reliance on simple prompting strategies and insufficient ToM knowledge, existing LLMs often fail to capture the intrinsic dependencies among mental states, leading to fragmented representations and unstable reasoning. To address these challenges, we introduce the ToM-based Persuasive Dialogue (ToM-PD) task, grounded in the Belief-Desire-Intention (BDI) framework, which explicitly models the sequential dependencies among mental states in multi-turn dialogues. To facilitate research on this task, we construct a large-scale annotated dataset, ToM-based Broad Persuasive Dialogues (ToM-BPD), capturing fine-grained mental states and corresponding persuasive strategies. We further propose Think Thrice Before You Speak (TTBYS), a knowledge-enhanced stepwise reasoning framework that leverages both explicit and implicit prior experiences to improve LLMs' inference of desires, beliefs, and persuasive strategies. Experimental results demonstrate that Qwen3-8B equipped with TTBYS outperforms GPT-5 by 1.20%, 22.80%, and 16.97% in predicting desires, beliefs, and persuasive strategies, respectively. Case studies further show that our approach enhances interpretability and consistency in reasoning.

Motion planning for autonomous driving must account for multi-modal uncertainty in both the intentions and trajectories of surrounding vehicles. Handling uncertainty in a worst-case manner guarantees robustness but often leads to excessive conservatism. Stochastic Model Predictive Control (SMPC) reduces trajectory-level conservatism through chance constraints, yet remains conservative with respect to intention uncertainty since constraints must hold across all intentions. We present a novel combination of SMPC and the branching structure, enabling the planner to generate distinct trajectories for different possible intentions while maintaining safety under trajectory uncertainty. A novel scenario clustering is proposed to merge prediction scenarios based on high-level decision similarity, thereby ensuring real-time tractability. Furthermore, an adaptive branching-time computation postpones commitment to separate plans until intention uncertainty is sufficiently reduced. Simulation studies in challenging highway scenarios demonstrate that the proposed method improves safety, reduces conservatism, and achieves real-time computational performance.