搜索结果：Ready

Estimating simulation-ready scenes from real-world observations is crucial for downstream planning and policy learning tasks. Regretfully, existing methods struggle in cluttered environments, often exhibiting prohibitive computational cost, poor robustness, and restricted generality when scaling to multiple interacting objects. We propose a unified optimization-based formulation for real-to-sim scene estimation that jointly recovers the shapes and poses of multiple rigid objects under physical constraints. Our method is built on two key technical innovations. First, we leverage the recently introduced shape-differentiable contact model, whose global differentiability permits joint optimization over object geometry and pose while modeling inter-object contacts. Second, we exploit the structured sparsity of the augmented Lagrangian Hessian to derive an efficient linear system solver whose computational cost scales favorably with scene complexity. Building on this formulation, we develop an end-to-end Simulation-ready Physics-Aware Reconstruction for Cluttered Scenes (SPARCS) pipeline, which integrates learning-based object initialization, physics-constrained joint shape-pose optimiza

Three-dimensional content generation has progressed from producing isolated, visually plausible shapes to constructing structured assets that can be deployed in real-time interactive environments. This trajectory is driven by converging demands from game development, embodied AI, world simulation, digital twins, and spatial computing, all of which require 3D content that goes beyond surface appearance to satisfy engine-level constraints on topology, UV parameterization, physically based materials, skeletal rigging, and physics-aware scene layout. Despite rapid advances in generative modeling, a persistent gap separates the outputs of current methods from the production-ready standard expected by interactive applications. This survey addresses that gap by organizing the literature around the asset production pipeline rather than algorithmic families. Along the horizontal axis we distinguish three asset tiers, namely general objects, characters, and scenes, while the vertical axis traces each tier through the full production lifecycle from data foundations and geometry synthesis through topology optimization, UV unwrapping, PBR appearance, rigging, and scene assembly. Through this tw

Visual navigation models often struggle in real-world dynamic environments due to limited robustness to the sim-to-real gap and the difficulty of training policies tailored to target deployment environments (e.g., households, restaurants, and factories). Although real-to-sim navigation simulation using 3D Gaussian Splatting (GS) can mitigate these challenges, prior GS-based works have considered only static scenes or non-photorealistic human obstacles built from simulator assets, despite the importance of safe navigation in dynamic environments. To address these issues, we propose ReaDy-Go, a novel real-to-sim simulation pipeline that synthesizes photorealistic dynamic scenarios in target environments by augmenting a reconstructed static GS scene with dynamic human GS obstacles, and trains navigation policies using the generated datasets. The pipeline provides three key contributions: (1) a dynamic GS simulator that integrates static scene GS with a human animation module, enabling the insertion of animatable human GS avatars and the synthesis of plausible human motions from 2D trajectories, (2) a navigation dataset generation framework that leverages the simulator along with a rob

We devise two complementary characterizations of hereditary history-preserving bisimilarity (HHPB): a denotational one, based on stable configuration structures, and an operational one, formulated in a reversible process calculus. Our characterizations rely on forward-reverse bisimilarity augmented with backward ready multiset equality. This shifts the emphasis from uniquely identifying events, as done in previous characterizations, to counting occurrences of identically labeled events associated with incoming transitions, which yields a more lightweight behavioral equivalence than HHPB. We show that our characterizations correctly distinguish between autoconcurrency and autocausation, but are valid only in the absence of non-local conflicts. We then study the logical foundations of these characterizations by relating event identifier logic, which captures the classical view of HHPB, and backward ready multiset logic, developed for our new equivalence.

Communication in times of crisis is essential. However, there is often a mismatch between the language of governments, aid providers, doctors, and those to whom they are providing aid. Commercial MT systems are reasonable tools to turn to in these scenarios. But how effective are these tools for translating to and from low resource languages, particularly in the crisis or medical domain? In this study, we evaluate four commercial MT systems using the TICO-19 dataset, which is composed of pandemic-related sentences from a large set of high priority languages spoken by communities most likely to be affected adversely in the next pandemic. We then assess the current degree of ``readiness'' for another pandemic (or epidemic) based on the usability of the output translations.

As quantum computing progresses, traditional cryptographic systems face the threat of obsolescence due to the capabilities of quantum algorithms. This paper introduces the Quantum-Ready Architecture for Security and Risk Management (QUASAR), a novel framework designed to help organizations prepare for the post-quantum era. QUASAR provides a structured approach to transition from current cryptographic systems to quantum-resistant alternatives, emphasizing technical, security, and operational readiness. The framework integrates a set of actionable components, a timeline for phased implementation, and continuous optimization strategies to ensure ongoing preparedness. Through performance indicators, readiness scores, and optimization functions, QUASAR enables organizations to assess their current state, identify gaps, and execute targeted actions to mitigate risks posed by quantum computing. By offering a comprehensive, adaptable, and quantifiable strategy, QUASAR equips organizations with the tools necessary to future-proof their operations and secure sensitive data against the impending rise of quantum technologies.

Optical lithography is the main enabler to semiconductor manufacturing. It requires extensive processing to perform the Resolution Enhancement Techniques (RETs) required to transfer the design data to a working Integrated Circuits (ICs). The processing power and computational runtime for RETs tasks is ever increasing due to the continuous reduction of the feature size and the expansion of the chip area. State-of-the-art research sought Machine Learning (ML) technologies to reduce runtime and computational power, however they are still not used in production yet. In this study, we analyze the reasons holding back ML computational lithography from being production ready and present a novel highly scalable end-to-end flow that enables production ready ML-RET correction.

Non-Fungible Tokens (NFTs) offer a promising mechanism to protect Australian and Indigenous artists' copyright. They represent and transfer the value of artwork in digital form. Before adopting NFTs to protect Australian artwork, we in this paper investigate them empericially. We focus on examining the details of NFT structure. We start from the underlying structure of NFTs to show how they represent copyright for both artists and production owners, as well as how they aim to safeguard or secure the value of digital artworks. We then involve data collection from various types of sources with different storage methods, including on-chain, centralized, and decentralized systems. Based on both metadata and artwork content, we present our analysis and discussion on the following key issues: copyright, security and artist identification. The final results of the evaluation, unfortnately, show that the NFT is NOT ready to protect Australian and Indigenous artists' copyright.

Globally, there is an increased need for guidelines to produce high-quality data outputs for analysis. No framework currently exists that provides guidelines for a comprehensive approach to producing analysis ready data (ARD). Through critically reviewing and summarising current literature, this paper proposes such guidelines for the creation of ARD. The guidelines proposed in this paper inform ten steps in the generation of ARD: ethics, project documentation, data governance, data management, data storage, data discovery and collection, data cleaning, quality assurance, metadata, and data dictionary. These steps are illustrated through a substantive case study that aimed to create ARD for a digital spatial platform: the Australian Child and Youth Wellbeing Atlas (ACYWA).

Quantum computing threatens to undermine classical cryptography by breaking widely deployed encryption and signature schemes. This paper examines enterprise readiness for quantum-safe cybersecurity through three perspectives: (i) the technologist view, assessing the maturity of post-quantum cryptography (PQC) and quantum key distribution (QKD); (ii) the enterprise (CISO/CIO) view, analyzing organizational awareness, risk management, and operational barriers; and (iii) the threat actor view, evaluating the evolving quantum threat and the urgency of migration. Using recent standards (e.g., NIST's 2024 PQC algorithms), industry surveys, and threat intelligence, we synthesize findings via a SWOT analysis to map strengths, weaknesses, opportunities, and threats. Results indicate uneven and generally insufficient preparedness: while PQC standards and niche QKD deployments signal technical progress, fewer than 5\% of enterprises have formal quantum-transition plans, and many underestimate "harvest now, decrypt later" risks. Financial, telecom, and government sectors have begun migration, but most industries remain exploratory or stalled by costs, complexity, and skills gaps. Expert consen

Workshop proceedings of two co-located workshops "Working with Troubles and Failures in Conversation with Humans and Robots" (WTF 2023) and "Is CUI Design Ready Yet?", both of which were part of the ACM conference on conversational user interfaces 2023. WTF 23 aimed at bringing together researchers from human-robot interaction, dialogue systems, human-computer interaction, and conversation analysis. Despite all progress, robotic speech interfaces continue to be brittle in a number of ways and the experience of failure of such interfaces is commonplace amongst roboticists. However, the technical literature is positively skewed toward their good performance. The workshop aims to provide a platform for discussing communicative troubles and failures in human-robot interactions and related failures in non-robotic speech interfaces. Aims include a scrupulous investigation into communicative failures, to begin working on a taxonomy of such failures, and enable a preliminary discussion on possible mitigating strategies. Workshop website: https://sites.google.com/view/wtf2023/overview Is CUI Design Ready Yet? As CUIs become more prevalent in both academic research and the commercial market,

A discussion on the readiness of Italian universities to address gender-related issues from a regional standpoint is proposed. A statistical analysis is conducted on data of all scholars enrolled in Italian universities from 2000 to 2023 to investigate why the glass ceiling of the full professor position remains so challenging to break in almost all scientific fields and across all regions of Italy.

Automatic summarization of legal case judgements has traditionally been attempted by using extractive summarization methods. However, in recent years, abstractive summarization models are gaining popularity since they can generate more natural and coherent summaries. Legal domain-specific pre-trained abstractive summarization models are now available. Moreover, general-domain pre-trained Large Language Models (LLMs), such as ChatGPT, are known to generate high-quality text and have the capacity for text summarization. Hence it is natural to ask if these models are ready for off-the-shelf application to automatically generate abstractive summaries for case judgements. To explore this question, we apply several state-of-the-art domain-specific abstractive summarization models and general-domain LLMs on Indian court case judgements, and check the quality of the generated summaries. In addition to standard metrics for summary quality, we check for inconsistencies and hallucinations in the summaries. We see that abstractive summarization models generally achieve slightly higher scores than extractive models in terms of standard summary evaluation metrics such as ROUGE and BLEU. However,

The emergence of quantum technologies has led to groundbreaking advancements in computing, sensing, secure communications, and simulation of advanced materials with practical applications in every industry sector. The rapid advancement of the quantum technologies ecosystem has made it imperative to assess the maturity of these technologies and their imminent acceleration towards commercial viability. The current status of quantum technologies is presented and the need for a quantum-ready ecosystem is emphasised. Standard Quantum Technology Readiness Levels (QTRLs) are formulated and innovative models and tools are defined to evaluate the readiness of specific quantum technology. In addition to QTRLs, Quantum Commercial Readiness Levels (QCRLs) is introduced to provide a robust framework for evaluating the commercial viability and market readiness of quantum technologies. Furthermore, relevant indicators concerning key stakeholders, including government, industry, and academia are discussed and ethics and protocols implications are described, to deepen our understanding of the readiness for quantum technology and support the development of a robust and effective quantum ecosystem.

We present evidence that learned density functional theory (``DFT'') force fields are ready for ground state catalyst discovery. Our key finding is that relaxation using forces from a learned potential yields structures with similar or lower energy to those relaxed using the RPBE functional in over 50\% of evaluated systems, despite the fact that the predicted forces differ significantly from the ground truth. This has the surprising implication that learned potentials may be ready for replacing DFT in challenging catalytic systems such as those found in the Open Catalyst 2020 dataset. Furthermore, we show that a force field trained on a locally harmonic energy surface with the same minima as a target DFT energy is also able to find lower or similar energy structures in over 50\% of cases. This ``Easy Potential'' converges in fewer steps than a standard model trained on true energies and forces, which further accelerates calculations. Its success illustrates a key point: learned potentials can locate energy minima even when the model has high force errors. The main requirement for structure optimisation is simply that the learned potential has the correct minima. Since learned pote

Currently, numerous approaches exist supporting the implementation of forensic readiness and, indirectly, forensic-ready software systems. However, the terminology used in the approaches and their focus tends to vary. To facilitate the design of forensic-ready software systems, the clarity of the underlying concepts needs to be established so that their requirements can be unambiguously formulated and assessed. This is especially important when considering forensic readiness as an add-on to information security. In this paper, the concepts relevant to forensic readiness are derived and aligned based on six existing approaches. The results then serve as a stepping stone for enhancing Information Systems Security Risk Management (ISSRM) with forensic readiness.

Many organizations that vitally depend on computation for their competitive advantage are keen to exploit the expected performance of quantum computers (QCs) as soon as quantum advantage is achieved. The best approach to deliver hardware quantum advantage for high-value problems is not yet clear. This work advocates establishing quantum-ready applications and underlying tools and formulations, so that software development can proceed now to ensure being ready for quantum advantage. This work can be done independently of which hardware approach delivers quantum advantage first. The quadratic unconstrained binary optimization (QUBO) problem is one such quantum-ready formulation. We developed the next generation of qbsolv, a tool that is widely used for sampling QUBOs on early QCs, focusing on its performance executing purely classically, and deliver it as a cloud service today. We find that it delivers highly competitive results in all of quality (low energy value), speed (time to solution), and diversity (variety of solutions). We believe these results give quantum-forward users a reason to switch to quantum-ready formulations today, reaping immediate benefits in performance and div