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In this paper, we study arbitrary infinite binary information systems each of which consists of an infinite set called universe and an infinite set of two-valued functions (attributes) defined on the universe. We consider the notion of a problem over information system, which is described by a finite number of attributes and a mapping associating a decision to each tuple of attribute values. As algorithms for problem solving, we investigate deterministic and nondeterministic decision trees that use only attributes from the problem description. Nondeterministic decision trees are representations of decision rule systems that sometimes have less space complexity than the original rule systems. As time and space complexity, we study the depth and the number of nodes in the decision trees. In the worst case, with the growth of the number of attributes in the problem description, (i) the minimum depth of deterministic decision trees grows either as a logarithm or linearly, (ii) the minimum depth of nondeterministic decision trees either is bounded from above by a constant or grows linearly, (iii) the minimum number of nodes in deterministic decision trees has either polynomial or exponential growth, and (iv) the minimum number of nodes in nondeterministic decision trees has either polynomial or exponential growth. Based on these results, we divide the set of all infinite binary information systems into three complexity classes. This allows us to identify nontrivial relationships between deterministic decision trees and decision rules systems represented by nondeterministic decision trees. For each class, we study issues related to time-space trade-off for deterministic and nondeterministic decision trees.

In this paper, we propose a method to extract physically-based rendering (PBR) materials from a single real-world image. We do so in two steps: first, we map regions of the image to material concepts using a diffusion model, which allows the sampling of texture images resembling each material in the scene. Second, we benefit from a separate network to decompose the generated textures into Spatially Varying BRDFs (SVBRDFs), providing us with materials ready to be used in rendering applications. Our approach builds on existing synthetic material libraries with SVBRDF ground truth, but also exploits a diffusion-generated RGB texture dataset to allow generalization to new samples using unsupervised domain adaptation (UDA). Our contributions are thoroughly evaluated on synthetic and real-world datasets. We further demonstrate the applicability of our method for editing 3D scenes with materials estimated from real photographs. The code and models will be made open-source. Project page: //astra-vision.github.io/MaterialPalette/

In this study, we explore the potential of Multimodal Large Language Models (MLLMs) in improving embodied decision-making processes for agents. While Large Language Models (LLMs) have been widely used due to their advanced reasoning skills and vast world knowledge, MLLMs like GPT4-Vision offer enhanced visual understanding and reasoning capabilities. We investigate whether state-of-the-art MLLMs can handle embodied decision-making in an end-to-end manner and whether collaborations between LLMs and MLLMs can enhance decision-making. To address these questions, we introduce a new benchmark called PCA-EVAL, which evaluates embodied decision-making from the perspectives of Perception, Cognition, and Action. Additionally, we propose HOLMES, a multi-agent cooperation framework that allows LLMs to leverage MLLMs and APIs to gather multimodal information for informed decision-making. We compare end-to-end embodied decision-making and HOLMES on our benchmark and find that the GPT4-Vision model demonstrates strong end-to-end embodied decision-making abilities, outperforming GPT4-HOLMES in terms of average decision accuracy (+3%). However, this performance is exclusive to the latest GPT4-Vision model, surpassing the open-source state-of-the-art MLLM by 26%. Our results indicate that powerful MLLMs like GPT4-Vision hold promise for decision-making in embodied agents, offering new avenues for MLLM research. Code and data are open at //github.com/pkunlp-icler/PCA-EVAL/.

In this paper, we address the problem of video super-resolution (VSR) using Diffusion Models (DM), and present StableVSR. Our method significantly enhances the perceptual quality of upscaled videos by synthesizing realistic and temporally-consistent details. We turn a pre-trained DM for single image super-resolution into a VSR method by introducing the Temporal Conditioning Module (TCM). TCM uses Temporal Texture Guidance, which provides spatially-aligned and detail-rich texture information synthesized in adjacent frames. This guides the generative process of the current frame toward high-quality and temporally-consistent results. We introduce a Frame-wise Bidirectional Sampling strategy to encourage the use of information from past to future and vice-versa. This strategy improves the perceptual quality of the results and the temporal consistency across frames. We demonstrate the effectiveness of StableVSR in enhancing the perceptual quality of upscaled videos compared to existing state-of-the-art methods for VSR. The code is available at //github.com/claudiom4sir/StableVSR.

In this paper, we develop a high-precision satellite orbit determination model for satellites orbiting the Earth. Solving this model entails numerically integrating the differential equation of motion governing a two-body system, employing Fehlberg's formulation and the Runge-Kutta class of embedded integrators with adaptive stepsize control. Relevant primary perturbing forces included in this mathematical model are the full force gravitational field model, Earth's atmospheric drag, third body gravitational effects and solar radiation pressure. Development of the high-precision model required accounting for the perturbing influences of Earth radiation pressure, Earth tides and relativistic effects. The model is then implemented to obtain a high-fidelity Earth orbiting satellite propagator, namely the Satellite Ephemeris Determiner (SED), which is comparable to the popular High Precision Orbit Propagator (HPOP). The architecture of SED, the methodology employed, and the numerical results obtained are presented.

In this paper, we analyze the regret incurred by a computationally efficient exploration strategy, known as naive exploration, for controlling unknown partially observable systems within the Linear Quadratic Gaussian (LQG) framework. We introduce a two-phase control algorithm called LQG-NAIVE, which involves an initial phase of injecting Gaussian input signals to obtain a system model, followed by a second phase of an interplay between naive exploration and control in an episodic fashion. We show that LQG-NAIVE achieves a regret growth rate of $\tilde{\mathcal{O}}(\sqrt{T})$, i.e., $\mathcal{O}(\sqrt{T})$ up to logarithmic factors after $T$ time steps, and we validate its performance through numerical simulations. Additionally, we propose LQG-IF2E, which extends the exploration signal to a `closed-loop' setting by incorporating the Fisher Information Matrix (FIM). We provide compelling numerical evidence of the competitive performance of LQG-IF2E compared to LQG-NAIVE.

This paper presents, for the first time, a novel Decentralized IDentifier (DID) Method called Over-The-Tangle and discusses its design and working principles that leverage the IOTA Tangle as the Root-of-Trust for identity data. The results of a long lasting experimental test campaign in real-world settings suggests the adoption of a private gateway node synchronised with the IOTA Tangle on the mainnet for efficient DID control. Moreover, the paper promotes the integration of the DID technology into OpenSSL through the use of Providers. A novel DID Operation and Provider is presented as a solution for building DID Method agility in OpenSSL.

In this paper, we conduct a comprehensive analysis of gender stereotypes in the character design of Honor of Kings, a popular multiplayer online battle arena (MOBA) game in China. We probe gender stereotypes through the lens of role assignments, visual designs, spoken lines, and background stories, combining qualitative analysis and text mining based on the moral foundation theory. Male heroes are commonly designed as masculine fighters with power and female heroes as feminine "ornaments" with ideal looks. We contribute with a culture-aware and multi-modal understanding of gender stereotypes in games, leveraging text-, visual-, and role-based evidence.

BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks. In this paper, we describe BERTSUM, a simple variant of BERT, for extractive summarization. Our system is the state of the art on the CNN/Dailymail dataset, outperforming the previous best-performed system by 1.65 on ROUGE-L. The codes to reproduce our results are available at //github.com/nlpyang/BertSum

In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects. First, a reattention mechanism is proposed to refine current attentions by directly accessing to past attentions that are temporally memorized in a multi-round alignment architecture, so as to avoid the problems of attention redundancy and attention deficiency. Second, a new optimization approach, called dynamic-critical reinforcement learning, is introduced to extend the standard supervised method. It always encourages to predict a more acceptable answer so as to address the convergence suppression problem occurred in traditional reinforcement learning algorithms. Extensive experiments on the Stanford Question Answering Dataset (SQuAD) show that our model achieves state-of-the-art results. Meanwhile, our model outperforms previous systems by over 6% in terms of both Exact Match and F1 metrics on two adversarial SQuAD datasets.