85 research outputs found

    Active Learning of Spin Network Models

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    The inverse statistical problem of finding direct interactions in complex networks is difficult. In the context of the experimental sciences, well-controlled perturbations can be applied to a system, probing the internal structure of the network. Therefore, we propose a general mathematical framework to study inference with iteratively applied perturbations to a network. Formulating active learning in the language of information geometry, our framework quantifies the difficulty of inference as well as the information gain due to perturbations through the curvature of the underlying parameter manifold as measured though the empirical Fisher information. Perturbations are then chosen that reduce most the variance of the Bayesian posterior. We apply this framework to a specific probabilistic graphical model where the nodes in the network are modeled as binary variables, "spins" with Ising-form pairwise interactions. Based on this strategy, we significantly improve the accuracy and efficiency of inference from a reasonable number of experimental queries for medium sized networks. Our active learning framework could be powerful in the analysis of complex networks as well as in the rational design of experiments

    Active Learning of Spin Network Models

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    The inverse statistical problem of finding direct interactions in complex networks is difficult. In the context of the experimental sciences, well-controlled perturbations can be applied to a system, probing the internal structure of the network. Therefore, we propose a general mathematical framework to study inference with iteratively applied perturbations to a network. Formulating active learning in the language of information geometry, our framework quantifies the difficulty of inference as well as the information gain due to perturbations through the curvature of the underlying parameter manifold as measured though the empirical Fisher information. Perturbations are then chosen that reduce most the variance of the Bayesian posterior. We apply this framework to a specific probabilistic graphical model where the nodes in the network are modeled as binary variables, "spins" with Ising-form pairwise interactions. Based on this strategy, we significantly improve the accuracy and efficiency of inference from a reasonable number of experimental queries for medium sized networks. Our active learning framework could be powerful in the analysis of complex networks as well as in the rational design of experiments

    FluentSpeech: Stutter-Oriented Automatic Speech Editing with Context-Aware Diffusion Models

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    Stutter removal is an essential scenario in the field of speech editing. However, when the speech recording contains stutters, the existing text-based speech editing approaches still suffer from: 1) the over-smoothing problem in the edited speech; 2) lack of robustness due to the noise introduced by stutter; 3) to remove the stutters, users are required to determine the edited region manually. To tackle the challenges in stutter removal, we propose FluentSpeech, a stutter-oriented automatic speech editing model. Specifically, 1) we propose a context-aware diffusion model that iteratively refines the modified mel-spectrogram with the guidance of context features; 2) we introduce a stutter predictor module to inject the stutter information into the hidden sequence; 3) we also propose a stutter-oriented automatic speech editing (SASE) dataset that contains spontaneous speech recordings with time-aligned stutter labels to train the automatic stutter localization model. Experimental results on VCTK and LibriTTS datasets demonstrate that our model achieves state-of-the-art performance on speech editing. Further experiments on our SASE dataset show that FluentSpeech can effectively improve the fluency of stuttering speech in terms of objective and subjective metrics. Code and audio samples can be found at https://github.com/Zain-Jiang/Speech-Editing-Toolkit.Comment: Accepted by ACL 2023 (Findings

    TextrolSpeech: A Text Style Control Speech Corpus With Codec Language Text-to-Speech Models

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    Recently, there has been a growing interest in the field of controllable Text-to-Speech (TTS). While previous studies have relied on users providing specific style factor values based on acoustic knowledge or selecting reference speeches that meet certain requirements, generating speech solely from natural text prompts has emerged as a new challenge for researchers. This challenge arises due to the scarcity of high-quality speech datasets with natural text style prompt and the absence of advanced text-controllable TTS models. In light of this, 1) we propose TextrolSpeech, which is the first large-scale speech emotion dataset annotated with rich text attributes. The dataset comprises 236,220 pairs of style prompt in natural text descriptions with five style factors and corresponding speech samples. Through iterative experimentation, we introduce a multi-stage prompt programming approach that effectively utilizes the GPT model for generating natural style descriptions in large volumes. 2) Furthermore, to address the need for generating audio with greater style diversity, we propose an efficient architecture called Salle. This architecture treats text controllable TTS as a language model task, utilizing audio codec codes as an intermediate representation to replace the conventional mel-spectrogram. Finally, we successfully demonstrate the ability of the proposed model by showing a comparable performance in the controllable TTS task. Audio samples are available at https://sall-e.github.io

    Persistent fluid flows defined by active matter boundaries

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    Biological systems achieve precise control over ambient fluids through the self-organization of active protein structures including flagella, cilia, and cytoskeletal networks. In active structures individual proteins consume chemical energy to generate force and motion at molecular length scales. Self-organization of protein components enables the control and modulation of fluid flow fields on micron scales. The physical principles underlying the organization and control of active-matter driven fluid flows are poorly understood. Here, we apply an optically-controlled active-matter system composed of microtubule filaments and light-switchable kinesin motor proteins to analyze the emergence of persistent flow fields in a model active matter system. Using light, we form contractile microtubule networks of varying shape. We analyze the fluid flow fields generated by a wide range of microtubule network geometries and explain the resulting flow fields within a unified theoretical framework. We specifically demonstrate that the geometry of microtubule flux at the boundary of contracting microtubule networks predicts the steady-state fluid flow fields across polygonal network geometries through finite-element simulations. Our work provides a foundation for programming microscopic fluid-flows with controllable active matter and could enable the engineering of versatile and dynamic microfluidic devices

    Symbolic Learning Enables Self-Evolving Agents

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    The AI community has been exploring a pathway to artificial general intelligence (AGI) by developing "language agents", which are complex large language models (LLMs) pipelines involving both prompting techniques and tool usage methods. While language agents have demonstrated impressive capabilities for many real-world tasks, a fundamental limitation of current language agents research is that they are model-centric, or engineering-centric. That's to say, the progress on prompts, tools, and pipelines of language agents requires substantial manual engineering efforts from human experts rather than automatically learning from data. We believe the transition from model-centric, or engineering-centric, to data-centric, i.e., the ability of language agents to autonomously learn and evolve in environments, is the key for them to possibly achieve AGI. In this work, we introduce agent symbolic learning, a systematic framework that enables language agents to optimize themselves on their own in a data-centric way using symbolic optimizers. Specifically, we consider agents as symbolic networks where learnable weights are defined by prompts, tools, and the way they are stacked together. Agent symbolic learning is designed to optimize the symbolic network within language agents by mimicking two fundamental algorithms in connectionist learning: back-propagation and gradient descent. Instead of dealing with numeric weights, agent symbolic learning works with natural language simulacrums of weights, loss, and gradients. We conduct proof-of-concept experiments on both standard benchmarks and complex real-world tasks and show that agent symbolic learning enables language agents to update themselves after being created and deployed in the wild, resulting in "self-evolving agents".Comment: Code available at https://github.com/aiwaves-cn/agent

    Agents: An Open-source Framework for Autonomous Language Agents

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    Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces. We consider language agents as a promising direction towards artificial general intelligence and release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience. Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control. Agents is user-friendly as it enables non-specialists to build, customize, test, tune, and deploy state-of-the-art autonomous language agents without much coding. The library is also research-friendly as its modularized design makes it easily extensible for researchers. Agents is available at https://github.com/aiwaves-cn/agents.Comment: Code available at https://github.com/aiwaves-cn/agent

    Changes of the adjacent discs and vertebrae in patients with osteoporotic vertebral compression fractures treated with or without bone cement augmentation

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    Background Context: Although vertebral augmentation with bone cement has been commonly used to treat symptomatic osteoporotic vertebral compression fractures, relatively little is known about the impact of augmentation on the adjacent spinal components. Purpose: To determine the imaging effects of vertebral augmentation on the adjacent discs, the augmented vertebra, and the involved spinal segment. Study Design: Retrospective radiographic study. Patient Sample: Patients with acute osteoporotic vertebral compression fractures who underwent vertebral augmentation or nonoperative treatments. Outcome Measures: On baseline and follow-up mid-sagittal T2W magnetic resonance images, quantitative measurements of disc degeneration, including disc height, bulging, and signal, vertebral height, wedge angle, and segmental kyphotic angle were acquired. Methods: Lumbar spine magnetic resonance images of patients with acute osteoporotic vertebral compression fractures at a local hospital in Eastern China between 2010 and 2017 were reviewed. Student’s t-tests and χ2 tests were used to examine the differences of baseline and changes over time between vertebrae underwent vertebral augmentation and those did not. Paired t-tests were used to examine the differences between baseline and follow-up to study the changes of adjacent disc degeneration, creep deformity of the vertebra and progression of segmental kyphosis. Results: There were 112 acute vertebral compression fractures (72 treated with kyphoplasty and 40 with nonoperative treatments) in 101 subjects. At final follow-up (mean 21.5 months), the cranial disc of the augmented vertebra decreased in height (p<0.001), and both cranial and caudal discs decreased in signal intensity (p≤0.02). The discs in the nonoperative group did not undergo such degenerative changes. For the fractured vertebra, vertebral height significantly decreased (p<0.01 for both) and vertebral wedge angle significantly increased (p≤0.01 for both), regardless of augmentation treatment or not. Segmental kyphotic angle significantly increased in vertebral fractures that underwent vertebral augmentation (p<0.001), but not in those underwent nonoperative treatments. Conclusions: Patients that underwent vertebral augmentation had more advanced disc degeneration at adjacent disc levels as compared to those without augmentation. The fractured vertebral body height decreased and the wedge angle increased, regardless of vertebral augmentation treatment or not. Vertebral augmentation may be associated with increased creep deformity of the adjacent vertebra and the progression of segmental kyphosis
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