470 research outputs found
sj-xlsx-1-tau-10.1177_17562872231217842 – Supplemental material for Surgical treatment of pelvic lipomatosis: a systematic review of 231 cases
No full textSupplemental material, sj-xlsx-1-tau-10.1177_17562872231217842 for Surgical treatment of pelvic lipomatosis: a systematic review of 231 cases by Mancheng Xia, Shengwei Xiong, Zhihua Li, Shubo Fan, Yuke Chen, Liqun Zhou, Kai Zhang and Xuesong Li in Therapeutic Advances in Urology</p
sj-docx-1-wso-10.1177_17474930241241994 – Supplemental material for Safety and efficacy of tight versus loose glycemic control in acute stroke patients: A meta-analysis of randomized controlled trials
No full textSupplemental material, sj-docx-1-wso-10.1177_17474930241241994 for Safety and efficacy of tight versus loose glycemic control in acute stroke patients: A meta-analysis of randomized controlled trials by Shuangzhe Wu, Yuke Mao, Sijia Chen, Peiyan Pan, Huiying Zhang, Siqi Chen, Jue Liu and Donghua Mi in International Journal of Stroke</p
MineDojo Internet Knowledge Base (Wiki)
No full textProject website: minedojo.org
Paper: arxiv.org/abs/2206.08853
GitHub: github.com/MineDojo/MineDojo
The Minecraft Wiki pages cover almost every aspect of the game mechanics, and supply a rich source of unstructured knowledge in multimodal tables, recipes, illustrations, and step-by-step tutorials. We scrape 6,735 pages that interleave text, images, tables, and diagrams. To preserve the layout information, we also save the screenshots of entire pages and extract bounding boxes of the visual elements.
There are two files in our Wiki knowledge base.
wiki_samples.zip: A sample version of the full knowledge base (10 pages).
wiki_full.zip: The full knowledge base (6,735 pages).
Cite Us
@article{fan2022minedojo,
title = {MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge},
author = {Linxi Fan and Guanzhi Wang and Yunfan Jiang and Ajay Mandlekar and Yuncong Yang and Haoyi Zhu and Andrew Tang and De-An Huang and Yuke Zhu and Anima Anandkumar},
year = {2022},
journal = {arXiv preprint arXiv: Arxiv-2206.08853}
MineDojo Internet Knowledge Base (Reddit)
No full textProject website: minedojo.org
Paper: arxiv.org/abs/2206.08853
GitHub: github.com/MineDojo/MineDojo
We collect 340K+ Reddit posts along with 6.6M comments under the “r/Minecraft” subreddit. These posts ask questions on how to solve certain tasks, showcase cool architectures and achievements in image/video snippets, and discuss general tips and tricks for players of all expertise levels. Large language models can be finetuned on our Reddit corpus to internalize Minecraft-specific concepts and develop sophisticated strategies.
Cite Us
@article{fan2022minedojo,
title = {MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge},
author = {Linxi Fan and Guanzhi Wang and Yunfan Jiang and Ajay Mandlekar and Yuncong Yang and Haoyi Zhu and Andrew Tang and De-An Huang and Yuke Zhu and Anima Anandkumar},
year = {2022},
journal = {arXiv preprint arXiv: Arxiv-2206.08853}
Sample Empirical Likelihood under Complex Survey Design and Bayesian Jackknife Empirical Likelihood-based Inference for Missing Data and Partial AUC
No full textThe empirical likelihood (EL), introduced by Owen (1988, 1990), is a powerful tool for constructing confidence intervals in nonparametric settings. Significant developments based on empirical likelihood have been made in recent years. In this dissertation, we investigate the performance of two extensions of EL, sample empirical likelihood (Chen and Kim, 2014) and Bayesian jackknife empirical likelihood (Cheng and Zhao, 2019) approaches, for several statistical problems.
One effective way to conduct statistical disclosure control is to use scrambled responses. Scrambled responses can be generated by using a controlled random device. We propose using the sample empirical likelihood approach to conduct statistical inference (using a Wilk-type confidence interval) under a complex survey design with scrambled responses.
Missing data, which are common in a variety of fields, reduce the representativeness of the sample and can lead to inference problems. We apply the Bayesian jackknife empirical likelihood method for inference with missing data and causal inference. The semiparametric fractional imputation estimator, proposed by Chen and Kim (2017), propensity score weighted estimator, and doubly robust estimator were used for inference with missing data.
The partial area under the receiver operating characteristic curve (pAUC) is a measure of diagnostic test performance. We propose using Bayesian jackknife empirical likelihood for inference for the pAUC and comparison of two tests.
Extensive simulation studies are conducted to compare the performance in terms of the coverage rate and average length of confidence interval between proposed methods and normal approximation/jackknife empirical likelihood methods. Furthermore, we demonstrate the application of the proposed approaches using real datasets.Doctor of Philosophy (PhD)Mathematics and Statistic
Plant commensal type VII secretion system causes iron leakage from roots to promote colonization
No full textCompetition for iron is an important factor for microbial niche establishment in the rhizosphere. Pathogenic and beneficial symbiotic bacteria use various secretion systems to interact with their hosts and acquire limited resources from the environment. Bacillus spp. are important plant commensals that encode a type VII secretion system (T7SS). However, the function of this secretion system in rhizobacteria–plant interactions is unclear. Here we use the beneficial rhizobacterium Bacillus velezensis SQR9 to show that the T7SS and the major secreted protein YukE are critical for root colonization. In planta experiments and liposome-based experiments demonstrate that secreted YukE inserts into the plant plasma membrane and causes root iron leakage in the early stage of inoculation. The increased availability of iron promotes root colonization by SQR9. Overall, our work reveals a previously undescribed role of the T7SS in a beneficial rhizobacterium to promote colonization and thus plant–microbe interactions
Travel Avatar: A Personalised Digital Service for FlyCo Frequent Business Travellers
No full textStrategic Product Desig
XXR: Further Extending Extended Reality with Sensory Perception
No full textThe goals of this graduation were to:- Develop a design tool that helps designers add additional senses to augmented and virtual reality project.- Test that tool using designers at Mobgen | Accenture Interactive.- Build one of the resulting concepts as a fully functioning demo.- Test that demo.In short, these goals were all achieved. Readers with more interest can read the first chapter of the report. While very interested readers can attempt to read the entire report
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Reinforcement learning beyond rewards : decision-making in the language of visitation distributions
No full textReinforcement Learning (RL) is traditionally framed as the problem of finding a policy that maximizes the
cumulative reward in the environment. The generality of the RL framework rests on the reward function being a
universal way to specify a decision-making task to an agent. While this notion of the universality of reward
function has been debated, reward functions can often be inconvenient for task specification. Small changes in
reward function can completely change optimal policy and it has been evidenced that humans frequently make
mistakes when specifying tasks as rewards, resulting in a policy that is misaligned with the human intention.
Alternatively, the environment is abundant with different forms of learning signals, and this thesis aims to
investigate an alternate framework for decision-making that allows for a unified way to directly learn from a
variety of learning signals not limited to reward functions. The core idea proposed in this thesis is to focus on
visitation distributions as the central object of optimization- the future state-action distribution of any policy
when interacting with the environment. Specifically, we show the generality of this framework by providing a
unified set of algorithms that are able to learn from the following learning signals -- rewards, goals, expert
demonstrations, and action-free demonstrations. Our algorithms simplify optimization and forgo reward inference
when learning from other signals and directly attempt to learn optimal policies. While environmental signals can
greatly influence learning a particular task, a bulk of an interactive agent's experience in the environment may
not have any associated learning signal. Even for this case, we hypothesize that the future state-action visitation
distribution of an agent captures information necessary for decision making. This insight allows us to propose a
self-supervised objective for decision-making that learns representations by learning to represent all possible
visitations in the environment using offline datasets without any learning signals. We show that such an
unsupervised learning approach can give rise to general-purpose RL agents that can perform any task specified by
a reward function, video demonstration, or language instruction near-optimally without any test-time planning or
learning. Finally, the thesis concludes by providing a solution to quickly adapt these near-optimal policies given by unsupervised RL agents rapidly for a test-time reward function.Computer Scienc
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Control-based factorization through causal interactions and hierarchical reinforcement learning
No full textFor robots to adapt to the real world, they must be able to handle complex, evolving scenes over extended sequences of actions. Reinforcement Learning (RL) handles this complexity by learning from environment interactions and reward—a scalar measure of task performance. RL is powerfully general—able to learn policies in a wide and disparate range of tasks from robotics and controls to logistics or language modeling. However, vanilla RL has no inductive biases about how the world is structured. Instead, statistical correlation between action sequences, scalar reward signal, and environment state must all be recovered from environment interactions. In practice, this means RL can struggle to capture many commonsense properties of these tasks. As a result, RL algorithms often have low sample efficiency, poor overall performance, and brittle generalization when applied to many real-world tasks. Hierarchical Reinforcement Learning methods offer a promising direction for resolving these challenges by learning multistep skills. Breaking down tasks into shorter subtasks or skills and reusing those skills can improve sample efficiency and generalization, while often making it possible for the agent to learn higher-performing policies overall. However, this requires selecting a good set of skills that both capture the space of useful behaviors, while still simplifying the overall problem. This thesis investigates applying the inductive bias of objects and their controllable interactions to HRL. This bias is motivated by the intuitive human breakdown of tasks in a complex scene into the key objects of interest and the sequence of interactions necessary to achieve the goal. For example, a person playing golf would identify how she should hold the club, swing it at the ball, and strike the ball to reach the hole. Alternatively, when cooking, a recipe often describes a sequence of object interactions, from cutting or washing to heating or mixing. Reasoning over object interactions allows for the powerful assumption that manipulation of one object is independent of many of the other scene objects that are not part of the interactions. This is how a golfer can hit on different terrain, or a chef can work in different kitchens. By contrast, RL policies often have to learn these intuitions from data, meaning that they must experience a vast quantity of different backgrounds or different kitchens before the policies learn to generalize. This limitation explains some of the sample efficiency and generalization limitations of learned policies. Even final performance might be hampered if the complexity of the state space makes it challenging for the learning algorithm to converge. To encode the prior causal interactions into RL, we focus on three aspects: (1) breaking state into objects, (2) breaking down dynamics into interactions, and (3) breaking down control into directed behavior towards those interactions. Altogether, we describe this as Interaction-guided Hierarchical Reinforcement Learning (HRL): learning skills to induce desired interactions between objects. Philosophically, these three breakdowns are unified by focusing on factorization. Factorization is the rearrangement of an entity into salient elements, such as parameterizing a complex space into the product of subspaces. The object factorization is a breakdown of state. The interaction factorization is a breakdown of transition dynamics into a series of interactions. The skill factorization is a breakdown of task actions into interaction-inducing skills. While factorization is a familiar concept in mathematics and computer science, this work offers the first comprehensive investigation of combining state factorization through causal variables, dynamics factorization through causal interactions, and action factorization through skills. In concert, this novel contribution is summarized as HRL guided by causal interactions. This factorization offers a promising direction for attacking the challenges of reinforcement learning, sample efficiency, challenging behaviors, and generalization. Each individual skill is more sample efficient to learn because it can leverage the learned capabilities of previous skills, as well as skill-specific state abstractions. In addition, by building skills up from simpler interactions to more complex ones, difficult behaviors such as the robot tool used for object manipulation can be handled as a chain of interactions. Finally, these object-interaction skills generalize and transfer between different tasks. The specific contributions of this work are sequenced as follows: First, we demonstrate that HRL structures trained using interactions perform well in complex environments, such as video games and simulated robotics tasks, with improvements in sample efficiency, task transfer, and final performance. Next, we formalize a definition of factor-factor interactions using concepts from the Causal Reasoning literature. Finally, we scale state factorization using controllability and introduce the robot air hockey domain, a challenging interaction-rich real-world platform. Altogether, this research demonstrates a novel paradigm for HRL to enable agents to reason about state-specific causal control.Computer Scienc
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