Between thoughts : on the interconnectivity of things

My furniture designs are sensory tools that offer a bridge between our mental world and the physical world. With a strong belief in systems of interconnectivity and equilibrium, I use my designs as tools to calibrate awareness to the flow of changes that occur as enduring undercurrents in our everyday lives. Using as stimuli basic mechanisms of measurement as well as sensory cues, my works are antennae to our perception, inviting finely-tuned attention to the subtleties of response between our body and the physical environment. In the process of our participation, we come to see the dynamics of action and reaction at play and anticipate for further awareness

Valorization of Renewable Carbon Resources for Chemicals

The overuse of fossil fuels has caused an energy crisis and associated environment issues. It is desirable to utilize renewable resources for the production of chemicals. This review mainly introduces our recent work on the transformation of renewable carbon resources including the conversion of cellulose, lignin, and chitin into sustainable chemicals. Various transformation routes have been established to form value-added chemicals, and accordingly a variety of effective catalytic systems have been developed, either based on metal catalysis and/or acid-base catalysis, to enable the desired transformation

Analysis of semi-volatile organic compounds in high elevation lake sediments

The transport and deposition of airborne contaminants, including semivolatile organic compounds (SOCs), to high elevation ecosystems is recognized as a potential threat to these ecosystems. The objectives of this research were to develop an analytical method to measure a wide range of SOCs in high elevation lake sediment and to evaluate decennial to century trends in SOC contaminant flux to a high elevation lake catchment, by dating and analyzing the sediment core. The new analytical method and its validation for the measurement of SOCs in high elevation lake sediments are described. The target compounds are 74 S005, including organochlorines, polycyclic aromatic hydrocarbons (PAHs), polychiorinated biphenyls (PCBs), carbamates, organophosphates, and striazines. Pressurized Liquid Extraction was used to extract SOCs from sediment. Gel Permeation Chromatography and silica gel adsorption chromatography were used to purify the sediment extract. Finally, GC/MS, with Electron Impact and Electron Capture Negative Ionization mass spectrometry, was used for the measurement and quantitation of SOCs in the sediment

Dynamic-Resolution Model Learning for Object Pile Manipulation

Dynamics models learned from visual observations have shown to be effective in various robotic manipulation tasks. One of the key questions for learning such dynamics models is what scene representation to use. Prior works typically assume representation at a fixed dimension or resolution, which may be inefficient for simple tasks and ineffective for more complicated tasks. In this work, we investigate how to learn dynamic and adaptive representations at different levels of abstraction to achieve the optimal trade-off between efficiency and effectiveness. Specifically, we construct dynamic-resolution particle representations of the environment and learn a unified dynamics model using graph neural networks (GNNs) that allows continuous selection of the abstraction level. During test time, the agent can adaptively determine the optimal resolution at each model-predictive control (MPC) step. We evaluate our method in object pile manipulation, a task we commonly encounter in cooking, agriculture, manufacturing, and pharmaceutical applications. Through comprehensive evaluations both in the simulation and the real world, we show that our method achieves significantly better performance than state-of-the-art fixed-resolution baselines at the gathering, sorting, and redistribution of granular object piles made with various instances like coffee beans, almonds, corn, etc.Comment: Accepted to Robotics: Science and Systems (RSS 2023). The first two authors contributed equally. Project Page: https://https://robopil.github.io/dyn-res-pile-mani

Research on the quality evaluation system and operating mechanism of graduate education in the new era

With the continuous expansion of graduate education scale and the continuous improvement of evaluation systems, corresponding educational evaluation activities have also been carried out, gradually forming a relatively standardized, wide-ranging, and various types of evaluation systems. This article introduces a new approach to the evaluation system of graduate education quality in the new era based on the actual situation of our school’s graduate students, and provides a reasonable operating mechanism

VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models

Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation in the form of reasoning and planning. Despite the progress, most still rely on pre-defined motion primitives to carry out the physical interactions with the environment, which remains a major bottleneck. In this work, we aim to synthesize robot trajectories, i.e., a dense sequence of 6-DoF end-effector waypoints, for a large variety of manipulation tasks given an open-set of instructions and an open-set of objects. We achieve this by first observing that LLMs excel at inferring affordances and constraints given a free-form language instruction. More importantly, by leveraging their code-writing capabilities, they can interact with a vision-language model (VLM) to compose 3D value maps to ground the knowledge into the observation space of the agent. The composed value maps are then used in a model-based planning framework to zero-shot synthesize closed-loop robot trajectories with robustness to dynamic perturbations. We further demonstrate how the proposed framework can benefit from online experiences by efficiently learning a dynamics model for scenes that involve contact-rich interactions. We present a large-scale study of the proposed method in both simulated and real-robot environments, showcasing the ability to perform a large variety of everyday manipulation tasks specified in free-form natural language. Videos and code at https://voxposer.github.i

Proteomic analysis of differentially expressed proteins in hepatitis B virus-related hepatocellular carcinoma tissues

<p>Abstract</p> <p>Background</p> <p>Hepatocellular carcinoma (HCC), a major cause of cancer death in China, is preceded by chronic hepatitis and liver cirrhosis (LC). Although hepatitis B virus (HBV) has been regarded as a clear etiology of human hepatocarcinogenesis, the mechanism is still needs to be further clarified. In this study, we used a proteomic approach to identify the differential expression protein profiles between HCC and the adjacent non-tumorous liver tissues.</p> <p>Methods</p> <p>Eighteen cases of HBV-related HCC including 12 cases of LC-developed HCC and 6 cases of chronic hepatitis B (CHB)-developed HCC were analyzed by two-dimensional electrophoresis (2-DE) combined with matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS), and the results were compared to those of paired adjacent non-tumorous liver tissues.</p> <p>Results</p> <p>A total of 17 differentially expressed proteins with diverse biological functions were identified. Among these, 10 proteins were up-regulated, whereas the other 7 proteins were down-regulated in cancerous tissues. Two proteins, c-Jun N-terminal kinase 2 and ADP/ATP carrier protein were found to be up-regulated only in CHB-developed HCC tissues. Insulin-like growth factor binding protein 2 and Rho-GTPase-activating protein 4 were down-regulated in LC-developed and CHB-developed HCC tissues, respectively. Although 11 out of these 17 proteins have been already described by previous studies, or are already known to be involved in hepatocarcinogenesis, this study revealed 6 new proteins differentially expressed in HBV-related HCC.</p> <p>Conclusion</p> <p>These findings elucidate that there are common features between CHB-developed HCC and LC-developed HCC. The identified proteins are valuable for studying the hepatocarcinogenesis, and may be potential diagnostic markers or therapeutic targets for HBV-related HCC.</p

RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation

Robots need to explore their surroundings to adapt to and tackle tasks in unknown environments. Prior work has proposed building scene graphs of the environment but typically assumes that the environment is static, omitting regions that require active interactions. This severely limits their ability to handle more complex tasks in household and office environments: before setting up a table, robots must explore drawers and cabinets to locate all utensils and condiments. In this work, we introduce the novel task of interactive scene exploration, wherein robots autonomously explore environments and produce an action-conditioned scene graph (ACSG) that captures the structure of the underlying environment. The ACSG accounts for both low-level information, such as geometry and semantics, and high-level information, such as the action-conditioned relationships between different entities in the scene. To this end, we present the Robotic Exploration (RoboEXP) system, which incorporates the Large Multimodal Model (LMM) and an explicit memory design to enhance our system's capabilities. The robot reasons about what and how to explore an object, accumulating new information through the interaction process and incrementally constructing the ACSG. We apply our system across various real-world settings in a zero-shot manner, demonstrating its effectiveness in exploring and modeling environments it has never seen before. Leveraging the constructed ACSG, we illustrate the effectiveness and efficiency of our RoboEXP system in facilitating a wide range of real-world manipulation tasks involving rigid, articulated objects, nested objects like Matryoshka dolls, and deformable objects like cloth.Comment: Project Page: https://jianghanxiao.github.io/roboexp-web

NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation

Learning a recommender system model from an item's raw modality features (such as image, text, audio, etc.), called MoRec, has attracted growing interest recently. One key advantage of MoRec is that it can easily benefit from advances in other fields, such as natural language processing (NLP) and computer vision (CV). Moreover, it naturally supports transfer learning across different systems through modality features, known as transferable recommender systems, or TransRec. However, so far, TransRec has made little progress, compared to groundbreaking foundation models in the fields of NLP and CV. The lack of large-scale, high-quality recommendation datasets poses a major obstacle. To this end, we introduce NineRec, a TransRec dataset suite that includes a large-scale source domain recommendation dataset and nine diverse target domain recommendation datasets. Each item in NineRec is represented by a text description and a high-resolution cover image. With NineRec, we can implement TransRec models in an end-to-end training manner instead of using pre-extracted invariant features. We conduct a benchmark study and empirical analysis of TransRec using NineRec, and our findings provide several valuable insights. To support further research, we make our code, datasets, benchmarks, and leaderboards publicly available at https://github.com/westlake-repl/NineRec

Introducing a chaotic component in the control system of soil respiration

Chaos theory has been proved to be of great significance in a series of critical applications although, until now, its applications in analyzing soil respiration have not been addressed. This study aims to introduce a chaotic component in the control system of soil respiration and explain control complexity of this nonlinear chaotic system. This also presents a theoretical framework for better understanding chaotic components of soil respiration in arid land. A concept model of processes and mechanisms associated with subterranean CO2 evolution are developed, and dynamics of the chaotic system is characterized as an extended Riccati equation. Controls of soil respiration and kinetics of the chaotic system are interpreted and as a first attempt, control complexity of this nonlinear chaotic system is tackled by introducing a period-regulator in partitioning components of soil respiration