Superwetting surfaces derived from sustainable materials for environmental and energy applications

This thesis focuses on the utilization of sustainable materials, including cellulose nanocrystals (CNC), cellulose nanofibers (CNF), and modified lycopodium pollen particles, for the development of superwettable surfaces with diverse properties. By incorporating surface modifications, such as grafting CNC with positively charged functional materials and incorporating superhydrophobic modified lycopodium pollen particles, these sustainable materials were used to fabricate superhydrophobic, superlyophobic, highly hydrophilic, and superhydrophilic surfaces. These superwettable surfaces hold great potential for environmental and energy applications, such as non-loss micro droplet transfer, oil/water emulsion separation, ions transfer and salinity energy harvesting

Feature-Based Matrix Factorization

Recommender system has been more and more popular and widely used in many applications recently. The increasing information available, not only in quantities but also in types, leads to a big challenge for recommender system that how to leverage these rich information to get a better performance. Most traditional approaches try to design a specific model for each scenario, which demands great efforts in developing and modifying models. In this technical report, we describe our implementation of feature-based matrix factorization. This model is an abstract of many variants of matrix factorization models, and new types of information can be utilized by simply defining new features, without modifying any lines of code. Using the toolkit, we built the best single model reported on track 1 of KDDCup'11.Comment: Minor update, add some related work

Learning Adaptive Display Exposure for Real-Time Advertising

In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal platform revenue and user experience. Consequently, major e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible ways to display ads. In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased? More specifically, we consider two types of constraints: request-level constraint ensures user experience for each user visit, and platform-level constraint controls the overall platform monetization rate. We model this problem as a Constrained Markov Decision Process with per-state constraint (psCMDP) and propose a constrained two-level reinforcement learning approach to decompose the original problem into two relatively independent sub-problems. To accelerate policy learning, we also devise a constrained hindsight experience replay mechanism. Experimental evaluations on industry-scale real-world datasets demonstrate the merits of our approach in both obtaining higher revenue under the constraints and the effectiveness of the constrained hindsight experience replay mechanism.Comment: accepted by CIKM201

Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning

Diffusion models have demonstrated highly-expressive generative capabilities in vision and NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are also powerful in modeling complex policies or trajectories in offline datasets. However, these works have been limited to single-task settings where a generalist agent capable of addressing multi-task predicaments is absent. In this paper, we aim to investigate the effectiveness of a single diffusion model in modeling large-scale multi-task offline data, which can be challenging due to diverse and multimodal data distribution. Specifically, we propose Multi-Task Diffusion Model (\textsc{MTDiff}), a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis in multi-task offline settings. \textsc{MTDiff} leverages vast amounts of knowledge available in multi-task data and performs implicit knowledge sharing among tasks. For generative planning, we find \textsc{MTDiff} outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D. For data synthesis, \textsc{MTDiff} generates high-quality data for testing tasks given a single demonstration as a prompt, which enhances the low-quality datasets for even unseen tasks.Comment: 21 page

Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation

Mapping two modalities, speech and text, into a shared representation space, is a research topic of using text-only data to improve end-to-end automatic speech recognition (ASR) performance in new domains. However, the length of speech representation and text representation is inconsistent. Although the previous method up-samples the text representation to align with acoustic modality, it may not match the expected actual duration. In this paper, we proposed novel representations match strategy through down-sampling acoustic representation to align with text modality. By introducing a continuous integrate-and-fire (CIF) module generating acoustic representations consistent with token length, our ASR model can learn unified representations from both modalities better, allowing for domain adaptation using text-only data of the target domain. Experiment results of new domain data demonstrate the effectiveness of the proposed method.Comment: Accepted by INTERSPEECH 2023. arXiv admin note: text overlap with arXiv:2309.0143

Ultrathin thermoresponsive self-folding 3D graphene

Graphene and other two-dimensional materials have unique physical and chemical properties of broad relevance. It has been suggested that the transformation of these atomically planar materials to three-dimensional (3D) geometries by bending, wrinkling, or folding could significantly alter their properties and lead to novel structures and devices with compact form factors, but strategies to enable this shape change remain limited. We report a benign thermally responsive method to fold and unfold monolayer graphene into predesigned, ordered 3D structures. The methodology involves the surface functionalization of monolayer graphene using ultrathin noncovalently bonded mussel-inspired polydopamine and thermoresponsive poly(N-isopropylacrylamide) brushes. The functionalized graphene is micropatterned and self-folds into ordered 3D structures with reversible deformation under a full control by temperature. The structures are characterized using spectroscopy and microscopy, and self-folding is rationalized using a multiscale molecular dynamics model. Our work demonstrates the potential to design and fabricate ordered 3D graphene structures with predictable shape and dynamics. We highlight applicability by encapsulating live cells and creating nonlinear resistor and creased transistor devices.United States. Office of Naval Research. Multidisciplinary University Research Initiative (FA9550-16-1-0031)United States. Office of Naval Research. Multidisciplinary University Research Initiative ( FA9550-15-1-0514)National Science Foundation (U.S.) (CMMI-1635443)United States. Office of Naval Research (N00014-16-1-2333

Interface-engineered ferroelectricity of epitaxial Hf\u3csub\u3e0.5\u3c/sub\u3eZr\u3csub\u3e0.5\u3c/sub\u3eO\u3csub\u3e2\u3c/sub\u3e thin films

Ferroelectric hafnia-based thin films have attracted intense attention due to their compatibility with complementary metal-oxide-semiconductor technology. However, the ferroelectric orthorhombic phase is thermodynamically metastable. Various efforts have been made to stabilize the ferroelectric orthorhombic phase of hafnia-based films such as controlling the growth kinetics and mechanical confinement. Here, we demonstrate a key interface engineering strategy to stabilize and enhance the ferroelectric orthorhombic phase of the Hf0.5Zr0.5O2 thin film by deliberately controlling the termination of the bottom La0.67Sr0.33MnO3 layer. We find that the Hf0.5Zr0.5O2 films on the MnO2-terminated La0.67Sr0.33MnO3 have more ferroelectric orthorhombic phase than those on the LaSrO-terminated La0.67Sr0.33MnO3, while with no wake-up effect. Even though the Hf0.5Zr0.5O2 thickness is as thin as 1.5nm, the clear ferroelectric orthorhombic (111) orientation is observed on the MnO2 termination. Our transmission electron microscopy characterization and theoretical modelling reveal that reconstruction at the Hf0.5Zr0.5O2/ La0.67Sr0.33MnO3 interface and hole doping of the Hf0.5Zr0.5O2 layer resulting from theMnO2 interface termination are responsible for the stabilization of the metastable ferroelectric phase of Hf0.5Zr0.5O2. We anticipate that these results will inspire further studies of interface-engineered hafnia-based systems