SRB Measures for A Class of Partially Hyperbolic Attractors in Hilbert spaces

In this paper, we study the existence of SRB measures and their properties for infinite dimensional dynamical systems in a Hilbert space. We show several results including (i) if the system has a partially hyperbolic attractor with nontrivial finite dimensional unstable directions, then it has at least one SRB measure; (ii) if the attractor is uniformly hyperbolic and the system is topological mixing and the splitting is H\"older continuous, then there exists a unique SRB measure which is mixing; (iii) if the attractor is uniformly hyperbolic and the system is non-wondering and and the splitting is H\"older continuous, then there exists at most finitely many SRB measures; (iv) for a given hyperbolic measure, there exist at most countably many ergodic components whose basin contains an observable set

Existence of SRB Measures for A Class of Partially Hyperbolic Attractors in Banach spaces

In this paper, we study the existence of SRB measures for infinite dimensional dynamical systems in a Banach space. We show that if the system has a partially hyperbolic attractor with nontrivial finite dimensional unstable directions, then it has an SRB measure.Comment: arXiv admin note: text overlap with arXiv:1508.0330

Nanowire Photoelectrochemistry.

Recent applications of photoelectrochemistry at the semiconductor/liquid interface provide a renewable route of mimicking natural photosynthesis and yielding chemicals from sunlight, water, and air. Nanowires, defined as one-dimensional nanostructures, exhibit multiple unique features for photoelectrochemical applications and promise better performance as compared to their bulk counterparts. This article reviews the use of semiconductor nanowires in photoelectrochemistry. After introducing fundamental concepts essential to understanding nanowires and photoelectrochemistry, the review considers answers to the following questions: (1) How can we interface semiconductor nanowires with other building blocks for enhanced photoelectrochemical responses? (2) How are nanowires utilized for photoelectrochemical half reactions? (3) What are the techniques that allow us to obtain fundamental insights of photoelectrochemistry at single-nanowire level? (4) What are the design strategies for an integrated nanosystem that mimics a closed cycle in artificial photosynthesis? This framework should help readers evaluate the salient features of nanowires for photoelectrochemical applications, promoting the sustainable development of solar-powered chemical plants that will benefit our society in the long run

Robust Dense Mapping for Large-Scale Dynamic Environments

We present a stereo-based dense mapping algorithm for large-scale dynamic urban environments. In contrast to other existing methods, we simultaneously reconstruct the static background, the moving objects, and the potentially moving but currently stationary objects separately, which is desirable for high-level mobile robotic tasks such as path planning in crowded environments. We use both instance-aware semantic segmentation and sparse scene flow to classify objects as either background, moving, or potentially moving, thereby ensuring that the system is able to model objects with the potential to transition from static to dynamic, such as parked cars. Given camera poses estimated from visual odometry, both the background and the (potentially) moving objects are reconstructed separately by fusing the depth maps computed from the stereo input. In addition to visual odometry, sparse scene flow is also used to estimate the 3D motions of the detected moving objects, in order to reconstruct them accurately. A map pruning technique is further developed to improve reconstruction accuracy and reduce memory consumption, leading to increased scalability. We evaluate our system thoroughly on the well-known KITTI dataset. Our system is capable of running on a PC at approximately 2.5Hz, with the primary bottleneck being the instance-aware semantic segmentation, which is a limitation we hope to address in future work. The source code is available from the project website (http://andreibarsan.github.io/dynslam).Comment: Presented at IEEE International Conference on Robotics and Automation (ICRA), 201

Efficient 2D-3D Matching for Multi-Camera Visual Localization

Visual localization, i.e., determining the position and orientation of a vehicle with respect to a map, is a key problem in autonomous driving. We present a multicamera visual inertial localization algorithm for large scale environments. To efficiently and effectively match features against a pre-built global 3D map, we propose a prioritized feature matching scheme for multi-camera systems. In contrast to existing works, designed for monocular cameras, we (1) tailor the prioritization function to the multi-camera setup and (2) run feature matching and pose estimation in parallel. This significantly accelerates the matching and pose estimation stages and allows us to dynamically adapt the matching efforts based on the surrounding environment. In addition, we show how pose priors can be integrated into the localization system to increase efficiency and robustness. Finally, we extend our algorithm by fusing the absolute pose estimates with motion estimates from a multi-camera visual inertial odometry pipeline (VIO). This results in a system that provides reliable and drift-less pose estimation. Extensive experiments show that our localization runs fast and robust under varying conditions, and that our extended algorithm enables reliable real-time pose estimation.Comment: 7 pages, 5 figure

Vector-Quantized Prompt Learning for Paraphrase Generation

Deep generative modeling of natural languages has achieved many successes, such as producing fluent sentences and translating from one language into another. However, the development of generative modeling techniques for paraphrase generation still lags behind largely due to the challenges in addressing the complex conflicts between expression diversity and semantic preservation. This paper proposes to generate diverse and high-quality paraphrases by exploiting the pre-trained models with instance-dependent prompts. To learn generalizable prompts, we assume that the number of abstract transforming patterns of paraphrase generation (governed by prompts) is finite and usually not large. Therefore, we present vector-quantized prompts as the cues to control the generation of pre-trained models. Extensive experiments demonstrate that the proposed method achieves new state-of-art results on three benchmark datasets, including Quora, Wikianswers, and MSCOCO. We will release all the code upon acceptance.Comment: EMNLP Findings, 202

Influence mechanism between information management technologies and green innovation: the role of sustainable firms practices in China

Despite the proposition of sustainable development goals by the United Nations, the progress shown by the organizations is not satisfactory. It also raised the attention of the policymakers to overcome those challenges by identifying potential solutions. Hence, the current study aims to assess the role of the information and knowledge management process in attaining green innovation in sustainable practices. For this purpose, the data is collected from the 395 organizations operating in China that are ISO 14001 certified. The application of PLS-SEM shows that information and knowledge management significantly and positively enhance all three sustainable practices, which eventually play an encouraging role in green innovation. Additionally, all three types of sustainable practices also reported mediating the relationships between the information and knowledge management process and green innovation. Based on the findings, organizations are recommended to integrate and align sustainable practices, information management, and green innovation with the mission, vision, and routine activities and objectives

ET3D: Efficient Text-to-3D Generation via Multi-View Distillation

Recent breakthroughs in text-to-image generation has shown encouraging results via large generative models. Due to the scarcity of 3D assets, it is hardly to transfer the success of text-to-image generation to that of text-to-3D generation. Existing text-to-3D generation methods usually adopt the paradigm of DreamFusion, which conducts per-asset optimization by distilling a pretrained text-to-image diffusion model. The generation speed usually ranges from several minutes to tens of minutes per 3D asset, which degrades the user experience and also imposes a burden to the service providers due to the high computational budget. In this work, we present an efficient text-to-3D generation method, which requires only around 8 $ms$ to generate a 3D asset given the text prompt on a consumer graphic card. The main insight is that we exploit the images generated by a large pre-trained text-to-image diffusion model, to supervise the training of a text conditioned 3D generative adversarial network. Once the network is trained, we are able to efficiently generate a 3D asset via a single forward pass. Our method requires no 3D training data and provides an alternative approach for efficient text-to-3D generation by distilling pre-trained image diffusion models

VERTICAL REPLENISHMENT BY UNMANNED AERIAL VEHICLES.

Master'sMASTER OF ENGINEERIN

Physical Biology of the Materials-Microorganism Interface.

Future solar-to-chemical production will rely upon a deep understanding of the material-microorganism interface. Hybrid technologies, which combine inorganic semiconductor light harvesters with biological catalysis to transform light, air, and water into chemicals, already demonstrate a wide product scope and energy efficiencies surpassing that of natural photosynthesis. But optimization to economic competitiveness and fundamental curiosity beg for answers to two basic questions: (1) how do materials transfer energy and charge to microorganisms, and (2) how do we design for bio- and chemocompatibility between these seemingly unnatural partners? This Perspective highlights the state-of-the-art and outlines future research paths to inform the cadre of spectroscopists, electrochemists, bioinorganic chemists, material scientists, and biologists who will ultimately solve these mysteries