Enhancing thermoelectric figure-of-merit by low-dimensional electrical transport in phonon-glass crystals

Low-dimensional electronic and glassy phononic transport are two important ingredients of highly-efficient thermoelectric material, from which two branches of the thermoelectric research emerge. One focuses on controlling electronic transport in the low dimension, while the other on multiscale phonon engineering in the bulk. Recent work has benefited much from combining these two approaches, e.g., phonon engineering in low-dimensional materials. Here, we propose to employ the low-dimensional electronic structure in bulk phonon-glass crystal as an alternative way to increase the thermoelectric efficiency. Through first-principles electronic structure calculation and classical molecular dynamics simulation, we show that the $\pi$-$\pi$ stacking Bis-Dithienothiophene molecular crystal is a natural candidate for such an approach. This is determined by the nature of its chemical bonding. Without any optimization of the material parameter, we obtain a maximum room-temperature figure of merit, $ZT$, of $1.48$ at optimal doping, thus validating our idea.Comment: Nano Lett.201

A Learned Index for Exact Similarity Search in Metric Spaces

Indexing is an effective way to support efficient query processing in large databases. Recently the concept of learned index has been explored actively to replace or supplement traditional index structures with machine learning models to reduce storage and search costs. However, accurate and efficient similarity query processing in high-dimensional metric spaces remains to be an open challenge. In this paper, a novel indexing approach called LIMS is proposed to use data clustering and pivot-based data transformation techniques to build learned indexes for efficient similarity query processing in metric spaces. The underlying data is partitioned into clusters such that each cluster follows a relatively uniform data distribution. Data redistribution is achieved by utilizing a small number of pivots for each cluster. Similar data are mapped into compact regions and the mapped values are totally ordinal. Machine learning models are developed to approximate the position of each data record on the disk. Efficient algorithms are designed for processing range queries and nearest neighbor queries based on LIMS, and for index maintenance with dynamic updates. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of LIMS compared with traditional indexes and state-of-the-art learned indexes.Comment: 14 pages, 14 figures, submitted to Transactions on Knowledge and Data Engineerin

Meta-Reinforcement Learning via Language Instructions

Although deep reinforcement learning has recently been very successful at learning complex behaviors, it requires a tremendous amount of data to learn a task. One of the fundamental reasons causing this limitation lies in the nature of the trial-and-error learning paradigm of reinforcement learning, where the agent communicates with the environment and progresses in the learning only relying on the reward signal. This is implicit and rather insufficient to learn a task well. On the contrary, humans are usually taught new skills via natural language instructions. Utilizing language instructions for robotic motion control to improve the adaptability is a recently emerged topic and challenging. In this paper, we present a meta-RL algorithm that addresses the challenge of learning skills with language instructions in multiple manipulation tasks. On the one hand, our algorithm utilizes the language instructions to shape its interpretation of the task, on the other hand, it still learns to solve task in a trial-and-error process. We evaluate our algorithm on the robotic manipulation benchmark (Meta-World) and it significantly outperforms state-of-the-art methods in terms of training and testing task success rates. Codes are available at \url{https://tumi6robot.wixsite.com/million}

PointNu-Net: Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification in the Clinical Wild

Automatic nuclei segmentation and classification plays a vital role in digital pathology. However, previous works are mostly built on data with limited diversity and small sizes, making the results questionable or misleading in actual downstream tasks. In this paper, we aim to build a reliable and robust method capable of dealing with data from the 'the clinical wild'. Specifically, we study and design a new method to simultaneously detect, segment, and classify nuclei from Haematoxylin and Eosin (H&E) stained histopathology data, and evaluate our approach using the recent largest dataset: PanNuke. We address the detection and classification of each nuclei as a novel semantic keypoint estimation problem to determine the center point of each nuclei. Next, the corresponding class-agnostic masks for nuclei center points are obtained using dynamic instance segmentation. By decoupling two simultaneous challenging tasks, our method can benefit from class-aware detection and class-agnostic segmentation, thus leading to a significant performance boost. We demonstrate the superior performance of our proposed approach for nuclei segmentation and classification across 19 different tissue types, delivering new benchmark results.Comment: 10 pages,7 figures, journa

Using Virtual Instrument in Teaching Automatic Measurement Technology Course

The use of an automatic measurement technology is highly important in current industries. The technology has been sued in various applications such as environment monitoring, quality control of production line, and medical disease analysis. Automatic measurement technology requires programming, facilities integration, control application, function innovation, and maintenance technology. Developing suitable teaching equipment that can satisfy the demand of industry-orientation Automatic Measurement Technology Course (AMTC) is a challenge. In this study, a virtual instrument is introduced to solve the problem. LabVIEW, which is utilized to design virtual instruments, provides powerful functions for instrument control and measurement. Therefore, in this proposed AMTC, anbsp LabVIEW-based virtual instrument system is established as teaching equipment for undergraduate students in colleges of engineering or technology

A Semi-Analytical Approach for State-Space Electromagnetic Transient Simulation Using the Differential Transformation

Electromagnetic transient (EMT) simulation is a crucial tool for power system dynamic analysis because of its detailed component modeling and high simulation accuracy. However, it suffers from computational burdens for large power grids since a tiny time step is typically required for accuracy. This paper proposes an efficient and accurate semi-analytical approach for state-space EMT simulations of power grids. It employs high-order semi-analytical solutions derived using the differential transformation from the state-space EMT grid model. The approach incorporates a proposed variable time step strategy based on equation imbalance, leveraging structural information of the grid model, to enlarge the time step and accelerate simulations, while high resolution is maintained by reconstructing detailed fast EMT dynamics through an efficient dense output mechanism. It also addresses limit-induced switches during large time steps by using a binary search-enhanced quadratic interpolation algorithm. Case studies are conducted on EMT models of the IEEE 39-bus system and a synthetic 390-bus system to demonstrate the merits of the new simulation approach against traditional methods

Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation

Single-source domain generalization (SDG) in medical image segmentation is a challenging yet essential task as domain shifts are quite common among clinical image datasets. Previous attempts most conduct global-only/random augmentation. Their augmented samples are usually insufficient in diversity and informativeness, thus failing to cover the possible target domain distribution. In this paper, we rethink the data augmentation strategy for SDG in medical image segmentation. Motivated by the class-level representation invariance and style mutability of medical images, we hypothesize that unseen target data can be sampled from a linear combination of $C$ (the class number) random variables, where each variable follows a location-scale distribution at the class level. Accordingly, data augmented can be readily made by sampling the random variables through a general form. On the empirical front, we implement such strategy with constrained B$\acute{\rm e}$zier transformation on both global and local (i.e. class-level) regions, which can largely increase the augmentation diversity. A Saliency-balancing Fusion mechanism is further proposed to enrich the informativeness by engaging the gradient information, guiding augmentation with proper orientation and magnitude. As an important contribution, we prove theoretically that our proposed augmentation can lead to an upper bound of the generalization risk on the unseen target domain, thus confirming our hypothesis. Combining the two strategies, our Saliency-balancing Location-scale Augmentation (SLAug) exceeds the state-of-the-art works by a large margin in two challenging SDG tasks. Code is available at https://github.com/Kaiseem/SLAug