pwc-archive/papers-with-abstracts huggingface dataset:
10000 gain of perovskite photodetector based on large-scale high-quality single-crystalline thin film
Authors: yuhao deng
Date: 2020-07-12 00:00:00

Abstract: Modern electronic and photonic devices rely on single-crystalline thin film semiconductors for high performance and reproducibility. The emerging halide perovskites have extraordinary electronic and photonic properties and can be synthesized via low cost solution-based methods. They have been used in a variety of devices with performance approaching or over the devices based on conventional materials. However, their solution based growth method is intrinsically challenge to grow large scale single-crystalline thin film due to the random nucleation and isotropous growth of the crystal. Here, we report the growth of centimeter-scale perovskite single-crystalline thin films by controlling the nucleation density and growth rate of the crystal under a spatially confined growth condition. The hydrophobic treatment on substrates inhibits nucleation and accelerates the growth of single-crystalline thin film, providing enough space for initial nucleus growing up quickly without touching each other. Single-crystalline perovskite thin-film with an aspect ratio of 1000 (1 cm in side length, 10 μm in thickness) has been successfully grown. The low trap density and the high mobility of the as-grown thin film show a high crystallinity. The photodetector based on the perovskite thin film has achieved a gain ~ 10^4, benefitting from the short transit time of the carries due to the high mobility and thin thickness of the active layer. Our work opens up a new route to grow large scale perovskite single-crystalline thin films, providing a platform to develop high- performance devices.

Cite this article: Deng, YH., Yang, ZQ. & Ma, RM. Growth of centimeter-scale perovskite single-crystalline thin film via surface engineering. Nano Convergence 7, 25 (2020). https://doi.org/10.1186/s40580-020-00236-5

DOI: https://doi.org/10.1186/s40580-020-00236-5
Endogenous structural transformation in economic development
Authors: Justin Y. F. Lin, Haipeng Xing
Date: 2020-11-07 00:00:00

This paper extends Xing's (2023abcd) optimal growth models of catching-up economies from the case of production function switching to that of economic structure switching and argues how a country develops its economy by endogenous structural transformation and efficient resource allocation in a market mechanism. To achieve this goal, the paper first summarizes three attributes of economic structures from the literature, namely, structurality, durationality, and transformality, and discuss their implications for methods of economic modeling. Then, with the common knowledge assumption, the paper extends Xing's (2023a) optimal growth model that is based on production function switching and considers an extended Ramsey model with endogenous structural transformation in which the social planner chooses the optimal industrial structure, recource allocation with the chosen structure, and consumption to maximize the representative household's total utility subject to the resource constraint. The paper next establishes the mathematical underpinning of the static, dynamic, and switching equilibria. The Ramsey growth model and its equilibria are then extended to economies with complicated economic structures consisting of hierarchical production, technology adoption and innovation, infrastructure, and economic and political institutions. The paper concludes with a brief discussion of applications of the proposed methodology to economic development problems in other scenarios.
The highest gain of single-crystalline perovskite photodetectors based on high-quality single-crystalline thin film
Authors: Zhenqian Yang, yuhao deng
Date: 2018-01-08 00:00:00

Abstract
The best performing modern optoelectronic devices rely on single‐crystalline thin‐film (SC‐TF) semiconductors grown epitaxially. The emerging halide perovskites, which can be synthesized via low‐cost solution‐based methods, have achieved substantial success in various optoelectronic devices including solar cells, lasers, light‐emitting diodes, and photodetectors. However, to date, the performance of these perovskite devices based on polycrystalline thin‐film active layers lags behind the epitaxially grown semiconductor devices. Here, a photodetector based on SC‐TF perovskite active layer is reported with a record performance of a 50 million gain, 70 GHz gain‐bandwidth product, and a 100‐photon level detection limit at 180 Hz modulation bandwidth, which as far as we know are the highest values among all the reported perovskite photodetectors. The superior performance of the device originates from replacing polycrystalline thin film by a thickness‐optimized SC‐TF with much higher mobility and longer recombination time. The results indicate that high‐performance perovskite devices based on SC‐TF may become competitive in modern optoelectronics.

Summary: In summary, we report that a photodetector based on perovskite SC‐TF can have superior performance than its polycrystalline counterpart. By simultaneously optimizing the perovskites thickness and crystallinity, the device achieves a record photoconductive gain of 50 million and a gain‐bandwidth product of 70 GHz. The superior performance of SC‐TF perovskite photodetectors has also been exemplified by their ultrahigh sensitivity with record detection limit down to 100‐photon level. Further, we systematically study the thickness‐dependent performance of photodetectors. We find that a suitable thickness of the perovskite SC‐TF is crucial in device performance. With the SC‐TF thickness decreasing from about 10 µm to hundreds of nanometers, the lowest detectable power and internal gain improve two and four orders of magnitude, respectively. Our results show that perovskite devices based on SC‐TF have a bright future for optoelectronics.

DOI: https://doi.org/10.1002/adma.201704333;  Yang Z, Deng Y, Zhang X, et al. High‐Performance Single‐Crystalline Perovskite Thin‐Film Photodetector[J]. Advanced Materials, 2018, 30(8): 1704333.
Defects in Perovskites Have Positive Impact on Optoelectronic Devices
Authors: Applied Physics
Date: 2020-12-08 00:00:00

Defects are generally regarded to have negative impact on carrier recombination, charge-transport and ion migration in perovskite materials, which thus lower the efficiency and stability of perovskite optoelectronic devices. Meanwhile, lots of efforts which focused on minimizing defects have greatly promoted the application of perovskite materials. Then, can defects be positive in perovskite optoelectronic devices? Herein, relying on in-depth understanding of defect-associated effects in semiconductors, trapping of photo-generated carriers by defects is applied to enlarge photoconductive gain in perovskite photodetector. The record photoconductive gain, gain-bandwidth product and detection limit were achieved in the photodetector. Exceeding the general concept that defects are harmful, we showed that the defects can be positive in perovskite optoelectronic devices.
Identifying and understanding the positive impact of defects and traps for perovskites optoelectronic devices
Authors: YH Deng
Date: 2020-12-10 00:00:00

Abstract: Defects are generally regarded to have negative impact on carrier recombination, charge-transport and ion migration in perovskite materials, which thus lower the efficiency and stability of perovskite optoelectronic devices. Meanwhile, lots of efforts which focused on minimizing defects have greatly promoted the application of perovskite materials. Then, can defects be positive in perovskite optoelectronic devices? Herein, relying on in-depth understanding of defect-associated effects in semiconductors, trapping of photo-generated carriers by defects is applied to enlarge photoconductive gain in perovskite photodetector. The record photoconductive gain, gain-bandwidth product and detection limit were achieved in the photodetector. Exceeding the general concept that defects are harmful, we open up a new view that the defects can be positive in perovskite optoelectronic devices.

Keywords: Defect, perovskite material, positive, optoelectronic device, photodetector.

Cite: Deng Y H. Can defects be positive in perovskite optoelectronic devices?[J]. arXiv preprint arXiv:2012.04346, 2020. https://arxiv.org/abs/2012.04346
Charge trapping in lead halide perovskites enhanced the performance of optoelectronic devices
Authors: Dr Yu-Hao Deng
Date: 2020-12-10 00:00:00

[Deng Y H. Can defects be positive in perovskite optoelectronic devices?[J]. arXiv preprint arXiv:2012.04346, 2020. https://arxiv.org/abs/2012.04346]

Abstract: Defects are generally regarded to have negative impact on carrier recombination, charge-transport and ion migration in perovskite materials, which thus lower the efficiency and stability of perovskite optoelectronic devices. Meanwhile, lots of efforts which focused on minimizing defects have greatly promoted the application of perovskite materials. Then, can defects be positive in perovskite optoelectronic devices? Herein, relying on in-depth understanding of defect-associated effects in semiconductors, trapping of photo-generated carriers by defects is applied to enlarge photoconductive gain in perovskite photodetector. The record photoconductive gain, gain-bandwidth product and detection limit were achieved in the photodetector. Exceeding the general concept that defects are harmful, we open up a new view that the defects can be positive in perovskite optoelectronic devices.

Keywords: Defect, perovskite material, positive, optoelectronic device, photodetector.

Conclusion: Exceeding the general concept that defects are harmful, we demonstrated that the defects can be positive in perovskite optoelectronic devices. With the help of surface defects, the record photoconductive gain, gain-bandwidth product and detection limit were achieved in our perovskite photodetector. The trapping of photo-carriers by defects decreases the performance of photovoltaic performance devices but improves the photoconductive gain of photodetectors. Therefore, in-depth understanding of defects evolution and delicate manipulation are the keys to maximize performances of perovskite optoelectronic devices.
Multi-scale convolutional neural network for multi-focus image fusion
Authors: Hafiz Tayyab Mustafa, Jie Yang, Masoumeh Zareapoor
Date: 2019-03-05 00:00:00
Tasks: Multi Focus Image Fusion, SSIM

In this study, we present new deep learning (DL) method for fusing multi-focus images. Current multi-focus image fusion (MFIF) approaches based on DL methods mainly treat MFIF as a classification task. These methods use a convolutional neural network (CNN) as a classifier to identify pixels as focused or defocused pixels. How- ever, due to unavailability of labeled data to train networks, existing DL-based supervised models for MFIF add Gaussian blur in focused images to produce training data. DL-based unsupervised models are also too simple and only applicable to perform fusion tasks other than MFIF. To address the above issues, we proposed a new MFIF method, which aims to learn feature extraction, fusion and reconstruction components together to produce a complete unsupervised end-to-end trainable deep CNN. To enhance the feature extraction capability of CNN, we introduce a Siamese multi-scale feature extraction module to achieve a promising performance. In our pro- posed network we applied multiscale convolutions along with skip connections to extract more useful common features from a multi-focus image pair. Instead of using basic loss functions to train the CNN, our model utilizes structure similarity (SSIM) measure as a training loss function. Moreover, the fused images are reconstructed in a multiscale manner to guarantee more accurate restoration of images. Our proposed model can process images with variable size during testing and validation. Experimental results on various test images validate that our pro- posed method yields better quality fused images that are superior to the fused images generated by compared state-of-the-art image fusion methods.
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
Authors: Jun Zeng, Bike Zhang, Koushil Sreenath
Date: 2020-07-22 00:00:00
Tasks: Car Racing, Model Predictive Control

The optimal performance of robotic systems is usually achieved near the limit of state and input bounds. Model predictive control (MPC) is a prevalent strategy to handle these operational constraints, however, safety still remains an open challenge for MPC as it needs to guarantee that the system stays within an invariant set. In order to obtain safe optimal performance in the context of set invariance, we present a safety-critical model predictive control strategy utilizing discrete-time control barrier functions (CBFs), which guarantees system safety and accomplishes optimal performance via model predictive control. We analyze the stability and the feasibility properties of our control design. We verify the properties of our method on a 2D double integrator model for obstacle avoidance. We also validate the algorithm numerically using a competitive car racing example, where the ego car is able to overtake other racing cars.
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models
Authors: Botelho, Joshi, A., Khara, Sarkar, S., Hegde, C., Rao, V., Adavani, S.S., & Ganapathysubramanian, B.
Date: 2020-11-12 00:00:00
Tasks: Decoder, Distributed Computing

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these
models, especially to make predictions at large output resolutions (≥ 1024 × 1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.
A Linear Approximation Method for the Shapley Value
Authors: S. Shaheen Fatima, Michael Wooldridge, Nicholas R. Jennings
Date: 2008-09-01 00:00:00

The Shapley value is a key solution concept for coalitional games in general and voting games in particular. Its main advantage is that it provides a unique and fair solution, but its main drawback is the complexity of computing it (e.g., for voting games this complexity is #p-complete). However, given the importance of the Shapley value and voting games, a number of approximation methods have been developed to overcome this complexity. Among these, Owen's multi-linear extension method is the most time efficient, being linear in the number of players. Now, in addition to speed, the other key criterion for an approximation algorithm is its approximation error. On this dimension, the multi-linear extension method is less impressive. Against this background, this paper presents a new approximation algorithm, based on randomization, for computing the Shapley value of voting games. This method has time complexity linear in the number of players, but has an approximation error that is, on average, lower than Owen's. In addition to this comparative study, we empirically evaluate the error for our method and show how the different parameters of the voting game affect it. Specifically, we show the following effects. First, as the number of players in a voting game increases, the average percentage error decreases. Second, as the quota increases, the average percentage error decreases. Third, the error is different for players with different weights; players with weight closer to the mean weight have a lower error than those with weight further away. We then extend our approximation to the more general k-majority voting games and show that, for n players, the method has time complexity O(k2n) and the upper bound on its approximation error is .
Hierarchical Human Action Classification with Network Pruning
Authors: Mahdi Davoodikakhki, KangKang Yin
Date: 2020-12-07 00:00:00
Tasks: Action Classification, Classification, General Classification, Network Pruning

Research on human action classification has made significant progresses in the past few years. Most deep learning methods focus on improving performance by adding more network components. We propose, however, to better utilize auxiliary mechanisms, including hierarchical classification, network pruning, and skeleton-based preprocessing, to boost the model robustness and performance. We test the effectiveness of our method on three commonly used testing datasets: NTU RGB+D 60, NTU RGB+D 120, and Northwestern-UCLA Multiview Action 3D. Our experiments show that our method can achieve either comparable or better performance than state-of-the-art methods on all three datasets. In particular, our method sets up a new baseline for NTU 120, the largest dataset among the three. We also analyze our method with extensive comparisons and ablation studies.
micompm: A MATLAB/Octave toolbox for multivariate independent comparison of observations
Authors: Nuno Fachada, Agostinho C. Rosa
Date: 2018-03-30 00:00:00
Tasks: Time Series, Time Series Analysis

micompm is a MATLAB / GNU Octave port of the original micompr R package for comparing multivariate samples associated with different groups. Its purpose is to determine if the compared samples are significantly different from a statistical point of view. This method uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using statistical tests and score plots. This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences, avoiding manual selection of specific points or summary statistics. The procedure is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations. It is aimed at researchers from all fields of science, although it requires some knowledge on design of experiments, statistical testing and multidimensional data analysis.
PerfAndPubTools – Tools for Software Performance Analysis and Publishing of Results
Authors: Nuno Fachada, Vitor V. Lopes, Rui C. Martins, Agostinho C. Rosa
Date: 2016-05-12 00:00:00

PerfAndPubTools consists of a set of MATLAB/Octave functions for the post-processing and analysis of software performance benchmark data and producing associated publication quality materials.
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps
Authors: Tri Dao, Nimit S. Sohoni, Albert Gu, Matthew Eichhorn, Amit Blonder, Megan Leszczynski, Atri Rudra, Christopher Ré
Date: 2020-12-29 00:00:00
Proceeding: ICLR 2020 1
Tasks: All, image-classification, Image Classification, speech-recognition, Speech Recognition

Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference speed and reduce memory usage compared to general linear maps. However, choosing which of the myriad structured transformations to use (and its associated parameterization) is a laborious task that requires trading off speed, space, and accuracy. We consider a different approach: we introduce a family of matrices called kaleidoscope matrices (K-matrices) that provably capture any structured matrix with near-optimal space (parameter) and time (arithmetic operation) complexity. We empirically validate that K-matrices can be automatically learned within end-to-end pipelines to replace hand-crafted procedures, in order to improve model quality. For example, replacing channel shuffles in ShuffleNet improves classification accuracy on ImageNet by up to 5%. K-matrices can also simplify hand-engineered pipelines -- we replace filter bank feature computation in speech data preprocessing with a learnable kaleidoscope layer, resulting in only 0.4% loss in accuracy on the TIMIT speech recognition task. In addition, K-matrices can capture latent structure in models: for a challenging permuted image classification task, a K-matrix based representation of permutations is able to learn the right latent structure and improves accuracy of a downstream convolutional model by over 9%. We provide a practically efficient implementation of our approach, and use K-matrices in a Transformer network to attain 36% faster end-to-end inference speed on a language translation task.
EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs
Authors: Ayaan Haque
Date: 2020-12-26 00:00:00
Tasks: Classification, Data Augmentation, General Classification, Image Generation, Semi-Supervised Image Classification, Synthetic Data Generation

Semi-supervised learning has been gaining attention as it allows for performing image analysis tasks such as classification with limited labeled data. Some popular algorithms using Generative Adversarial Networks (GANs) for semi-supervised classification share a single architecture for classification and discrimination. However, this may require a model to converge to a separate data distribution for each task, which may reduce overall performance. While progress in semi-supervised learning has been made, less addressed are small-scale, fully-supervised tasks where even unlabeled data is unavailable and unattainable. We therefore, propose a novel GAN model namely External Classifier GAN (EC-GAN), that utilizes GANs and semi-supervised algorithms to improve classification in fully-supervised regimes. Our method leverages a GAN to generate artificial data used to supplement supervised classification. More specifically, we attach an external classifier, hence the name EC-GAN, to the GAN's generator, as opposed to sharing an architecture with the discriminator. Our experiments demonstrate that EC-GAN's performance is comparable to the shared architecture method, far superior to the standard data augmentation and regularization-based approach, and effective on a small, realistic dataset.