pwc-archive/papers-with-abstracts huggingface dataset:
Online Photometric Calibration of Automatic Gain Thermal Infrared Cameras
Authors: Manash Pratim Das, Larry Matthies, Shreyansh Daftry
Date: 2020-12-07 00:00:00
Tasks: Camera Auto-Calibration, Thermal Image Denoising, Visual Odometry, Visual Tracking

Thermal infrared cameras are increasingly being used in various applications such as robot vision, industrial inspection and medical imaging, thanks to their improved resolution and portability. However, the performance of traditional computer vision techniques developed for electro-optical imagery does not directly translate to the thermal domain due to two major reasons: these algorithms require photometric assumptions to hold, and methods for photometric calibration of RGB cameras cannot be applied to thermal-infrared cameras due to difference in data acquisition and sensor phenomenology. In this paper, we take a step in this direction, and introduce a novel algorithm for online photometric calibration of thermal-infrared cameras. Our proposed method does not require any specific driver/hardware support and hence can be applied to any commercial off-the-shelf thermal IR camera. We present this in the context of visual odometry and SLAM algorithms, and demonstrate the efficacy of our proposed system through extensive experiments for both standard benchmark datasets, and real-world field tests with a thermal-infrared camera in natural outdoor environments.
A Survey on Performance Metrics for Object-Detection Algorithms
Authors: R. Padilla, S. L. Netto, E. A. B. da Silva
Date: 2020-07-21 00:00:00
Tasks: Benchmarking, Object, object-detection, Object Detection, Survey

This work explores and compares the plethora of metrics for the performance evaluation of object-detection algorithms. Average precision (AP),for instance, is a popular metric for evaluating the accuracy of object detectors by estimating the area under the curve (AUC) of the precision × recall relationship. Depending on the point interpolation used in the plot, two different AP variants can be defined and, therefore, different results are generated. AP has six additional variants increasing the possibilities of benchmarking. The lack of consensus in different works and AP implementations is a problem faced by the academic and scientific communities. Metric implementations written in different computational languages and platforms are usually distributed with corresponding datasets sharing a given bounding-box description. Such projects indeed help the community with evaluation tools, but demand extra work to be adapted for other datasets and bounding-box formats. This work reviews the most used metrics for object detection detaching their differences, applications, and main concepts. It also proposes a standard implementation that can be used as a benchmark among different datasets with minimum adaptation on the annotation files.
Adversarial Machine Learning in Wireless Communications using RF Data: A Review
Authors: Damilola Adesina, Chung-Chu Hsieh, Yalin E. Sagduyu, Lijun Qian
Date: 2020-12-28 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML) provides effective means to learn from spectrum data and solve complex tasks involved in wireless communications. Supported by recent advances in computational resources and algorithmic designs, deep learning (DL) has found success in performing various wireless communication tasks such as signal recognition, spectrum sensing and waveform design. However, ML in general and DL in particular have been found vulnerable to manipulations thus giving rise to a field of study called adversarial machine learning (AML). Although AML has been extensively studied in other data domains such as computer vision and natural language processing, research for AML in the wireless communications domain is still in its early stage. This paper presents a comprehensive review of the latest research efforts focused on AML in wireless communications while accounting for the unique characteristics of wireless systems. First, the background of AML attacks on deep neural networks is discussed and a taxonomy of AML attack types is provided. Various methods of generating adversarial examples and attack mechanisms are also described. In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented. Finally, as new attacks and defense techniques are developed, recent research trends and the overarching future outlook for AML for next-generation wireless communications are discussed.
Fuzzy Conviction Score for Discriminating Decision-Tree-Classified Feature Vectors w.r.t. Relative Distances from Decision Boundaries
Authors: Poomjai Nacaskul, Kongkan Kalakan
Date: 2020-12-29 00:00:00
Tasks: Diagnostic

We augment decision tree classification analysis with fuzzy membership functions that quantitatively qualify, at each binary decision boundary, the degree of "conviction" to which each data point (i.e. feature vector) is deemed to be on either side of said decision boundary, the further away from the decision threshold, relative to peers, the higher the fuzzy membership value (i.e. the closer to 1). Our fuzzy "conviction" score is analogous to the measure of "confidence" as per traditional statistical methods, whilst handily accommodates the nonlinear discriminant surface created by a decision tree.

Although our method has been successfully deployed in confidential commercial setting, here we demonstrate the concept and computation on the benchmark "Breast Cancer Wisconsin (Original/Diagnostic)" Data Sets archived and made available publicly on the UCI (University of California, Irvine) Machine Learning Repository. In addition, we will as well demonstrate that w/o introducing any additional learning loops, our fuzzification of decision tree classifier improves the AUC (Area Under the ROC (Receiver Operating Characteristic) Curve) performance over that of the original decision tree classifier, provided the latter is decently capable of discriminating classes within the relevant data set to begin with.
Active learning via informed search in movement parameter space for efficient robot task learning and transfer
Authors: Nemanja Rakicevic, Petar Kormushev
Date: 2019-02-21 00:00:00
Tasks: Active Learning

Learning complex physical tasks via trial-and-error is still challenging for high-degree-of-freedom robots. Greatest challenges are devising a suitable objective function that defines the task, and the high sample complexity of learning the task. We propose a novel active learning framework, consisting of decoupled task model and exploration components, which does not require an objective function. The task model is specific to a task and maps the parameter space, defining a trial, to the trial outcome space. The exploration component enables efficient search in the trial-parameter space to generate the subsequent most informative trials, by simultaneously exploiting all the information gained from previous trials and reducing the task model’s overall uncertainty. We analyse the performance of our framework in a simulation environment and further validate it on a challenging bimanual-robot puck-passing task. Results show that the robot successfully acquires the necessary skills after only 100 trials without any prior information about the task or target positions. Decoupling the framework’s components also enables efficient skill transfer to new environments which is validated experimentally.
Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation
Authors: Yun Liu, Yu-Huan Wu, Pei-Song Wen, Yu-Jun Shi, Yu Qiu, Ming-Ming Cheng
Date: 2020-09-10 00:00:00
Tasks: Image-level Supervised Instance Segmentation, Instance Segmentation, Multiple Instance Learning, Segmentation, Semantic Segmentation, Weakly-supervised instance segmentation, Weakly-Supervised Semantic Segmentation

Weakly supervised semantic instance segmentation with only image-level supervision, instead of relying on expensive pixel wise masks or bounding box annotations, is an important problem to alleviate the data-hungry nature of deep learning. In this paper, we tackle this challenging problem by aggregating the image-level information of all training images into a large knowledge graph and exploiting semantic relationships from this graph. Specifically, our effort starts with some generic segment-based object proposals (SOP) without category priors. We propose a multiple instance learning (MIL) framework, which can be trained in an end-to-end manner using training images with image-level labels. For each proposal, this MIL framework can simultaneously compute probability distributions and category-aware semantic features, with which we can formulate a large undirected graph. The category of background is also included in this graph to remove the massive noisy object proposals. An optimal multi-way cut of this graph can thus assign a reliable category label to each proposal. The denoised SOP with assigned category labels can be viewed as pseudo instance segmentation of training images, which are used to train fully supervised models. The proposed approach achieves state-of-the-art performance for both weakly supervised instance segmentation and semantic segmentation.
ResCovNet: A Deep Learning-Based Architecture For COVID-19 Detection From Chest CT Scan Images
Authors: Ankan Ghosh Dastider, Mohseu Rashid Subah, Farhan Sadik, Tanvir Mahmud, Shaikh Anowarul Fattah
Date: 2020-11-17 00:00:00
Tasks: BIG-bench Machine Learning, Computed Tomography (CT), Diagnostic

Automatic disease detection using machine learning-based techniques from X-ray and computed tomography (CT) can play a major role in the frontline to assist medical professionals during the current outbreak of COVID-19. Fast diagnosis of the disease is the key to reduce the uncontrollable spread of this life-threatening disease, where machine learning-based applications can contribute greatly by predicting the situation of patients so that professionals can decide accordingly. The major drawbacks of detecting COVID-19 are its similarities with different types of pneumonia, and the absence of properly labeled data. Considering the ResNet152V2 as a backbone network, an efficient architecture, namely ResCovNet is proposed to detect COVID-19 accurately from chest CT scan images by separating it from three types of pneumonia and normal cases. Otsu’s thresholding is applied in the pre-processing step to strengthen the features for the classification network. With the use of proposed architecture, a very satisfactory classification accuracy of 88.1% is achieved to separate COVID-19 from all other four classes. Evaluating the performance of this study by 3-fold cross-validation, and comparison with related works prove that this adroit algorithm provides an effective way to be implemented as a diagnostic tool in the COVID-19 screening.
Common Phase and Structure Misidentifications in High-Resolution TEM Characterization of Perovskite Materials
Authors: Yu-Hao Deng
Date: 2020-12-29 00:00:00

Abstract：High-resolution TEM (HRTEM) is a powerful tool for structure characterization. However, methylammonium lead iodide (MAPbI3) perovskite is highly sensitive to electron beams and easily decomposes into lead iodide (PbI2). Misidentifications, such as PbI2 being incorrectly labeled as perovskite, are widely present in HRTEM characterization and would negatively affect the development of perovskite research field. Here misidentifications in MAPbI3 perovskite are summarized, classified, and corrected based on low-dose imaging and electron diffraction (ED) simulations. Corresponding crystallographic parameters of intrinsic tetragonal MAPbI3 and the confusable hexagonal PbI2 are presented unambiguously. Finally, the method of proper phase identification and some strategies to control the radiation damage in HRTEM are provided. This warning paves the way to avoid future misinterpretations in HRTEM characterization of perovskite and other electron beam-sensitive materials.

Keywords: MAPbI3 perovskite; transmission electron microscopy (TEM); electron diffraction (ED); phase identification; electron beam-sensitive
Forecasting COVID-19 Chile's second outbreak by a generalized SIR model with constant time delays and a fitted positivity rate
Authors: Patricio Cumsille, Oscar Rojas-Díaz, Pablo Moisset de Espanés
Date: 2020-12-28 00:00:00

The COVID-19 disease has forced countries to make a considerable collaborative effort between scientists and governments to provide indicators to suitable follow-up the pandemic's consequences. Mathematical modeling plays a crucial role in quantifying indicators describing diverse aspects of the pandemic. Consequently, this work aims to develop a clear, efficient, and reproducible methodology for parameter optimization, whose implementation is illustrated using data from three representative regions from Chile and a suitable generalized SIR model together with a fitted positivity rate. Our results reproduce the general trend of the infected's curve, distinguishing the reported and real cases. Finally, our methodology is robust, and it allows us to forecast a second outbreak of COVID-19 and the infection fatality rate of COVID-19 qualitatively according to the reported dead cases.
Machine Learning that Matters
Authors: Kiri Wagstaff
Date: 2012-06-18 00:00:00
Tasks: BIG-bench Machine Learning

Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring limitations in the data sets we investigate, the metrics we employ for evaluation, and the degree to which results are communicated back to their originating domains. What changes are needed to how we conduct research to increase the impact that ML has? We present six Impact Challenges to explicitly focus the field?s energy and attention, and we discuss existing obstacles that must be addressed. We aim to inspire ongoing discussion and focus on ML that matters.
High-performance photodetector based on centimeter-scale perovskite single-crystalline thin film
Authors: yuhao deng
Date: 2020-07-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

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 ~ 104, 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.

Keywords: Perovskite, Single-crystalline thin film, Hydrophobic, Photodetector, Gain

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
Large-scale perovskite single-crystalline thin films & growth of perovskite single crystals & high-performance photodetectors
Authors: perovskite
Date: 2020-06-12 00:00:00

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 ~ 104, 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.

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
High-performance photodetector based on large-scale perovskite single-crystalline thin films
Authors: deng yuhao
Date: 2020-07-20 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.

Keywords: Perovskite, Single-crystalline thin film, Hydrophobic, Photodetector, Gain

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
Large-scale & centimeter-scale halide perovskite single-crystal thin films: growth method and optoelectronic application
Authors: Nano Convergence
Date: 2020-07-04 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
High-gain and low-driving-voltage photodetectors based on large-scale perovskite single-crystalline thin films
Authors: Yu-Hao Deng、Zhen-Qian Yang、 Ren-Min Ma
Date: 2020-06-12 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 ~ 104, 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