pwc-archive/papers-with-abstracts huggingface dataset:
A multicenter study on radiomic features from T2‐weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics
Authors: Bianchini, Linda; Santinha, João; Loução, Nuno; Figueiredo, Mário; Botta, Francesca; Origgi, Daniela; Cremonesi, Marta; Cassano, Enrico; Papanikolaou, Nikolaos; Lascialfari, Alessandro
Date: 2020-09-24 00:00:00

Purpose
To investigate the repeatability and reproducibility of radiomic features extracted from MR images and provide a workflow to identify robust features.

Methods
T2‐weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of features were assessed by the intraclass correlation coefficient and the concordance correlation coefficient, respectively, and by the within‐subject coefficient of variation, considering repeated acquisitions with and without phantom repositioning, and with different scanner and acquisition parameters. The features showing intraclass correlation coefficient or concordance correlation coefficient >0.9 were selected, and their dependence on shape information (Spearman’s ρ > 0.8) analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities of images.

Results
From 944 two‐dimensional features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. A much lower range (11.2% to 85.4%) was obtained after phantom repositioning. Three‐dimensional extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. In addition, 82.4% to 94.9% of the features showed excellent agreement when extracted from images acquired with echo times 5 ms apart, but decreased with increasing echo‐time intervals, and 90.7% of the features exhibited excellent reproducibility for changes in pulse repetition time. Of nonshape features, 2.0% was identified as providing only shape information.

Conclusion
We showed that radiomic features are affected by MRI protocols and propose a general workflow to identify repeatable, reproducible, and informative radiomic features to ensure robustness of clinical studies.
Java application for the superposition T-matrix code to study the optical properties of cosmic dust aggregates
Authors: P. Halder, A. Chakraborty, P. Deb Roy, H.S. Das
Date: 2014-05-09 00:00:00

In this paper, we report the development of a java application for the Superposition T-matrix code, JaSTA (Java Superposition T-matrix App), to study the light scattering properties of aggregate structures. It has been developed using Netbeans 7.1.2, which is a java integrated development environment (IDE). The JaSTA uses double precession superposition codes for multi-sphere clusters in random orientation developed by Mackowski and Mischenko (1996). It consists of a graphical user interface (GUI) in the front hand and a database of related data in the back hand. Both the interactive GUI and database package directly enable a user to model by self-monitoring respective input parameters (namely, wavelength, complex refractive indices, grain size, etc.) to study the related optical properties of cosmic dust (namely, extinction, polarization, etc.) instantly, i.e., with zero computational time. This increases the efficiency of the user. The database of JaSTA is now created for a few sets of input parameters with a plan to create a large database in future. This application also has an option where users can compile and run the scattering code directly for aggregates in GUI environment. The JaSTA aims to provide convenient and quicker data analysis of the optical properties which can be used in different fields like planetary science, atmospheric science, nano science, etc. The current version of this software is developed for the Linux and Windows platform to study the light scattering properties of small aggregates which will be extended for larger aggregates using parallel codes in future.
JaSTA-2: Second version of the Java Superposition T-matrix Application
Authors: Prithish Halder, Himadri Sekhar Das
Date: 2017-09-12 00:00:00

In this article, we announce the development of a new version of the Java Superposition T-matrix App (JaSTA-2), to study the light scattering properties of porous aggregate particles. It has been developed using Netbeans 7.1.2, which is a java integrated development environment (IDE). The JaSTA uses double precision superposition T-matrix codes for multi-sphere clusters in random orientation, developed by Mackowski and Mischenko (1996). The new version consists of two options as part of the input parameters: (i) single wavelength and (ii) multiple wavelengths. The first option (which retains the applicability of older version of JaSTA) calculates the light scattering properties of aggregates of spheres for a single wavelength at a given instant of time whereas the second option can execute the code for a multiple numbers of wavelengths in a single run. JaSTA-2 provides convenient and quicker data analysis which can be used in diverse fields like Planetary Science, Atmospheric Physics, Nanoscience, etc. This version of the software is developed for Linux platform only, and it can be operated over all the cores of a processor using the multi-threading option.
Sneak Preview: PowerDynamics.jl -= An Open-Source library for analyzing dynamic stability in power grids with high shares of renewable energy
Authors: Tim Kittel, Sabine Auer, Christina Horn
Date: 2020-12-09 00:00:00

PowerDynamics.jl is an Open-Source library for dynamic power grid modeling built in the latest scientific programming language, Julia. It provides all the tools necessary to analyze the dynamical stability of power grids with high share of renewable energy. In contrast to conventional tools, it makes full use of the simplicity and generality that Julia combines with highly-optimized just-in-time compiled Code. Additionally, its ecosystem provides DifferentialEquations.jl, a high-performance library for solving differential equations with built-in solvers and interfaces to industrial grade solvers like Sundials. PowerDynamics.jl provides a multitude of dynamics for different node/bus-types, e.g. rotating masses, droop-control in inverters, and is able to explicitly model time delays of inverters. Furthermore, it includes realistic models of fluctuations from renewable energy sources. In this paper, we demonstrate how to use PowerDynamics.jl for the IEEE 14-bus distribution grid feeder.
Cooperative System Identification via Correctional Learning
Authors: Inês Lourenço, Robert Mattila, Cristian R. Rojas, Bo Wahlberg
Date: 2020-12-09 00:00:00

We consider a cooperative system identification scenario in which an expert agent (teacher) knows a correct, or at least a good, model of the system and aims to assist a learner-agent (student), but cannot directly transfer its knowledge to the student. For example, the teacher's knowledge of the system might be abstract or the teacher and student might be employing different model classes, which renders the teacher's parameters uninformative to the student. In this paper, we propose correctional learning as an approach to the above problem: Suppose that in order to assist the student, the teacher can intercept the observations collected from the system and modify them to maximize the amount of information the student receives about the system. We formulate a general solution as an optimization problem, which for a multinomial system instantiates itself as an integer program. Furthermore, we obtain finite-sample results on the improvement that the assistance from the teacher results in (as measured by the reduction in the variance of the estimator) for a binomial system.
Modeling asset allocation strategies and a new portfolio performance score
Authors: Apostolos Chalkis, Emmanouil Christoforou, Ioannis Z. Emiris, Theodore Dalamagas
Date: 2020-12-09 00:00:00
Tasks: Anomaly Detection, Management, Portfolio Optimization

We discuss and extend a powerful, geometric framework to represent the set of portfolios, which identifies the space of asset allocations with the points lying in a convex polytope. Based on this viewpoint, we survey certain state-of-the-art tools from geometric and statistical computing in order to handle important and difficult problems in digital finance. Although our tools are quite general, in this paper we focus on two specific questions. The first concerns crisis detection, which is of prime interest for the public in general and for policy makers in particular because of the significant impact that crises have on the economy. Certain features in stock markets lead to this type of anomaly detection: Given the assets' returns, we describe the relationship between portfolios' return and volatility by means of a copula, without making any assumption on investor strategies. We examine a recent method relying on copulae to construct an appropriate indicator that allows us to automate crisis detection. On real data, the indicator detects all past crashes in the cryptocurrency market, whereas from the DJ600-Europe index, from 1990 to 2008, the indicator identifies correctly 4 crises and issues one false positive for which we offer an explanation. Our second contribution is to introduce an original computational framework to model asset allocation strategies, which is of independent interest for digital finance and its applications. Our approach addresses the crucial question of evaluating portfolio management, and is relevant to individual managers as well as financial institutions. To evaluate portfolio performance, we provide a new portfolio score, based on the aforementioned framework and concepts. In particular, our score relies on the statistical properties of portfolios, and we show how they can be computed efficiently.
Liability Design with Information Acquisition
Authors: Francisco Poggi, Bruno Strulovici
Date: 2020-12-09 00:00:00

How to guarantee that firms perform due diligence before launching potentially dangerous products? We study the design of liability rules when (i) limited liability prevents firms from internalizing the full damage they may cause, (ii) penalties are paid only if damage occurs, regardless of the product's inherent riskiness, (iii) firms have private information about their products' riskiness before performing due diligence. We show that (i) any liability mechanism can be implemented by a tariff that depends only on the evidence acquired by the firm if a damage occurs, not on any initial report by the firm about its private information, (ii) firms that assign a higher prior to product riskiness always perform more due diligence but less than is socially optimal, and (iii) under a simple and intuitive condition, any type-specific launch thresholds can be implemented by a monotonic tariff.
Full C-Band WDM Transmission of Nonlinearity-Tolerant Probabilistically Shaped QAM over 2824-km Dispersion-Managed Fiber
Authors: Junho Cho, Xi Chen, Greg Raybon, Di Che, Ellsworth Burrows, Robert Tkach
Date: 2020-12-09 00:00:00

By tailoring probabilistic constellation shaping (PCS) for nonlinearity tolerance, we experimentally demonstrate up to 1.1 dB increase in signal-to-noise ratio (SNR) and 6.4% increase in total net data rate (NDR) compared to linear-channel-optimized PCS on a 2824-km dispersion-managed wavelength-division multiplexed (WDM) optical fiber link.
Frequency Separation based Adaptive Feedforward Control for Rejecting Wideband Vibration with Application to Hard Disk Drives
Authors: Jinwen Pan, Zhi Chen, Yong Wang, Roberto Horowitz
Date: 2020-12-09 00:00:00

In this paper, a frequency separation based adaptive feedforward control algorithm is developed with the ability to identify the plant and do compensation region by region. In this algorithm, the accelerometer signal is filtered by a series of uniformly distributed bandpass filters to generate a bunch of subband signals which are mutually exclusive in spectrum. In each subband, the corresponding subband signal acts as the feedforward signal and only the frequency response of system in that region needs to be identified, thus a pretty low order model can be expected to have efficient compensation. Starting from the first region, the feedforward control parameters are learned simultaneously with the low order plant model in the same region and then moves to the next region until all the regions are performed.
Cost-efficiency trade-offs of the human brain network revealed by a multiobjective evolutionary algorithm
Authors: Junji Ma, Jinbo Zhang, Ying Lin, Zhengjia Dai
Date: 2020-12-09 00:00:00

It is widely believed that the formation of brain network structure is under the pressure of optimal trade-off between reducing wiring cost and promoting communication efficiency. However, the question of whether this trade-off exists in empirical human brain networks and, if so, how it takes effect is still not well understood. Here, we employed a multiobjective evolutionary algorithm to directly and quantitatively explore the cost-efficiency trade-off in human brain networks. Using this algorithm, we generated a population of synthetic networks with optimal but diverse cost-efficiency trade-offs. It was found that these synthetic networks could not only reproduce a large portion of connections in the empirical brain networks but also embed a resembling small-world structure. Moreover, the synthetic and empirical brain networks were found similar in terms of the spatial arrangement of hub regions and the modular structure, which are two important topological features widely assumed to be outcomes of cost-efficiency trade-offs. The synthetic networks had high robustness against random attack as the empirical brain networks did. Additionally, we also revealed some differences of the synthetic networks from the empirical brain networks, including lower segregated processing capacity and weaker robustness against targeted attack. These findings provide direct and quantitative evidence that the structure of human brain networks is indeed largely influenced by optimal cost-efficiency trade-offs. We also suggest that some additional factors (e.g., segregated processing capacity) might jointly determine the network organization with cost and efficiency.
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
Authors: Pengfei Chen, Junjie Ye, Guangyong Chen, Jingwei Zhao, Pheng-Ann Heng
Date: 2020-12-10 00:00:00
Tasks: Image Classification

Supervised learning under label noise has seen numerous advances recently, while existing theoretical findings and empirical results broadly build up on the class-conditional noise (CCN) assumption that the noise is independent of input features given the true label. In this work, we present a theoretical hypothesis testing and prove that noise in real-world dataset is unlikely to be CCN, which confirms that label noise should depend on the instance and justifies the urgent need to go beyond the CCN assumption.The theoretical results motivate us to study the more general and practical-relevant instance-dependent noise (IDN). To stimulate the development of theory and methodology on IDN, we formalize an algorithm to generate controllable IDN and present both theoretical and empirical evidence to show that IDN is semantically meaningful and challenging. As a primary attempt to combat IDN, we present a tiny algorithm termed self-evolution average label (SEAL), which not only stands out under IDN with various noise fractions, but also improves the generalization on real-world noise benchmark Clothing1M. Our code is released. Notably, our theoretical analysis in Section 2 provides rigorous motivations for studying IDN, which is an important topic that deserves more research attention in future.
Portrait Neural Radiance Fields from a Single Image
Authors: Chen Gao, YiChang Shih, Wei-Sheng Lai, Chia-Kai Liang, Jia-Bin Huang
Date: 2020-12-10 00:00:00
Tasks: Meta-Learning, NeRF

We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. In this work, we propose to pretrain the weights of a multilayer perceptron (MLP), which implicitly models the volumetric density and colors, with a meta-learning framework using a light stage portrait dataset. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts.
Robust Consistent Video Depth Estimation
Authors: Johannes Kopf, Xuejian Rong, Jia-Bin Huang
Date: 2020-12-10 00:00:00
Proceeding: CVPR 2021 1
Tasks: Depth Estimation

We present an algorithm for estimating consistent dense depth maps and camera poses from a monocular video. We integrate a learning-based depth prior, in the form of a convolutional neural network trained for single-image depth estimation, with geometric optimization, to estimate a smooth camera trajectory as well as detailed and stable depth reconstruction. Our algorithm combines two complementary techniques: (1) flexible deformation-splines for low-frequency large-scale alignment and (2) geometry-aware depth filtering for high-frequency alignment of fine depth details. In contrast to prior approaches, our method does not require camera poses as input and achieves robust reconstruction for challenging hand-held cell phone captures containing a significant amount of noise, shake, motion blur, and rolling shutter deformations. Our method quantitatively outperforms state-of-the-arts on the Sintel benchmark for both depth and pose estimations and attains favorable qualitative results across diverse wild datasets.
Are Fewer Labels Possible for Few-shot Learning?
Authors: Suichan Li, Dongdong Chen, Yinpeng Chen, Lu Yuan, Lei Zhang, Qi Chu, Nenghai Yu
Date: 2020-12-10 00:00:00
Tasks: Clustering, Few-Shot Learning

Few-shot learning is challenging due to its very limited data and labels. Recent studies in big transfer (BiT) show that few-shot learning can greatly benefit from pretraining on large scale labeled dataset in a different domain. This paper asks a more challenging question: "can we use as few as possible labels for few-shot learning in both pretraining (with no labels) and fine-tuning (with fewer labels)?". Our key insight is that the clustering of target samples in the feature space is all we need for few-shot finetuning. It explains why the vanilla unsupervised pretraining (poor clustering) is worse than the supervised one. In this paper, we propose transductive unsupervised pretraining that achieves a better clustering by involving target data even though its amount is very limited. The improved clustering result is of great value for identifying the most representative samples ("eigen-samples") for users to label, and in return, continued finetuning with the labeled eigen-samples further improves the clustering. Thus, we propose eigen-finetuning to enable fewer shot learning by leveraging the co-evolution of clustering and eigen-samples in the finetuning. We conduct experiments on 10 different few-shot target datasets, and our average few-shot performance outperforms both vanilla inductive unsupervised transfer and supervised transfer by a large margin. For instance, when each target category only has 10 labeled samples, the mean accuracy gain over the above two baselines is 9.2% and 3.42 respectively.
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation
Authors: Hugues Thomas, Ben Agro, Mona Gridseth, Jian Zhang, Timothy D. Barfoot
Date: 2020-12-10 00:00:00
Tasks: Navigate, Point Cloud Segmentation, Segmentation, Self-Supervised Learning, Semantic Segmentation, Simultaneous Localization and Mapping

We present a self-supervised learning approach for the semantic segmentation of lidar frames. Our method is used to train a deep point cloud segmentation architecture without any human annotation. The annotation process is automated with the combination of simultaneous localization and mapping (SLAM) and ray-tracing algorithms. By performing multiple navigation sessions in the same environment, we are able to identify permanent structures, such as walls, and disentangle short-term and long-term movable objects, such as people and tables, respectively. New sessions can then be performed using a network trained to predict these semantic labels. We demonstrate the ability of our approach to improve itself over time, from one session to the next. With semantically filtered point clouds, our robot can navigate through more complex scenarios, which, when added to the training pool, help to improve our network predictions. We provide insights into our network predictions and show that our approach can also improve the performances of common localization techniques.
Probing Few-Shot Generalization with Attributes
Authors: Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard Zemel
Date: 2020-12-10 00:00:00
Tasks: Attribute, Few-Shot Learning, Zero-Shot Learning

Despite impressive progress in deep learning, generalizing far beyond the training distribution is an important open challenge. In this work, we consider few-shot classification, and aim to shed light on what makes some novel classes easier to learn than others, and what types of learned representations generalize better. To this end, we define a new paradigm in terms of attributes -- simple building blocks of which concepts are formed -- as a means of quantifying the degree of relatedness of different concepts. Our empirical analysis reveals that supervised learning generalizes poorly to new attributes, but a combination of self-supervised pretraining with supervised finetuning leads to stronger generalization. The benefit of self-supervised pretraining and supervised finetuning is further investigated through controlled experiments using random splits of the attribute space, and we find that predictability of test attributes provides an informative estimate of a model's generalization ability.
AutoSelect: Automatic and Dynamic Detection Selection for 3D Multi-Object Tracking
Authors: Xinshuo Weng, Kris Kitani
Date: 2020-12-10 00:00:00
Tasks: 3D Multi-Object Tracking, Multi-Object Tracking, Object, Object Tracking

3D multi-object tracking is an important component in robotic perception systems such as self-driving vehicles. Recent work follows a tracking-by-detection pipeline, which aims to match past tracklets with detections in the current frame. To avoid matching with false positive detections, prior work filters out detections with low confidence scores via a threshold. However, finding a proper threshold is non-trivial, which requires extensive manual search via ablation study. Also, this threshold is sensitive to many factors such as target object category so we need to re-search the threshold if these factors change. To ease this process, we propose to automatically select high-quality detections and remove the efforts needed for manual threshold search. Also, prior work often uses a single threshold per data sequence, which is sub-optimal in particular frames or for certain objects. Instead, we dynamically search threshold per frame or per object to further boost performance. Through experiments on KITTI and nuScenes, our method can filter out $45.7\%$ false positives while maintaining the recall, achieving new S.O.T.A. performance and removing the need for manually threshold tuning.