pwc-archive/papers-with-abstracts huggingface dataset:
Spectral GUI for Automated Tissue and Lesion Segmentation of T1 Weighted Breast MR Images
Authors: Prajval Koul
Date: 2020-04-19 00:00:00
Tasks: BIG-bench Machine Learning, Image Segmentation, Lesion Segmentation, Segmentation, Semantic Segmentation

We present Spectral GUI, a multiplatform breast MR image analysis tool designed to facilitate the segmentation of fibro glandular tissues and lesions in T1 weighted breast MR images via a graphical user interface (GUI). Spectral GUIR uses spectrum loft method [1] for breast MR image segmentation. Not only is it interactive, but robust and expeditious at the same time. Being devoid of any machine learning algorithm, it shows exceptionally high execution speed with minimal overheads. The accuracy of the results has been simultaneously measured using performance metrics and expert entailment. The validity and applicability of the tool are discussed in the paper along with a crisp contrast with traditional machine learning principles, establishing the unequivocal foundation of it as a competent tool in the field of image analysis.
Reconstruction of high-resolution 6x6-mm OCT angiograms using deep learning
Authors: Min Gao, Yukun Guo, Tristan T. Hormel, Jiande Sun, Thomas Hwang, Yali Jia
Date: 2020-04-19 00:00:00
Tasks: Deep Learning, Image Enhancement, Vocal Bursts Intensity Prediction

Typical optical coherence tomographic angiography (OCTA) acquisition areas on commercial devices are 3x3- or 6x6-mm. Compared to 3x3-mm angiograms with proper sampling density, 6x6-mm angiograms have significantly lower scan quality, with reduced signal-to-noise ratio and worse shadow artifacts due to undersampling. Here, we propose a deep-learning-based high-resolution angiogram reconstruction network (HARNet) to generate enhanced 6x6-mm superficial vascular complex (SVC) angiograms. The network was trained on data from 3x3-mm and 6x6-mm angiograms from the same eyes. The reconstructed 6x6-mm angiograms have significantly lower noise intensity and better vascular connectivity than the original images. The algorithm did not generate false flow signal at the noise level presented by the original angiograms. The image enhancement produced by our algorithm may improve biomarker measurements and qualitative clinical assessment of 6x6-mm OCTA.
ResNeSt: Split-Attention Networks
Authors: Hang Zhang, Chongruo wu, Zhongyue Zhang, Yi Zhu, Haibin Lin, Zhi Zhang, Yue Sun, Tong He, Jonas Mueller, R. Manmatha, Mu Li, Alexander Smola
Date: 2020-04-19 00:00:00
Tasks: image-classification, Image Classification, Instance Segmentation, Object Detection, Panoptic Segmentation, Semantic Segmentation, Transfer Learning

It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies the channel-wise attention on different network branches to leverage their success in capturing cross-feature interactions and learning diverse representations. Our design results in a simple and unified computation block, which can be parameterized using only a few variables. Our model, named ResNeSt, outperforms EfficientNet in accuracy and latency trade-off on image classification. In addition, ResNeSt has achieved superior transfer learning results on several public benchmarks serving as the backbone, and has been adopted by the winning entries of COCO-LVIS challenge. The source code for complete system and pretrained models are publicly available.
Desmoking laparoscopy surgery images using an image-to-image translation guided by an embedded dark channel
Authors: Sebastián Salazar-Colores, Hugo Alberto-Moreno, César Javier Ortiz-Echeverri, Gerardo Flores
Date: 2020-04-19 00:00:00
Tasks: Generative Adversarial Network, Image-to-Image Translation, SSIM, Translation

In laparoscopic surgery, the visibility in the image can be severely degraded by the smoke caused by the $CO_2$ injection, and dissection tools, thus reducing the visibility of organs and tissues. This lack of visibility increases the surgery time and even the probability of mistakes conducted by the surgeon, then producing negative consequences on the patient's health. In this paper, a novel computational approach to remove the smoke effects is introduced. The proposed method is based on an image-to-image conditional generative adversarial network in which a dark channel is used as an embedded guide mask. Obtained experimental results are evaluated and compared quantitatively with other desmoking and dehazing state-of-art methods using the metrics of the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index. Based on these metrics, it is found that the proposed method has improved performance compared to the state-of-the-art. Moreover, the processing time required by our method is 92 frames per second, and thus, it can be applied in a real-time medical system trough an embedded device.
Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation
Authors: Seyma Yucer, Samet Akçay, Noura Al-Moubayed, Toby P. Breckon
Date: 2020-04-19 00:00:00
Tasks: Data Augmentation, Face Recognition

Whilst face recognition applications are becoming increasingly prevalent within our daily lives, leading approaches in the field still suffer from performance bias to the detriment of some racial profiles within society. In this study, we propose a novel adversarial derived data augmentation methodology that aims to enable dataset balance at a per-subject level via the use of image-to-image transformation for the transfer of sensitive racial characteristic facial features. Our aim is to automatically construct a synthesised dataset by transforming facial images across varying racial domains, while still preserving identity-related features, such that racially dependant features subsequently become irrelevant within the determination of subject identity. We construct our experiments on three significant face recognition variants: Softmax, CosFace and ArcFace loss over a common convolutional neural network backbone. In a side-by-side comparison, we show the positive impact our proposed technique can have on the recognition performance for (racial) minority groups within an originally imbalanced training dataset by reducing the pre-race variance in performance.
On the Theoretical Properties of the Network Jackknife
Authors: Qiaohui Lin, Robert Lunde, Purnamrita Sarkar
Date: 2020-04-19 00:00:00
Proceeding: ICML 2020 1
Tasks: valid

We study the properties of a leave-node-out jackknife procedure for network data. Under the sparse graphon model, we prove an Efron-Stein-type inequality, showing that the network jackknife leads to conservative estimates of the variance (in expectation) for any network functional that is invariant to node permutation. For a general class of count functionals, we also establish consistency of the network jackknife. We complement our theoretical analysis with a range of simulated and real-data examples and show that the network jackknife offers competitive performance in cases where other resampling methods are known to be valid. In fact, for several network statistics, we see that the jackknife provides more accurate inferences compared to related methods such as subsampling.
Calculating Pose with Vanishing Points of Visual-Sphere Perspective Model
Authors: Jakub Maksymilian Fober
Date: 2020-04-19 00:00:00
Tasks: 3D Reconstruction, Camera Calibration, Pose Estimation

The goal of the proposed method is to directly obtain a pose matrix of a known rectangular target, without estimation, using geometric techniques. This method is specifically tailored for real-time, extreme imaging setups exceeding 180{\deg} field of view, such as a fish-eye camera view. The introduced algorithm employs geometric algebra to determine the pose for a pair of coplanar parallel lines (ideally a tangent pair as in a rectangle). This is achieved by computing vanishing points on a visual unit sphere, which correspond to pose matrix vectors. The algorithm can determine pose for an extremely distorted view source without prior rectification, owing to a visual-sphere perspective model mapping of view coordinates. Mapping can be performed using either a perspective map lookup or a parametric universal perspective distortion model, which is also presented in this paper. The outcome is a robust pose matrix computation that can be executed on an embedded system using a microcontroller, offering high accuracy and low latency. This method can be further extended to a cubic target setup for comprehensive camera calibration. It may also prove valuable in other applications requiring low latency and extreme viewing angles.
Space of Functions Computed by Deep-Layered Machines
Authors: Alexander Mozeika, Bo Li, David Saad
Date: 2020-04-19 00:00:00

We study the space of functions computed by random-layered machines, including deep neural networks and Boolean circuits. Investigating the distribution of Boolean functions computed on the recurrent and layer-dependent architectures, we find that it is the same in both models. Depending on the initial conditions and computing elements used, we characterize the space of functions computed at the large depth limit and show that the macroscopic entropy of Boolean functions is either monotonically increasing or decreasing with the growing depth.
Tree Echo State Autoencoders with Grammars
Authors: Benjamin Paassen, Irena Koprinska, Kalina Yacef
Date: 2020-04-19 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

Tree data occurs in many forms, such as computer programs, chemical molecules, or natural language. Unfortunately, the non-vectorial and discrete nature of trees makes it challenging to construct functions with tree-formed output, complicating tasks such as optimization or time series prediction. Autoencoders address this challenge by mapping trees to a vectorial latent space, where tasks are easier to solve, and then mapping the solution back to a tree structure. However, existing autoencoding approaches for tree data fail to take the specific grammatical structure of tree domains into account and rely on deep learning, thus requiring large training datasets and long training times. In this paper, we propose tree echo state autoencoders (TES-AE), which are guided by a tree grammar and can be trained within seconds by virtue of reservoir computing. In our evaluation on three datasets, we demonstrate that our proposed approach is not only much faster than a state-of-the-art deep learning autoencoding approach (D-VAE) but also has less autoencoding error if little data and time is given.
VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback
Authors: Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael. I. Jordan, Ion Stoica
Date: 2020-04-19 00:00:00

We study a multi-round welfare-maximising mechanism design problem in instances where agents do not know their values. On each round, a mechanism first assigns an allocation each to a set of agents and charges them a price; at the end of the round, the agents provide (stochastic) feedback to the mechanism for the allocation they received. This setting is motivated by applications in cloud markets and online advertising where an agent may know her value for an allocation only after experiencing it. Therefore, the mechanism needs to explore different allocations for each agent so that it can learn their values, while simultaneously attempting to find the socially optimal set of allocations. Our focus is on truthful and individually rational mechanisms which imitate the classical VCG mechanism in the long run. To that end, we first define three notions of regret for the welfare, the individual utilities of each agent and that of the mechanism. We show that these three terms are interdependent via an $\Omega(T^{\frac{2}{3}})$ lower bound for the maximum of these three terms after $T$ rounds of allocations, and describe an algorithm which essentially achieves this rate. Our framework also provides flexibility to control the pricing scheme so as to trade-off between the agent and seller regrets. Next, we define asymptotic variants for the truthfulness and individual rationality requirements and provide asymptotic rates to quantify the degree to which both properties are satisfied by the proposed algorithm.
DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction
Authors: Kexin Huang, Tianfan Fu, Lucas Glass, Marinka Zitnik, Cao Xiao, Jimeng Sun
Date: 2020-04-19 00:00:00
Tasks: Deep Learning, Drug Discovery, Prediction

Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use deep learning library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets.
MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network
Authors: Ling Lo, Hong-Xia Xie, Hong-Han Shuai, Wen-Huang Cheng
Date: 2020-04-19 00:00:00
Tasks: Graph Classification, Micro Expression Recognition, Micro-Expression Recognition, Relation

Micro-Expression (ME) is the spontaneous, involuntary movement of a face that can reveal the true feeling. Recently, increasing researches have paid attention to this field combing deep learning techniques. Action units (AUs) are the fundamental actions reflecting the facial muscle movements and AU detection has been adopted by many researches to classify facial expressions. However, the time-consuming annotation process makes it difficult to correlate the combinations of AUs to specific emotion classes. Inspired by the nodes relationship building Graph Convolutional Networks (GCN), we propose an end-to-end AU-oriented graph classification network, namely MER-GCN, which uses 3D ConvNets to extract AU features and applies GCN layers to discover the dependency laying between AU nodes for ME categorization. To our best knowledge, this work is the first end-to-end architecture for Micro-Expression Recognition (MER) using AUs based GCN. The experimental results show that our approach outperforms CNN-based MER networks.
MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG signal Classification
Authors: Seyed Ahmad Mirsalari, Sima Sinaei, Mostafa E. Salehi, Masoud Daneshtalab
Date: 2020-04-19 00:00:00
Tasks: Binarization, Classification, EEG, EEG Signal Classification, Electroencephalogram (EEG), General Classification

Recurrent Neural Networks (RNN) are widely used for learning sequences in applications such as EEG classification. Complex RNNs could be hardly deployed on wearable devices due to their computation and memory-intensive processing patterns. Generally, reduction in precision leads much more efficiency and binarized RNNs are introduced as energy-efficient solutions. However, naive binarization methods lead to significant accuracy loss in EEG classification. In this paper, we propose a multi-level binarized LSTM, which significantly reduces computations whereas ensuring an accuracy pretty close to the full precision LSTM. Our method reduces the delay of the 3-bit LSTM cell operation 47* with less than 0.01% accuracy loss.
Autonomous task planning and situation awareness in robotic surgery
Authors: Michele Ginesi, Daniele Meli, Andrea Roberti, Nicola Sansonetto, Paolo Fiorini
Date: 2020-04-19 00:00:00
Tasks: Motion Planning, Task Planning

The use of robots in minimally invasive surgery has improved the quality of standard surgical procedures. So far, only the automation of simple surgical actions has been investigated by researchers, while the execution of structured tasks requiring reasoning on the environment and the choice among multiple actions is still managed by human surgeons. In this paper, we propose a framework to implement surgical task automation. The framework consists of a task-level reasoning module based on answer set programming, a low-level motion planning module based on dynamic movement primitives, and a situation awareness module. The logic-based reasoning module generates explainable plans and is able to recover from failure conditions, which are identified and explained by the situation awareness module interfacing to a human supervisor, for enhanced safety. Dynamic Movement Primitives allow to replicate the dexterity of surgeons and to adapt to obstacles and changes in the environment. The framework is validated on different versions of the standard surgical training peg-and-ring task.
HCM: Hardware-Aware Complexity Metric for Neural Network Architectures
Authors: Alex Karbachevsky, Chaim Baskin, Evgenii Zheltonozhskii, Yevgeny Yermolin, Freddy Gabbay, Alex M. Bronstein, Avi Mendelson
Date: 2020-04-19 00:00:00
Tasks: Quantization, speech-recognition

Convolutional Neural Networks (CNNs) have become common in many fields including computer vision, speech recognition, and natural language processing. Although CNN hardware accelerators are already included as part of many SoC architectures, the task of achieving high accuracy on resource-restricted devices is still considered challenging, mainly due to the vast number of design parameters that need to be balanced to achieve an efficient solution. Quantization techniques, when applied to the network parameters, lead to a reduction of power and area and may also change the ratio between communication and computation. As a result, some algorithmic solutions may suffer from lack of memory bandwidth or computational resources and fail to achieve the expected performance due to hardware constraints. Thus, the system designer and the micro-architect need to understand at early development stages the impact of their high-level decisions (e.g., the architecture of the CNN and the amount of bits used to represent its parameters) on the final product (e.g., the expected power saving, area, and accuracy). Unfortunately, existing tools fall short of supporting such decisions. This paper introduces a hardware-aware complexity metric that aims to assist the system designer of the neural network architectures, through the entire project lifetime (especially at its early stages) by predicting the impact of architectural and micro-architectural decisions on the final product. We demonstrate how the proposed metric can help evaluate different design alternatives of neural network models on resource-restricted devices such as real-time embedded systems, and to avoid making design mistakes at early stages.
The Cost of Training NLP Models: A Concise Overview
Authors: Or Sharir, Barak Peleg, Yoav Shoham
Date: 2020-04-19 00:00:00

We review the cost of training large-scale language models, and the drivers of these costs. The intended audience includes engineers and scientists budgeting their model-training experiments, as well as non-practitioners trying to make sense of the economics of modern-day Natural Language Processing (NLP).
The Moral Burden of Ambiguity Aversion
Authors: Brian Jabarian
Date: 2020-04-19 00:00:00
Tasks: Ethics, Philosophy

In their article, "Egalitarianism under Severe Uncertainty", Philosophy and Public Affairs, 46:3, 2018, Thomas Rowe and Alex Voorhoeve develop an original moral decision theory for cases under uncertainty, called "pluralist egalitarianism under uncertainty". In this paper, I firstly sketch their views and arguments. I then elaborate on their moral decision theory by discussing how it applies to choice scenarios in health ethics. Finally, I suggest a new two-stage Ellsberg thought experiment challenging the core of the principle of their theory. In such an experiment pluralist egalitarianism seems to suggest the wrong, morally and rationally speaking, course of action -- no matter whether I consider my thought experiment in a simultaneous or a sequential setting.
Hedging with Linear Regressions and Neural Networks
Authors: Johannes Ruf, Weiguan Wang
Date: 2020-04-19 00:00:00

We study neural networks as nonparametric estimation tools for the hedging of options. To this end, we design a network, named HedgeNet, that directly outputs a hedging strategy. This network is trained to minimise the hedging error instead of the pricing error. Applied to end-of-day and tick prices of S&P 500 and Euro Stoxx 50 options, the network is able to reduce the mean squared hedging error of the Black-Scholes benchmark significantly. However, a similar benefit arises by simple linear regressions that incorporate the leverage effect.
Sequential hypothesis testing in machine learning, and crude oil price jump size detection
Authors: Michael Roberts, Indranil SenGupta
Date: 2020-04-19 00:00:00
Tasks: BIG-bench Machine Learning, Two-sample testing

In this paper we present a sequential hypothesis test for the detection of general jump size distrubution. Infinitesimal generators for the corresponding log-likelihood ratios are presented and analyzed. Bounds for infinitesimal generators in terms of super-solutions and sub-solutions are computed. This is shown to be implementable in relation to various classification problems for a crude oil price data set. Machine and deep learning algorithms are implemented to extract a specific deterministic component from the crude oil data set, and the deterministic component is implemented to improve the Barndorff-Nielsen and Shephard model, a commonly used stochastic model for derivative and commodity market analysis.
A Biologically Interpretable Two-stage Deep Neural Network (BIT-DNN) For Vegetation Recognition From Hyperspectral Imagery
Authors: Yue Shi, Liangxiu Han, Wenjiang Huang, Sheng Chang, Yingying Dong, Darren Dancey, Lianghao Han
Date: 2020-04-19 00:00:00
Tasks: Classification, Deep Learning, General Classification, Land Cover Classification, Scene Recognition

Spectral-spatial based deep learning models have recently proven to be effective in hyperspectral image (HSI) classification for various earth monitoring applications such as land cover classification and agricultural monitoring. However, due to the nature of "black-box" model representation, how to explain and interpret the learning process and the model decision, especially for vegetation classification, remains an open challenge. This study proposes a novel interpretable deep learning model -- a biologically interpretable two-stage deep neural network (BIT-DNN), by incorporating the prior-knowledge (i.e. biophysical and biochemical attributes and their hierarchical structures of target entities) based spectral-spatial feature transformation into the proposed framework, capable of achieving both high accuracy and interpretability on HSI based classification tasks. The proposed model introduces a two-stage feature learning process: in the first stage, an enhanced interpretable feature block extracts the low-level spectral features associated with the biophysical and biochemical attributes of target entities; and in the second stage, an interpretable capsule block extracts and encapsulates the high-level joint spectral-spatial features representing the hierarchical structure of biophysical and biochemical attributes of these target entities, which provides the model an improved performance on classification and intrinsic interpretability with reduced computational complexity. We have tested and evaluated the model using four real HSI datasets for four separate tasks (i.e. plant species classification, land cover classification, urban scene recognition, and crop disease recognition tasks). The proposed model has been compared with five state-of-the-art deep learning models.