pwc-archive/papers-with-abstracts huggingface dataset:
Non-stationary GARCH modelling for fitting higher order moments of financial series within moving time windows
Authors: Luke De Clerk, Sergey Savel'ev
Date: 2021-02-23 00:00:00
Tasks: Time Series, Time Series Analysis

Here, we have analysed a GARCH(1,1) model with the aim to fit higher order moments for different companies' stock prices. When we assume a gaussian conditional distribution, we fail to capture any empirical data when fitting the first three even moments of financial time series. We show instead that a double gaussian conditional probability distribution better captures the higher order moments of the data. To demonstrate this point, we construct regions (phase diagrams), in the fourth and sixth order standardised moment space, where a GARCH(1,1) model can be used to fit these moments and compare them with the corresponding moments from empirical data for different sectors of the economy. We found that the ability of the GARCH model with a double gaussian conditional distribution to fit higher order moments is dictated by the time window our data spans. We can only fit data collected within specific time window lengths and only with certain parameters of the conditional double gaussian distribution. In order to incorporate the non-stationarity of financial series, we assume that the parameters of the GARCH model have time dependence.
Assessment of the Effectiveness of State Participation in Economic Clusters
Authors: A. R. Baghirzade, B. Kushbakov
Date: 2021-03-11 00:00:00
Tasks: Specificity

In the article we made an attempt to reveal the contents and development of the concept of economic clusters, to characterize the specificity of the regional cluster as a project. We have identified features of an estimation of efficiency of state participation in the cluster, where the state is an institution representing the interests of society.
Convergence of Computed Dynamic Models with Unbounded Shock
Authors: Kenichiro McAlinn, Kosaku Takanashi
Date: 2021-03-11 00:00:00

This paper studies the asymptotic convergence of computed dynamic models when the shock is unbounded. Most dynamic economic models lack a closed-form solution. As such, approximate solutions by numerical methods are utilized. Since the researcher cannot directly evaluate the exact policy function and the associated exact likelihood, it is imperative that the approximate likelihood asymptotically converges -- as well as to know the conditions of convergence -- to the exact likelihood, in order to justify and validate its usage. In this regard, Fernandez-Villaverde, Rubio-Ramirez, and Santos (2006) show convergence of the likelihood, when the shock has compact support. However, compact support implies that the shock is bounded, which is not an assumption met in most dynamic economic models, e.g., with normally distributed shocks. This paper provides theoretical justification for most dynamic models used in the literature by showing the conditions for convergence of the approximate invariant measure obtained from numerical simulations to the exact invariant measure, thus providing the conditions for convergence of the likelihood.
Dual Labor Market and the "Phillips Curve Puzzle"
Authors: Hideaki Aoyama, Corrado Di Guilmi, Yoshi Fujiwara, Hiroshi Yoshikawa
Date: 2021-03-11 00:00:00

Low inflation was once a welcome to both policy makers and the public. However, Japan's experience during the 1990's changed the consensus view on price of economists and central banks around the world. Facing deflation and zero interest bound at the same time, Bank of Japan had difficulty in conducting effective monetary policy. It made Japan's stagnation unusually prolonged. Too low inflation which annoys central banks today is translated into the "Phillips curve puzzle". In the US and Japan, in the course of recovery from the Great Recession after the 2008 global financial crisis, the unemployment rate had steadily declined to the level which was commonly regarded as lower than the natural rate or NAIRU. And yet, inflation stayed low. In this paper, we consider a minimal model of dual labor market to explore what kind of change in the economy makes the Phillips curve flat. The level of bargaining power of workers, the elasticity of the supply of labor to wage in the secondary market, and the composition of the workforce are the main factors in explaining the flattening of the Phillips curve. We argue that the changes we consider in the model, in fact, has plausibly made the Phillips curve flat in recent years.
Deep Multiway Canonical Correlation Analysis for Multi-Subject EEG Normalization
Authors: Jaswanth Reddy Katthi, Sriram Ganapathy
Date: 2021-03-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The normalization of brain recordings from multiple subjects responding to the natural stimuli is one of the key challenges in auditory neuroscience. The objective of this normalization is to transform the brain data in such a way as to remove the inter-subject redundancies and to boost the component related to the stimuli. In this paper, we propose a deep learning framework to improve the correlation of electroencephalography (EEG) data recorded from multiple subjects engaged in an audio listening task. The proposed model extends the linear multi-way canonical correlation analysis (CCA) for audio-EEG analysis using an auto-encoder network with a shared encoder layer. The model is trained to optimize a combined loss involving correlation and reconstruction. The experiments are performed on EEG data collected from subjects listening to natural speech and music. In these experiments, we show that the proposed deep multi-way CCA (DMCCA) based model significantly improves the correlations over the linear multi-way CCA approach with absolute improvements of 0.08 and 0.29 in terms of the Pearson correlation values for speech and music tasks respectively.
More Robust Estimators for Instrumental-Variable Panel Designs, With An Application to the Effect of Imports from China on US Employment
Authors: Clément de Chaisemartin, Ziteng Lei
Date: 2021-03-11 00:00:00
Tasks: regression

We show that first-difference two-stages-least-squares regressions identify non-convex combinations of location-and-period-specific treatment effects. Thus, those regressions could be biased if effects are heterogeneous. We propose an alternative instrumental-variable correlated-random-coefficient (IV-CRC) estimator, that is more robust to heterogeneous effects. We revisit Autor et al. (2013), who use a first-difference two-stages-least-squares regression to estimate the effect of imports from China on US manufacturing employment. Their regression estimates a highly non-convex combination of effects. Our more robust IV-CRC estimator is small and insignificant. Though its confidence interval is wide, it significantly differs from the first-difference two-stages-least-squares estimator.
Automated segmentation of choroidal layers from 3-dimensional macular optical coherence tomography scans
Authors: Kyungmoo Lee, Alexis K. Warren, Michael D. Abramoff, Andreas Wahle, S. Scott Whitmore, Ian C. Han, John H. Fingert, Todd E. Scheetz, Robert F. Mullins, Milan Sonka, Elliott H. Sohn
Date: 2021-03-11 00:00:00
Tasks: Segmentation

Background: Changes in choroidal thickness are associated with various ocular diseases and the choroid can be imaged using spectral-domain optical coherence tomography (SDOCT) and enhanced depth imaging OCT (EDIOCT). New Method: Eighty macular SDOCT volumes from 80 patients were obtained using the Zeiss Cirrus machine. Eleven additional control subjects had two Cirrus scans done in one visit along with EDIOCT using the Heidelberg Spectralis machine. To automatically segment choroidal layers from the OCT volumes, our graph-theoretic approach was utilized. The segmentation results were compared with reference standards from two graders, and the accuracy of automated segmentation was calculated using unsigned to signed border positioning thickness errors and Dice similarity coefficient (DSC). The repeatability and reproducibility of our choroidal thicknesses were determined by intraclass correlation coefficient (ICC), coefficient of variation (CV), and repeatability coefficient (RC). Results: The mean unsigned to signed border positioning errors for the choroidal inner and outer surfaces are 3.39plusminus1.26microns (mean plusminus SD) to minus1.52 plusminus 1.63microns and 16.09 plusminus 6.21microns to 4.73 plusminus 9.53microns, respectively. The mean unsigned to signed choroidal thickness errors are 16.54 plusminus 6.47microns to 6.25 plusminus 9.91microns, and the mean DSC is 0.949 plusminus 0.025. The ICC (95% CI), CV, RC values are 0.991 (0.977 to 0.997), 2.48%, 3.15microns for the repeatability and 0.991 (0.977 to 0.997), 2.49%, 0.53microns for the reproducibility studies, respectively. Comparison with Existing Method(s): The proposed method outperformed our previous method using choroidal vessel segmentation and inter-grader variability. Conclusions: This automated segmentation method can reliably measure choroidal thickness using different OCT platforms.
Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets
Authors: Mohamed Chetoui, Moulay A. Akhloufi
Date: 2020-08-20 00:00:00
Tasks: Diabetic Retinopathy Detection, Diabetic Retinopathy Grading, Specificity

Purpose: Diabetic retinopathy (DR) is characterized by retinal lesions affecting people having diabetes for several years. It is one of the leading causes of visual impairment worldwide. To diagnose this disease, ophthalmologists need to manually analyze retinal fundus images. Computer-aided diagnosis systems can help alleviate this burden by automatically detecting DR on retinal images, thus saving physicians’ precious time and reducing costs. The objective of this study is to develop a deep learning algorithm capable of detecting DR on retinal fundus images. Nine public datasets and more than 90,000 images are used to assess the efficiency of the proposed technique. In addition, an explainability algorithm is developed to visually show the DR signs detected by the deep model.

Approach: The proposed deep learning algorithm fine-tunes a pretrained deep convolutional neural network for DR detection. The model is trained on a subset of EyePACS dataset using a cosine annealing strategy for decaying the learning rate with warm up, thus improving the training accuracy. Tests are conducted on the nine datasets. An explainability algorithm based on gradient-weighted class activation mapping is developed to visually show the signs selected by the model to classify the retina images as DR.

Result: The proposed network leads to higher classification rates with an area under curve (AUC) of 0.986, sensitivity = 0.958, and specificity = 0.971 for EyePACS. For MESSIDOR, MESSIDOR-2, DIARETDB0, DIARETDB1, STARE, IDRID, E-ophtha, and UoA-DR, the AUC is 0.963, 0.979, 0.986, 0.988, 0.964, 0.957, 0.984, and 0.990, respectively.

Conclusions: The obtained results achieve state-of-the-art performance and outperform past published works relying on training using only publicly available datasets. The proposed approach can robustly classify fundus images and detect DR. An explainability model was developed and showed that our model was able to efficiently identify different signs of DR and detect this health issue.
Model-free Approach for Sensor Network Localization with Noisy Distance Measurement
Authors: Xu Fang, Chen Wang, Thien-Minh Nguyen, Lihua Xie
Date: 2018-11-18 00:00:00

A model-free localization method with noisy distance measurement is proposed for estimating a moving robot in 3D space. Considering that the traditional filter-based sensor network localization algorithms can not provide acceptable estimation accuracy in altitude in 3D space, the proposed method utilizes not only current measurements but also previous measurements to localize a robot. This character adds more constraints to localization to avoid local minimum. In addition, different from the traditional filter-based localization methods which need kinetic model for localization, our proposed method is model-free and converts the localization problem to graph optimization problem. The advantage is that we avoid the possible estimation error caused by inaccurate or simplified kinetic model. Considering that the communication limitation in application makes many graph optimization theories such as distributed localization theory and trilateration difficult to be realized, our method proposes to add constrained equation between adjacent positions to solve this problem. Experiments under a variety of scenarios verify the stability of this method and show that the algorithm achieves better localization accuracy than filter-based methods.
Unsupervised Discovery of Sign Terms by K-Nearest Neighbours Approach
Authors: Korhan Polat, Murat Saraclar
Date: 2020-08-25 00:00:00

In order to utilize the large amount of unlabeled sign language resources, unsupervised learning methods are needed. Motivated by the successful results of unsupervised term discovery (UTD) in spoken  languages,  here  we  explore  how  to  apply  similar  methods  for  sign terms discovery. Our goal is to find the repeating terms from continuous sign videos without any supervision. Using visual features extracted from  RGB  videos,  we  show  that  a  k-nearest  neighbours  based  discovery algorithm designed for speech can also discover sign terms. We also run experiments using a baseline UTD algorithm and comment on their differences.
Consistent Video Style Transfer via Relaxation and Regularization
Authors: Wenjing Wang, Shuai Yang, Jizheng Xu, Jiaying Liu
Date: 2020-09-23 00:00:00
Tasks: Style Transfer, Video Style Transfer

In recent years, neural style transfer has attracted more and more attention, especially for image style transfer. However, temporally consistent style transfer for videos is still a challenging problem. Existing methods, either relying on a significant amount of video data with optical flows or using single-frame regularizers, fail to handle strong motions or complex variations, therefore have limited performance on real videos. In this article, we address the problem by jointly considering the intrinsic properties of stylization and temporal consistency. We first identify the cause of the conflict between style transfer and temporal consistency, and propose to reconcile this contradiction by relaxing the objective function, so as to make the stylization loss term more robust to motions. Through relaxation, style transfer is more robust to inter-frame variation without degrading the subjective effect. Then, we provide a novel formulation and understanding of temporal consistency. Based on the formulation, we analyze the drawbacks of existing training strategies and derive a new regularization. We show by experiments that the proposed regularization can better balance the spatial and temporal performance. Based on relaxation and regularization, we design a zero-shot video style transfer framework. Moreover, for better feature migration, we introduce a new module to dynamically adjust inter-channel distributions. Quantitative and qualitative results demonstrate the superiority of our method over state-of-the-art style transfer methods.
Learning Task Grouping and Overlap in Multi-task Learning
Authors: Abhishek Kumar, Hal Daume III
Date: 2012-06-27 00:00:00
Tasks: Multi-Task Learning

In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks. We propose a framework for multi-task learn- ing that enables one to selectively share the information across the tasks. We assume that each task parameter vector is a linear combi- nation of a finite number of underlying basis tasks. The coefficients of the linear combina- tion are sparse in nature and the overlap in the sparsity patterns of two tasks controls the amount of sharing across these. Our model is based on on the assumption that task pa- rameters within a group lie in a low dimen- sional subspace but allows the tasks in differ- ent groups to overlap with each other in one or more bases. Experimental results on four datasets show that our approach outperforms competing methods.
Deep Dual Consecutive Network for Human Pose Estimation
Authors: Zhenguang Liu, Haoming Chen, Runyang Feng, Shuang Wu, Shouling Ji, Bailin Yang, Xun Wang
Date: 2021-03-12 00:00:00
Proceeding: CVPR 2021 1
Tasks: Keypoint Detection, Multi-Person Pose Estimation, Pose Estimation

Multi-frame human pose estimation in complicated situations is challenging. Although state-of-the-art human joints detectors have demonstrated remarkable results for static images, their performances come short when we apply these models to video sequences. Prevalent shortcomings include the failure to handle motion blur, video defocus, or pose occlusions, arising from the inability in capturing the temporal dependency among video frames. On the other hand, directly employing conventional recurrent neural networks incurs empirical difficulties in modeling spatial contexts, especially for dealing with pose occlusions. In this paper, we propose a novel multi-frame human pose estimation framework, leveraging abundant temporal cues between video frames to facilitate keypoint detection. Three modular components are designed in our framework. A Pose Temporal Merger encodes keypoint spatiotemporal context to generate effective searching scopes while a Pose Residual Fusion module computes weighted pose residuals in dual directions. These are then processed via our Pose Correction Network for efficient refining of pose estimations. Our method ranks No.1 in the Multi-frame Person Pose Estimation Challenge on the large-scale benchmark datasets PoseTrack2017 and PoseTrack2018. We have released our code, hoping to inspire future research.
Neural Networks with Complex-Valued Weights Have No Spurious Local Minima
Authors: Xingtu Liu
Date: 2021-01-31 00:00:00

We study the benefits of complex-valued weights for neural networks. We prove that shallow complex neural networks with quadratic activations have no spurious local minima. In contrast, shallow real neural networks with quadratic activations have infinitely many spurious local minima under the same conditions. In addition, we provide specific examples to demonstrate that complex-valued weights turn poor local minima into saddle points.
Large Batch Simulation for Deep Reinforcement Learning
Authors: Brennan Shacklett, Erik Wijmans, Aleksei Petrenko, Manolis Savva, Dhruv Batra, Vladlen Koltun, Kayvon Fatahalian
Date: 2021-03-12 00:00:00
Proceeding: ICLR 2021 1
Tasks: Deep Reinforcement Learning, GPU, PointGoal Navigation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We accelerate deep reinforcement learning-based training in visually complex 3D environments by two orders of magnitude over prior work, realizing end-to-end training speeds of over 19,000 frames of experience per second on a single GPU and up to 72,000 frames per second on a single eight-GPU machine. The key idea of our approach is to design a 3D renderer and embodied navigation simulator around the principle of "batch simulation": accepting and executing large batches of requests simultaneously. Beyond exposing large amounts of work at once, batch simulation allows implementations to amortize in-memory storage of scene assets, rendering work, data loading, and synchronization costs across many simulation requests, dramatically improving the number of simulated agents per GPU and overall simulation throughput. To balance DNN inference and training costs with faster simulation, we also build a computationally efficient policy DNN that maintains high task performance, and modify training algorithms to maintain sample efficiency when training with large mini-batches. By combining batch simulation and DNN performance optimizations, we demonstrate that PointGoal navigation agents can be trained in complex 3D environments on a single GPU in 1.5 days to 97% of the accuracy of agents trained on a prior state-of-the-art system using a 64-GPU cluster over three days. We provide open-source reference implementations of our batch 3D renderer and simulator to facilitate incorporation of these ideas into RL systems.
Genetic algorithm for feature selection of EEG heterogeneous data
Authors: Aurora Saibene, Francesca Gasparini
Date: 2021-03-12 00:00:00
Tasks: Benchmarking, EEG, Electroencephalogram (EEG), feature selection

The electroencephalographic (EEG) signals provide highly informative data on brain activities and functions. However, their heterogeneity and high dimensionality may represent an obstacle for their interpretation. The introduction of a priori knowledge seems the best option to mitigate high dimensionality problems, but could lose some information and patterns present in the data, while data heterogeneity remains an open issue that often makes generalization difficult. In this study, we propose a genetic algorithm (GA) for feature selection that can be used with a supervised or unsupervised approach. Our proposal considers three different fitness functions without relying on expert knowledge. Starting from two publicly available datasets on cognitive workload and motor movement/imagery, the EEG signals are processed, normalized and their features computed in the time, frequency and time-frequency domains. The feature vector selection is performed by applying our GA proposal and compared with two benchmarking techniques. The results show that different combinations of our proposal achieve better results in respect to the benchmark in terms of overall performance and feature reduction. Moreover, the proposed GA, based on a novel fitness function here presented, outperforms the benchmark when the two different datasets considered are merged together, showing the effectiveness of our proposal on heterogeneous data.
Neural Architecture Search based on Cartesian Genetic Programming Coding Method
Authors: Xuan Wu, Linhan Jia, Xiuyi Zhang, Liang Chen, Yanchun Liang, You Zhou, Chunguo Wu
Date: 2021-03-12 00:00:00
Tasks: AutoML, BIG-bench Machine Learning, Neural Architecture Search, Sentence, Sentence Classification

Neural architecture search (NAS) is a hot topic in the field of automated machine learning and outperforms humans in designing neural architectures on quite a few machine learning tasks. Motivated by the natural representation form of neural networks by the Cartesian genetic programming (CGP), we propose an evolutionary approach of NAS based on CGP, called CGPNAS, to solve sentence classification task. To evolve the architectures under the framework of CGP, the operations such as convolution are identified as the types of function nodes of CGP, and the evolutionary operations are designed based on Evolutionary Strategy. The experimental results show that the searched architectures are comparable with the performance of human-designed architectures. We verify the ability of domain transfer of our evolved architectures. The transfer experimental results show that the accuracy deterioration is lower than 2-5%. Finally, the ablation study identifies the Attention function as the single key function node and the linear transformations along could keep the accuracy similar with the full evolved architectures, which is worthy of investigation in the future.