pwc-archive/papers-with-abstracts huggingface dataset:
Imitation Learning for High Precision Peg-in-Hole Tasks
Authors: Sagar Gubbi, Shishir Kolathaya, Bharadwaj Amrutur
Date: 2020-12-26 00:00:00
Tasks: Imitation Learning, Vocal Bursts Intensity Prediction

Industrial robot manipulators are not able to match the precision and speed with which humans are able to execute contact rich tasks even to this day. Therefore, as a means overcome this gap, we demonstrate generative methods for imitating a peg-in-hole insertion task in a 6-DOF robot manipulator. In particular, generative adversarial imitation learning (GAIL) is used to successfully achieve this task with a 10 um, and a 6 um peg-hole clearance on the Yaskawa GP8 industrial robot. Experimental results show that the policy successfully learns within 20 episodes from a handful of human expert demonstrations on the robot (i.e., < 10 tele-operated robot demonstrations). The insertion time improves from > 20 seconds (which also includes failed insertions) to < 15 seconds, thereby validating the effectiveness of this approach.
Multi-Instance Aware Localization for End-to-End Imitation Learning
Authors: Sagar Gubbi Venkatesh, Raviteja Upadrashta, Shishir Kolathaya, Bharadwaj Amrutur
Date: 2020-12-26 00:00:00
Tasks: Imitation Learning

Existing architectures for imitation learning using image-to-action policy networks perform poorly when presented with an input image containing multiple instances of the object of interest, especially when the number of expert demonstrations available for training are limited. We show that end-to-end policy networks can be trained in a sample efficient manner by (a) appending the feature map output of the vision layers with an embedding that can indicate instance preference or take advantage of an implicit preference present in the expert demonstrations, and (b) employing an autoregressive action generator network for the control layers. The proposed architecture for localization has improved accuracy and sample efficiency and can generalize to the presence of more instances of objects than seen during training. When used for end-to-end imitation learning to perform reach, push, and pick-and-place tasks on a real robot, training is achieved with as few as 15 expert demonstrations.
Stochastic Action Prediction for Imitation Learning
Authors: Sagar Gubbi Venkatesh, Nihesh Rathod, Shishir Kolathaya, Bharadwaj Amrutur
Date: 2020-12-26 00:00:00
Tasks: Action Generation, Imitation Learning, Prediction

Imitation learning is a data-driven approach to acquiring skills that relies on expert demonstrations to learn a policy that maps observations to actions. When performing demonstrations, experts are not always consistent and might accomplish the same task in slightly different ways. In this paper, we demonstrate inherent stochasticity in demonstrations collected for tasks including line following with a remote-controlled car and manipulation tasks including reaching, pushing, and picking and placing an object. We model stochasticity in the data distribution using autoregressive action generation, generative adversarial nets, and variational prediction and compare the performance of these approaches. We find that accounting for stochasticity in the expert data leads to substantial improvement in the success rate of task completion.
A Comparative Study of AHP and Fuzzy AHP Method for Inconsistent Data
Authors: Md. Ashek-Al-Aziz, Sagar Mahmud, Md. Azizul Islam, Jubayer Al Mahmud, Khan Md. Hasib
Date: 2020-12-23 00:00:00
Tasks: Decision Making

In various cases of decision analysis we use two popular methods: Analytical Hierarchical Process (AHP) and Fuzzy based AHP or Fuzzy AHP. Both the methods deal with stochastic data and can determine decision result through Multi Criteria Decision Making (MCDM) process. Obviously resulting values of the two methods are not same though same set of data is fed into them. In this research work, we have tried to observe similarities and dissimilarities between two methods outputs. Almost same trend or fluctuations in outputs have been seen for both methods for same set of input data which are not consistent. Both method outputs ups and down fluctuations are same for fifty percent cases.
VIS30K: A Collection of Figures and Tables from IEEE Visualization Conference Publications
Authors: Jian Chen, Meng Ling, Rui Li, Petra Isenberg, Tobias Isenberg, Michael Sedlmair, Torsten Möller, Robert S. Laramee, Han-Wei Shen, Katharina Wünsche, Qiru Wang
Date: 2020-12-22 00:00:00

We present the VIS30K dataset, a collection of 29,689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST). VIS30K's comprehensive coverage of the scientific literature in visualization not only reflects the progress of the field but also enables researchers to study the evolution of the state-of-the-art and to find relevant work based on graphical content. We describe the dataset and our semi-automatic collection process, which couples convolutional neural networks (CNN) with curation. Extracting figures and tables semi-automatically allows us to verify that no images are overlooked or extracted erroneously. To improve quality further, we engaged in a peer-search process for high-quality figures from early IEEE Visualization papers. With the resulting data, we also contribute VISImageNavigator (VIN, visimagenavigator.github.io), a web-based tool that facilitates searching and exploring VIS30K by author names, paper keywords, title and abstract, and years.
Live Detection of Face Using Machine Learning with Multi-feature Method
Authors: Sandeep Kumar, Sukhwinder Singh, Jagdish Kumar
Date: 2018-07-27 00:00:00
Tasks: BIG-bench Machine Learning, Face Detection, Face Image Quality, Face Recognition, Learning-To-Rank

Facial expression detection (FED) and extraction show the most important role in face recognition. This research proposed a new algorithm for automatic live FED using radial basis function; Haar discrete wavelet transform and Gray-level difference method is used for feature extraction and classification. Detect edges of the facial image by Otsu algorithm. The implementation results worked on Japanese Female Facial Expressions and Cohn–Kanade Extended (CK+) database for facial expression. The other database used for face detection process, namely, CMU, BioID, Long Distance, and FEI. It is usually possible for practical recognition system to record (by a camera or by computer) multiple face images from each subject. Choosing face images with high tone for recognition is a promising strategy for improving the system performance. We propose a learning to rank based (solid basic structure on which bigger things can be built) for evaluating the face image quality. But we improved limitations of this algorithm using contrast enhancement. We solved the problem of long distance and low contrast images. In the initial preprocess stage; perform median filtering for removing noise from an image. This step enhances the feature extraction process. Finding an image from the image components is a typical task in pattern recognition. The detection rate has reached up to 100% for expression recognition. The proposed system estimates the value of precision and recall. This algorithm is compared with the previous algorithm and our proposed proved better than previous algorithms.
Better Bunching, Nicer Notching
Authors: Marinho Bertanha, Andrew H. McCallum, Nathan Seegert
Date: 2021-01-04 00:00:00

This paper studies the bunching identification strategy for an elasticity parameter that summarizes agents' responses to changes in slope (kink) or intercept (notch) of a schedule of incentives. We show that current bunching methods may be very sensitive to implicit assumptions in the literature about unobserved individual heterogeneity. We overcome this sensitivity concern with new non- and semi-parametric estimators. Our estimators allow researchers to show how bunching elasticities depend on different identifying assumptions and when elasticities are robust to them. We follow the literature and derive our methods in the context of the iso-elastic utility model and an income tax schedule that creates a piece-wise linear budget constraint. We demonstrate bunching behavior provides robust estimates for self-employed and not-married taxpayers in the context of the U.S. Earned Income Tax Credit. In contrast, estimates for self-employed and married taxpayers depend on specific identifying assumptions, which highlight the value of our approach. We provide the Stata package "bunching" to implement our procedures.
Bus operators in competition: a directed location approach
Authors: Fernanda Herrera, Sergio I. López
Date: 2021-01-04 00:00:00

We present a directed variant of Salop (1979) model to analyze bus transport dynamics. The players are operators competing in cooperative and non-cooperative games. Utility, like in most bus concession schemes in emerging countries, is proportional to the total fare collection. Competition for picking up passengers leads to well documented and dangerous driving practices that cause road accidents, traffic congestion and pollution. We obtain theoretical results that support the existence and implementation of such practices, and give a qualitative description of how they come to occur. In addition, our results allow to compare the current or base transport system with a more cooperative one.
Deep Learning for Latent Events Forecasting in Twitter Aided Caching Networks
Authors: Zhong Yang, Yuanwei Liu, Yue Chen, Joey Tianyi Zhou
Date: 2021-01-04 00:00:00
Tasks: Popularity Forecasting

A novel Twitter context aided content caching (TAC) framework is proposed for enhancing the caching efficiency by taking advantage of the legibility and massive volume of Twitter data. For the purpose of promoting the caching efficiency, three machine learning models are proposed to predict latent events and events popularity, utilizing collect Twitter data with geo-tags and geographic information of the adjacent base stations (BSs). Firstly, we propose a latent Dirichlet allocation (LDA) model for latent events forecasting taking advantage of the superiority of the LDA model in natural language processing (NLP). Then, we conceive long short-term memory (LSTM) with skip-gram embedding approach and LSTM with continuous skip-gram-Geo-aware embedding approach for the events popularity forecasting. Lastly, we associate the predicted latent events and the popularity of the events with the caching strategy. Extensive practical experiments demonstrate that: (1) The proposed TAC framework outperforms the conventional caching framework and is capable of being employed in practical applications thanks to the associating ability with public interests. (2) The proposed LDA approach conserves superiority for natural language processing (NLP) in Twitter data. (3) The perplexity of the proposed skip-gram-based LSTM is lower compared with the conventional LDA approach. (4) Evaluation of the model demonstrates that the hit rates of tweets of the model vary from 50% to 65% and the hit rate of the caching contents is up to approximately 75\% with smaller caching space compared to conventional algorithms.
Shoiuld Humans Lie to Machines: The Incentive Compatibility of Lasso and General Weighted Lasso
Authors: Mehmet Caner, Kfir Eliaz
Date: 2021-01-04 00:00:00
Tasks: BIG-bench Machine Learning

We consider situations where a user feeds her attributes to a machine learning method that tries to predict her best option based on a random sample of other users. The predictor is incentive-compatible if the user has no incentive to misreport her covariates. Focusing on the popular Lasso estimation technique, we borrow tools from high-dimensional statistics to characterize sufficient conditions that ensure that Lasso is incentive compatible in large samples. We extend our results to the Conservative Lasso estimator and provide new moment bounds for this generalized weighted version of Lasso. Our results show that incentive compatibility is achieved if the tuning parameter is kept above some threshold. We present simulations that illustrate how this can be done in practice.
The OxyContin Reformulation Revisited: New Evidence From Improved Definitions of Markets and Substitutes
Authors: Shiyu Zhang, Daniel Guth
Date: 2021-01-04 00:00:00

The opioid epidemic began with prescription pain relievers. In 2010 Purdue Pharma reformulated OxyContin to make it more difficult to abuse. OxyContin misuse fell dramatically, and concurrently heroin deaths began to rise. Previous research overlooked generic oxycodone and argued that the reformulation induced OxyContin users to switch directly to heroin. Using a novel and fine-grained source of all oxycodone sales from 2006-2014, we show that the reformulation led users to substitute from OxyContin to generic oxycodone, and the reformulation had no overall impact on opioid or heroin mortality. In fact, generic oxycodone, instead of OxyContin, was the driving factor in the transition to heroin. Finally, we show that by omitting generic oxycodone we recover the results of the literature. These findings highlight the important role generic oxycodone played in the opioid epidemic and the limited effectiveness of a partial supply-side intervention.
Mind the wealth gap: a new allocation method to match micro and macro statistics for household wealth
Authors: Michele Cantarella, Andrea Neri, Maria Giovanna Ranalli
Date: 2021-01-04 00:00:00
Tasks: Imputation

The financial and economic crisis recently experienced by many European countries has increased demand for timely, coherent and consistent distributional information for the household sector. In the Euro area, most of the NCBs collect such information through income and wealth surveys, which are often used to inform their decisions. These surveys, however, can often suffer from biases, usually caused by non-response and under-reporting behaviours, leading to a mismatch with macroeconomic aggregates. In this paper, we develop a novel allocation method which combines information from a power law (Pareto) model and imputation procedures so to address these issues simultaneously, when only limited external information is available. We provide two important contributions: first, we adjust the weights of observed survey households for non-response bias, then, we correct for measurement error. Finally, we produce distributional indicators for four Euro-Area countries.
An Integrated Optimization Framework for Multi-Component Predictive Analytics in Wind Farm Operations & Maintenance
Authors: Ilke Bakir, Murat Yildirim, Evrim Ursavas
Date: 2021-01-04 00:00:00

Recent years have seen an unprecedented growth in the use of sensor data to guide wind farm operations and maintenance. Emerging sensor-driven approaches typically focus on optimal maintenance procedures for single turbine systems, or model multiple turbines in wind farms as single component entities. In reality, turbines are composed of multiple components that dynamically interact throughout their lifetime. These interactions are central for realistic assessment and control of turbine failure risks. In this paper, an integrated framework that combines i) real-time degradation models used for predicting remaining life distribution of each component, with ii) mixed integer optimization models and solution algorithms used for identifying optimal wind farm maintenance and operations is proposed. Maintenance decisions identify optimal times to repair every component, which in turn, determine the failure risk of the turbines. More specifically, optimization models that characterize a turbine's failure time as the first time that one of its constituent components fail - a systems reliability concept called competing risk is developed. The resulting turbine failures impact the optimization of wind farm operations and revenue. Extensive experiments conducted for multiple wind farms with 300 wind turbines - 1200 components - showcases the performance of the proposed framework over conventional methods.
Experimental Characterization of Air-to-ground Propagation at mm-Wave Frequencies in Dense Urban Environment
Authors: Enrico M. Vitucci, Vasilii Semkin, Maximilian J. Arpaio, Marina Barbiroli, Franco Fuschini, Claude Oestges, Vittorio Degli-Esposti
Date: 2021-01-04 00:00:00

In the present study, a measurement setup utilizing mm-wave transceivers with steerable directive antennas, mounted on both a customized UAV and a ground station has been used to study Air-to-Ground (A2G) radio links and, more generally, full-3D mm-wave propagation in urban environment. We evaluate the double-directional characteristics of the channel by rotating the antennas, deriving Power-Angle Profiles at both link ends. Preliminary results provide useful understanding of A2G propagation, e.g. the influence of the antenna tilt angles, or the mechanisms allowing for the signal to propagate from street canyons to the air.
Model-free price bounds under dynamic option trading
Authors: Ariel Neufeld, Julian Sester
Date: 2021-01-04 00:00:00

In this paper we extend discrete time semi-static trading strategies by also allowing for dynamic trading in a finite amount of options, and we study the consequences for the model-independent super-replication prices of exotic derivatives. These include duality results as well as a precise characterization of pricing rules for the dynamically tradable options triggering an improvement of the price bounds for exotic derivatives in comparison with the conventional price bounds obtained through the martingale optimal transport approach.
Super-relaxation of space-time-quantized ensemble of energy loads to curtail their synchronization after demand response perturbation
Authors: Ilia Luchnikov, David Métivier, Henni Ouerdane, Michael Chertkov
Date: 2020-08-03 00:00:00
Tasks: Quantization

Ensembles of thermostatically controlled loads (TCL) provide a significant demand response reserve for the system operator to balance power grids. However, this also results in the parasitic synchronization of individual devices within the ensemble leading to long post-demand-response oscillations in the integrated energy consumption of the ensemble. The synchronization is eventually destructed by fluctuations, thus leading to the (pre-demand response) steady state; however, this natural desynchronization, or relaxation to a statistically steady state, is too long. A resolution of this problem consists in measuring the ensemble's instantaneous consumption and using it as a feedback to stochastic switching of the ensemble's devices between on- and off- states. A simplified continuous-time model showed that carefully tuned nonlinear feedback results in a fast (super-) relaxation of the ensemble energy consumption. Since both state information and control signals are discrete, the actual TCL devices operation is space-time quantized, and this must be considered for realistic TCL ensemble modelling. Here, assuming that states are characterized by indoor temperature (quantifying comfort) and air conditioner regime (on, off), we construct a discrete model based on the probabilistic description of state transitions. We demonstrate that super-relaxation holds in such a more realistic setting, and that while it is stable against randomness in the stochastic matrix of the quantized model, it remains sensitive to the time discretization scheme. Aiming to achieve a balance between super-relaxation and customer's comfort, we analyze the dependence of super-relaxation on details of the space-time quantization, and provide a simple analytical criterion to avoid undesirable oscillations in consumption.
Design and analysis of momentum trading strategies
Authors: Richard J. Martin
Date: 2021-01-04 00:00:00

We give a complete description of the third-moment (skewness) characteristics of both linear and nonlinear momentum trading strategies, the latter being understood as transformations of a normalised moving-average filter (EMA). We explain in detail why the skewness is generally positive and has a term structure. This paper is a synthesis of two papers published by the author in RISK in 2012, with some updates and comments.
Coherence and Concentration in Tightly-Connected Networks
Authors: Hancheng Min, Richard Pates, Enrique Mallada
Date: 2021-01-04 00:00:00

The ability to achieve coordinated behavior -- engineered or emergent -- on networked systems has attracted widespread interest over several fields. This interest has led to remarkable advances in developing a theoretical understanding of the conditions under which agents within a network can reach an agreement (consensus) or develop coordinated behavior, such as synchronization. However, much less understood is the phenomenon of network coherence. Network coherence generally refers to nodes' ability in a network to have a similar dynamic response despite heterogeneity in their individual behavior. In this paper, we develop a general framework to analyze and quantify the level of network coherence that a system exhibits by relating coherence with a low-rank property of the system. More precisely, for a networked system with linear dynamics and coupling, we show that, as the network connectivity grows, the system transfer matrix converges to a rank-one transfer matrix representing the coherent behavior. Interestingly, the non-zero eigenvalue of such a rank-one matrix is given by the harmonic mean of individual nodal dynamics, and we refer to it as the coherent dynamics. Our analysis unveils the frequency-dependent nature of coherence and a non-trivial interplay between dynamics and network topology. We further show that many networked systems can exhibit similar coherent behavior by establishing a concentration result in a setting with randomly chosen individual nodal dynamics.
Analysis of Filter Size Effect In Deep Learning
Authors: Yunus Camgözlü, Yakup Kutlu
Date: 2020-12-12 00:00:00
Tasks: Deep Learning

With the use of deep learning in many areas, how to improve this technology or how to develop the structure used more effectively and in a shorter time is an issue that is of interest to many people working in this field. Many studies are carried out on this subject, it is aimed to reduce the duration of the operation and the processing power required, except to obtain the best result with the changes made in the variables, functions and data in the models used. In this study, in the leaf classification made using Mendeley data set consisting of leaf images with a fixed background, all other variables such as layer number, iteration, number of layers in the model and pooling process were kept constant, except for the filter dimensions of the convolution layers in the determined model. Convolution layers in 3 different filter sizes and in addition to this, many results obtained in 2 different structures, increasing and decreasing, and 3 different image sizes were examined. In the literature, it is seen that different uses of pooling layers, changes due to increase or decrease in the number of layers, the difference in the size of the data used, and the results of many functions used with different parameters are evaluated. In the leaf classification of the determined data set with CNN, the change in the filter size of the convolution layer together with the change in different filter combinations and in different sized images was focused. Using the data set and data reproduction methods, it was aimed to make the differences in filter sizes and image sizes more distinct. Using the fixed number of iterations, model and data set, the effect of different filter sizes has been observed.