pwc-archive/papers-with-abstracts huggingface dataset:
Learning and Adaptation for Millimeter-Wave Beam Tracking and Training: a Dual Timescale Variational Framework
Authors: Muddassar Hussain, Nicolo Michelusi
Date: 2021-06-27 00:00:00

Millimeter-wave vehicular networks incur enormous beam-training overhead to enable narrow-beam communications. This paper proposes a learning and adaptation framework in which the dynamics of the communication beams are learned and then exploited to design adaptive beam-tracking and training with low overhead: on a long-timescale, a deep recurrent variational autoencoder (DR-VAE) uses noisy beam-training feedback to learn a probabilistic model of beam dynamics and enable predictive beam-tracking; on a short-timescale, an adaptive beam-training procedure is formulated as a partially observable (PO-) Markov decision process (MDP) and optimized via point-based value iteration (PBVI) by leveraging beam-training feedback and a probabilistic prediction of the strongest beam pair provided by the DR-VAE. In turn, beam-training feedback is used to refine the DR-VAE via stochastic gradient ascent in a continuous process of learning and adaptation. The proposed DR-VAE learning framework learns accurate beam dynamics: it reduces the Kullback-Leibler divergence between the ground truth and the learned model of beam dynamics by 95% over the Baum-Welch algorithm and a naive learning approach that neglects feedback errors. Numerical results on a line-of-sight (LOS) scenario with multipath reveal that the proposed dual timescale approach yields near-optimal spectral efficiency, and improves it by 130% over a policy that scans exhaustively over the dominant beam pairs, and by 20% over a state-of-the-art POMDP policy. Finally, a low-complexity policy is proposed by reducing the POMDP to an error-robust MDP, and is shown to perform well in regimes with infrequent feedback errors.
Comparing seven methods for state-of-health time series prediction for the lithium-ion battery packs of forklifts
Authors: Matti Huotari, Shashank Arora, Avleen Malhi, Kary Främling
Date: 2021-07-06 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

A key aspect for the forklifts is the state-of-health (SoH) assessment to ensure the safety and the reliability of uninterrupted power source. Forecasting the battery SoH well is imperative to enable preventive maintenance and hence to reduce the costs. This paper demonstrates the capabilities of gradient boosting regression for predicting the SoH timeseries under circumstances when there is little prior information available about the batteries. We compared the gradient boosting method with light gradient boosting, extra trees, extreme gradient boosting, random forests, long short-term memory networks and with combined convolutional neural network and long short-term memory networks methods. We used multiple predictors and lagged target signal decomposition results as additional predictors and compared the yielded prediction results with different sets of predictors for each method. For this work, we are in possession of a unique data set of 45 lithium-ion battery packs with large variation in the data. The best model that we derived was validated by a novel walk-forward algorithm that also calculates point-wise confidence intervals for the predictions; we yielded reasonable predictions and confidence intervals for the predictions. Furthermore, we verified this model against five other lithium-ion battery packs; the best model generalised to greater extent to this set of battery packs. The results about the final model suggest that we were able to enhance the results in respect to previously developed models. Moreover, we further validated the model for extracting cycle counts presented in our previous work with data from new forklifts; their battery packs completed around 3000 cycles in a 10-year service period, which corresponds to the cycle life for commercial Nickel-Cobalt-Manganese (NMC) cells.
FibAR: Embedding Optical Fibers in 3D Printed Objects for Active Markers in Dynamic Projection Mapping
Authors: Daiki Tone, Daisuke Iwai, Shinsaku Hiura, Kosuke Sato
Date: 2020-02-06 00:00:00
Tasks: Object

This paper presents a novel active marker for dynamic projection mapping (PM) that emits a temporal blinking pattern of infrared (IR) light representing its ID. We used a multi-material three dimensional (3D) printer to fabricate a projection object with optical fibers that can guide IR light from LEDs attached on the bottom of the object. The aperture of an optical fiber is typically very small; thus, it is unnoticeable to human observers under projection and can be placed on a strongly curved part of a projection surface. In addition, the working range of our system can be larger than previous marker-based methods as the blinking patterns can theoretically be recognized by a camera placed at a wide range of distances from markers. We propose an automatic marker placement algorithm to spread multiple active markers over the surface of a projection object such that its pose can be robustly estimated using captured images from arbitrary directions. We also propose an optimization framework for determining the routes of the optical fibers in such a way that collisions of the fibers can be avoided while minimizing the loss of light intensity in the fibers. Through experiments conducted using three fabricated objects containing strongly curved surfaces, we confirmed that the proposed method can achieve accurate dynamic PMs in a significantly wide working range.
An Accurate Car Counting in Aerial Images Based on Convolutional Neural Networks
Authors: Ersin Kılıç, Serkan Öztürk
Date: 2021-07-13 00:00:00
Proceeding: Journal of Ambient Intelligence and Humanized Computing 2021 7
Tasks: Data Augmentation, Object Counting

This paper proposes a simple and effective single-shot detector model to detect and
count cars in aerial images. The proposed model, called heatmap learner convolutional
neural network (HLCNN), is used to predict the heatmap of target car instances. In
order to learn the heatmap of the target cars, we have improved CNN architecture by
adding three convolutional layers as adaptation layers instead of fully connected
layers. The VGG-16 has been used as a backbone convolutional neural network in the
proposed model. The proposed method successfully determines the number of cars
and precisely detects the center of target cars. Experiments on the two different car
datasets (PUCPR+ and CARPK) show the state-of-the-art counting and localizing
performance of the proposed method in comparison with existing methods. Also,
experiments have been conducted to examine the effect of data augmentation and
batch normalization on the success of the proposed method. The code and data will be
made available here [https://www.github.com/ekilic/Heatmap-Learner-CNN-for-Object-Counting].
Detect and Defense Against Adversarial Examples in Deep Learning using Natural Scene Statistics and Adaptive Denoising
Authors: Anouar Kherchouche, Sid Ahmed Fezza, Wassim Hamidouche
Date: 2021-07-12 00:00:00
Tasks: Denoising

Despite the enormous performance of deepneural networks (DNNs), recent studies have shown theirvulnerability to adversarial examples (AEs), i.e., care-fully perturbed inputs designed to fool the targetedDNN. Currently, the literature is rich with many ef-fective attacks to craft such AEs. Meanwhile, many de-fenses strategies have been developed to mitigate thisvulnerability. However, these latter showed their effec-tiveness against specific attacks and does not general-ize well to different attacks. In this paper, we proposea framework for defending DNN classifier against ad-versarial samples. The proposed method is based on atwo-stage framework involving a separate detector anda denoising block. The detector aims to detect AEs bycharacterizing them through the use of natural scenestatistic (NSS), where we demonstrate that these statis-tical features are altered by the presence of adversarialperturbations. The denoiser is based on block matching3D (BM3D) filter fed by an optimum threshold valueestimated by a convolutional neural network (CNN) toproject back the samples detected as AEs into theirdata manifold. We conducted a complete evaluation onthree standard datasets namely MNIST, CIFAR-10 andTiny-ImageNet. The experimental results show that theproposed defense method outperforms the state-of-the-art defense techniques by improving the robustnessagainst a set of attacks under black-box, gray-box and white-box settings. The source code is available at: https://github.com/kherchouche-anouar/2DAE
Per-Pixel Classification is Not All You Need for Semantic Segmentation
Authors: Bowen Cheng, Alexander G. Schwing, Alexander Kirillov
Date: 2021-07-13 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: All, Classification, Panoptic Segmentation, Segmentation, Semantic Segmentation

Modern approaches typically formulate semantic segmentation as a per-pixel classification task, while instance-level segmentation is handled with an alternative mask classification. Our key insight: mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks in a unified manner using the exact same model, loss, and training procedure. Following this observation, we propose MaskFormer, a simple mask classification model which predicts a set of binary masks, each associated with a single global class label prediction. Overall, the proposed mask classification-based method simplifies the landscape of effective approaches to semantic and panoptic segmentation tasks and shows excellent empirical results. In particular, we observe that MaskFormer outperforms per-pixel classification baselines when the number of classes is large. Our mask classification-based method outperforms both current state-of-the-art semantic (55.6 mIoU on ADE20K) and panoptic segmentation (52.7 PQ on COCO) models.
'CADSketchNet' -- An Annotated Sketch dataset for 3D CAD Model Retrieval with Deep Neural Networks
Authors: Bharadwaj Manda, Shubham Dhayarkar, Sai Mitheran, V. K. Viekash, Ramanathan Muthuganapathy
Date: 2021-07-13 00:00:00
Tasks: Retrieval

Ongoing advancements in the fields of 3D modelling and digital archiving have led to an outburst in the amount of data stored digitally. Consequently, several retrieval systems have been developed depending on the type of data stored in these databases. However, unlike text data or images, performing a search for 3D models is non-trivial. Among 3D models, retrieving 3D Engineering/CAD models or mechanical components is even more challenging due to the presence of holes, volumetric features, presence of sharp edges etc., which make CAD a domain unto itself. The research work presented in this paper aims at developing a dataset suitable for building a retrieval system for 3D CAD models based on deep learning. 3D CAD models from the available CAD databases are collected, and a dataset of computer-generated sketch data, termed 'CADSketchNet', has been prepared. Additionally, hand-drawn sketches of the components are also added to CADSketchNet. Using the sketch images from this dataset, the paper also aims at evaluating the performance of various retrieval system or a search engine for 3D CAD models that accepts a sketch image as the input query. Many experimental models are constructed and tested on CADSketchNet. These experiments, along with the model architecture, choice of similarity metrics are reported along with the search results.
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability
Authors: Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine
Date: 2021-07-13 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Reinforcement Learning (RL)

Generalization is a central challenge for the deployment of reinforcement learning (RL) systems in the real world. In this paper, we show that the sequential structure of the RL problem necessitates new approaches to generalization beyond the well-studied techniques used in supervised learning. While supervised learning methods can generalize effectively without explicitly accounting for epistemic uncertainty, we show that, perhaps surprisingly, this is not the case in RL. We show that generalization to unseen test conditions from a limited number of training conditions induces implicit partial observability, effectively turning even fully-observed MDPs into POMDPs. Informed by this observation, we recast the problem of generalization in RL as solving the induced partially observed Markov decision process, which we call the epistemic POMDP. We demonstrate the failure modes of algorithms that do not appropriately handle this partial observability, and suggest a simple ensemble-based technique for approximately solving the partially observed problem. Empirically, we demonstrate that our simple algorithm derived from the epistemic POMDP achieves significant gains in generalization over current methods on the Procgen benchmark suite.
Fairness-aware Summarization for Justified Decision-Making
Authors: Moniba Keymanesh, Tanya Berger-Wolf, Micha Elsner, Srinivasan Parthasarathy
Date: 2021-07-13 00:00:00
Tasks: Data Poisoning, Decision Making, Fairness

In consequential domains such as recidivism prediction, facility inspection, and benefit assignment, it's important for individuals to know the decision-relevant information for the model's prediction. In addition, predictions should be fair both in terms of the outcome and the justification of the outcome. In other words, decision-relevant features should provide sufficient information for the predicted outcome and should be independent of the membership of individuals in protected groups such as race and gender. In this work, we focus on the problem of (un)fairness in the justification of the text-based neural models. We tie the explanatory power of the model to fairness in the outcome and propose a fairness-aware summarization mechanism to detect and counteract the bias in such models. Given a potentially biased natural language explanation for a decision, we use a multi-task neural model and an attribution mechanism based on integrated gradients to extract high-utility and low-bias justifications in form of a summary. The extracted summary is then used for training a model to make decisions for individuals. Results on several real world datasets suggest that our method drastically limits the demographic leakage in the input (fairness in justification) while moderately enhancing the fairness in the outcome. Our model is also effective in detecting and counteracting several types of data poisoning attacks that synthesize race-coded reasoning or irrelevant justifications.
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage
Authors: Masatoshi Uehara, Wen Sun
Date: 2021-07-13 00:00:00
Proceeding: ICLR 2022 4
Tasks: Offline RL, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

We study model-based offline Reinforcement Learning with general function approximation without a full coverage assumption on the offline data distribution. We present an algorithm named Constrained Pessimistic Policy Optimization (CPPO)which leverages a general function class and uses a constraint over the model class to encode pessimism. Under the assumption that the ground truth model belongs to our function class (i.e., realizability in the function class), CPPO has a PAC guarantee with offline data only providing partial coverage, i.e., it can learn a policy that competes against any policy that is covered by the offline data. We then demonstrate that this algorithmic framework can be applied to many specialized Markov Decision Processes where additional structural assumptions can further refine the concept of partial coverage. Two notable examples are: (1) low-rank MDP with representation learning where the partial coverage condition is defined using a relative condition number measured by the unknown ground truth feature representation; (2) factored MDP where the partial coverage condition is defined using density ratio based concentrability coefficients associated with individual factors.
What classifiers know what they don't?
Authors: Mohamed Ishmael Belghazi, David Lopez-Paz
Date: 2021-07-13 00:00:00
Tasks: Decision Making

Being uncertain when facing the unknown is key to intelligent decision making. However, machine learning algorithms lack reliable estimates about their predictive uncertainty. This leads to wrong and overly-confident decisions when encountering classes unseen during training. Despite the importance of equipping classifiers with uncertainty estimates ready for the real world, prior work has focused on small datasets and little or no class discrepancy between training and testing data. To close this gap, we introduce UIMNET: a realistic, ImageNet-scale test-bed to evaluate predictive uncertainty estimates for deep image classifiers. Our benchmark provides implementations of eight state-of-the-art algorithms, six uncertainty measures, four in-domain metrics, three out-domain metrics, and a fully automated pipeline to train, calibrate, ensemble, select, and evaluate models. Our test-bed is open-source and all of our results are reproducible from a fixed commit in our repository. Adding new datasets, algorithms, measures, or metrics is a matter of a few lines of code-in so hoping that UIMNET becomes a stepping stone towards realistic, rigorous, and reproducible research in uncertainty estimation. Our results show that ensembles of ERM classifiers as well as single MIMO classifiers are the two best alternatives currently available to measure uncertainty about both in-domain and out-domain classes.
Correlation Analysis between the Robustness of Sparse Neural Networks and their Random Hidden Structural Priors
Authors: M. Ben Amor, J. Stier, M. Granitzer
Date: 2021-07-13 00:00:00

Deep learning models have been shown to be vulnerable to adversarial attacks. This perception led to analyzing deep learning models not only from the perspective of their performance measures but also their robustness to certain types of adversarial attacks. We take another step forward in relating the architectural structure of neural networks from a graph theoretic perspective to their robustness. We aim to investigate any existing correlations between graph theoretic properties and the robustness of Sparse Neural Networks. Our hypothesis is, that graph theoretic properties as a prior of neural network structures are related to their robustness. To answer to this hypothesis, we designed an empirical study with neural network models obtained through random graphs used as sparse structural priors for the networks. We additionally investigated the evaluation of a randomly pruned fully connected network as a point of reference. We found that robustness measures are independent of initialization methods but show weak correlations with graph properties: higher graph densities correlate with lower robustness, but higher average path lengths and average node eccentricities show negative correlations with robustness measures. We hope to motivate further empirical and analytical research to tightening an answer to our hypothesis.
Deep learning approaches to Earth Observation change detection
Authors: Antonio Di Pilato, Nicolò Taggio, Alexis Pompili, Michele Iacobellis, Adriano Di Florio, Davide Passarelli, Sergio Samarelli
Date: 2021-07-13 00:00:00
Tasks: Anomaly Detection, Change Detection, Deep Learning, Earth Observation, Semantic Segmentation

The interest for change detection in the field of remote sensing has increased in the last few years. Searching for changes in satellite images has many useful applications, ranging from land cover and land use analysis to anomaly detection. In particular, urban change detection provides an efficient tool to study urban spread and growth through several years of observation. At the same time, change detection is often a computationally challenging and time-consuming task, which requires innovative methods to guarantee optimal results with unquestionable value and within reasonable time. In this paper we present two different approaches to change detection (semantic segmentation and classification) that both exploit convolutional neural networks to achieve good results, which can be further refined and used in a post-processing workflow for a large variety of applications.
A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization
Authors: Zhenning Li, Chengzhong Xu, Guohui Zhang
Date: 2021-07-13 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Traffic Signal Control

Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy. Reinforcement learning (RL) is a trending data-driven approach for adaptive traffic signal control in complex urban traffic networks. Although the development of deep neural networks (DNN) further enhances its learning capability, there are still some challenges in applying deep RLs to transportation networks with multiple signalized intersections, including non-stationarity environment, exploration-exploitation dilemma, multi-agent training schemes, continuous action spaces, etc. In order to address these issues, this paper first proposes a multi-agent deep deterministic policy gradient (MADDPG) method by extending the actor-critic policy gradient algorithms. MADDPG has a centralized learning and decentralized execution paradigm in which critics use additional information to streamline the training process, while actors act on their own local observations. The model is evaluated via simulation on the Simulation of Urban MObility (SUMO) platform. Model comparison results show the efficiency of the proposed algorithm in controlling traffic lights.
Indian Legal NLP Benchmarks : A Survey
Authors: Prathamesh Kalamkar, Janani Venugopalan Ph. D., Vivek Raghavan Ph. D
Date: 2021-07-13 00:00:00
Tasks: Survey

Availability of challenging benchmarks is the key to advancement of AI in a specific field.Since Legal Text is significantly different than normal English text, there is a need to create separate Natural Language Processing benchmarks for Indian Legal Text which are challenging and focus on tasks specific to Legal Systems. This will spur innovation in applications of Natural language Processing for Indian Legal Text and will benefit AI community and Legal fraternity. We review the existing work in this area and propose ideas to create new benchmarks for Indian Legal Natural Language Processing.
Parallelisable Existential Rules: a Story of Pieces
Authors: Maxime Buron, Marie-Laure Mugnier, Michaël Thomazo
Date: 2021-07-13 00:00:00
Tasks: Data Integration

In this paper, we consider existential rules, an expressive formalism well suited to the representation of ontological knowledge and data-to-ontology mappings in the context of ontology-based data integration. The chase is a fundamental tool to do reasoning with existential rules as it computes all the facts entailed by the rules from a database instance. We introduce parallelisable sets of existential rules, for which the chase can be computed in a single breadth-first step from any instance. The question we investigate is the characterization of such rule sets. We show that parallelisable rule sets are exactly those rule sets both bounded for the chase and belonging to a novel class of rules, called pieceful. The pieceful class includes in particular frontier-guarded existential rules and (plain) datalog. We also give another characterization of parallelisable rule sets in terms of rule composition based on rewriting.