pwc-archive/papers-with-abstracts huggingface dataset:
High-Speed Robot Navigation using Predicted Occupancy Maps
Authors: Kapil D. Katyal, Adam Polevoy, Joseph Moore, Craig Knuth, Katie M. Popek
Date: 2020-12-22 00:00:00
Tasks: Robot Navigation, Vocal Bursts Intensity Prediction

Safe and high-speed navigation is a key enabling capability for real world deployment of robotic systems. A significant limitation of existing approaches is the computational bottleneck associated with explicit mapping and the limited field of view (FOV) of existing sensor technologies. In this paper, we study algorithmic approaches that allow the robot to predict spaces extending beyond the sensor horizon for robust planning at high speeds. We accomplish this using a generative neural network trained from real-world data without requiring human annotated labels. Further, we extend our existing control algorithms to support leveraging the predicted spaces to improve collision-free planning and navigation at high speeds. Our experiments are conducted on a physical robot based on the MIT race car using an RGBD sensor where were able to demonstrate improved performance at 4 m/s compared to a controller not operating on predicted regions of the map.
Learning to Initialize Gradient Descent Using Gradient Descent
Authors: Kartik Ahuja, Amit Dhurandhar, Kush R. Varshney
Date: 2020-12-22 00:00:00

Non-convex optimization problems are challenging to solve; the success and computational expense of a gradient descent algorithm or variant depend heavily on the initialization strategy. Often, either random initialization is used or initialization rules are carefully designed by exploiting the nature of the problem class. As a simple alternative to hand-crafted initialization rules, we propose an approach for learning "good" initialization rules from previous solutions. We provide theoretical guarantees that establish conditions that are sufficient in all cases and also necessary in some under which our approach performs better than random initialization. We apply our methodology to various non-convex problems such as generating adversarial examples, generating post hoc explanations for black-box machine learning models, and allocating communication spectrum, and show consistent gains over other initialization techniques.
Image to Bengali Caption Generation Using Deep CNN and Bidirectional Gated Recurrent Unit
Authors: Al Momin Faruk, Hasan Al Faraby, Md. Muzahidul Azad, Md. Riduyan Fedous, Md. Kishor Morol
Date: 2020-12-22 00:00:00
Tasks: Caption Generation, Decoder, Image Captioning

There is very little notable research on generating descriptions of the Bengali language. About 243 million people speak in Bengali, and it is the 7th most spoken language on the planet. The purpose of this research is to propose a CNN and Bidirectional GRU based architecture model that generates natural language captions in the Bengali language from an image. Bengali people can use this research to break the language barrier and better understand each other's perspectives. It will also help many blind people with their everyday lives. This paper used an encoder-decoder approach to generate captions. We used a pre-trained Deep convolutional neural network (DCNN) called InceptonV3image embedding model as the encoder for analysis, classification, and annotation of the dataset's images Bidirectional Gated Recurrent unit (BGRU) layer as the decoder to generate captions. Argmax and Beam search is used to produce the highest possible quality of the captions. A new dataset called BNATURE is used, which comprises 8000 images with five captions per image. It is used for training and testing the proposed model. We obtained BLEU-1, BLEU-2, BLEU-3, BLEU-4 and Meteor is 42.6, 27.95, 23, 66, 16.41, 28.7 respectively.
Projection-Free Bandit Optimization with Privacy Guarantees
Authors: Alina Ene, Huy L. Nguyen, Adrian Vladu
Date: 2020-12-22 00:00:00

We design differentially private algorithms for the bandit convex optimization problem in the projection-free setting. This setting is important whenever the decision set has a complex geometry, and access to it is done efficiently only through a linear optimization oracle, hence Euclidean projections are unavailable (e.g. matroid polytope, submodular base polytope). This is the first differentially-private algorithm for projection-free bandit optimization, and in fact our bound of $\widetilde{O}(T^{3/4})$ matches the best known non-private projection-free algorithm (Garber-Kretzu, AISTATS `20) and the best known private algorithm, even for the weaker setting when projections are available (Smith-Thakurta, NeurIPS `13).
Projected Stochastic Gradient Langevin Algorithms for Constrained Sampling and Non-Convex Learning
Authors: Andrew Lamperski
Date: 2020-12-22 00:00:00

Langevin algorithms are gradient descent methods with additive noise. They have been used for decades in Markov chain Monte Carlo (MCMC) sampling, optimization, and learning. Their convergence properties for unconstrained non-convex optimization and learning problems have been studied widely in the last few years. Other work has examined projected Langevin algorithms for sampling from log-concave distributions restricted to convex compact sets. For learning and optimization, log-concave distributions correspond to convex losses. In this paper, we analyze the case of non-convex losses with compact convex constraint sets and IID external data variables. We term the resulting method the projected stochastic gradient Langevin algorithm (PSGLA). We show the algorithm achieves a deviation of $O(T^{-1/4}(\log T)^{1/2})$ from its target distribution in 1-Wasserstein distance. For optimization and learning, we show that the algorithm achieves $\epsilon$-suboptimal solutions, on average, provided that it is run for a time that is polynomial in $\epsilon^{-1}$ and slightly super-exponential in the problem dimension.
A Computational Framework for Solving Nonlinear Binary OptimizationProblems in Robust Causal Inference
Authors: Md Saiful Islam, Md Sarowar Morshed, Md. Noor-E-Alam
Date: 2020-12-22 00:00:00
Tasks: Causal Inference, Decision Making

Identifying cause-effect relations among variables is a key step in the decision-making process. While causal inference requires randomized experiments, researchers and policymakers are increasingly using observational studies to test causal hypotheses due to the wide availability of observational data and the infeasibility of experiments. The matching method is the most used technique to make causal inference from observational data. However, the pair assignment process in one-to-one matching creates uncertainty in the inference because of different choices made by the experimenter. Recently, discrete optimization models are proposed to tackle such uncertainty. Although a robust inference is possible with discrete optimization models, they produce nonlinear problems and lack scalability. In this work, we propose greedy algorithms to solve the robust causal inference test instances from observational data with continuous outcomes. We propose a unique framework to reformulate the nonlinear binary optimization problems as feasibility problems. By leveraging the structure of the feasibility formulation, we develop greedy schemes that are efficient in solving robust test problems. In many cases, the proposed algorithms achieve global optimal solutions. We perform experiments on three real-world datasets to demonstrate the effectiveness of the proposed algorithms and compare our result with the state-of-the-art solver. Our experiments show that the proposed algorithms significantly outperform the exact method in terms of computation time while achieving the same conclusion for causal tests. Both numerical experiments and complexity analysis demonstrate that the proposed algorithms ensure the scalability required for harnessing the power of big data in the decision-making process.
The Control of Lung Branching Morphogenesis
Authors: Dagmar Iber
Date: 2020-12-22 00:00:00

Branching morphogenesis generates epithelial trees which facilitate gas exchange, filtering, as well as secretion processes with their large surface to volume ratio. In this review, we focus on the developmental mechanisms that control the early stages of lung branching morphogenesis. Lung branching morphogenesis involves the stereotypic, recurrent definition of new branch points, subsequent epithelial budding, and lung tube elongation. We discuss current models and experimental evidence for each of these steps. Finally, we discuss the role of the mesenchyme in determining the organ-specific shape.
Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages
Authors: Cheng Yi, Jianzhong Wang, Ning Cheng, Shiyu Zhou, Bo Xu
Date: 2020-12-22 00:00:00
Tasks: speech-recognition, Speech Recognition

There are several domains that own corresponding widely used feature extractors, such as ResNet, BERT, and GPT-x. These models are usually pre-trained on large amounts of unlabeled data by self-supervision and can be effectively applied to downstream tasks. In the speech domain, wav2vec2.0 starts to show its powerful representation ability and feasibility of ultra-low resource speech recognition on the Librispeech corpus, which belongs to the audiobook domain. However, wav2vec2.0 has not been examined on real spoken scenarios and languages other than English. To verify its universality over languages, we apply pre-trained models to solve low-resource speech recognition tasks in various spoken languages. We achieve more than 20% relative improvements in six languages compared with previous work. Among these languages, English achieves a gain of 52.4%. Moreover, using coarse-grained modeling units, such as subword or character, achieves better results than fine-grained modeling units, such as phone or letter.
Social distancing in networks: A web-based interactive experiment
Authors: Edoardo Gallo, Darija Barak, Alastair Langtry
Date: 2020-12-22 00:00:00

Governments have used social distancing to stem the spread of COVID-19, but lack evidence on the most effective policy to ensure compliance. We examine the effectiveness of fines and informational messages (nudges) in promoting social distancing in a web-based interactive experiment conducted during the first wave of the pandemic on a near-representative sample of the US population. Fines promote distancing, but nudges only have a marginal impact. Individuals do more social distancing when they are aware they are a superspreader. Using an instrumental variable approach, we argue progressives are more likely to practice distancing, and they are marginally more responsive to fines.
Domain Adaptation of NMT models for English-Hindi Machine Translation Task at AdapMT ICON 2020
Authors: Ramchandra Joshi, Rushabh Karnavat, Kaustubh Jirapure, Raviraj Joshi
Date: 2020-12-22 00:00:00
Tasks: Domain Adaptation, Machine Translation, NMT, Translation

Recent advancements in Neural Machine Translation (NMT) models have proved to produce a state of the art results on machine translation for low resource Indian languages. This paper describes the neural machine translation systems for the English-Hindi language presented in AdapMT Shared Task ICON 2020. The shared task aims to build a translation system for Indian languages in specific domains like Artificial Intelligence (AI) and Chemistry using a small in-domain parallel corpus. We evaluated the effectiveness of two popular NMT models i.e, LSTM, and Transformer architectures for the English-Hindi machine translation task based on BLEU scores. We train these models primarily using the out of domain data and employ simple domain adaptation techniques based on the characteristics of the in-domain dataset. The fine-tuning and mixed-domain data approaches are used for domain adaptation. Our team was ranked first in the chemistry and general domain En-Hi translation task and second in the AI domain En-Hi translation task.
TorchMD: A deep learning framework for molecular simulations
Authors: Stefan Doerr, Maciej Majewsk, Adrià Pérez, Andreas Krämer, Cecilia Clementi, Frank Noe, Toni Giorgino, Gianni de Fabritiis
Date: 2020-12-22 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Protein Folding

Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All of force computations including bond, angle, dihedral, Lennard-Jones and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab-initio potential, performing an end-to-end training and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool-set to support molecular simulations of machine learning potentials. Code and data are freely available at \url{github.com/torchmd}.
Prediction of Chronic Kidney Disease Using Deep Neural Network
Authors: Iliyas Ibrahim Iliyas, Isah Rambo Saidu, Ali Baba Dauda, Suleiman Tasiu
Date: 2020-12-22 00:00:00

Deep neural Network (DNN) is becoming a focal point in Machine Learning research. Its application is penetrating into different fields and solving intricate and complex problems. DNN is now been applied in health image processing to detect various ailment such as cancer and diabetes. Another disease that is causing threat to our health is the kidney disease. This disease is becoming prevalent due to substances and elements we intake. Death is imminent and inevitable within few days without at least one functioning kidney. Ignoring the kidney malfunction can cause chronic kidney disease leading to death. Frequently, Chronic Kidney Disease (CKD) and its symptoms are mild and gradual, often go unnoticed for years only to be realized lately. Bade, a Local Government of Yobe state in Nigeria has been a center of attention by medical practitioners due to the prevalence of CKD. Unfortunately, a technical approach in culminating the disease is yet to be attained. We obtained a record of 400 patients with 10 attributes as our dataset from Bade General Hospital. We used DNN model to predict the absence or presence of CKD in the patients. The model produced an accuracy of 98%. Furthermore, we identified and highlighted the Features importance to provide the ranking of the features used in the prediction of the CKD. The outcome revealed that two attributes; Creatinine and Bicarbonate have the highest influence on the CKD prediction.
Design and Comparative Analysis of a Two-Stage Ultra-Low-Power Subthreshold Operational Amplifier in 180nm, 90nm, and 45nm technology
Authors: Sumukh Nitundil, Nihal Singh, Rushabha Balaji, Pankaj Arora
Date: 2020-12-22 00:00:00

In this paper, a two-stage ultra-low-power operational amplifier is designed, and a comparative analysis of the proposed subthreshold complementary amplifier is presented between 180nm, 90nm, and 45nm CMOS technology. The proposed operational amplifier is compared across several different parameters to determine the optimal design. It achieves a maximum gain of around 75 dB and a phase margin of 76{\deg}, dissipating just 140nW with a supply voltage of 0.5 V which is well suited for biomedical applications that require low power and high gain. The proposed operational amplifier has been designed using a SPICE-based circuit simulator.
Limitation of Acyclic Oriented Graphs Matching as Cell Tracking Accuracy Measure when Evaluating Mitosis
Authors: Ye Chen, Yuankai Huo
Date: 2020-12-22 00:00:00
Tasks: Cell Tracking, Multi-Object Tracking, Multiple Object Tracking, Object, object-detection, Object Detection, Object Tracking

Multi-object tracking (MOT) in computer vision and cell tracking in biomedical image analysis are two similar research fields, whose common aim is to achieve instance level object detection/segmentation and associate such objects across different video frames. However, one major difference between these two tasks is that cell tracking also aim to detect mitosis (cell division), which is typically not considered in MOT tasks. Therefore, the acyclic oriented graphs matching (AOGM) has been used as de facto standard evaluation metrics for cell tracking, rather than directly using the evaluation metrics in computer vision, such as multiple object tracking accuracy (MOTA), ID Switches (IDS), ID F1 Score (IDF1) etc. However, based on our experiments, we realized that AOGM did not always function as expected for mitosis events. In this paper, we exhibit the limitations of evaluating mitosis with AOGM using both simulated and real cell tracking data.
Unsupervised Machine learning methods for city vitality index
Authors: Jean-Sébastien Dessureault, Jonathan Simard, Daniel Massicotte
Date: 2020-12-22 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

This paper concerns the challenge to evaluate and predict a district vitality index (VI) over the years. There is no standard method to do it, and it is even more complicated to do it retroactively in the last decades. Although, it is essential to evaluate and learn features of the past to predict a VI in the future. This paper proposes a method to evaluate such a VI, based on a k-mean clustering algorithm. The meta parameters of this unsupervised machine learning technique are optimized by a genetic algorithm method. Based on the resulting clusters and VI, a linear regression is applied to predict the VI of each district of a city. The weights of each feature used in the clustering are calculated using a random forest regressor algorithm. This method can be a powerful insight for urbanists and inspire the redaction of a city plan in the smart city context.
MetaAugment: Sample-Aware Data Augmentation Policy Learning
Authors: Fengwei Zhou, Jiawei Li, Chuanlong Xie, Fei Chen, Lanqing Hong, Rui Sun, Zhenguo Li
Date: 2020-12-22 00:00:00
Tasks: Data Augmentation, Meta-Learning

Automated data augmentation has shown superior performance in image recognition. Existing works search for dataset-level augmentation policies without considering individual sample variations, which are likely to be sub-optimal. On the other hand, learning different policies for different samples naively could greatly increase the computing cost. In this paper, we learn a sample-aware data augmentation policy efficiently by formulating it as a sample reweighting problem. Specifically, an augmentation policy network takes a transformation and the corresponding augmented image as inputs, and outputs a weight to adjust the augmented image loss computed by a task network. At training stage, the task network minimizes the weighted losses of augmented training images, while the policy network minimizes the loss of the task network on a validation set via meta-learning. We theoretically prove the convergence of the training procedure and further derive the exact convergence rate. Superior performance is achieved on widely-used benchmarks including CIFAR-10/100, Omniglot, and ImageNet.
Event-Driven Query Expansion
Authors: Guy D. Rosin, Ido Guy, Kira Radinsky
Date: 2020-12-22 00:00:00
Tasks: Ad-Hoc Information Retrieval, Retrieval

A significant number of event-related queries are issued in Web search. In this paper, we seek to improve retrieval performance by leveraging events and specifically target the classic task of query expansion. We propose a method to expand an event-related query by first detecting the events related to it. Then, we derive the candidates for expansion as terms semantically related to both the query and the events. To identify the candidates, we utilize a novel mechanism to simultaneously embed words and events in the same vector space. We show that our proposed method of leveraging events improves query expansion performance significantly compared with state-of-the-art methods on various newswire TREC datasets.
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning
Authors: Pascal Leroy, Damien Ernst, Pierre Geurts, Gilles Louppe, Jonathan Pisane, Matthia Sabatelli
Date: 2020-12-22 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), SMAC, SMAC+, Starcraft

This paper introduces four new algorithms that can be used for tackling multi-agent reinforcement learning (MARL) problems occurring in cooperative settings. All algorithms are based on the Deep Quality-Value (DQV) family of algorithms, a set of techniques that have proven to be successful when dealing with single-agent reinforcement learning problems (SARL). The key idea of DQV algorithms is to jointly learn an approximation of the state-value function $V$, alongside an approximation of the state-action value function $Q$. We follow this principle and generalise these algorithms by introducing two fully decentralised MARL algorithms (IQV and IQV-Max) and two algorithms that are based on the centralised training with decentralised execution training paradigm (QVMix and QVMix-Max). We compare our algorithms with state-of-the-art MARL techniques on the popular StarCraft Multi-Agent Challenge (SMAC) environment. We show competitive results when QVMix and QVMix-Max are compared to well-known MARL techniques such as QMIX and MAVEN and show that QVMix can even outperform them on some of the tested environments, being the algorithm which performs best overall. We hypothesise that this is due to the fact that QVMix suffers less from the overestimation bias of the $Q$ function.
Wideband Channel Estimation with A Generative Adversarial Network
Authors: Eren Balevi, Jeffrey G. Andrews
Date: 2020-12-22 00:00:00
Tasks: Generative Adversarial Network

Communication at high carrier frequencies such as millimeter wave (mmWave) and terahertz (THz) requires channel estimation for very large bandwidths at low SNR. Hence, allocating an orthogonal pilot tone for each coherence bandwidth leads to excessive number of pilots. We leverage generative adversarial networks (GANs) to accurately estimate frequency selective channels with few pilots at low SNR. The proposed estimator first learns to produce channel samples from the true but unknown channel distribution via training the generative network, and then uses this trained network as a prior to estimate the current channel by optimizing the network's input vector in light of the current received signal. Our results show that at an SNR of -5 dB, even if a transceiver with one-bit phase shifters is employed, our design achieves the same channel estimation error as an LS estimator with SNR = 20 dB or the LMMSE estimator at 2.5 dB, both with fully digital architectures. Additionally, the GAN-based estimator reduces the required number of pilots by about 70% without significantly increasing the estimation error and required SNR. We also show that the generative network does not appear to require retraining even if the number of clusters and rays change considerably.