pwc-archive/papers-with-abstracts huggingface dataset:
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and Optimization
Authors: Prathamesh Mayekar, Shubham Jha, Ananda Theertha Suresh, Himanshu Tyagi
Date: 2020-11-24 00:00:00
Tasks: Distributed Optimization, Federated Learning

Communication efficient distributed mean estimation is an important primitive that arises in many distributed learning and optimization scenarios such as federated learning. Without any probabilistic assumptions on the underlying data, we study the problem of distributed mean estimation where the server has access to side information. We propose \emph{Wyner-Ziv estimators}, which are communication and computationally efficient and near-optimal when an upper bound for the distance between the side information and the data is known. As a corollary, we also show that our algorithms provide efficient schemes for the classic Wyner-Ziv problem in information theory. In a different direction, when there is no knowledge assumed about the distance between side information and the data, we present an alternative Wyner-Ziv estimator that uses correlated sampling. This latter setting offers {\em universal recovery guarantees}, and perhaps will be of interest in practice when the number of users is large and keeping track of the distances between the data and the side information may not be possible. With this mean estimator at our disposal, we revisit basic problems in decentralized optimization and compression where our Wyner-Ziv estimator yields algorithms with almost optimal performance. First, we consider the problem of communication constrained distributed optimization and provide an algorithm which attains the optimal convergence rate by exploiting the fact that the gradient estimates are close to each other. Specifically, the gradient compression scheme in our algorithm first uses half of the parties to form side information and then uses our Wyner-Ziv estimator to compress the remaining half of the gradient estimates.
Tensor Kernel Recovery for Spatio-Temporal Hawkes Processes
Authors: Heejune Sheen, Xiaonan Zhu, Yao Xie
Date: 2020-11-24 00:00:00

We estimate the general influence functions for spatio-temporal Hawkes processes using a tensor recovery approach by formulating the location dependent influence function that captures the influence of historical events as a tensor kernel. We assume a low-rank structure for the tensor kernel and cast the estimation problem as a convex optimization problem using the Fourier transformed nuclear norm (TNN). We provide theoretical performance guarantees for our approach and present an algorithm to solve the optimization problem. Moreover, we demonstrate the efficiency of our estimation with numerical simulations.
A Reusable Framework Based on Reinforcement Learning to Design Antennas for Curved Surfaces
Authors: Enrique Lizarraga, Walter Herrera
Date: 2020-11-24 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

The design and implementation of low-profile antennas has been analyzed in past decades from different perspectives while the purpose is to have a small size in the device, and an adequate electromagnetic behavior. This work pursues a methodology to identify small antennas and consequently presents some similarities. Meanwhile, curved surfaces are considered for a certain variety of antennas with reduced size. The so-called deep reinforcement learning technique is used as an assistance against morphological variations that are specifically taken into account in this work. The objective is to identify antennas that can be efficiently mounted on the surface of metal tubes such as those frequently present in public infrastructure (e.g. traffic lights and luminaries). The motivation is to reduce the visual impact and optimize the radiation pattern of the antenna. It is analyzed that if changes in variables such as the radius of curvature, or the electromagnetic properties of the materials appear, an automatic identification of the underlying characteristics of the problem (by means of machine learning techniques) can readjust the design efficiently. The results obtained in this work are analyzed based on variables that are typically used to characterize antennas, such as their impedance and radiation pattern.
CASU2Net: Cascaded Unification Network by a Two-step Early Fusion for Fault Detection in Offshore Wind Turbines
Authors: Soorena Salari, Nasser Sadati
Date: 2020-11-24 00:00:00
Tasks: Fault Detection, Time Series, Time Series Analysis

This paper presents a novel feature fusion-based deep learning model (called CASU2Net) for fault detection in offshore wind turbines. The proposed CASU2Net model benefits of a two-step early fusion to enrich features in the final stage. Moreover, since previous studies did not consider uncertainty while model developing and also predictions, we take advantage of Monte Carlo dropout (MC dropout) to enhance the certainty of the results. To design fault detection model, we use five sensors and a sliding window to exploit the inherent temporal information contained in the raw time-series data obtained from sensors. The proposed model uses the nonlinear relationships among multiple sensor variables and the temporal dependency of each sensor on others which considerably increases the performance of fault detection model. A 10-fold cross-validation approach is used to verify the generalization of the model and evaluate the classification metrics. To evaluate the performance of the model, simulated data from a benchmark floating offshore wind turbine (FOWT) with supervisory control and data acquisition (SCADA) are used. The results illustrate that the proposed model would accurately disclose and classify more than 99% of the faults. Moreover, it is generalizable and can be used to detect faults for different types of systems.
Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation
Authors: Nina Lukashina, Alisa Alenicheva, Elizaveta Vlasova, Artem Kondiukov, Aigul Khakimova, Emil Magerramov, Nikita Churikov, Aleksei Shpilman
Date: 2020-11-24 00:00:00
Tasks: Prediction

Lipophilicity is one of the factors determining the permeability of the cell membrane to a drug molecule. Hence, accurate lipophilicity prediction is an essential step in the development of new drugs. In this paper, we introduce a novel approach to encoding additional graph information by extracting molecular substructures. By adding a set of generalized atomic features of these substructures to an established Direct Message Passing Neural Network (D-MPNN) we were able to achieve a new state-of-the-art result at the task of prediction of two main lipophilicity coefficients, namely logP and logD descriptors. We further improve our approach by employing a multitask approach to predict logP and logD values simultaneously. Additionally, we present a study of the model performance on symmetric and asymmetric molecules, that may yield insight for further research.
Learning of Long-Horizon Sparse-Reward Robotic Manipulator Tasks with Base Controllers
Authors: Guangming Wang, Minjian Xin, Wenhua Wu, Zhe Liu, Hesheng Wang
Date: 2020-11-24 00:00:00
Tasks: Deep Reinforcement Learning, Robot Manipulation

Deep Reinforcement Learning (DRL) enables robots to perform some intelligent tasks end-to-end. However, there are still many challenges for long-horizon sparse-reward robotic manipulator tasks. On the one hand, a sparse-reward setting causes exploration inefficient. On the other hand, exploration using physical robots is of high cost and unsafe. In this paper, we propose a method of learning long-horizon sparse-reward tasks utilizing one or more existing traditional controllers named base controllers in this paper. Built upon Deep Deterministic Policy Gradients (DDPG), our algorithm incorporates the existing base controllers into stages of exploration, value learning, and policy update. Furthermore, we present a straightforward way of synthesizing different base controllers to integrate their strengths. Through experiments ranging from stacking blocks to cups, it is demonstrated that the learned state-based or image-based policies steadily outperform base controllers. Compared to previous works of learning from demonstrations, our method improves sample efficiency by orders of magnitude and improves the performance. Overall, our method bears the potential of leveraging existing industrial robot manipulation systems to build more flexible and intelligent controllers.
Recurrent Multi-view Alignment Network for Unsupervised Surface Registration
Authors: Wanquan Feng, Juyong Zhang, Hongrui Cai, Haofei Xu, Junhui Hou, Hujun Bao
Date: 2020-11-24 00:00:00
Proceeding: CVPR 2021 1
Tasks: Deformable Object Manipulation, Neural Rendering, Point Cloud Registration

Learning non-rigid registration in an end-to-end manner is challenging due to the inherent high degrees of freedom and the lack of labeled training data. In this paper, we resolve these two challenges simultaneously. First, we propose to represent the non-rigid transformation with a point-wise combination of several rigid transformations. This representation not only makes the solution space well-constrained but also enables our method to be solved iteratively with a recurrent framework, which greatly reduces the difficulty of learning. Second, we introduce a differentiable loss function that measures the 3D shape similarity on the projected multi-view 2D depth images so that our full framework can be trained end-to-end without ground truth supervision. Extensive experiments on several different datasets demonstrate that our proposed method outperforms the previous state-of-the-art by a large margin. The source codes are available at https://github.com/WanquanF/RMA-Net.
Do You Live a Healthy Life? Analyzing Lifestyle by Visual Life Logging
Authors: Qing Gao, Mingtao Pei, Hongyu Shen
Date: 2020-11-24 00:00:00

A healthy lifestyle is the key to better health and happiness and has a considerable effect on quality of life and disease prevention. Current lifelogging/egocentric datasets are not suitable for lifestyle analysis; consequently, there is no research on lifestyle analysis in the field of computer vision. In this work, we investigate the problem of lifestyle analysis and build a visual lifelogging dataset for lifestyle analysis (VLDLA). The VLDLA contains images captured by a wearable camera every 3 seconds from 8:00 am to 6:00 pm for seven days. In contrast to current lifelogging/egocentric datasets, our dataset is suitable for lifestyle analysis as images are taken with short intervals to capture activities of short duration; moreover, images are taken continuously from morning to evening to record all the activities performed by a user. Based on our dataset, we classify the user activities in each frame and use three latent fluents of the user, which change over time and are associated with activities, to measure the healthy degree of the user's lifestyle. The scores for the three latent fluents are computed based on recognized activities, and the healthy degree of the lifestyle for the day is determined based on the scores for the latent fluents. Experimental results show that our method can be used to analyze the healthiness of users' lifestyles.
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
Authors: Michael Niemeyer, Andreas Geiger
Date: 2020-11-24 00:00:00
Proceeding: CVPR 2021 1
Tasks: Image Generation, Neural Rendering

Deep generative models allow for photorealistic image synthesis at high resolutions. But for many applications, this is not enough: content creation also needs to be controllable. While several recent works investigate how to disentangle underlying factors of variation in the data, most of them operate in 2D and hence ignore that our world is three-dimensional. Further, only few works consider the compositional nature of scenes. Our key hypothesis is that incorporating a compositional 3D scene representation into the generative model leads to more controllable image synthesis. Representing scenes as compositional generative neural feature fields allows us to disentangle one or multiple objects from the background as well as individual objects' shapes and appearances while learning from unstructured and unposed image collections without any additional supervision. Combining this scene representation with a neural rendering pipeline yields a fast and realistic image synthesis model. As evidenced by our experiments, our model is able to disentangle individual objects and allows for translating and rotating them in the scene as well as changing the camera pose.
Learning to Sample the Most Useful Training Patches from Images
Authors: Shuyang Sun, Liang Chen, Gregory Slabaugh, Philip Torr
Date: 2020-11-24 00:00:00
Tasks: Demosaicking, Image Restoration

Some image restoration tasks like demosaicing require difficult training samples to learn effective models. Existing methods attempt to address this data training problem by manually collecting a new training dataset that contains adequate hard samples, however, there are still hard and simple areas even within one single image. In this paper, we present a data-driven approach called PatchNet that learns to select the most useful patches from an image to construct a new training set instead of manual or random selection. We show that our simple idea automatically selects informative samples out from a large-scale dataset, leading to a surprising 2.35dB generalisation gain in terms of PSNR. In addition to its remarkable effectiveness, PatchNet is also resource-friendly as it is applied only during training and therefore does not require any additional computational cost during inference.
Gender bias in magazines oriented to men and women: a computational approach
Authors: Diego Kozlowski, Gabriela Lozano, Carla M. Felcher, Fernando Gonzalez, Edgar Altszyler
Date: 2020-11-24 00:00:00
Tasks: Articles

Cultural products are a source to acquire individual values and behaviours. Therefore, the differences in the content of the magazines aimed specifically at women or men are a means to create and reproduce gender stereotypes. In this study, we compare the content of a women-oriented magazine with that of a men-oriented one, both produced by the same editorial group, over a decade (2008-2018). With Topic Modelling techniques we identify the main themes discussed in the magazines and quantify how much the presence of these topics differs between magazines over time. Then, we performed a word-frequency analysis to validate this methodology and extend the analysis to other subjects that did not emerge automatically. Our results show that the frequency of appearance of the topics Family, Business and Women as sex objects, present an initial bias that tends to disappear over time. Conversely, in Fashion and Science topics, the initial differences between both magazines are maintained. Besides, we show that in 2012, the content associated with horoscope increased in the women-oriented magazine, generating a new gap that remained open over time. Also, we show a strong increase in the use of words associated with feminism since 2015 and specifically the word abortion in 2018. Overall, these computational tools allowed us to analyse more than 24,000 articles. Up to our knowledge, this is the first study to compare magazines in such a large dataset, a task that would have been prohibitive using manual content analysis methodologies.
SEA: Sentence Encoder Assembly for Video Retrieval by Textual Queries
Authors: Xirong Li, Fangming Zhou, Chaoxi Xu, Jiaqi Ji, Gang Yang
Date: 2020-11-24 00:00:00
Tasks: Ad-hoc video search, Management, Representation Learning, Retrieval, Semantic Similarity, Semantic Textual Similarity, Sentence, Video Retrieval

Retrieving unlabeled videos by textual queries, known as Ad-hoc Video Search (AVS), is a core theme in multimedia data management and retrieval. The success of AVS counts on cross-modal representation learning that encodes both query sentences and videos into common spaces for semantic similarity computation. Inspired by the initial success of previously few works in combining multiple sentence encoders, this paper takes a step forward by developing a new and general method for effectively exploiting diverse sentence encoders. The novelty of the proposed method, which we term Sentence Encoder Assembly (SEA), is two-fold. First, different from prior art that use only a single common space, SEA supports text-video matching in multiple encoder-specific common spaces. Such a property prevents the matching from being dominated by a specific encoder that produces an encoding vector much longer than other encoders. Second, in order to explore complementarities among the individual common spaces, we propose multi-space multi-loss learning. As extensive experiments on four benchmarks (MSR-VTT, TRECVID AVS 2016-2019, TGIF and MSVD) show, SEA surpasses the state-of-the-art. In addition, SEA is extremely ease to implement. All this makes SEA an appealing solution for AVS and promising for continuously advancing the task by harvesting new sentence encoders.
AI Discovering a Coordinate System of Chemical Elements: Dual Representation by Variational Autoencoders
Authors: Alex Glushkovsky
Date: 2020-11-24 00:00:00

The periodic table is a fundamental representation of chemical elements that plays essential theoretical and practical roles. The research article discusses the experiences of unsupervised training of neural networks to represent elements on the 2D latent space based on their electron configurations. To emphasize chemical properties of the elements, the original data of electron configurations has been realigned towards valence orbitals. Recognizing seven shells and four subshells, the input data has been arranged as 7x4 images. Latent space representation has been performed using a convolutional beta variational autoencoder (beta-VAE). Despite discrete and sparse input data, the beta-VAE disentangles elements of different periods, blocks, groups, and types. The unsupervised representation of elements on the latent space reveals pairwise symmetries of periods and elements related to the invariance of quantum numbers of corresponding elements. In addition, it isolates outliers that turned out to be known cases of Madelung's rule violations for lanthanide and actinide elements. Considering the generative capabilities of beta-VAE, the supervised machine learning has been set to find out if there are insightful patterns distinguishing electron configurations between real elements and decoded artificial ones. Also, the article addresses the capability of dual representation by autoencoders. Conventionally, autoencoders represent observations of input data on the latent space. By transposing and duplicating original input data, it is possible to represent variables on the latent space which can lead to the discovery of meaningful patterns among input variables. Applying that unsupervised learning for transposed data of electron configurations, the order of input variables that has been arranged by the encoder on the latent space has turned out to exactly match the sequence of Madelung's rule.
A Convenient Infinite Dimensional Framework for Generative Adversarial Learning
Authors: Hayk Asatryan, Hanno Gottschalk, Marieke Lippert, Matthias Rottmann
Date: 2020-11-24 00:00:00
Tasks: Learning Theory

In recent years, generative adversarial networks (GANs) have demonstrated impressive experimental results while there are only a few works that foster statistical learning theory for GANs. In this work, we propose an infinite dimensional theoretical framework for generative adversarial learning. We assume that the probability density functions of the underlying measure are uniformly bounded, $k$-times $\alpha$-H\"{o}lder differentiable ($C^{k,\alpha}$) and uniformly bounded away from zero. Under these assumptions, we show that the Rosenblatt transformation induces an optimal generator, which is realizable in the hypothesis space of $C^{k,\alpha}$-generators. With a consistent definition of the hypothesis space of discriminators, we further show that the Jensen-Shannon divergence between the distribution induced by the generator from the adversarial learning procedure and the data generating distribution converges to zero. Under certain regularity assumptions on the density of the data generating process, we also provide rates of convergence based on chaining and concentration.
Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis
Authors: Michael A. Lepori
Date: 2020-11-24 00:00:00
Tasks: Word Embeddings

We present a new approach for detecting human-like social biases in word embeddings using representational similarity analysis. Specifically, we probe contextualized and non-contextualized embeddings for evidence of intersectional biases against Black women. We show that these embeddings represent Black women as simultaneously less feminine than White women, and less Black than Black men. This finding aligns with intersectionality theory, which argues that multiple identity categories (such as race or sex) layer on top of each other in order to create unique modes of discrimination that are not shared by any individual category.
Computational efficient deep neural network with difference attention maps for facial action unit detection
Authors: Jing Chen, Chenhui Wang, Kejun Wang, Meichen Liu
Date: 2020-11-24 00:00:00
Tasks: Action Unit Detection, Facial Action Unit Detection

In this paper, we propose a computational efficient end-to-end training deep neural network (CEDNN) model and spatial attention maps based on difference images. Firstly, the difference image is generated by image processing. Then five binary images of difference images are obtained using different thresholds, which are used as spatial attention maps. We use group convolution to reduce model complexity. Skip connection and $\text{1}\times \text{1}$ convolution are used to ensure good performance even if the network model is not deep. As an input, spatial attention map can be selectively fed into the input of each block. The feature maps tend to focus on the parts that are related to the target task better. In addition, we only need to adjust the parameters of classifier to train different numbers of AU. It can be easily extended to varying datasets without increasing too much computation. A large number of experimental results show that the proposed CEDNN is obviously better than the traditional deep learning method on DISFA+ and CK+ datasets. After adding spatial attention maps, the result is better than the most advanced AU detection method. At the same time, the scale of the network is small, the running speed is fast, and the requirement for experimental equipment is low.
Tackling Domain-Specific Winograd Schemas with Knowledge-Based Reasoning and Machine Learning
Authors: Suk Joon Hong, Brandon Bennett
Date: 2020-11-24 00:00:00
Tasks: BIG-bench Machine Learning, Common Sense Reasoning, Sentence

The Winograd Schema Challenge (WSC) is a common-sense reasoning task that requires background knowledge. In this paper, we contribute to tackling WSC in four ways. Firstly, we suggest a keyword method to define a restricted domain where distinctive high-level semantic patterns can be found. A thanking domain was defined by key-words, and the data set in this domain is used in our experiments. Secondly, we develop a high-level knowledge-based reasoning method using semantic roles which is based on the method of Sharma [2019]. Thirdly, we propose an ensemble method to combine knowledge-based reasoning and machine learning which shows the best performance in our experiments. As a machine learning method, we used Bidirectional Encoder Representations from Transformers (BERT) [Kocijan et al., 2019]. Lastly, in terms of evaluation, we suggest a "robust" accuracy measurement by modifying that of Trichelair et al. [2018]. As with their switching method, we evaluate a model by considering its performance on trivial variants of each sentence in the test set.