pwc-archive/papers-with-abstracts huggingface dataset:
Feature Fusion via Multiresolution Compressive Measurement Matrix Analysis For Spectral Image Classification
Authors: Juan Marcos Ramirez, Jose Ignacio Martinez-Torre, Henry Arguello
Date: 2020-09-15 00:00:00
Tasks: Classification, image-classification, Image Classification

In the compressive spectral imaging (CSI) framework, different architectures have been proposed to recover high-resolution spectral images from compressive measurements. Since CSI architectures compactly capture the relevant information of the spectral image, various methods that extract classification features from compressive samples have been recently proposed. However, these techniques require a feature extraction procedure that reorders measurements using the information embedded in the coded aperture patterns. In this paper, a method that fuses features directly from multiresolution compressive measurements is proposed for spectral image classification. More precisely, the fusion method is formulated as an inverse problem that estimates high-spatial-resolution and low-dimensional feature bands from compressive measurements. To this end, the decimation matrices that describe the compressive measurements as degraded versions of the fused features are mathematically modeled using the information embedded in the coded aperture patterns. Furthermore, we include both a sparsity-promoting and a total-variation (TV) regularization terms to the fusion problem in order to consider the correlations between neighbor pixels, and therefore, improve the accuracy of pixel-based classifiers. To solve the fusion problem, we describe an algorithm based on the accelerated variant of the alternating direction method of multipliers (accelerated-ADMM). Additionally, a classification approach that includes the developed fusion method and a multilayer neural network is introduced. Finally, the proposed approach is evaluated on three remote sensing spectral images and a set of compressive measurements captured in the laboratory. Extensive simulations show that the proposed classification approach outperforms other approaches under various performance metrics.
Detection of Maternal and Fetal Stress from the Electrocardiogram with Self-Supervised Representation Learning
Authors: Pritam Sarkar, Silvia Lobmaier, Bibiana Fabre, Diego González, Alexander Mueller, Martin G. Frasch, Marta C. Antonelli, Ali Etemad
Date: 2020-11-03 00:00:00
Tasks: Representation Learning, Self-Supervised Learning

In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical data with high accuracy in noisy real-life environments, but little is known about DL's utility in non-invasive biometric monitoring during pregnancy. A recently established self-supervised learning (SSL) approach to DL provides emotional recognition from electrocardiogram (ECG). We hypothesized that SSL will identify chronically stressed mother-fetus dyads from the raw maternal abdominal electrocardiograms (aECG), containing fetal and maternal ECG. Chronically stressed mothers and controls matched at enrolment at 32 weeks of gestation were studied. We validated the chronic stress exposure by psychological inventory, maternal hair cortisol and FSI. We tested two variants of SSL architecture, one trained on the generic ECG features for emotional recognition obtained from public datasets and another transfer-learned on a subset of our data. Our DL models accurately detect the chronic stress exposure group (AUROC=0.982+/-0.002), the individual psychological stress score (R2=0.943+/-0.009) and FSI at 34 weeks of gestation (R2=0.946+/-0.013), as well as the maternal hair cortisol at birth reflecting chronic stress exposure (0.931+/-0.006). The best performance was achieved with the DL model trained on the public dataset and using maternal ECG alone. The present DL approach provides a novel source of physiological insights into complex multi-modal relationships between different regulatory systems exposed to chronic stress. The final DL model can be deployed in low-cost regular ECG biosensors as a simple, ubiquitous early stress detection and monitoring tool during pregnancy. This discovery should enable early behavioral interventions.
Move to See Better: Self-Improving Embodied Object Detection
Authors: Zhaoyuan Fang, Ayush Jain, Gabriel Sarch, Adam W. Harley, Katerina Fragkiadaki
Date: 2020-11-30 00:00:00
Tasks: Object, object-detection, Object Detection

Passive methods for object detection and segmentation treat images of the same scene as individual samples and do not exploit object permanence across multiple views. Generalization to novel or difficult viewpoints thus requires additional training with lots of annotations. In contrast, humans often recognize objects by simply moving around, to get more informative viewpoints. In this paper, we propose a method for improving object detection in testing environments, assuming nothing but an embodied agent with a pre-trained 2D object detector. Our agent collects multi-view data, generates 2D and 3D pseudo-labels, and fine-tunes its detector in a self-supervised manner. Experiments on both indoor and outdoor datasets show that (1) our method obtains high-quality 2D and 3D pseudo-labels from multi-view RGB-D data; (2) fine-tuning with these pseudo-labels improves the 2D detector significantly in the test environment; (3) training a 3D detector with our pseudo-labels outperforms a prior self-supervised method by a large margin; (4) given weak supervision, our method can generate better pseudo-labels for novel objects.
Fast Class-wise Updating for Online Hashing
Authors: Mingbao Lin, Rongrong Ji, Xiaoshuai Sun, Baochang Zhang, Feiyue Huang, Yonghong Tian, DaCheng Tao
Date: 2020-12-01 00:00:00

Online image hashing has received increasing research attention recently, which processes large-scale data in a streaming fashion to update the hash functions on-the-fly. To this end, most existing works exploit this problem under a supervised setting, i.e., using class labels to boost the hashing performance, which suffers from the defects in both adaptivity and efficiency: First, large amounts of training batches are required to learn up-to-date hash functions, which leads to poor online adaptivity. Second, the training is time-consuming, which contradicts with the core need of online learning. In this paper, a novel supervised online hashing scheme, termed Fast Class-wise Updating for Online Hashing (FCOH), is proposed to address the above two challenges by introducing a novel and efficient inner product operation. To achieve fast online adaptivity, a class-wise updating method is developed to decompose the binary code learning and alternatively renew the hash functions in a class-wise fashion, which well addresses the burden on large amounts of training batches. Quantitatively, such a decomposition further leads to at least 75% storage saving. To further achieve online efficiency, we propose a semi-relaxation optimization, which accelerates the online training by treating different binary constraints independently. Without additional constraints and variables, the time complexity is significantly reduced. Such a scheme is also quantitatively shown to well preserve past information during updating hashing functions. We have quantitatively demonstrated that the collective effort of class-wise updating and semi-relaxation optimization provides a superior performance comparing to various state-of-the-art methods, which is verified through extensive experiments on three widely-used datasets.
A Privacy-Preserving Trust Model Based on Blockchain for VANETs
Authors: ZHAOJUN LU1, WENCHAO LIU1, QIAN WANG 2, GANG QU 2, (Senior Member, IEEE), AND ZHENGLIN LIU 1
Date: 2018-08-07 00:00:00
Tasks: Privacy Preserving

The public key infrastructure-based authentication protocol provides basic security services
for the vehicular ad hoc networks (VANETs). However, trust and privacy are still open issues due to
the unique characteristics of VANETs. It is crucial to prevent internal vehicles from broadcasting forged
messages while simultaneously preserving the privacy of vehicles against the tracking attacks. In this paper,
we propose a blockchain-based anonymous reputation system (BARS) to establish a privacy-preserving
trust model for VANETs. The certificate and revocation transparency is implemented efficiently with the
proofs of presence and absence based on the extended blockchain technology. The public keys are used
as pseudonyms in communications without any information about real identities for conditional anonymity.
In order to prevent the distribution of forged messages, a reputation evaluation algorithm is presented relying
on both direct historical interactions and indirect opinions about vehicles. A set of experiments is conducted
to evaluate BARS in terms of security, validity, and performance, and the results show that BARS is able to
establish a trust model with transparency, conditional anonymity, efficiency, and robustness for VANETs.
CreditCoin: A Privacy-Preserving Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles
Authors: Lun Li, Jiqiang Liu, Lichen Cheng, Shuo Qiu, Wei Wang, Xiangliang Zhang, and Zonghua Zhang
Date: 2018-07-07 00:00:00
Tasks: Privacy Preserving

The vehicular announcement network is one of
the most promising utilities in the communications of smart
vehicles and in the smart transportation systems. In general,
there are two major issues in building an effective vehicular
announcement network. First, it is difficult to forward reliable
announcements without revealing users’ identities. Second, users
usually lack the motivation to forward announcements. In this
paper, we endeavor to resolve these two issues through proposing
an effective announcement network called CreditCoin, a novel
privacy-preserving incentive announcement network based on
Blockchain via an efficient anonymous vehicular announcement
aggregation protocol. On the one hand, CreditCoin allows nondeterministic different signers (i.e., users) to generate the signatures
and to send announcements anonymously in the nonfully trusted
environment. On the other hand, with Blockchain, CreditCoin
motivates users with incentives to share traffic information.
In addition, transactions and account information in CreditCoin
are tamper-resistant. CreditCoin also achieves conditional privacy
since Trace manager in CreditCoin traces malicious users’ identities in anonymous announcements with related transactions.
CreditCoin thus is able to motivate users to forward announcements anonymously and reliably. Extensive experimental results
show that CreditCoin is efficient and practical in simulations of
smart transportation.
Facilitating the Communication of Politeness through Fine-Grained Paraphrasing
Authors: Liye Fu, Susan R. Fussell, Cristian Danescu-Niculescu-Mizil
Date: 2020-11-30 00:00:00
Proceeding: EMNLP 2020 11

Aided by technology, people are increasingly able to communicate across geographical, cultural, and language barriers. This ability also results in new challenges, as interlocutors need to adapt their communication approaches to increasingly diverse circumstances. In this work, we take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance. As a case study, we focus on facilitating the accurate transmission of pragmatic intentions and introduce a methodology for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance. We demonstrate the feasibility of this approach by evaluating our method in two realistic communication scenarios and show that it can reduce the potential for misalignment between the speaker's intentions and the listener's perceptions in both cases.
Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
Authors: Raghav Kansal, Javier Duarte, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, Dimitrios Gunopulos
Date: 2020-11-30 00:00:00
Tasks: Generative Adversarial Network

We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training on and generating sparse representations of MNIST handwritten digit images and jets of particles in proton-proton collisions like those at the LHC. We find the model successfully generates sparse MNIST digits and particle jet data. We quantify agreement between real and generated data with a graph-based Fr\'echet Inception distance, and the particle and jet feature-level 1-Wasserstein distance for the MNIST and jet datasets respectively.
Unpaired Image-to-Image Translation via Latent Energy Transport
Authors: Yang Zhao, Changyou Chen
Date: 2020-12-01 00:00:00
Proceeding: CVPR 2021 1
Tasks: Image Reconstruction, Image-to-Image Translation, Translation

Image-to-image translation aims to preserve source contents while translating to discriminative target styles between two visual domains. Most works apply adversarial learning in the ambient image space, which could be computationally expensive and challenging to train. In this paper, we propose to deploy an energy-based model (EBM) in the latent space of a pretrained autoencoder for this task. The pretrained autoencoder serves as both a latent code extractor and an image reconstruction worker. Our model, LETIT, is based on the assumption that two domains share the same latent space, where latent representation is implicitly decomposed as a content code and a domain-specific style code. Instead of explicitly extracting the two codes and applying adaptive instance normalization to combine them, our latent EBM can implicitly learn to transport the source style code to the target style code while preserving the content code, an advantage over existing image translation methods. This simplified solution is also more efficient in the one-sided unpaired image translation setting. Qualitative and quantitative comparisons demonstrate superior translation quality and faithfulness for content preservation. Our model is the first to be applicable to 1024$\times$1024-resolution unpaired image translation to the best of our knowledge.
MYSTIKO : : Cloud-Mediated, Private, Federated Gradient Descent
Authors: K. R. Jayaram, Archit Verma, Ashish Verma, Gegi Thomas, Colin Sutcher-Shepard
Date: 2020-12-01 00:00:00
Tasks: Federated Learning

Federated learning enables multiple, distributed participants (potentially on different clouds) to collaborate and train machine/deep learning models by sharing parameters/gradients. However, sharing gradients, instead of centralizing data, may not be as private as one would expect. Reverse engineering attacks on plaintext gradients have been demonstrated to be practically feasible. Existing solutions for differentially private federated learning, while promising, lead to less accurate models and require nontrivial hyperparameter tuning. In this paper, we examine the use of additive homomorphic encryption (specifically the Paillier cipher) to design secure federated gradient descent techniques that (i) do not require addition of statistical noise or hyperparameter tuning, (ii) does not alter the final accuracy or utility of the final model, (iii) ensure that the plaintext model parameters/gradients of a participant are never revealed to any other participant or third party coordinator involved in the federated learning job, (iv) minimize the trust placed in any third party coordinator and (v) are efficient, with minimal overhead, and cost effective.
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution
Authors: Kelvin C. K. Chan, Xintao Wang, Xiangyu Xu, Jinwei Gu, Chen Change Loy
Date: 2020-12-01 00:00:00
Proceeding: CVPR 2021 1
Tasks: Decoder, Image Super-Resolution, Super-Resolution

We show that pre-trained Generative Adversarial Networks (GANs), e.g., StyleGAN, can be used as a latent bank to improve the restoration quality of large-factor image super-resolution (SR). While most existing SR approaches attempt to generate realistic textures through learning with adversarial loss, our method, Generative LatEnt bANk (GLEAN), goes beyond existing practices by directly leveraging rich and diverse priors encapsulated in a pre-trained GAN. But unlike prevalent GAN inversion methods that require expensive image-specific optimization at runtime, our approach only needs a single forward pass to generate the upscaled image. GLEAN can be easily incorporated in a simple encoder-bank-decoder architecture with multi-resolution skip connections. Switching the bank allows the method to deal with images from diverse categories, e.g., cat, building, human face, and car. Images upscaled by GLEAN show clear improvements in terms of fidelity and texture faithfulness in comparison to existing methods.
Convergence and Sample Complexity of SGD in GANs
Authors: Vasilis Kontonis, Sihan Liu, Christos Tzamos
Date: 2020-12-01 00:00:00
Tasks: Bilevel Optimization

We provide theoretical convergence guarantees on training Generative Adversarial Networks (GANs) via SGD. We consider learning a target distribution modeled by a 1-layer Generator network with a non-linear activation function $\phi(\cdot)$ parametrized by a $d \times d$ weight matrix $\mathbf W_*$, i.e., $f_*(\mathbf x) = \phi(\mathbf W_* \mathbf x)$. Our main result is that by training the Generator together with a Discriminator according to the Stochastic Gradient Descent-Ascent iteration proposed by Goodfellow et al. yields a Generator distribution that approaches the target distribution of $f_*$. Specifically, we can learn the target distribution within total-variation distance $\epsilon$ using $\tilde O(d^2/\epsilon^2)$ samples which is (near-)information theoretically optimal. Our results apply to a broad class of non-linear activation functions $\phi$, including ReLUs and is enabled by a connection with truncated statistics and an appropriate design of the Discriminator network. Our approach relies on a bilevel optimization framework to show that vanilla SGDA works.
mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms
Authors: Mike Ludkovski
Date: 2020-12-01 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning, regression

We introduce mlOSP, a computational template for Machine Learning for Optimal Stopping Problems. The template is implemented in the R statistical environment and publicly available via a GitHub repository. mlOSP presents a unified numerical implementation of Regression Monte Carlo (RMC) approaches to optimal stopping, providing a state-of-the-art, open-source, reproducible and transparent platform. Highlighting its modular nature, we present multiple novel variants of RMC algorithms, especially in terms of constructing simulation designs for training the regressors, as well as in terms of machine learning regression modules. Furthermore, mlOSP nests most of the existing RMC schemes, allowing for a consistent and verifiable benchmarking of extant algorithms. The article contains extensive R code snippets and figures, and serves as a vignette to the underlying software package.
Intrinsic analysis for dual word embedding space models
Authors: Mohit Mayank
Date: 2020-12-01 00:00:00
Tasks: Word Embeddings

Recent word embeddings techniques represent words in a continuous vector space, moving away from the atomic and sparse representations of the past. Each such technique can further create multiple varieties of embeddings based on different settings of hyper-parameters like embedding dimension size, context window size and training method. One additional variety appears when we especially consider the Dual embedding space techniques which generate not one but two-word embeddings as output. This gives rise to an interesting question - "is there one or a combination of the two word embeddings variety, which works better for a specific task?". This paper tries to answer this question by considering all of these variations. Herein, we compare two classical embedding methods belonging to two different methodologies - Word2Vec from window-based and Glove from count-based. For an extensive evaluation after considering all variations, a total of 84 different models were compared against semantic, association and analogy evaluations tasks which are made up of 9 open-source linguistics datasets. The final Word2vec reports showcase the preference of non-default model for 2 out of 3 tasks. In case of Glove, non-default models outperform in all 3 evaluation tasks.
RAFT-3D: Scene Flow using Rigid-Motion Embeddings
Authors: Zachary Teed, Jia Deng
Date: 2020-12-01 00:00:00
Proceeding: CVPR 2021 1
Tasks: Optical Flow Estimation, Scene Flow Estimation

We address the problem of scene flow: given a pair of stereo or RGB-D video frames, estimate pixelwise 3D motion. We introduce RAFT-3D, a new deep architecture for scene flow. RAFT-3D is based on the RAFT model developed for optical flow but iteratively updates a dense field of pixelwise SE3 motion instead of 2D motion. A key innovation of RAFT-3D is rigid-motion embeddings, which represent a soft grouping of pixels into rigid objects. Integral to rigid-motion embeddings is Dense-SE3, a differentiable layer that enforces geometric consistency of the embeddings. Experiments show that RAFT-3D achieves state-of-the-art performance. On FlyingThings3D, under the two-view evaluation, we improved the best published accuracy (d < 0.05) from 34.3% to 83.7%. On KITTI, we achieve an error of 5.77, outperforming the best published method (6.31), despite using no object instance supervision. Code is available at https://github.com/princeton-vl/RAFT-3D.
Assessing and Accelerating Coverage in Deep Reinforcement Learning
Authors: Arpan Kusari
Date: 2020-12-01 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Current deep reinforcement learning (DRL) algorithms utilize randomness in simulation environments to assume complete coverage in the state space. However, particularly in high dimensions, relying on randomness may lead to gaps in coverage of the trained DRL neural network model, which in turn may lead to drastic and often fatal real-world situations. To the best of the author's knowledge, the assessment of coverage for DRL is lacking in current research literature. Therefore, in this paper, a novel measure, Approximate Pseudo-Coverage (APC), is proposed for assessing the coverage in DRL applications. We propose to calculate APC by projecting the high dimensional state space on to a lower dimensional manifold and quantifying the occupied space. Furthermore, we utilize an exploration-exploitation strategy for coverage maximization using Rapidly-Exploring Random Tree (RRT). The efficacy of the assessment and the acceleration of coverage is demonstrated on standard tasks such as Cartpole, highway-env.