pwc-archive/papers-with-abstracts huggingface dataset:
Deep Embedding Data Fusion Scheme Using Variational Graph Auto-Encoder in IoT Environments
Authors: Asmaa Mohamed fathy, Ahmed. A. A. Gad-Elrab, Sawsan Mohammed Aziz, Heba F. Eid
Date: 2020-09-04 00:00:00
Tasks: Decision Making, Management

Internet of things (IoT) is a promised paradigm for developing
smart systems and architectures. IoT is a framework where
everyday objects can be equipped with capabilities for
identifying, sensing, networking and processing that will
allow them to interact over the Internet with each other and
with other devices and services to achieve some objectives. In
IoT, the sensing devices can collect and manage data that exist
in their surrounding environments. So, data fusion problem is
a major challenge for the future in order to allow highly
effective, reliable and accurate management and
decision-making of IoT environments. To meet this challenge,
this paper introduces a new data fusion scheme uses a
variational graph auto-encoder as a deep learning method for
creating deep embedding data graph. This graph represents
the similarities among data items which can be divided into
groups that can be fused together to minimize the amount of
transferred data and latency time of the collected data to the
cloud server and the energy consumption by IoT devices. The
conducted simulations experiments and results show that the
proposed scheme can achieve a reasonable performance in
terms of latency time, energy consumption, and data fusion
accuracy compared to non-fusion schemes.
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification
Authors: Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
Date: 2020-12-16 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis, Time Series Classification

Until recently, the most accurate methods for time series classification were limited by high computational complexity. ROCKET achieves state-of-the-art accuracy with a fraction of the computational expense of most existing methods by transforming input time series using random convolutional kernels, and using the transformed features to train a linear classifier. We reformulate ROCKET into a new method, MINIROCKET, making it up to 75 times faster on larger datasets, and making it almost deterministic (and optionally, with additional computational expense, fully deterministic), while maintaining essentially the same accuracy. Using this method, it is possible to train and test a classifier on all of 109 datasets from the UCR archive to state-of-the-art accuracy in less than 10 minutes. MINIROCKET is significantly faster than any other method of comparable accuracy (including ROCKET), and significantly more accurate than any other method of even roughly-similar computational expense. As such, we suggest that MINIROCKET should now be considered and used as the default variant of ROCKET.
Discovering New Intents with Deep Aligned Clustering
Authors: Hanlei Zhang, Hua Xu, Ting-En Lin, Rui Lyu
Date: 2020-12-16 00:00:00
Tasks: Clustering, Open Intent Discovery, Short Text Clustering, Text Clustering

Discovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. They also have difficulties in providing high-quality supervised signals to learn clustering-friendly features for grouping unlabeled intents. In this work, we propose an effective method, Deep Aligned Clustering, to discover new intents with the aid of the limited known intent data. Firstly, we leverage a few labeled known intent samples as prior knowledge to pre-train the model. Then, we perform k-means to produce cluster assignments as pseudo-labels. Moreover, we propose an alignment strategy to tackle the label inconsistency problem during clustering assignments. Finally, we learn the intent representations under the supervision of the aligned pseudo-labels. With an unknown number of new intents, we predict the number of intent categories by eliminating low-confidence intent-wise clusters. Extensive experiments on two benchmark datasets show that our method is more robust and achieves substantial improvements over the state-of-the-art methods. The codes are released at https://github.com/thuiar/DeepAligned-Clustering.
FuseVis: Interpreting neural networks for image fusion using per-pixel saliency visualization
Authors: Nishant Kumar, Stefan Gumhold
Date: 2020-12-06 00:00:00
Tasks: Autonomous Driving, Multi-Exposure Image Fusion

Image fusion helps in merging two or more images to construct a more informative single fused image. Recently, unsupervised learning based convolutional neural networks (CNN) have been utilized for different types of image fusion tasks such as medical image fusion, infrared-visible image fusion for autonomous driving as well as multi-focus and multi-exposure image fusion for satellite imagery. However, it is challenging to analyze the reliability of these CNNs for the image fusion tasks since no groundtruth is available. This led to the use of a wide variety of model architectures and optimization functions yielding quite different fusion results. Additionally, due to the highly opaque nature of such neural networks, it is difficult to explain the internal mechanics behind its fusion results. To overcome these challenges, we present a novel real-time visualization tool, named FuseVis, with which the end-user can compute per-pixel saliency maps that examine the influence of the input image pixels on each pixel of the fused image. We trained several image fusion based CNNs on medical image pairs and then using our FuseVis tool, we performed case studies on a specific clinical application by interpreting the saliency maps from each of the fusion methods. We specifically visualized the relative influence of each input image on the predictions of the fused image and showed that some of the evaluated image fusion methods are better suited for the specific clinical application. To the best of our knowledge, currently, there is no approach for visual analysis of neural networks for image fusion. Therefore, this work opens up a new research direction to improve the interpretability of deep fusion networks. The FuseVis tool can also be adapted in other deep neural network based image processing applications to make them interpretable.
Attentional Local Contrast Networks for Infrared Small Target Detection
Authors: Yimian Dai, Yiquan Wu, Fei Zhou, Kobus Barnard
Date: 2020-12-15 00:00:00

To mitigate the issue of minimal intrinsic features for pure data-driven methods, in this paper, we propose a novel model-driven deep network for infrared small target detection, which combines discriminative networks and conventional model-driven methods to make use of both labeled data and the domain knowledge. By designing a feature map cyclic shift scheme, we modularize a conventional local contrast measure method as a depth-wise parameterless nonlinear feature refinement layer in an end-to-end network, which encodes relatively long-range contextual interactions with clear physical interpretability. To highlight and preserve the small target features, we also exploit a bottom-up attentional modulation integrating the smaller scale subtle details of low-level features into high-level features of deeper layers. We conduct detailed ablation studies with varying network depths to empirically verify the effectiveness and efficiency of the design of each component in our network architecture. We also compare the performance of our network against other model-driven methods and deep networks on the open SIRST dataset as well. The results suggest that our network yields a performance boost over its competitors. Our code, trained models, and results are available online.
CUDA-Optimized real-time rendering of a Foveated Visual System
Authors: Elian Malkin, Arturo Deza, Tomaso Poggio
Date: 2020-12-15 00:00:00
Proceeding: NeurIPS Workshop SVRHM 2020 12
Tasks: Foveation, GPU

The spatially-varying field of the human visual system has recently received a resurgence of interest with the development of virtual reality (VR) and neural networks. The computational demands of high resolution rendering desired for VR can be offset by savings in the periphery, while neural networks trained with foveated input have shown perceptual gains in i.i.d and o.o.d generalization. In this paper, we present a technique that exploits the CUDA GPU architecture to efficiently generate Gaussian-based foveated images at high definition (1920x1080 px) in real-time (165 Hz), with a larger number of pooling regions than previous Gaussian-based foveation algorithms by several orders of magnitude, producing a smoothly foveated image that requires no further blending or stitching, and that can be well fit for any contrast sensitivity function. The approach described can be adapted from Gaussian blurring to any eccentricity-dependent image processing and our algorithm can meet demand for experimentation to evaluate the role of spatially-varying processing across biological and artificial agents, so that foveation can be added easily on top of existing systems rather than forcing their redesign (emulated foveated renderer). Altogether, this paper demonstrates how a GPU, with a CUDA block-wise architecture, can be employed for radially-variant rendering, with opportunities for more complex post-processing to ensure a metameric foveation scheme. Code is provided.
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts
Authors: Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie
Date: 2020-12-16 00:00:00
Proceeding: CVPR 2021 1
Tasks: 3D Semantic Segmentation, Instance Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

The rapid progress in 3D scene understanding has come with growing demand for data; however, collecting and annotating 3D scenes (e.g. point clouds) are notoriously hard. For example, the number of scenes (e.g. indoor rooms) that can be accessed and scanned might be limited; even given sufficient data, acquiring 3D labels (e.g. instance masks) requires intensive human labor. In this paper, we explore data-efficient learning for 3D point cloud. As a first step towards this direction, we propose Contrastive Scene Contexts, a 3D pre-training method that makes use of both point-level correspondences and spatial contexts in a scene. Our method achieves state-of-the-art results on a suite of benchmarks where training data or labels are scarce. Our study reveals that exhaustive labelling of 3D point clouds might be unnecessary; and remarkably, on ScanNet, even using 0.1% of point labels, we still achieve 89% (instance segmentation) and 96% (semantic segmentation) of the baseline performance that uses full annotations.
Point Transformer
Authors: Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip Torr, Vladlen Koltun
Date: 2020-12-16 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, 3D Semantic Segmentation, General Classification, image-classification, Image Classification, Object, Object Detection, Point Cloud Segmentation, Scene Segmentation, Segmentation, Semantic Segmentation

Self-attention networks have revolutionized natural language processing and are making impressive strides in image analysis tasks such as image classification and object detection. Inspired by this success, we investigate the application of self-attention networks to 3D point cloud processing. We design self-attention layers for point clouds and use these to construct self-attention networks for tasks such as semantic scene segmentation, object part segmentation, and object classification. Our Point Transformer design improves upon prior work across domains and tasks. For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70.4% on Area 5, outperforming the strongest prior model by 3.3 absolute percentage points and crossing the 70% mIoU threshold for the first time.
Learning Continuous Image Representation with Local Implicit Image Function
Authors: Yinbo Chen, Sifei Liu, Xiaolong Wang
Date: 2020-12-16 00:00:00
Proceeding: CVPR 2021 1
Tasks: 3D Reconstruction, Image Super-Resolution, Super-Resolution

How to represent an image? While the visual world is presented in a continuous manner, machines store and see the images in a discrete way with 2D arrays of pixels. In this paper, we seek to learn a continuous representation for images. Inspired by the recent progress in 3D reconstruction with implicit neural representation, we propose Local Implicit Image Function (LIIF), which takes an image coordinate and the 2D deep features around the coordinate as inputs, predicts the RGB value at a given coordinate as an output. Since the coordinates are continuous, LIIF can be presented in arbitrary resolution. To generate the continuous representation for images, we train an encoder with LIIF representation via a self-supervised task with super-resolution. The learned continuous representation can be presented in arbitrary resolution even extrapolate to x30 higher resolution, where the training tasks are not provided. We further show that LIIF representation builds a bridge between discrete and continuous representation in 2D, it naturally supports the learning tasks with size-varied image ground-truths and significantly outperforms the method with resizing the ground-truths.
DECOR-GAN: 3D Shape Detailization by Conditional Refinement
Authors: Zhiqin Chen, Vladimir G. Kim, Matthew Fisher, Noam Aigerman, Hao Zhang, Siddhartha Chaudhuri
Date: 2020-12-16 00:00:00
Proceeding: CVPR 2021 1
Tasks: 3D geometry, Generative Adversarial Network

We introduce a deep generative network for 3D shape detailization, akin to stylization with the style being geometric details. We address the challenge of creating large varieties of high-resolution and detailed 3D geometry from a small set of exemplars by treating the problem as that of geometric detail transfer. Given a low-resolution coarse voxel shape, our network refines it, via voxel upsampling, into a higher-resolution shape enriched with geometric details. The output shape preserves the overall structure (or content) of the input, while its detail generation is conditioned on an input "style code" corresponding to a detailed exemplar. Our 3D detailization via conditional refinement is realized by a generative adversarial network, coined DECOR-GAN. The network utilizes a 3D CNN generator for upsampling coarse voxels and a 3D PatchGAN discriminator to enforce local patches of the generated model to be similar to those in the training detailed shapes. During testing, a style code is fed into the generator to condition the refinement. We demonstrate that our method can refine a coarse shape into a variety of detailed shapes with different styles. The generated results are evaluated in terms of content preservation, plausibility, and diversity. Comprehensive ablation studies are conducted to validate our network designs. Code is available at https://github.com/czq142857/DECOR-GAN.
LIREx: Augmenting Language Inference with Relevant Explanation
Authors: Xinyan Zhao, V. G. Vinod Vydiswaran
Date: 2020-12-16 00:00:00
Tasks: Explanation Generation, Natural Language Inference

Natural language explanations (NLEs) are a special form of data annotation in which annotators identify rationales (most significant text tokens) when assigning labels to data instances, and write out explanations for the labels in natural language based on the rationales. NLEs have been shown to capture human reasoning better, but not as beneficial for natural language inference (NLI). In this paper, we analyze two primary flaws in the way NLEs are currently used to train explanation generators for language inference tasks. We find that the explanation generators do not take into account the variability inherent in human explanation of labels, and that the current explanation generation models generate spurious explanations. To overcome these limitations, we propose a novel framework, LIREx, that incorporates both a rationale-enabled explanation generator and an instance selector to select only relevant, plausible NLEs to augment NLI models. When evaluated on the standardized SNLI data set, LIREx achieved an accuracy of 91.87%, an improvement of 0.32 over the baseline and matching the best-reported performance on the data set. It also achieves significantly better performance than previous studies when transferred to the out-of-domain MultiNLI data set. Qualitative analysis shows that LIREx generates flexible, faithful, and relevant NLEs that allow the model to be more robust to spurious explanations. The code is available at https://github.com/zhaoxy92/LIREx.
Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning
Authors: Nathan O. Lambert, Albert Wilcox, Howard Zhang, Kristofer S. J. Pister, Roberto Calandra
Date: 2020-12-16 00:00:00
Tasks: Model-based Reinforcement Learning, Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Accurately predicting the dynamics of robotic systems is crucial for model-based control and reinforcement learning. The most common way to estimate dynamics is by fitting a one-step ahead prediction model and using it to recursively propagate the predicted state distribution over long horizons. Unfortunately, this approach is known to compound even small prediction errors, making long-term predictions inaccurate. In this paper, we propose a new parametrization to supervised learning on state-action data to stably predict at longer horizons -- that we call a trajectory-based model. This trajectory-based model takes an initial state, a future time index, and control parameters as inputs, and directly predicts the state at the future time index. Experimental results in simulated and real-world robotic tasks show that trajectory-based models yield significantly more accurate long term predictions, improved sample efficiency, and the ability to predict task reward. With these improved prediction properties, we conclude with a demonstration of methods for using the trajectory-based model for control.
MagBB: Wireless Charging for Batteryless Sensors using Magnetic Blind Beamforming
Authors: Albert Aninagyei Ofori, Hongzhi Guo
Date: 2020-12-16 00:00:00

Tiny batteryless sensors are desirable since they create negligible impacts on the operation of the system being monitored or the surrounding environment. Wireless energy transfer for batteryless sensors is challenging since they cannot cooperate with the charger due to the lack of energy. In this paper, a Magnetic Blind Beamforming (MagBB) algorithm is developed for wireless energy transfer for batteryless sensors in inhomogeneous media. Batteryless sensors with randomly orientated coils may experience significant orientation losses and they may not receive any energy from the charger. MagBB uses a set of optimized current vectors to generate rotating magnetic fields which can ensure that coils on batteryless sensors with arbitrary orientations can receive sufficient voltages for charging. It does not require any information regarding the batteryless sensor's coil orientation or location. The efficiency of MagBB is proven by extensive numerical simulations.
Generative Neural Network Channel Modeling for Millimeter-Wave UAV Communication
Authors: William Xia, Sundeep Rangan, Marco Mezzavillla, Angel Lozano, Giovanni Geraci, Vasilii Semkin, Giuseppe Loianno
Date: 2020-12-16 00:00:00

The millimeter wave bands are being increasingly considered for wireless communication to unmanned aerial vehicles (UAVs). Critical to this undertaking are statistical channel models that describe the distribution of constituent parameters in scenarios of interest. This paper presents a general modeling methodology based on data-training a generative neural network. The proposed generative model has a two-stage structure that first predicts the link state (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoencoder (VAE) that generates the path losses, delays, and angles of arrival and departure for all the propagation paths. The methodology is demonstrated for 28 GHz air-to-ground channels between UAVs and a cellular system in representative urban environments, with training datasets produced through ray tracing. The demonstration extends to both standard base stations (installed at street level and downtilted) as well as dedicated base stations (mounted on rooftops and uptilted). The proposed approach is able to capture complex statistical relations in the data and it significantly outperforms standard 3GPP models, even after refitting the parameters of those models to the data.
Planning from Pixels in Atari with Learned Symbolic Representations
Authors: Andrea Dittadi, Frederik K. Drachmann, Thomas Bolander
Date: 2020-12-16 00:00:00

Width-based planning methods have been shown to yield state-of-the-art performance in the Atari 2600 domain using pixel input. One successful approach, RolloutIW, represents states with the B-PROST boolean feature set. An augmented version of RolloutIW, $\pi$-IW, shows that learned features can be competitive with handcrafted ones for width-based search. In this paper, we leverage variational autoencoders (VAEs) to learn features directly from pixels in a principled manner, and without supervision. The inference model of the trained VAEs extracts boolean features from pixels, and RolloutIW plans with these features. The resulting combination outperforms the original RolloutIW and human professional play on Atari 2600 and drastically reduces the size of the feature set.
Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media
Authors: Lei Cao, Huijun Zhang, Ling Feng
Date: 2020-12-16 00:00:00

A large number of individuals are suffering from suicidal ideation in the world. There are a number of causes behind why an individual might suffer from suicidal ideation. As the most popular platform for self-expression, emotion release, and personal interaction, individuals may exhibit a number of symptoms of suicidal ideation on social media. Nevertheless, challenges from both data and knowledge aspects remain as obstacles, constraining the social media-based detection performance. Data implicitness and sparsity make it difficult to discover the inner true intentions of individuals based on their posts. Inspired by psychological studies, we build and unify a high-level suicide-oriented knowledge graph with deep neural networks for suicidal ideation detection on social media. We further design a two-layered attention mechanism to explicitly reason and establish key risk factors to individual's suicidal ideation. The performance study on microblog and Reddit shows that: 1) with the constructed personal knowledge graph, the social media-based suicidal ideation detection can achieve over 93% accuracy; and 2) among the six categories of personal factors, post, personality, and experience are the top-3 key indicators. Under these categories, posted text, stress level, stress duration, posted image, and ruminant thinking contribute to one's suicidal ideation detection.
AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models
Authors: Fabian Fröhlich, Daniel Weindl, Yannik Schälte, Dilan Pathirana, Łukasz Paszkowski, Glenn Terje Lines, Paul Stapor, Jan Hasenauer
Date: 2020-12-16 00:00:00
Tasks: parameter estimation, Sensitivity, Uncertainty Quantification, Vocal Bursts Intensity Prediction

Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification. AMICI is published under the permissive BSD-3-Clause license with source code publicly available on https://github.com/AMICI-dev/AMICI. Citeable releases are archived on Zenodo.