pwc-archive/papers-with-abstracts huggingface dataset:
Design and Analysis of a Novel Control Moment Gyroscope (CMG) For Small Satellite Applications
Authors: Mihindukulasooriya Sheral Crescent Tissera, Kay Soon Low, Ankit Srivastava, Abhishek Rai, Jun Hao Tan and Yan Rui Tan
Date: 2018-06-06 00:00:00

This paper presents a novel Single Gimbal Control Moment Gyroscope (CMG) to increase the agility of small satellites while consuming a low power. The first prototype design of the CMG has been fabricated and tested successfully. The key features of the CMG design include the use of a high precision gear head, absolute encoders, and integrated speed controllers. These features make the proposed design more attractive to small satellites by lowering its power consumption and offering better control of satellite attitude. Through simulations, it was shown that a cluster of four proposed CMGs provide five times more toque output when compared to a cluster of four reaction wheels. The experimental results have shown that the proposed CMG not only provides a higher torque but demands lower power consumption when it is compared to a reaction Wheel.
Analyzing Representations inside Convolutional Neural Networks
Authors: Uday Singh Saini, Evangelos E. Papalexakis
Date: 2020-12-23 00:00:00

How can we discover and succinctly summarize the concepts that a neural network has learned? Such a task is of great importance in applications of networks in areas of inference that involve classification, like medical diagnosis based on fMRI/x-ray etc. In this work, we propose a framework to categorize the concepts a network learns based on the way it clusters a set of input examples, clusters neurons based on the examples they activate for, and input features all in the same latent space. This framework is unsupervised and can work without any labels for input features, it only needs access to internal activations of the network for each input example, thereby making it widely applicable. We extensively evaluate the proposed method and demonstrate that it produces human-understandable and coherent concepts that a ResNet-18 has learned on the CIFAR-100 dataset.
Augmenting Policy Learning with Routines Discovered from a Single Demonstration
Authors: Zelin Zhao, Chuang Gan, Jiajun Wu, Xiaoxiao Guo, Joshua B. Tenenbaum
Date: 2020-12-23 00:00:00
Tasks: Atari Games, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Humans can abstract prior knowledge from very little data and use it to boost skill learning. In this paper, we propose routine-augmented policy learning (RAPL), which discovers routines composed of primitive actions from a single demonstration and uses discovered routines to augment policy learning. To discover routines from the demonstration, we first abstract routine candidates by identifying grammar over the demonstrated action trajectory. Then, the best routines measured by length and frequency are selected to form a routine library. We propose to learn policy simultaneously at primitive-level and routine-level with discovered routines, leveraging the temporal structure of routines. Our approach enables imitating expert behavior at multiple temporal scales for imitation learning and promotes reinforcement learning exploration. Extensive experiments on Atari games demonstrate that RAPL improves the state-of-the-art imitation learning method SQIL and reinforcement learning method A2C. Further, we show that discovered routines can generalize to unseen levels and difficulties on the CoinRun benchmark.
Investigating the Impact of Electric Vehicles on the Voltage Profile of Distribution Networks
Authors: Arash Farokhi Soofi, Reza Bayani, Saeed D. Manshadi
Date: 2020-12-23 00:00:00

This paper investigates the impact of high penetration of Electric Vehicles (EVs) on the distribution network in the presence of photovoltaic (PV) systems. Two models for EVs are presented and the voltage profile of buses is investigated considering both models and various penetration levels of EVs within the distribution network. The analysis is conducted by presenting an exact convex relaxed form of the full ACOPF problem of the distribution network with fixed power EVs and presenting the full ACOPF problem of the distribution network with fixed current EVs. The performance of each model is illustrated in the case studies leveraging the modified IEEE 33-bus system and considering time-of-use (TOU) pricing. Besides the sensitivity of voltage profile of buses in the distribution network on the time-of-use prices is investigated in the case studies.
Vehicle Re-identification Based on Dual Distance Center Loss
Authors: Zhijun Hu, Yong Xu, Jie Wen, Lilei Sun, Raja S P
Date: 2020-12-23 00:00:00
Tasks: Person Re-Identification, Vehicle Re-Identification

Recently, deep learning has been widely used in the field of vehicle re-identification. When training a deep model, softmax loss is usually used as a supervision tool. However, the softmax loss performs well for closed-set tasks, but not very well for open-set tasks. In this paper, we sum up five shortcomings of center loss and solved all of them by proposing a dual distance center loss (DDCL). Especially we solve the shortcoming that center loss must combine with the softmax loss to supervise training the model, which provides us with a new perspective to examine the center loss. In addition, we verify the inconsistency between the proposed DDCL and softmax loss in the feature space, which makes the center loss no longer be limited by the softmax loss in the feature space after removing the softmax loss. To be specifically, we add the Pearson distance on the basis of the Euclidean distance to the same center, which makes all features of the same class be confined to the intersection of a hypersphere and a hypercube in the feature space. The proposed Pearson distance strengthens the intra-class compactness of the center loss and enhances the generalization ability of center loss. Moreover, by designing a Euclidean distance threshold between all center pairs, which not only strengthens the inter-class separability of center loss, but also makes the center loss (or DDCL) works well without the combination of softmax loss. We apply DDCL in the field of vehicle re-identification named VeRi-776 dataset and VehicleID dataset. And in order to verify its good generalization ability, we also verify it in two datasets commonly used in the field of person re-identification named MSMT17 dataset and Market1501 dataset.
Localization in the Crowd with Topological Constraints
Authors: Shahira Abousamra, Minh Hoai, Dimitris Samaras, Chao Chen
Date: 2020-12-23 00:00:00
Tasks: Crowd Counting

We address the problem of crowd localization, i.e., the prediction of dots corresponding to people in a crowded scene. Due to various challenges, a localization method is prone to spatial semantic errors, i.e., predicting multiple dots within a same person or collapsing multiple dots in a cluttered region. We propose a topological approach targeting these semantic errors. We introduce a topological constraint that teaches the model to reason about the spatial arrangement of dots. To enforce this constraint, we define a persistence loss based on the theory of persistent homology. The loss compares the topographic landscape of the likelihood map and the topology of the ground truth. Topological reasoning improves the quality of the localization algorithm especially near cluttered regions. On multiple public benchmarks, our method outperforms previous localization methods. Additionally, we demonstrate the potential of our method in improving the performance in the crowd counting task.
QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors
Authors: Benjamin Maas, Erfan Zabeh, Soroush Arabshahi
Date: 2020-12-22 00:00:00
Tasks: Brain Tumor Segmentation, Diagnostic, Segmentation, Tumor Segmentation

Non-invasive techniques such as magnetic resonance imaging (MRI) are widely employed in brain tumor diagnostics. However, manual segmentation of brain tumors from 3D MRI volumes is a time-consuming task that requires trained expert radiologists. Due to the subjectivity of manual segmentation, there is low inter-rater reliability which can result in diagnostic discrepancies. As the success of many brain tumor treatments depends on early intervention, early detection is paramount. In this context, a fully automated segmentation method for brain tumor segmentation is necessary as an efficient and reliable method for brain tumor detection and quantification. In this study, we propose an end-to-end approach for brain tumor segmentation, capitalizing on a modified version of QuickNAT, a brain tissue type segmentation deep convolutional neural network (CNN). Our method was evaluated on a data set of 233 patient's T1 weighted images containing three tumor type classes annotated (meningioma, glioma, and pituitary). Our model, QuickTumorNet, demonstrated fast, reliable, and accurate brain tumor segmentation that can be utilized to assist clinicians in diagnosis and treatment.
How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?
Authors: Jingling Li, Mozhi Zhang, Keyulu Xu, John P. Dickerson, Jimmy Ba
Date: 2020-12-23 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Image Classification, Learning with noisy labels

Noisy labels are inevitable in large real-world datasets. In this work, we explore an area understudied by previous works -- how the network's architecture impacts its robustness to noisy labels. We provide a formal framework connecting the robustness of a network to the alignments between its architecture and target/noise functions. Our framework measures a network's robustness via the predictive power in its representations -- the test performance of a linear model trained on the learned representations using a small set of clean labels. We hypothesize that a network is more robust to noisy labels if its architecture is more aligned with the target function than the noise. To support our hypothesis, we provide both theoretical and empirical evidence across various neural network architectures and different domains. We also find that when the network is well-aligned with the target function, its predictive power in representations could improve upon state-of-the-art (SOTA) noisy-label-training methods in terms of test accuracy and even outperform sophisticated methods that use clean labels.
A Modern Analysis of Hutchinson's Trace Estimator
Authors: Maciej Skorski
Date: 2020-12-23 00:00:00

The paper establishes the new state-of-art in the accuracy analysis of Hutchinson's trace estimator. Leveraging tools that have not been previously used in this context, particularly hypercontractive inequalities and concentration properties of sub-gamma distributions, we offer an elegant and modular analysis, as well as numerically superior bounds. Besides these improvements, this work aims to better popularize the aforementioned techniques within the CS community.
ANR: Articulated Neural Rendering for Virtual Avatars
Authors: Amit Raj, Julian Tanke, James Hays, Minh Vo, Carsten Stoll, Christoph Lassner
Date: 2020-12-23 00:00:00
Proceeding: CVPR 2021 1
Tasks: Neural Rendering

The combination of traditional rendering with neural networks in Deferred Neural Rendering (DNR) provides a compelling balance between computational complexity and realism of the resulting images. Using skinned meshes for rendering articulating objects is a natural extension for the DNR framework and would open it up to a plethora of applications. However, in this case the neural shading step must account for deformations that are possibly not captured in the mesh, as well as alignment inaccuracies and dynamics -- which can confound the DNR pipeline. We present Articulated Neural Rendering (ANR), a novel framework based on DNR which explicitly addresses its limitations for virtual human avatars. We show the superiority of ANR not only with respect to DNR but also with methods specialized for avatar creation and animation. In two user studies, we observe a clear preference for our avatar model and we demonstrate state-of-the-art performance on quantitative evaluation metrics. Perceptually, we observe better temporal stability, level of detail and plausibility.
Vid2Actor: Free-viewpoint Animatable Person Synthesis from Video in the Wild
Authors: Chung-Yi Weng, Brian Curless, Ira Kemelmacher-Shlizerman
Date: 2020-12-23 00:00:00
Tasks: Image Generation, Image-to-Image Translation, Translation

Given an "in-the-wild" video of a person, we reconstruct an animatable model of the person in the video. The output model can be rendered in any body pose to any camera view, via the learned controls, without explicit 3D mesh reconstruction. At the core of our method is a volumetric 3D human representation reconstructed with a deep network trained on input video, enabling novel pose/view synthesis. Our method is an advance over GAN-based image-to-image translation since it allows image synthesis for any pose and camera via the internal 3D representation, while at the same time it does not require a pre-rigged model or ground truth meshes for training, as in mesh-based learning. Experiments validate the design choices and yield results on synthetic data and on real videos of diverse people performing unconstrained activities (e.g. dancing or playing tennis). Finally, we demonstrate motion re-targeting and bullet-time rendering with the learned models.
Exploring Instance-Level Uncertainty for Medical Detection
Authors: Jiawei Yang, Yuan Liang, Yao Zhang, Weinan Song, Kun Wang, Lei He
Date: 2020-12-23 00:00:00
Tasks: Lung Nodule Detection

The ability of deep learning to predict with uncertainty is recognized as key for its adoption in clinical routines. Moreover, performance gain has been enabled by modelling uncertainty according to empirical evidence. While previous work has widely discussed the uncertainty estimation in segmentation and classification tasks, its application on bounding-box-based detection has been limited, mainly due to the challenge of bounding box aligning. In this work, we explore to augment a 2.5D detection CNN with two different bounding-box-level (or instance-level) uncertainty estimates, i.e., predictive variance and Monte Carlo (MC) sample variance. Experiments are conducted for lung nodule detection on LUNA16 dataset, a task where significant semantic ambiguities can exist between nodules and non-nodules. Results show that our method improves the evaluating score from 84.57% to 88.86% by utilizing a combination of both types of variances. Moreover, we show the generated uncertainty enables superior operating points compared to using the probability threshold only, and can further boost the performance to 89.52%. Example nodule detections are visualized to further illustrate the advantages of our method.
Training data-efficient image transformers & distillation through attention
Authors: Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou
Date: 2020-12-23 00:00:00
Tasks: Document Image Classification, Document Layout Analysis, Efficient ViTs, Fine-Grained Image Classification, image-classification, Image Classification

Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained with hundreds of millions of images using an expensive infrastructure, thereby limiting their adoption. In this work, we produce a competitive convolution-free transformer by training on Imagenet only. We train them on a single computer in less than 3 days. Our reference vision transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evaluation) on ImageNet with no external data. More importantly, we introduce a teacher-student strategy specific to transformers. It relies on a distillation token ensuring that the student learns from the teacher through attention. We show the interest of this token-based distillation, especially when using a convnet as a teacher. This leads us to report results competitive with convnets for both Imagenet (where we obtain up to 85.2% accuracy) and when transferring to other tasks. We share our code and models.
A Multimodal Framework for the Detection of Hateful Memes
Authors: Phillip Lippe, Nithin Holla, Shantanu Chandra, Santhosh Rajamanickam, Georgios Antoniou, Ekaterina Shutova, Helen Yannakoudakis
Date: 2020-12-23 00:00:00
Tasks: Ensemble Learning, Multimodal Reasoning

An increasingly common expression of online hate speech is multimodal in nature and comes in the form of memes. Designing systems to automatically detect hateful content is of paramount importance if we are to mitigate its undesirable effects on the society at large. The detection of multimodal hate speech is an intrinsically difficult and open problem: memes convey a message using both images and text and, hence, require multimodal reasoning and joint visual and language understanding. In this work, we seek to advance this line of research and develop a multimodal framework for the detection of hateful memes. We improve the performance of existing multimodal approaches beyond simple fine-tuning and, among others, show the effectiveness of upsampling of contrastive examples to encourage multimodality and ensemble learning based on cross-validation to improve robustness. We furthermore analyze model misclassifications and discuss a number of hypothesis-driven augmentations and their effects on performance, presenting important implications for future research in the field. Our best approach comprises an ensemble of UNITER-based models and achieves an AUROC score of 80.53, placing us 4th on phase 2 of the 2020 Hateful Memes Challenge organized by Facebook.
Deep manifold learning reveals hidden dynamics of proteasome autoregulation
Authors: Zhaolong Wu, Shuwen Zhang, Wei Li Wang, Yinping Ma, Yuanchen Dong, Youdong Mao
Date: 2020-12-23 00:00:00
Tasks: 3D Classification, Clustering, Cryogenic Electron Microscopy (cryo-EM)

The 2.5-MDa 26S proteasome maintains proteostasis and regulates myriad cellular processes. How polyubiquitylated substrate interactions regulate proteasome activity is not understood. Here we introduce a deep manifold learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy (cryo-EM) reconstructions of nonequilibrium conformational continuum and reconstitutes hidden dynamics of proteasome autoregulation in the act of substrate degradation. AlphaCryo4D integrates 3D deep residual learning with manifold embedding of free-energy landscapes, which directs 3D clustering via an energy-based particle-voting algorithm. In blind assessments using simulated heterogeneous cryo-EM datasets, AlphaCryo4D achieved 3D classification accuracy three times that of conventional method and reconstructed continuous conformational changes of a 130-kDa protein at sub-3-angstrom resolution. By using AlphaCryo4D to analyze a single experimental cryo-EM dataset, we identified 64 conformers of the substrate-bound human 26S proteasome, revealing conformational entanglement of two regulatory particles in the doubly capped holoenzymes and their energetic differences with singly capped ones. Novel ubiquitin-binding sites are discovered on the RPN2, RPN10 and Alpha5 subunits to remodel polyubiquitin chains for deubiquitylation and recycle. Importantly, AlphaCryo4D choreographs single-nucleotide-exchange dynamics of proteasomal AAA-ATPase motor during translocation initiation, which upregulates proteolytic activity by allosterically promoting nucleophilic attack. Our systemic analysis illuminates a grand hierarchical allostery for proteasome autoregulation.
EQ-Net: A Unified Deep Learning Framework for Log-Likelihood Ratio Estimation and Quantization
Authors: Marius Arvinte, Ahmed H. Tewfik, Sriram Vishwanath
Date: 2020-12-23 00:00:00
Tasks: GPU, Quantization

In this work, we introduce EQ-Net: the first holistic framework that solves both the tasks of log-likelihood ratio (LLR) estimation and quantization using a data-driven method. We motivate our approach with theoretical insights on two practical estimation algorithms at the ends of the complexity spectrum and reveal a connection between the complexity of an algorithm and the information bottleneck method: simpler algorithms admit smaller bottlenecks when representing their solution. This motivates us to propose a two-stage algorithm that uses LLR compression as a pretext task for estimation and is focused on low-latency, high-performance implementations via deep neural networks. We carry out extensive experimental evaluation and demonstrate that our single architecture achieves state-of-the-art results on both tasks when compared to previous methods, with gains in quantization efficiency as high as $20\%$ and reduced estimation latency by up to $60\%$ when measured on general purpose and graphical processing units (GPU). In particular, our approach reduces the GPU inference latency by more than two times in several multiple-input multiple-output (MIMO) configurations. Finally, we demonstrate that our scheme is robust to distributional shifts and retains a significant part of its performance when evaluated on 5G channel models, as well as channel estimation errors.
Focal Frequency Loss for Image Reconstruction and Synthesis
Authors: Liming Jiang, Bo Dai, Wayne Wu, Chen Change Loy
Date: 2020-12-23 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Generation, Image Reconstruction, Image-to-Image Translation

Image reconstruction and synthesis have witnessed remarkable progress thanks to the development of generative models. Nonetheless, gaps could still exist between the real and generated images, especially in the frequency domain. In this study, we show that narrowing gaps in the frequency domain can ameliorate image reconstruction and synthesis quality further. We propose a novel focal frequency loss, which allows a model to adaptively focus on frequency components that are hard to synthesize by down-weighting the easy ones. This objective function is complementary to existing spatial losses, offering great impedance against the loss of important frequency information due to the inherent bias of neural networks. We demonstrate the versatility and effectiveness of focal frequency loss to improve popular models, such as VAE, pix2pix, and SPADE, in both perceptual quality and quantitative performance. We further show its potential on StyleGAN2.