pwc-archive/papers-with-abstracts huggingface dataset:
Exploiting complex pattern features for interactive pattern mining
Authors: Arnold Hien, Samir Loudni, Noureddine Aribi, Abdelkader Ouali, Albrecht Zimmermann
Date: 2022-04-08 00:00:00

Recent years have seen a shift from a pattern mining process that has users define constraints before-hand, and sift through the results afterwards, to an interactive one. This new framework depends on exploiting user feedback to learn a quality function for patterns. Existing approaches have a weakness in that they use static pre-defined low-level features, and attempt to learn independent weights representing their importance to the user. As an alternative, we propose to work with more complex features that are derived directly from the pattern ranking imposed by the user. Learned weights are then aggregated onto lower-level features and help to drive the quality function in the right direction. We explore the effect of different parameter choices experimentally and find that using higher-complexity features leads to the selection of patterns that are better aligned with a hidden quality function while not adding significantly to the run times of the method. Getting good user feedback requires to quickly present diverse patterns, something that we achieve but pushing an existing diversity constraint into the sampling component of the interactive mining system LetSip. Resulting patterns allow in most cases to converge to a good solution more quickly. Combining the two improvements, finally, leads to an algorithm showing clear advantages over the existing state-of-the-art.
Semantic Exploration from Language Abstractions and Pretrained Representations
Authors: Allison C. Tam, Neil C. Rabinowitz, Andrew K. Lampinen, Nicholas A. Roy, Stephanie C. Y. Chan, DJ Strouse, Jane X. Wang, Andrea Banino, Felix Hill
Date: 2022-04-08 00:00:00
Tasks: Image Captioning, Reinforcement Learning (RL)

Effective exploration is a challenge in reinforcement learning (RL). Novelty-based exploration methods can suffer in high-dimensional state spaces, such as continuous partially-observable 3D environments. We address this challenge by defining novelty using semantically meaningful state abstractions, which can be found in learned representations shaped by natural language. In particular, we evaluate vision-language representations, pretrained on natural image captioning datasets. We show that these pretrained representations drive meaningful, task-relevant exploration and improve performance on 3D simulated environments. We also characterize why and how language provides useful abstractions for exploration by considering the impacts of using representations from a pretrained model, a language oracle, and several ablations. We demonstrate the benefits of our approach in two very different task domains -- one that stresses the identification and manipulation of everyday objects, and one that requires navigational exploration in an expansive world. Our results suggest that using language-shaped representations could improve exploration for various algorithms and agents in challenging environments.
The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system
Authors: A. M. Vorontsov, G. A. Filimonov
Date: 2022-04-08 00:00:00
Tasks: Self-Learning

In this study we consider adaptive power beaming with fiber-array laser transmitter system in presence of atmospheric turbulence. For optimization of power transition through the atmosphere fiber-array is traditionally controlled by stochastic parallel gradient descent (SPGD) algorithm where control feedback is provided via radio frequency link by an optical-to-electrical power conversion sensor, attached to a cooperative target. The SPGD algorithm continuously and randomly perturbs voltages applied to fiber-array phase shifters and fiber tip positioners in order to maximize sensor signal, i.e. uses, so-called, "blind" optimization principle. In opposite to this approach a perspective artificially intelligent (AI) control systems for synthesis of optimal control can utilize various pupil- or target-plane data available for the analysis including wavefront sensor data, photo-voltaic array (PVA) data, other optical or atmospheric parameters, and potentially can eliminate well-known drawbacks of SPGD-based controllers. In this study an optimal control is synthesized by a deep neural network (DNN) using target-plane PVA sensor data as its input. A DNN training is occurred online in sync with control system operation and is performed by applying of small perturbations to DNN's outputs. This approach does not require initial DNN's pre-training as well as guarantees optimization of system performance in time. All theoretical results are verified by numerical experiments.
ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models
Authors: Francesco Martinuzzi, Chris Rackauckas, Anas Abdelrehim, Miguel D. Mahecha, Karin Mora
Date: 2022-04-08 00:00:00

We introduce ReservoirComputing.jl, an open source Julia library for reservoir computing models. The software offers a great number of algorithms presented in the literature, and allows to expand on them with both internal and external tools in a simple way. The implementation is highly modular, fast and comes with a comprehensive documentation, which includes reproduced experiments from literature. The code and documentation are hosted on Github under an MIT license https://github.com/SciML/ReservoirComputing.jl.
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment
Authors: Qiang Hu, Yuejun Guo, Maxime Cordy, Xiaofei Xie, Wei Ma, Mike Papadakis, Yves Le Traon
Date: 2022-04-08 00:00:00
Tasks: Image to text, Language Modeling, Language Modelling, Model Compression, Quantization

Deep Neural Networks (DNNs) have gained considerable attention in the past decades due to their astounding performance in different applications, such as natural language modeling, self-driving assistance, and source code understanding. With rapid exploration, more and more complex DNN architectures have been proposed along with huge pre-trained model parameters. The common way to use such DNN models in user-friendly devices (e.g., mobile phones) is to perform model compression before deployment. However, recent research has demonstrated that model compression, e.g., model quantization, yields accuracy degradation as well as outputs disagreements when tested on unseen data. Since the unseen data always include distribution shifts and often appear in the wild, the quality and reliability of quantized models are not ensured. In this paper, we conduct a comprehensive study to characterize and help users understand the behaviors of quantized models. Our study considers 4 datasets spanning from image to text, 8 DNN architectures including feed-forward neural networks and recurrent neural networks, and 42 shifted sets with both synthetic and natural distribution shifts. The results reveal that 1) data with distribution shifts happen more disagreements than without. 2) Quantization-aware training can produce more stable models than standard, adversarial, and Mixup training. 3) Disagreements often have closer top-1 and top-2 output probabilities, and $Margin$ is a better indicator than the other uncertainty metrics to distinguish disagreements. 4) Retraining with disagreements has limited efficiency in removing disagreements. We opensource our code and models as a new benchmark for further studying the quantized models.
Feature-enhanced Adversarial Semi-supervised Semantic Segmentation Network for Pulmonary Embolism Annotation
Authors: Ting-Wei Cheng, Jerry Chang, Ching-Chun Huang, Chin Kuo, Yun-Chien Cheng
Date: 2022-04-08 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation, Sensitivity

This study established a feature-enhanced adversarial semi-supervised semantic segmentation model to automatically annotate pulmonary embolism lesion areas in computed tomography pulmonary angiogram (CTPA) images. In current studies, all of the PE CTPA image segmentation methods are trained by supervised learning. However, the supervised learning models need to be retrained and the images need to be relabeled when the CTPA images come from different hospitals. This study proposed a semi-supervised learning method to make the model applicable to different datasets by adding a small amount of unlabeled images. By training the model with both labeled and unlabeled images, the accuracy of unlabeled images can be improved and the labeling cost can be reduced. Our semi-supervised segmentation model includes a segmentation network and a discriminator network. We added feature information generated from the encoder of segmentation network to the discriminator so that it can learn the similarity between predicted mask and ground truth mask. This HRNet-based architecture can maintain a higher resolution for convolutional operations so the prediction of small PE lesion areas can be improved. We used the labeled open-source dataset and the unlabeled National Cheng Kung University Hospital (NCKUH) (IRB number: B-ER-108-380) dataset to train the semi-supervised learning model, and the resulting mean intersection over union (mIOU), dice score, and sensitivity achieved 0.3510, 0.4854, and 0.4253, respectively on the NCKUH dataset. Then, we fine-tuned and tested the model with a small amount of unlabeled PE CTPA images from China Medical University Hospital (CMUH) (IRB number: CMUH110-REC3-173) dataset. Comparing the results of our semi-supervised model with the supervised model, the mIOU, dice score, and sensitivity improved from 0.2344, 0.3325, and 0.3151 to 0.3721, 0.5113, and 0.4967, respectively.
Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization
Authors: Yefei He, Luoming Zhang, Weijia Wu, Hong Zhou
Date: 2022-04-08 00:00:00
Tasks: Data Free Quantization, Quantization

Data-free quantization is a task that compresses the neural network to low bit-width without access to original training data. Most existing data-free quantization methods cause severe performance degradation due to inaccurate activation clipping range and quantization error, especially for low bit-width. In this paper, we present a simple yet effective data-free quantization method with accurate activation clipping and adaptive batch normalization. Accurate activation clipping (AAC) improves the model accuracy by exploiting accurate activation information from the full-precision model. Adaptive batch normalization firstly proposes to address the quantization error from distribution changes by updating the batch normalization layer adaptively. Extensive experiments demonstrate that the proposed data-free quantization method can yield surprisingly performance, achieving 64.33% top-1 accuracy of ResNet18 on ImageNet dataset, with 3.7% absolute improvement outperforming the existing state-of-the-art methods.
Structure-aware Protein Self-supervised Learning
Authors: Can Chen, Jingbo Zhou, Fan Wang, Xue Liu, Dejing Dou
Date: 2022-04-06 00:00:00
Tasks: Graph Neural Network, Language Modeling, Language Modelling, Protein Language Model, Representation Learning, Self-Supervised Learning

Protein representation learning methods have shown great potential to yield useful representation for many downstream tasks, especially on protein classification. Moreover, a few recent studies have shown great promise in addressing insufficient labels of proteins with self-supervised learning methods. However, existing protein language models are usually pretrained on protein sequences without considering the important protein structural information. To this end, we propose a novel structure-aware protein self-supervised learning method to effectively capture structural information of proteins. In particular, a well-designed graph neural network (GNN) model is pretrained to preserve the protein structural information with self-supervised tasks from a pairwise residue distance perspective and a dihedral angle perspective, respectively. Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning. Specifically, we identify the relation between the sequential information in the protein language model and the structural information in the specially designed GNN model via a novel pseudo bi-level optimization scheme. Experiments on several supervised downstream tasks verify the effectiveness of our proposed method.The code of the proposed method is available in \url{https://github.com/GGchen1997/STEPS_Bioinformatics}.
Comparative Survey of Multigraph Integration Methods for Holistic Brain Connectivity Mapping
Authors: Nada Chaari, Hatice Camgoz Akdag, Islem Rekik
Date: 2022-04-05 00:00:00

One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: centeredness, biomarker-reproducibility, node-level similarity, global-level similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels.
ESCM$^2$: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation
Authors: Hao Wang, Tai-Wei Chang, Tianqiao Liu, Jianmin Huang, Zhichao Chen, Chao Yu, Ruopeng Li, Wei Chu
Date: 2022-04-03 00:00:00
Tasks: counterfactual, Recommendation Systems, Selection bias

Accurate estimation of post-click conversion rate is critical for building recommender systems, which has long been confronted with sample selection bias and data sparsity issues. Methods in the Entire Space Multi-task Model (ESMM) family leverage the sequential pattern of user actions, i.e. $impression\rightarrow click \rightarrow conversion$ to address data sparsity issue. However, they still fail to ensure the unbiasedness of CVR estimates. In this paper, we theoretically demonstrate that ESMM suffers from the following two problems: (1) Inherent Estimation Bias (IEB), where the estimated CVR of ESMM is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where there is a risk that the ESMM overlooks the causality from click to conversion. To this end, we devise a principled approach named Entire Space Counterfactual Multi-task Modelling (ESCM$^2$), which employs a counterfactual risk miminizer as a regularizer in ESMM to address both IEB and PIP issues simultaneously. Extensive experiments on offline datasets and online environments demonstrate that our proposed ESCM$^2$ can largely mitigate the inherent IEB and PIP issues and achieve better performance than baseline models.
On the Generalization of BasicVSR++ to Video Deblurring and Denoising
Authors: Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy
Date: 2022-04-11 00:00:00
Tasks: Deblurring, Denoising, Super-Resolution, Video Deblurring, Video Restoration, Video Super-Resolution

The exploitation of long-term information has been a long-standing problem in video restoration. The recent BasicVSR and BasicVSR++ have shown remarkable performance in video super-resolution through long-term propagation and effective alignment. Their success has led to a question of whether they can be transferred to different video restoration tasks. In this work, we extend BasicVSR++ to a generic framework for video restoration tasks. In tasks where inputs and outputs possess identical spatial size, the input resolution is reduced by strided convolutions to maintain efficiency. With only minimal changes from BasicVSR++, the proposed framework achieves compelling performance with great efficiency in various video restoration tasks including video deblurring and denoising. Notably, BasicVSR++ achieves comparable performance to Transformer-based approaches with up to 79% of parameter reduction and 44x speedup. The promising results demonstrate the importance of propagation and alignment in video restoration tasks beyond just video super-resolution. Code and models are available at https://github.com/ckkelvinchan/BasicVSR_PlusPlus.
Single-Photon Structured Light
Authors: Varun Sundar, Sizhuo Ma, Aswin C. Sankaranarayanan, Mohit Gupta
Date: 2022-04-11 00:00:00
Proceeding: CVPR 2022 1
Tasks: Temporal Sequences

We present a novel structured light technique that uses Single Photon Avalanche Diode (SPAD) arrays to enable 3D scanning at high-frame rates and low-light levels. This technique, called "Single-Photon Structured Light", works by sensing binary images that indicates the presence or absence of photon arrivals during each exposure; the SPAD array is used in conjunction with a high-speed binary projector, with both devices operated at speeds as high as 20~kHz. The binary images that we acquire are heavily influenced by photon noise and are easily corrupted by ambient sources of light. To address this, we develop novel temporal sequences using error correction codes that are designed to be robust to short-range effects like projector and camera defocus as well as resolution mismatch between the two devices. Our lab prototype is capable of 3D imaging in challenging scenarios involving objects with extremely low albedo or undergoing fast motion, as well as scenes under strong ambient illumination.
Towards Online Domain Adaptive Object Detection
Authors: Vibashan VS, Poojan Oza, Vishal M. Patel
Date: 2022-04-11 00:00:00
Tasks: Domain Adaptation, Object, object-detection, Object Detection, Representation Learning, Unsupervised Domain Adaptation

Existing object detection models assume both the training and test data are sampled from the same source domain. This assumption does not hold true when these detectors are deployed in real-world applications, where they encounter new visual domain. Unsupervised Domain Adaptation (UDA) methods are generally employed to mitigate the adverse effects caused by domain shift. Existing UDA methods operate in an offline manner where the model is first adapted towards the target domain and then deployed in real-world applications. However, this offline adaptation strategy is not suitable for real-world applications as the model frequently encounters new domain shifts. Hence, it becomes critical to develop a feasible UDA method that generalizes to these domain shifts encountered during deployment time in a continuous online manner. To this end, we propose a novel unified adaptation framework that adapts and improves generalization on the target domain in online settings. In particular, we introduce MemXformer - a cross-attention transformer-based memory module where items in the memory take advantage of domain shifts and record prototypical patterns of the target distribution. Further, MemXformer produces strong positive and negative pairs to guide a novel contrastive loss, which enhances target specific representation learning. Experiments on diverse detection benchmarks show that the proposed strategy can produce state-of-the-art performance in both online and offline settings. To the best of our knowledge, this is the first work to address online and offline adaptation settings for object detection. Code at https://github.com/Vibashan/memXformer-online-da
Physically Disentangled Representations
Authors: Tzofi Klinghoffer, Kushagra Tiwary, Arkadiusz Balata, Vivek Sharma, Ramesh Raskar
Date: 2022-04-11 00:00:00
Tasks: Attribute, Classification, Disentanglement, Emotion Recognition, Inverse Rendering, Representation Learning

State-of-the-art methods in generative representation learning yield semantic disentanglement, but typically do not consider physical scene parameters, such as geometry, albedo, lighting, or camera. We posit that inverse rendering, a way to reverse the rendering process to recover scene parameters from an image, can also be used to learn physically disentangled representations of scenes without supervision. In this paper, we show the utility of inverse rendering in learning representations that yield improved accuracy on downstream clustering, linear classification, and segmentation tasks with the help of our novel Leave-One-Out, Cycle Contrastive loss (LOOCC), which improves disentanglement of scene parameters and robustness to out-of-distribution lighting and viewpoints. We perform a comparison of our method with other generative representation learning methods across a variety of downstream tasks, including face attribute classification, emotion recognition, identification, face segmentation, and car classification. Our physically disentangled representations yield higher accuracy than semantically disentangled alternatives across all tasks and by as much as 18%. We hope that this work will motivate future research in applying advances in inverse rendering and 3D understanding to representation learning.
MONCE Tracking Metrics: a comprehensive quantitative performance evaluation methodology for object tracking
Authors: Kenneth Rapko, Wanlin Xie, Andrew Walsh
Date: 2022-04-11 00:00:00
Tasks: Diagnostic, Object, Object Tracking

Evaluating tracking model performance is a complicated task, particularly for non-contiguous, multi-object trackers that are crucial in defense applications. While there are various excellent tracking benchmarks available, this work expands them to quantify the performance of long-term, non-contiguous, multi-object and detection model assisted trackers. We propose a suite of MONCE (Multi-Object Non-Contiguous Entities) image tracking metrics that provide both objective tracking model performance benchmarks as well as diagnostic insight for driving tracking model development in the form of Expected Average Overlap, Short/Long Term Re-Identification, Tracking Recall, Tracking Precision, Longevity, Localization and Absence Prediction.