pwc-archive/papers-with-abstracts huggingface dataset:
Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding
Authors: Jingya Zhou, Ling Liu, Wenqi Wei, Jianxi Fan
Date: 2021-10-14 00:00:00
Tasks: Graph Mining, Knowledge Graphs, Link Prediction, Node Clustering, Representation Learning, Survey

Network representation learning (NRL) advances the conventional graph mining of social networks, knowledge graphs, and complex biomedical and physics information networks. Over dozens of network representation learning algorithms have been reported in the literature. Most of them focus on learning node embeddings for homogeneous networks, but they differ in the specific encoding schemes and specific types of node semantics captured and used for learning node embedding. This survey paper reviews the design principles and the different node embedding techniques for network representation learning over homogeneous networks. To facilitate the comparison of different node embedding algorithms, we introduce a unified reference framework to divide and generalize the node embedding learning process on a given network into preprocessing steps, node feature extraction steps and node embedding model training for a NRL task such as link prediction and node clustering. With this unifying reference framework, we highlight the representative methods, models, and techniques used at different stages of the node embedding model learning process. This survey not only helps researchers and practitioners to gain an in-depth understanding of different network representation learning techniques but also provides practical guidelines for designing and developing the next generation of network representation learning algorithms and systems.
SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures
Authors: Megan Ung, Jing Xu, Y-Lan Boureau
Date: 2021-10-14 00:00:00
Proceeding: ACL 2022 5

Current open-domain conversational models can easily be made to talk in inadequate ways. Online learning from conversational feedback given by the conversation partner is a promising avenue for a model to improve and adapt, so as to generate fewer of these safety failures. However, current state-of-the-art models tend to react to feedback with defensive or oblivious responses. This makes for an unpleasant experience and may discourage conversation partners from giving feedback in the future. This work proposes SaFeRDialogues, a task and dataset of graceful responses to conversational feedback about safety failures. We collect a dataset of 10k dialogues demonstrating safety failures, feedback signaling them, and a response acknowledging the feedback. We show how fine-tuning on this dataset results in conversations that human raters deem considerably more likely to lead to a civil conversation, without sacrificing engagingness or general conversational ability.
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding
Authors: Sijia Wang, Mo Yu, Shiyu Chang, Lichao Sun, Lifu Huang
Date: 2021-10-14 00:00:00
Proceeding: Findings (ACL) 2022 5
Tasks: Event Extraction, Multi-class Classification, Natural Language Queries, Vocal Bursts Type Prediction, Zero-shot Event Extraction

Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbols. These approaches are usually limited to a set of pre-defined types. We propose a novel event extraction framework that uses event types and argument roles as natural language queries to extract candidate triggers and arguments from the input text. With the rich semantics in the queries, our framework benefits from the attention mechanisms to better capture the semantic correlation between the event types or argument roles and the input text. Furthermore, the query-and-extract formulation allows our approach to leverage all available event annotations from various ontologies as a unified model. Experiments on ACE and ERE demonstrate that our approach achieves state-of-the-art performance on each dataset and significantly outperforms existing methods on zero-shot event extraction.
Domain Adaptation on Semantic Segmentation with Separate Affine Transformation in Batch Normalization
Authors: Junhao Yan, Woonsok Lee
Date: 2021-10-14 00:00:00
Tasks: Domain Adaptation, Semantic Segmentation, Unsupervised Domain Adaptation

In recent years, unsupervised domain adaptation (UDA) for semantic segmentation has brought many researchers'attention. Many of them take an approach to design a complex system so as to better align the gap between source and target domain. Instead, we focus on the very basic structure of the deep neural network, Batch Normalization, and propose to replace the Sharing Affine Transformation with our proposed Separate Affine Transformation (SEAT). The proposed SEAT is simple, easily implemented and easy to integrate into existing adversarial learning based UDA methods. Also, to further improve the adaptation quality, we introduce multi level adaptation by adding the lower-level features to the higher-level ones before feeding them to the discriminator, without adding extra discriminator like others. Experiments show that the proposed methods is less complex without losing performance accuracy when compared with other UDA methods.
Plug-Tagger: A Pluggable Sequence Labeling Framework Using Language Models
Authors: Xin Zhou, Ruotian Ma, Tao Gui, Yiding Tan, Qi Zhang, Xuanjing Huang
Date: 2021-10-14 00:00:00
Tasks: Language Modelling, Text Generation

Plug-and-play functionality allows deep learning models to adapt well to different tasks without requiring any parameters modified. Recently, prefix-tuning was shown to be a plug-and-play method on various text generation tasks by simply inserting corresponding continuous vectors into the inputs. However, sequence labeling tasks invalidate existing plug-and-play methods since different label sets demand changes to the architecture of the model classifier. In this work, we propose the use of label word prediction instead of classification to totally reuse the architecture of pre-trained models for sequence labeling tasks. Specifically, for each task, a label word set is first constructed by selecting a high-frequency word for each class respectively, and then, task-specific vectors are inserted into the inputs and optimized to manipulate the model predictions towards the corresponding label words. As a result, by simply switching the plugin vectors on the input, a frozen pre-trained language model is allowed to perform different tasks. Experimental results on three sequence labeling tasks show that the performance of the proposed method can achieve comparable performance with standard fine-tuning with only 0.1\% task-specific parameters. In addition, our method is up to 70 times faster than non-plug-and-play methods while switching different tasks under the resource-constrained scenario.
DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-Reflectors
Authors: Anargyros Chatzitofis, Dimitrios Zarpalas, Stefanos Kollias, Petros Daras
Date: 2021-10-14 00:00:00
Tasks: Optical Flow Estimation

In this paper, a marker-based, single-person optical motion capture method (DeepMoCap) is proposed using multiple spatio-temporally aligned infrared-depth sensors and retro-reflective straps and patches (reflectors). DeepMoCap explores motion capture by automatically localizing and labeling reflectors on depth images and, subsequently, on 3D space. Introducing a non-parametric representation to encode the temporal correlation among pairs of colorized depthmaps and 3D optical flow frames, a multi-stage Fully Convolutional Network (FCN) architecture is proposed to jointly learn reflector locations and their temporal dependency among sequential frames. The extracted reflector 2D locations are spatially mapped in 3D space, resulting in robust 3D optical data extraction. The subject's motion is efficiently captured by applying a template-based fitting technique on the extracted optical data. Two datasets have been created and made publicly available for evaluation purposes; one comprising multi-view depth and 3D optical flow annotated images (DMC2.5D), and a second, consisting of spatio-temporally aligned multi-view depth images along with skeleton, inertial and ground truth MoCap data (DMC3D). The FCN model outperforms its competitors on the DMC2.5D dataset using 2D Percentage of Correct Keypoints (PCK) metric, while the motion capture outcome is evaluated against RGB-D and inertial data fusion approaches on DMC3D, outperforming the next best method by 4.5% in total 3D PCK accuracy.
Carousel Memory: Rethinking the Design of Episodic Memory for Continual Learning
Authors: Soobee Lee, Minindu Weerakoon, Jonghyun Choi, Minjia Zhang, Di Wang, Myeongjae Jeon
Date: 2021-10-14 00:00:00
Tasks: Continual Learning, Management

Continual Learning (CL) is an emerging machine learning paradigm that aims to learn from a continuous stream of tasks without forgetting knowledge learned from the previous tasks. To avoid performance decrease caused by forgetting, prior studies exploit episodic memory (EM), which stores a subset of the past observed samples while learning from new non-i.i.d. data. Despite the promising results, since CL is often assumed to execute on mobile or IoT devices, the EM size is bounded by the small hardware memory capacity and makes it infeasible to meet the accuracy requirements for real-world applications. Specifically, all prior CL methods discard samples overflowed from the EM and can never retrieve them back for subsequent training steps, incurring loss of information that would exacerbate catastrophic forgetting. We explore a novel hierarchical EM management strategy to address the forgetting issue. In particular, in mobile and IoT devices, real-time data can be stored not just in high-speed RAMs but in internal storage devices as well, which offer significantly larger capacity than the RAMs. Based on this insight, we propose to exploit the abundant storage to preserve past experiences and alleviate the forgetting by allowing CL to efficiently migrate samples between memory and storage without being interfered by the slow access speed of the storage. We call it Carousel Memory (CarM). As CarM is complementary to existing CL methods, we conduct extensive evaluations of our method with seven popular CL methods and show that CarM significantly improves the accuracy of the methods across different settings by large margins in final average accuracy (up to 28.4%) while retaining the same training efficiency.
Order Constraints in Optimal Transport
Authors: Fabian Lim, Laura Wynter, Shiau Hong Lim
Date: 2021-10-14 00:00:00
Tasks: Natural Language Inference

Optimal transport is a framework for comparing measures whereby a cost is incurred for transporting one measure to another. Recent works have aimed to improve optimal transport plans through the introduction of various forms of structure. We introduce novel order constraints into the optimal transport formulation to allow for the incorporation of structure. We define an efficient method for obtaining explainable solutions to the new formulation that scales far better than standard approaches. The theoretical properties of the method are provided. We demonstrate experimentally that order constraints improve explainability using the e-SNLI (Stanford Natural Language Inference) dataset that includes human-annotated rationales as well as on several image color transfer examples.
Building Chinese Biomedical Language Models via Multi-Level Text Discrimination
Authors: Quan Wang, Songtai Dai, Benfeng Xu, Yajuan Lyu, Yong Zhu, Hua Wu, Haifeng Wang
Date: 2021-10-14 00:00:00
Tasks: Domain Adaptation

Pre-trained language models (PLMs), such as BERT and GPT, have revolutionized the field of NLP, not only in the general domain but also in the biomedical domain. Most prior efforts in building biomedical PLMs have resorted simply to domain adaptation and focused mainly on English. In this work we introduce eHealth, a Chinese biomedical PLM built from scratch with a new pre-training framework. This new framework pre-trains eHealth as a discriminator through both token- and sequence-level discrimination. The former is to detect input tokens corrupted by a generator and recover their original identities from plausible candidates, while the latter is to further distinguish corruptions of a same original sequence from those of others. As such, eHealth can learn language semantics at both token and sequence levels. Extensive experiments on 11 Chinese biomedical language understanding tasks of various forms verify the effectiveness and superiority of our approach. We release the pre-trained model at \url{https://github.com/PaddlePaddle/Research/tree/master/KG/eHealth} and will also release the code later.
HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media
Authors: Anargyros Chatzitofis, Leonidas Saroglou, Prodromos Boutis, Petros Drakoulis, Nikolaos Zioulis, Shishir Subramanyam, Bart Kevelham, Caecilia Charbonnier, Pablo Cesar, Dimitrios Zarpalas, Stefanos Kollias, Petros Daras
Date: 2021-10-14 00:00:00
Tasks: 3D Pose Estimation, Benchmarking, Pose Estimation

We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and $2$ male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets captured with the use of hardware (HW) synchronization, to the best of our knowledge, HUMAN4D is the first and only public resource that provides volumetric depth maps with high synchronization precision due to the use of intra- and inter-sensor HW-SYNC. Moreover, a spatio-temporally aligned scanned and rigged 3D character complements HUMAN4D to enable joint research on time-varying and high-quality dynamic meshes. We provide evaluation baselines by benchmarking HUMAN4D with state-of-the-art human pose estimation and 3D compression methods. For the former, we apply 2D and 3D pose estimation algorithms both on single- and multi-view data cues. For the latter, we benchmark open-source 3D codecs on volumetric data respecting online volumetric video encoding and steady bit-rates. Furthermore, qualitative and quantitative visual comparison between mesh-based volumetric data reconstructed in different qualities showcases the available options with respect to 4D representations. HUMAN4D is introduced to the computer vision and graphics research communities to enable joint research on spatio-temporally aligned pose, volumetric, mRGBD and audio data cues. The dataset and its code are available https://tofis.github.io/myurls/human4d.
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints
Authors: Wenjie Xu, Colin N Jones, Bratislav Svetozarevic, Christopher R. Laughman, Ankush Chakrabarty
Date: 2021-10-14 00:00:00
Tasks: Bayesian Optimization

We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated effective for improving closed-loop performance by automatically tuning controller gains or reference setpoints in a model-free manner. However, BO methods have rarely been tested on dynamical systems with unmodeled constraints. In this paper, we propose a violation-aware BO algorithm (VABO) that optimizes closed-loop performance while simultaneously learning constraint-feasible solutions. Unlike classical constrained BO methods which allow an unlimited constraint violations, or safe BO algorithms that are conservative and try to operate with near-zero violations, we allow budgeted constraint violations to improve constraint learning and accelerate optimization. We demonstrate the effectiveness of our proposed VABO method for energy minimization of industrial vapor compression systems.
A Data-driven Probabilistic-based Flexibility Region Estimation Method for Aggregated Distributed Energy Resources
Authors: Mingzhi Zhang, Xiangqi Zhu, Ning Lu
Date: 2021-10-14 00:00:00

This paper presents a data-driven, distributionally robust chance-constrained optimization method for estimating the real and reactive power controllability of aggregated distributed energy resources (DER). At the DER-level, a two-dimensional flexibility region can be formed based on the real and reactive power regulating limits of each DER considering forecast uncertainty. At the feeder-level, an aggregated flexibility region is computed via a multi-directional search method. In each search direction, extend the real and reactive power of each controllable DER towards its operational limits until: i) all DERs' maximum operational limits are reached or, ii) one or more of the distribution network operational limits are violated. The method enables three key features for operating aggregated DER resources: controllability estimation, visualization, and risk quantification. Simulation results demonstrated the effectiveness of the algorithm and quantified the impact of different parameter settings on the flexibility region boundary changes. The proposed algorithm is robust and computationally efficient and can meet real-time computing needs.
Soft robotic shell with active thermal display
Authors: Yukiko Osawa, Yuho Kinbara, Masakazu Kageoka, Kenji Iida, Abderrahmane Kheddar
Date: 2021-10-14 00:00:00

Almost all robotic systems in use have hard shells, which is limiting in many ways their full potential of physical interaction with humans or their surrounding environment. Robots with soft-shell covers offer an alternative morphology which is more pleasant in both appearance and for haptic human interaction. A persisting challenge in such soft-shell robotic covers is the simultaneous realization of softness and heat-conducting properties. Such heat-conducting properties are important for enabling temperature-control of robotic covers in the range that is comfortable for human touch. The presented soft-shell robotic cover is composed of a linked two-layer structure: (i) the inner layer, with built-in pipes for water circulation, is soft and acts as a thermal-isolation layer between the cover and the robot structure, whereas (ii) the outer layer, which can be patterned with a given desired texture and color, allows heat transfer from the circulating water of the inner part to the surface. Moreover, we demonstrate the ability to integrate our prototype cover with a humanoid robot equipped with capacitance sensors. This fabrication technique enables robotic cover possibilities, including tunable color, surface texture, and size, that are likely to have applications in a variety of robotic systems.
A Dual-Attention Neural Network for Pun Location and Using Pun-Gloss Pairs for Interpretation
Authors: Shen Liu, Meirong Ma, Hao Yuan, Jianchao Zhu, Yuanbin Wu, Man Lan
Date: 2021-10-14 00:00:00
Tasks: Word Sense Disambiguation

Pun location is to identify the punning word (usually a word or a phrase that makes the text ambiguous) in a given short text, and pun interpretation is to find out two different meanings of the punning word. Most previous studies adopt limited word senses obtained by WSD(Word Sense Disambiguation) technique or pronunciation information in isolation to address pun location. For the task of pun interpretation, related work pays attention to various WSD algorithms. In this paper, a model called DANN (Dual-Attentive Neural Network) is proposed for pun location, effectively integrates word senses and pronunciation with context information to address two kinds of pun at the same time. Furthermore, we treat pun interpretation as a classification task and construct pungloss pairs as processing data to solve this task. Experiments on the two benchmark datasets show that our proposed methods achieve new state-of-the-art results. Our source code is available in the public code repository.
Distribution Locational Marginal Pricing Under Uncertainty Considering Coordination of Distribution and Wholesale Markets
Authors: Zongzheng Zhao, Yixin Liu, Li Guo, Linquan Bai, Chengshan Wang
Date: 2021-10-14 00:00:00
Tasks: Scheduling

An effective distribution electricity market (DEM) is required to manage the rapidly growing small-scale distributed energy resources (DERs) in distribution systems (DSs). This paper proposes a day-ahead DEM clearing and pricing mechanism to account for the uncertainty of DERs and the coordination with the wholesale electricity market (WEM) through a bi-level model. The upper-level model clears the WEM in the transmission system (TS) and forms the locational marginal price (LMP) and uncertainty LMP (ULMP) for energy and uncertainty/reserve, respectively. In the lower level, a robust scheduling model considering WEM-DEM coordination and uncertainties is proposed to clear the DEM. Accordingly, the distribution LMPs (DLMPs) for active power, reactive power and uncertainty/reserve are derived to reward the energy/reserve provision and charge uncertain resources in the DEM, which provide effective price signals for managing not only the voltage and congestion, but also the uncertainty in DSs. A heterogeneous decomposition (HGD) algorithm is utilized to solve the bi-level model in a decentralized manner with limited information interaction between TS and DSs, which guarantees the solution efficiency and information privacy. The effectiveness of the proposed method is verified via numerous case studies.
Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution
Authors: Yunshi Huang, Emilie Chouzenoux, Jean-Christophe Pesquet
Date: 2021-10-14 00:00:00
Tasks: Rolling Shutter Correction

In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution. Our generic framework incorporates smoothness priors on the unknown blur/image and possible affine constraints (e.g., sum to one) on the blur kernel. One of our main contributions is the integration of VBA within a neural network paradigm, following an unrolling methodology. The proposed architecture is trained in a supervised fashion, which allows us to optimally set two key hyperparameters of the VBA model and lead to further improvements in terms of resulting visual quality. Various experiments involving grayscale/color images and diverse kernel shapes, are performed. The numerical examples illustrate the high performance of our approach when compared to state-of-the-art techniques based on optimization, Bayesian estimation, or deep learning.