pwc-archive/papers-with-abstracts huggingface dataset:
Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning
Authors: R Faghihpirayesh, M Yarossi, T Imbiriba, E Tunik, D Brooks, D Erdoğmuş
Date: 2021-08-18 00:00:00
Proceeding: IEEE Transactions on Neural Systems and Rehabilitation Engineering 2021 8
Tasks: Active Learning, Electromyography (EMG)

Transcranial Magnetic Stimulation (TMS) can be used to map cortical motor topography by spatially sampling the sensorimotor cortex while recording Motor Evoked Potentials (MEP) with surface electromyography (EMG). Traditional sampling strategies are time-consuming and inefficient, as they ignore the fact that responsive sites are typically sparse and highly spatially correlated. An alternative approach, commonly employed when TMS mapping is used for presurgical planning, is to leverage the expertise of the coil operator to use MEPs elicited by previous stimuli as feedback to decide which loci to stimulate next. In this paper, we propose to automatically infer optimal future stimulus loci using active learning Gaussian Process-based sampling in place of user expertise. We first compare the user-guided (USRG) method to the traditional grid selection method and randomized sampling to verify that the USRG approach has superior performance. We then compare several novel active Gaussian Process (GP) strategies with the USRG approach. Experimental results using real data show that, as expected, the USRG method is superior to the grid and random approach in both time efficiency and MEP map accuracy. We also found that an active warped GP entropy and a GP random-based strategy performed equally as well as, or even better than, the USRG method. These methods were completely automatic, and succeeded in efficiently sampling the regions in which the MEP response variations are largely confined. This work provides the foundation for highly efficient, fully automatized TMS mapping, especially when considered in the context of advances in robotic coil operation.
REBEL: Relation Extraction By End-to-end Language generation
Authors: Pere-Lluis Huguet Cabot, Roberto Navigli
Date: 2021-10-29 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Entity Linking, Joint Entity and Relation Extraction, Relation, Relation Classification, Relation Extraction, Text Generation

Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types. To overcome these issues, we propose the use of autoregressive seq2seq models. Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks.
In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types. We show our model's flexibility by fine-tuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Authors: Dawei Du, Pengfei Zhu, Longyin Wen, Xiao Bian, Haibin Lin, QinGhua Hu, Tao Peng, Jiayu Zheng, Xinyao Wang, Yue Zhang, Liefeng Bo, Hailin Shi, Rui Zhu, Aashish Kumar, Aijin Li, Almaz Zinollayev, Anuar Askergaliyev, Arne Schumann, Binjie Mao, Byeongwon Lee, Chang Liu, Changrui Chen, Chunhong Pan, Chunlei Huo, Da Yu, Dechun Cong, Dening Zeng, Dheeraj Reddy Pailla, Di Li, Dong Wang, Donghyeon Cho, Dongyu Zhang, Furui Bai, George Jose, Guangyu Gao, Guizhong Liu, Haitao Xiong, Hao Qi, Haoran Wang, Heqian Qiu, Hongliang Li, Huchuan Lu, Ildoo Kim, Jaekyum Kim, Jane Shen, Jihoon Lee, Jing Ge, Jingjing Xu, Jingkai Zhou, Jonas Meier, Jun Won Choi, Junhao Hu, Junyi Zhang, Junying Huang, Kaiqi Huang, Keyang Wang, Lars Sommer, Lei Jin, Lei Zhang
Date: 2019-10-27 00:00:00
Proceeding: International Conference on Computer Vision Workshops 2019 10
Tasks: Object, object-detection, Object Detection

Recently, automatic visual data understanding from drone platforms becomes highly demanding. To facilitate the study, the Vision Meets Drone Object Detection in Image Challenge is held the second time in conjunction with the 17-th International Conference on Computer Vision (ICCV 2019), focuses on image object detection on drones. Results of 33 object detection algorithms are presented. For each participating detector, a short description is provided in the appendix. Our goal is to advance the state-of-the-art detection algorithms and provide a comprehensive evaluation platform for them. The evaluation protocol of the VisDrone-DET2019 Challenge and the comparison results of all the submitted detectors on the released dataset are publicly available at the website: http: //www.aiskyeye.com/. The results demonstrate that there still remains a large room for improvement for object detection algorithms on drones.
Tiny Object Detection in Aerial Images
Authors: Jinwang Wang, Wen Yang, Haowen Guo, Ruixiang Zhang, Gui-Song Xia
Date: 2021-01-10 00:00:00
Proceeding: International Conference on Pattern Recognition (ICPR) 2021 1
Tasks: Object, object-detection, Object Detection, Object Detection In Aerial Images

Object detection in Earth Vision has achieved great progress in recent years. However, tiny object detection in aerial images remains a very challenging problem since the tiny objects contain a small number of pixels and are easily confused with the background. To advance tiny object detection research in aerial images, we present a new dataset for Tiny Object Detection in Aerial Images (AI-TOD). Specifically, AI-TOD comes with 700,621 object instances for eight categories across 28,036 aerial images. Compared to existing object detection datasets in aerial images, the mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than others. To build a benchmark for tiny object detection in aerial images, we evaluate the state-of-the-art object detectors on our AI-TOD dataset. Experimental results show that direct application of these approaches on AI-TOD produces suboptimal object detection results, thus new specialized detectors for tiny object detection need to be designed. Therefore, we propose a multiple center points based learning network (M-CenterNet) to improve the localization performance of tiny object detection, and experimental results show the significant performance gain over the competitors.
DARCNN: Domain Adaptive Region-based Convolutional Neural Network forUnsupervised Instance Segmentation in Biomedical Images
Authors: Joy Hsu
Date: 2021-04-03 00:00:00
Proceeding: CVPR 2021 2021 4
Tasks: Instance Segmentation, Medical Image Segmentation, scientific discovery, Segmentation, Semantic Segmentation, Unsupervised Instance Segmentation

In  the  biomedical  domain,  there  is  an  abundance  ofdense, complex data where objects of interest may be chal-lenging to detect or constrained by limits of human knowl-edge.    Labelled  domain  specific  datasets  for  supervisedtasks  are  often  expensive  to  obtain,  and  furthermore  dis-covery  of  novel  distinct  objects  may  be  desirable  for  un-biased scientific discovery.  Therefore, we propose leverag-ing the wealth of annotations in benchmark computer visiondatasets to conduct unsupervised instance segmentation fordiverse biomedical datasets.  The key obstacle is thus over-coming the large domain shift from common to biomedicalimages. We propose a Domain Adaptive Region-based Con-volutional Neural Network (DARCNN), that adapts knowl-edge of object definition from COCO, a large labelled visiondataset, to multiple biomedical datasets. We introduce a do-main  separation  module,  a  self-supervised  representationconsistency loss, and an augmented pseudo-labelling stagewithin DARCNN to effectively perform domain adaptationacross such large domain shifts.  We showcase DARCNN’sperformance for unsupervised instance segmentation on nu-merous biomedical datasets.
Probabilistic Deep Learning for Electric-Vehicle Energy-Use Prediction
Authors: Linas Petkevicius, Simonas Saltenis, Alminas Civilis, Kristian Torp
Date: 2021-08-23 00:00:00
Proceeding: International Symposium on Spatial and Temporal Databases 2021 8
Tasks: Deep Learning, Prediction, Probabilistic Deep Learning

The continued spread of electric vehicles raises new challenges for the supporting digital infrastructure. For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel time as well as energy use. We envision a two-tier architecture to produce such predictions. First, a routing and travel-time-prediction subsystem generates a suggested route and predicts how the speed will vary along the route. Next, the expected energy use is predicted from the speed profile and other contextual characteristics, such as weather information and slope.

To this end, the paper proposes deep-learning models that are built from EV tracking data. First, as the speed profile of a route is one of the main predictors for energy use, different simple ways to build speed profiles are explored. Next, eight different deep-learning models for energy-use prediction are proposed. Four of the models are probabilistic in that they predict not a single-point estimate but parameters of a probability distribution of energy use on the route. This is particularly relevant when predicting EV energy use, which is highly sensitive to many input characteristics and, thus, can hardly be predicted precisely. Extensive experiments with two real-world EV tracking datasets validate the proposed methods. The code for this research has been made available on GitHub.
BLT: Balancing Long-Tailed Datasets with Adversarially-Perturbed Images
Authors: Jedrzej Kozerawski, Victor Fragoso, Nikolaos Karianakis, Gaurav Mittal, Matthew Turk, Mei Chen
Date: 2020-10-30 00:00:00
Proceeding: ACCV 2020 10
Tasks: Data Augmentation, Image Generation, Long-tail Learning

Real visual-world datasets tend to have few classes with large numbers of samples (i.e., head classes) and many others with smaller
numbers of samples (i.e., tail classes). Unfortunately, this imbalance enables a visual recognition system to perform well on head classes but poorly on tail classes. To alleviate this imbalance, we present BLT, a novel data augmentation technique that generates extra training samples for tail classes to improve the generalization performance of a classifier. Unlike prior long-tail approaches that rely on generative models (e.g., GANs or VQ-VAEs) to augment a dataset, BLT uses a gradient-ascent based image generation algorithm that requires significantly less training time and computational resources. BLT avoids the use of dedicated generative networks, which adds significant computational overhead and require elaborate training procedures. Our experiments on natural and synthetic long-tailed datasets and across different network architectures demonstrate that BLT consistently improves the average classification performance of tail classes by 11% w.r.t. the common approach that balances the dataset by oversampling tail-class images. BLT maintains the accuracy on head classes while improving the performance on tail classes.
Social Media Reveals Urban-Rural Differences in Stress across China
Authors: Jesse Cui, Tingdan Zhang, Kokil Jaidka, Dandan Pang, Garrick Sherman, Vinit Jakhetiya, Lyle Ungar, Sharath Chandra Guntuku
Date: 2021-10-19 00:00:00

Modeling differential stress expressions in urban and rural regions in China can provide a better understanding of the effects of urbanization on psychological well-being in a country that has rapidly grown economically in the last two decades. This paper studies linguistic differences in the experiences and expressions of stress in urban-rural China from Weibo posts from over 65,000 users across 329 counties using hierarchical mixed-effects models. We analyzed phrases, topical themes, and psycho-linguistic word choices in Weibo posts mentioning stress to better understand appraisal differences surrounding psychological stress in urban and rural communities in China; we then compared them with large-scale polls from Gallup. After controlling for socioeconomic and gender differences, we found that rural communities tend to express stress in emotional and personal themes such as relationships, health, and opportunity while users in urban areas express stress using relative, temporal, and external themes such as work, politics, and economics. These differences exist beyond controlling for GDP and urbanization, indicating a fundamentally different lifestyle between rural and urban residents in very specific environments, arguably having different sources of stress. We found corroborative trends in physical, financial, and social wellness with urbanization in Gallup polls.
LayerCAM: Exploring Hierarchical Class Activation Maps for Localization
Authors: Peng-Tao Jiang, Chang-Bin Zhang, Qibin Hou, Ming-Ming Cheng, Yunchao Wei
Date: 2021-06-22 00:00:00
Proceeding: IEEE 2021 6
Tasks: Object, Object Localization, Semantic Segmentation, Weakly-Supervised Object Localization

The class activation maps are generated from the final convolutional layer of CNN. They can highlight discriminative object regions for the class of interest. These discovered object regions have been widely used for weakly-supervised tasks. However, due to the small spatial resolution of the final convolutional layer, such class activation maps often locate coarse regions of the target objects, limiting the performance of weakly-supervised tasks that need pixel-accurate object locations. Thus, we aim to generate more fine-grained object localization information from the class activation maps to locate the target objects more accurately. In this paper, by rethinking the relationships between the feature maps and their corresponding gradients, we propose a simple yet effective method, called LayerCAM. It can produce reliable class activation maps for different layers of CNN. This property enables us to collect object localization information from coarse (rough spatial localization) to fine (precise fine-grained details) levels. We further integrate them into a high-quality class activation map, where the object-related pixels can be better highlighted. To evaluate the quality of the class activation maps produced by LayerCAM, we apply them to weakly-supervised object localization and semantic segmentation. Experiments demonstrate that the class activation maps generated by our method are more effective and reliable than those by the existing attention methods. The code will be made publicly available.
Learning Personal Food Preferences via Food Logs Embedding
Authors: Ahmed A. Metwally, Ariel K. Leong, Aman Desai, Anvith Nagarjuna, Dalia Perelman, Michael Snyder
Date: 2021-10-29 00:00:00
Tasks: Management, Nutrition, Recommendation Systems, Word Embeddings

Diet management is key to managing chronic diseases such as diabetes. Automated food recommender systems may be able to assist by providing meal recommendations that conform to a user's nutrition goals and food preferences. Current recommendation systems suffer from a lack of accuracy that is in part due to a lack of knowledge of food preferences, namely foods users like to and are able to eat frequently. In this work, we propose a method for learning food preferences from food logs, a comprehensive but noisy source of information about users' dietary habits. We also introduce accompanying metrics. The method generates and compares word embeddings to identify the parent food category of each food entry and then calculates the most popular. Our proposed approach identifies 82% of a user's ten most frequently eaten foods. Our method is publicly available on (https://github.com/aametwally/LearningFoodPreferences)
Recommender System for Online Dating Service
Authors: Lukas Brozovsky, Vaclav Petricek
Date: 2007-03-09 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Users of online dating sites are facing information overload that requires them to manually construct queries and browse huge amount of matching user profiles. This becomes even more problematic for multimedia profiles. Although matchmaking is frequently cited as a typical application for recommender systems, there is a surprising lack of work published in this area. In this paper we describe a recommender system we implemented and perform a quantitative comparison of two collaborative filtering (CF) and two global algorithms. Results show that collaborative filtering recommenders significantly outperform global algorithms that are currently used by dating sites. A blind experiment with real users also confirmed that users prefer CF based recommendations to global popularity recommendations. Recommender systems show a great potential for online dating where they could improve the value of the service to users and improve monetization of the service.
Integrating Deep Event-Level and Script-Level Information for Script Event Prediction
Authors: Long Bai, Saiping Guan, Jiafeng Guo, Zixuan Li, Xiaolong Jin, Xueqi Cheng
Date: 2021-09-24 00:00:00
Proceeding: EMNLP 2021 11

Scripts are structured sequences of events together with the participants, which are extracted from the texts.Script event prediction aims to predict the subsequent event given the historical events in the script. Two kinds of information facilitate this task, namely, the event-level information and the script-level information. At the event level, existing studies view an event as a verb with its participants, while neglecting other useful properties, such as the state of the participants. At the script level, most existing studies only consider a single event sequence corresponding to one common protagonist. In this paper, we propose a Transformer-based model, called MCPredictor, which integrates deep event-level and script-level information for script event prediction. At the event level, MCPredictor utilizes the rich information in the text to obtain more comprehensive event semantic representations. At the script-level, it considers multiple event sequences corresponding to different participants of the subsequent event. The experimental results on the widely-used New York Times corpus demonstrate the effectiveness and superiority of the proposed model.
Deep Neural Networks for Web Page Information Extraction
Authors: Tomas Gogar, Hubacek Ondrej, and Jan Sedivy
Date: 2016-09-01 00:00:00
Proceeding: IFIP International Conference on Artificial Intelligence Applications and Innovations 2016 9
Tasks: Webpage Object Detection

Web wrappers are systems for extracting structured information from web pages. Currently, wrappers need to be adapted to a  particular website template before they can start the extraction process. In this work we present a new method, which uses convolutional neural networks to learn a wrapper that can extract information from previously unseen templates. Therefore, this wrapper does not need any site-specific initialization and is able to extract information from a single web page. We also propose a method for spatial text encoding, which allows us to encode visual and textual content of a web page into a single neural net. The first experiments with product information extraction showed very promising results and suggest that this approach can lead to a general site-independent web wrapper.
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains
Authors: Christian Gumbsch, Martin V. Butz, Georg Martius
Date: 2021-10-29 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Inductive Bias

A common approach to prediction and planning in partially observable domains is to use recurrent neural networks (RNNs), which ideally develop and maintain a latent memory about hidden, task-relevant factors. We hypothesize that many of these hidden factors in the physical world are constant over time, changing only sparsely. To study this hypothesis, we propose Gated $L_0$ Regularized Dynamics (GateL0RD), a novel recurrent architecture that incorporates the inductive bias to maintain stable, sparsely changing latent states. The bias is implemented by means of a novel internal gating function and a penalty on the $L_0$ norm of latent state changes. We demonstrate that GateL0RD can compete with or outperform state-of-the-art RNNs in a variety of partially observable prediction and control tasks. GateL0RD tends to encode the underlying generative factors of the environment, ignores spurious temporal dependencies, and generalizes better, improving sampling efficiency and overall performance in model-based planning and reinforcement learning tasks. Moreover, we show that the developing latent states can be easily interpreted, which is a step towards better explainability in RNNs.
MetaICL: Learning to Learn In Context
Authors: Sewon Min, Mike Lewis, Luke Zettlemoyer, Hannaneh Hajishirzi
Date: 2021-10-29 00:00:00
Proceeding: NAACL 2022 7
Tasks: Few-Shot Learning, In-Context Learning, Language Modelling, Multi-Task Learning, Natural Language Inference, Question Answering

We introduce MetaICL (Meta-training for In-Context Learning), a new meta-training framework for few-shot learning where a pretrained language model is tuned to do in-context learning on a large set of training tasks. This meta-training enables the model to more effectively learn a new task in context at test time, by simply conditioning on a few training examples with no parameter updates or task-specific templates. We experiment on a large, diverse collection of tasks consisting of 142 NLP datasets including classification, question answering, natural language inference, paraphrase detection and more, across seven different meta-training/target splits. MetaICL outperforms a range of baselines including in-context learning without meta-training and multi-task learning followed by zero-shot transfer. We find that the gains are particularly significant for target tasks that have domain shifts from the meta-training tasks, and that using a diverse set of the meta-training tasks is key to improvements. We also show that MetaICL approaches (and sometimes beats) the performance of models fully finetuned on the target task, and outperforms much bigger models with nearly 8x parameters. Finally, we show that MetaICL is complementary to human-written instructions, and the best performance can be achieved by combining both approaches.
Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems
Authors: Wenqing Zheng, Qiangqiang Guo, Hao Yang, Peihao Wang, Zhangyang Wang
Date: 2021-10-29 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Inductive Bias, Traffic Signal Control

Multi-agent control is a central theme in the Cyber-Physical Systems (CPS). However, current control methods either receive non-Markovian states due to insufficient sensing and decentralized design, or suffer from poor convergence. This paper presents the Delayed Propagation Transformer (DePT), a new transformer-based model that specializes in the global modeling of CPS while taking into account the immutable constraints from the physical world. DePT induces a cone-shaped spatial-temporal attention prior, which injects the information propagation and aggregation principles and enables a global view. With physical constraint inductive bias baked into its design, our DePT is ready to plug and play for a broad class of multi-agent systems. The experimental results on one of the most challenging CPS -- network-scale traffic signal control system in the open world -- show that our model outperformed the state-of-the-art expert methods on synthetic and real-world datasets. Our codes are released at: https://github.com/VITA-Group/DePT.