pwc-archive/papers-with-abstracts huggingface dataset:
HumBugDB: A Large-scale Acoustic Mosquito Dataset
Authors: Ivan Kiskin, Marianne Sinka, Adam D. Cobb, Waqas Rafique, Lawrence Wang, Davide Zilli, Benjamin Gutteridge, Rinita Dam, Theodoros Marinos, Yunpeng Li, Dickson Msaky, Emmanuel Kaindoa, Gerard Killeen, Eva Herreros-Moya, Kathy J. Willis, Stephen J. Roberts
Date: 2021-10-14 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

This paper presents the first large-scale multi-species dataset of acoustic recordings of mosquitoes tracked continuously in free flight. We present 20 hours of audio recordings that we have expertly labelled and tagged precisely in time. Significantly, 18 hours of recordings contain annotations from 36 different species. Mosquitoes are well-known carriers of diseases such as malaria, dengue and yellow fever. Collecting this dataset is motivated by the need to assist applications which utilise mosquito acoustics to conduct surveys to help predict outbreaks and inform intervention policy. The task of detecting mosquitoes from the sound of their wingbeats is challenging due to the difficulty in collecting recordings from realistic scenarios. To address this, as part of the HumBug project, we conducted global experiments to record mosquitoes ranging from those bred in culture cages to mosquitoes captured in the wild. Consequently, the audio recordings vary in signal-to-noise ratio and contain a broad range of indoor and outdoor background environments from Tanzania, Thailand, Kenya, the USA and the UK. In this paper we describe in detail how we collected, labelled and curated the data. The data is provided from a PostgreSQL database, which contains important metadata such as the capture method, age, feeding status and gender of the mosquitoes. Additionally, we provide code to extract features and train Bayesian convolutional neural networks for two key tasks: the identification of mosquitoes from their corresponding background environments, and the classification of detected mosquitoes into species. Our extensive dataset is both challenging to machine learning researchers focusing on acoustic identification, and critical to entomologists, geo-spatial modellers and other domain experts to understand mosquito behaviour, model their distribution, and manage the threat they pose to humans.
Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels
Authors: Chu Han, Jiatai Lin, Jinhai Mai, Yi Wang, Qingling Zhang, Bingchao Zhao, Xin Chen, Xipeng Pan, Zhenwei Shi, Xiaowei Xu, Su Yao, Lixu Yan, Huan Lin, Zeyan Xu, Xiaomei Huang, Guoqiang Han, Changhong Liang, Zaiyi Liu
Date: 2021-10-14 00:00:00
Tasks: Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation, whole slide images

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance with dense pixel-level annotations. However, drawing such labels on the giga-pixel whole slide images is extremely expensive and time-consuming. In this paper, we use only patch-level classification labels to achieve tissue semantic segmentation on histopathology images, finally reducing the annotation efforts. We proposed a two-step model including a classification and a segmentation phases. In the classification phase, we proposed a CAM-based model to generate pseudo masks by patch-level labels. In the segmentation phase, we achieved tissue semantic segmentation by our proposed Multi-Layer Pseudo-Supervision. Several technical novelties have been proposed to reduce the information gap between pixel-level and patch-level annotations. As a part of this paper, we introduced a new weakly-supervised semantic segmentation (WSSS) dataset for lung adenocarcinoma (LUAD-HistoSeg). We conducted several experiments to evaluate our proposed model on two datasets. Our proposed model outperforms two state-of-the-art WSSS approaches. Note that we can achieve comparable quantitative and qualitative results with the fully-supervised model, with only around a 2\% gap for MIoU and FwIoU. By comparing with manual labeling, our model can greatly save the annotation time from hours to minutes. The source code is available at: \url{https://github.com/ChuHan89/WSSS-Tissue}.
Adversarial Scene Reconstruction and Object Detection System for Assisting Autonomous Vehicle
Authors: Md Foysal Haque, Hay-Youn Lim, Dae-Seong Kang
Date: 2021-10-13 00:00:00
Tasks: Deep Learning, object-detection, Object Detection, Scene Understanding

In the current computer vision era classifying scenes through video surveillance systems is a crucial task. Artificial Intelligence (AI) Video Surveillance technologies have been advanced remarkably while artificial intelligence and deep learning ascended into the system. Adopting the superior compounds of deep learning visual classification methods achieved enormous accuracy in classifying visual scenes. However, the visual classifiers face difficulties examining the scenes in dark visible areas, especially during the nighttime. Also, the classifiers face difficulties in identifying the contexts of the scenes. This paper proposed a deep learning model that reconstructs dark visual scenes to clear scenes like daylight, and the method recognizes visual actions for the autonomous vehicle. The proposed model achieved 87.3 percent accuracy for scene reconstruction and 89.2 percent in scene understanding and detection tasks.
Adversarial Attack across Datasets
Authors: Yunxiao Qin, Yuanhao Xiong, JinFeng Yi, Lihong Cao, Cho-Jui Hsieh
Date: 2021-10-13 00:00:00
Tasks: Adversarial Attack, image-classification, Image Classification

Existing transfer attack methods commonly assume that the attacker knows the training set (e.g., the label set, the input size) of the black-box victim models, which is usually unrealistic because in some cases the attacker cannot know this information. In this paper, we define a Generalized Transferable Attack (GTA) problem where the attacker doesn't know this information and is acquired to attack any randomly encountered images that may come from unknown datasets. To solve the GTA problem, we propose a novel Image Classification Eraser (ICE) that trains a particular attacker to erase classification information of any images from arbitrary datasets. Experiments on several datasets demonstrate that ICE greatly outperforms existing transfer attacks on GTA, and show that ICE uses similar texture-like noises to perturb different images from different datasets. Moreover, fast fourier transformation analysis indicates that the main components in each ICE noise are three sine waves for the R, G, and B image channels. Inspired by this interesting finding, we then design a novel Sine Attack (SA) method to optimize the three sine waves. Experiments show that SA performs comparably to ICE, indicating that the three sine waves are effective and enough to break DNNs under the GTA setting.
Application of Homomorphic Encryption in Medical Imaging
Authors: Francis Dutil, Alexandre See, Lisa Di Jorio, Florent Chandelier
Date: 2021-10-12 00:00:00
Tasks: Federated Learning

In this technical report, we explore the use of homomorphic encryption (HE) in the context of training and predicting with deep learning (DL) models to deliver strict \textit{Privacy by Design} services, and to enforce a zero-trust model of data governance. First, we show how HE can be used to make predictions over medical images while preventing unauthorized secondary use of data, and detail our results on a disease classification task with OCT images. Then, we demonstrate that HE can be used to secure the training of DL models through federated learning, and report some experiments using 3D chest CT-Scans for a nodule detection task.
Reliable Shot Identification for Complex Event Detection via Visual-Semantic Embedding
Authors: Minnan Luo, Xiaojun Chang, Chen Gong
Date: 2021-10-12 00:00:00
Tasks: Event Detection, Multiple Instance Learning

Multimedia event detection is the task of detecting a specific event of interest in an user-generated video on websites. The most fundamental challenge facing this task lies in the enormously varying quality of the video as well as the high-level semantic abstraction of event inherently. In this paper, we decompose the video into several segments and intuitively model the task of complex event detection as a multiple instance learning problem by representing each video as a "bag" of segments in which each segment is referred to as an instance. Instead of treating the instances equally, we associate each instance with a reliability variable to indicate its importance and then select reliable instances for training. To measure the reliability of the varying instances precisely, we propose a visual-semantic guided loss by exploiting low-level feature from visual information together with instance-event similarity based high-level semantic feature. Motivated by curriculum learning, we introduce a negative elastic-net regularization term to start training the classifier with instances of high reliability and gradually taking the instances with relatively low reliability into consideration. An alternative optimization algorithm is developed to solve the proposed challenging non-convex non-smooth problem. Experimental results on standard datasets, i.e., TRECVID MEDTest 2013 and TRECVID MEDTest 2014, demonstrate the effectiveness and superiority of the proposed method to the baseline algorithms.
Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules
Authors: Rangeet Pan, Hridesh Rajan
Date: 2021-10-11 00:00:00
Tasks: image-classification, Image Classification

Training from scratch is the most common way to build a Convolutional Neural Network (CNN) based model. What if we can build new CNN models by reusing parts from previously build CNN models? What if we can improve a CNN model by replacing (possibly faulty) parts with other parts? In both cases, instead of training, can we identify the part responsible for each output class (module) in the model(s) and reuse or replace only the desired output classes to build a model? Prior work has proposed decomposing dense-based networks into modules (one for each output class) to enable reusability and replaceability in various scenarios. However, this work is limited to the dense layers and based on the one-to-one relationship between the nodes in consecutive layers. Due to the shared architecture in the CNN model, prior work cannot be adapted directly. In this paper, we propose to decompose a CNN model used for image classification problems into modules for each output class. These modules can further be reused or replaced to build a new model. We have evaluated our approach with CIFAR-10, CIFAR-100, and ImageNet tiny datasets with three variations of ResNet models and found that enabling decomposition comes with a small cost (2.38% and 0.81% for top-1 and top-5 accuracy, respectively). Also, building a model by reusing or replacing modules can be done with a 2.3% and 0.5% average loss of accuracy. Furthermore, reusing and replacing these modules reduces CO2e emission by ~37 times compared to training the model from scratch.
EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation
Authors: Hechen Yang, Chen Li, Xin Zhao, Bencheng Cai, Jiawei Zhang, Pingli Ma, Peng Zhao, Ao Chen, Hongzan Sun, Yueyang Teng, Shouliang Qi, Tao Jiang, Marcin Grzegorzek
Date: 2021-10-11 00:00:00
Tasks: Articles, Object, object-detection, Object Detection

The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set, including the original Environmental Microorganism images (EMs) and the corresponding object labeling files in ".XML" format file. The EMDS-7 data set consists of 41 types of EMs, which has a total of 2365 images and 13216 labeled objects. The EMDS-7 database mainly focuses on the object detection. In order to prove the effectiveness of EMDS-7, we select the most commonly used deep learning methods (Faster-RCNN, YOLOv3, YOLOv4, SSD and RetinaNet) and evaluation indices for testing and evaluation. EMDS-7 is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/EMDS-7_DataSet/16869571
Probabilistic Time Series Forecasts with Autoregressive Transformation Models
Authors: David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn
Date: 2021-10-15 00:00:00
Tasks: Time Series, Time Series Analysis

Probabilistic forecasting of time series is an important matter in many applications and research fields. In order to draw conclusions from a probabilistic forecast, we must ensure that the model class used to approximate the true forecasting distribution is expressive enough. Yet, characteristics of the model itself, such as its uncertainty or its feature-outcome relationship are not of lesser importance. This paper proposes Autoregressive Transformation Models (ATMs), a model class inspired by various research directions to unite expressive distributional forecasts using a semi-parametric distribution assumption with an interpretable model specification. We demonstrate the properties of ATMs both theoretically and through empirical evaluation on several simulated and real-world forecasting datasets.
Learn Proportional Derivative Controllable Latent Space from Pixels
Authors: Weiyao Wang, Marin Kobilarov, Gregory D. Hager
Date: 2021-10-15 00:00:00
Tasks: Model Predictive Control

Recent advances in latent space dynamics model from pixels show promising progress in vision-based model predictive control (MPC). However, executing MPC in real time can be challenging due to its intensive computational cost in each timestep. We propose to introduce additional learning objectives to enforce that the learned latent space is proportional derivative controllable. In execution time, the simple PD-controller can be applied directly to the latent space encoded from pixels, to produce simple and effective control to systems with visual observations. We show that our method outperforms baseline methods to produce robust goal reaching and trajectory tracking in various environments.
Simultaneous Missing Value Imputation and Structure Learning with Groups
Authors: Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
Date: 2021-10-15 00:00:00
Tasks: Causal Discovery, Graph Neural Network, Imputation, Missing Values

Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve. One typical scenario is discovering the structure among topics in the education domain to identify learning pathways. Here, the observations are student performances for questions under each topic which contain missing values. However, most existing methods focus on learning structures between a few individual variables from the complete data. In this work, we propose VISL, a novel scalable structure learning approach that can simultaneously infer structures between groups of variables under missing data and perform missing value imputations with deep learning. Particularly, we propose a generative model with a structured latent space and a graph neural network-based architecture, scaling to a large number of variables. Empirically, we conduct extensive experiments on synthetic, semi-synthetic, and real-world education data sets. We show improved performances on both imputation and structure learning accuracy compared to popular and recent approaches.
Choice functions based multi-objective Bayesian optimisation
Authors: Alessio Benavoli, Dario Azzimonti, Dario Piga
Date: 2021-10-15 00:00:00
Tasks: Bayesian Optimisation

In this work we introduce a new framework for multi-objective Bayesian optimisation where the multi-objective functions can only be accessed via choice judgements, such as ``I pick options A,B,C among this set of five options A,B,C,D,E''. The fact that the option D is rejected means that there is at least one option among the selected ones A,B,C that I strictly prefer over D (but I do not have to specify which one). We assume that there is a latent vector function f for some dimension $n_e$ which embeds the options into the real vector space of dimension n, so that the choice set can be represented through a Pareto set of non-dominated options. By placing a Gaussian process prior on f and deriving a novel likelihood model for choice data, we propose a Bayesian framework for choice functions learning. We then apply this surrogate model to solve a novel multi-objective Bayesian optimisation from choice data problem.
NNK-Means: Data summarization using dictionary learning with non-negative kernel regression
Authors: Sarath Shekkizhar, Antonio Ortega
Date: 2021-10-15 00:00:00
Tasks: Data Summarization, Dictionary Learning, regression

An increasing number of systems are being designed by gathering significant amounts of data and then optimizing the system parameters directly using the obtained data. Often this is done without analyzing the dataset structure. As task complexity, data size, and parameters all increase to millions or even billions, data summarization is becoming a major challenge. In this work, we investigate data summarization via dictionary learning~(DL), leveraging the properties of recently introduced non-negative kernel regression (NNK) graphs. Our proposed NNK-Means, unlike previous DL techniques, such as kSVD, learns geometric dictionaries with atoms that are representative of the input data space. Experiments show that summarization using NNK-Means can provide better class separation compared to linear and kernel versions of kMeans and kSVD. Moreover, NNK-Means is scalable, with runtime complexity similar to that of kMeans.
Multitask Prompted Training Enables Zero-Shot Task Generalization
Authors: Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Tali Bers, Stella Biderman, Leo Gao, Thomas Wolf, Alexander M. Rush
Date: 2021-10-15 00:00:00
Proceeding: ICLR 2022 4
Tasks: Benchmarking, Decoder, Language Modelling, Prompt Engineering, Zero-shot Generalization

Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models' pretraining (Radford et al., 2019). Can zero-shot generalization instead be directly induced by explicit multitask learning? To test this question at scale, we develop a system for easily mapping any natural language tasks into a human-readable prompted form. We convert a large set of supervised datasets, each with multiple prompts with diverse wording. These prompted datasets allow for benchmarking the ability of a model to perform completely held-out tasks. We fine-tune a pretrained encoder-decoder model (Raffel et al., 2020; Lester et al., 2021) on this multitask mixture covering a wide variety of tasks. The model attains strong zero-shot performance on several standard datasets, often outperforming models up to 16x its size. Further, our approach attains strong performance on a subset of tasks from the BIG-bench benchmark, outperforming models up to 6x its size. All trained models are available at https://github.com/bigscience-workshop/t-zero and all prompts are available at https://github.com/bigscience-workshop/promptsource.
Propagation on Multi-relational Graphs for Node Regression
Authors: Eda Bayram
Date: 2021-10-15 00:00:00
Tasks: Node Regression, regression, Relational Reasoning

Recent years have witnessed a rise in real-world data captured with rich structural information that can be conveniently depicted by multi-relational graphs. While inference of continuous node features across a simple graph is rather under-studied by the current relational learning research, we go one step further and focus on node regression problem on multi-relational graphs. We take inspiration from the well-known label propagation algorithm aiming at completing categorical features across a simple graph and propose a novel propagation framework for completing missing continuous features at the nodes of a multi-relational and directed graph. Our multi-relational propagation algorithm is composed of iterative neighborhood aggregations which originate from a relational local generative model. Our findings show the benefit of exploiting the multi-relational structure of the data in several node regression scenarios in different settings.
Collaborating with Humans without Human Data
Authors: DJ Strouse, Kevin R. McKee, Matt Botvinick, Edward Hughes, Richard Everett
Date: 2021-10-15 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Multi-agent Reinforcement Learning

Collaborating with humans requires rapidly adapting to their individual strengths, weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement learning techniques, such as self-play (SP) or population play (PP), produce agents that overfit to their training partners and do not generalize well to humans. Alternatively, researchers can collect human data, train a human model using behavioral cloning, and then use that model to train "human-aware" agents ("behavioral cloning play", or BCP). While such an approach can improve the generalization of agents to new human co-players, it involves the onerous and expensive step of collecting large amounts of human data first. Here, we study the problem of how to train agents that collaborate well with human partners without using human data. We argue that the crux of the problem is to produce a diverse set of training partners. Drawing inspiration from successful multi-agent approaches in competitive domains, we find that a surprisingly simple approach is highly effective. We train our agent partner as the best response to a population of self-play agents and their past checkpoints taken throughout training, a method we call Fictitious Co-Play (FCP). Our experiments focus on a two-player collaborative cooking simulator that has recently been proposed as a challenge problem for coordination with humans. We find that FCP agents score significantly higher than SP, PP, and BCP when paired with novel agent and human partners. Furthermore, humans also report a strong subjective preference to partnering with FCP agents over all baselines.