pwc-archive/papers-with-abstracts huggingface dataset:
The language of race, ethnicity, and ancestry in human genetic research
Authors: Ewan Birney, Michael Inouye, Jennifer Raff, Adam Rutherford, Aylwyn Scally
Date: 2021-06-18 00:00:00

The language commonly used in human genetics can inadvertently pose problems for multiple reasons. Terms like "ancestry", "ethnicity", and other ways of grouping people can have complex, often poorly understood, or multiple meanings within the various fields of genetics, between different domains of biological sciences and medicine, and between scientists and the general public. Furthermore, some categories in frequently used datasets carry scientifically misleading, outmoded or even racist perspectives derived from the history of science. Here, we discuss examples of problematic lexicon in genetics, and how commonly used statistical practices to control for the non-genetic environment may exacerbate difficulties in our terminology, and therefore understanding. Our intention is to stimulate a much-needed discussion about the language of genetics, to begin a process to clarify existing terminology, and in some cases adopt a new lexicon that both serves scientific insight, and cuts us loose from various aspects of a pernicious past.
XRP Network and Proposal of Flow Index
Authors: Hideaki Aoyama
Date: 2021-06-18 00:00:00

XRP is a modern crypto-asset (crypto-currency) developed by Ripple Labs, which has been increasing its financial presence. We study its transaction history available as ledger data. An analysis of its basic statistics, correlations, and network properties are presented. Motivated by the behavior of some nodes with histories of large transactions, we propose a new index: the ``Flow Index.'' The Flow Index is a pair of indices suitable for characterizing transaction frequencies as a source and destination of a node. Using this Flow Index, we study the global structure of the XRP network and construct bow-tie/walnut structure.
Proof-of-Work Cryptocurrencies: Does Mining Technology Undermine Decentralization?
Authors: Agostino Capponi, Sveinn Olafsson, Humoud Alsabah
Date: 2021-06-17 00:00:00

Does the proof-of-work protocol serve its intended purpose of supporting decentralized cryptocurrency mining? To address this question, we develop a game-theoretical model where miners first invest in hardware to improve the efficiency of their operations, and then compete for mining rewards in a rent-seeking game. We argue that because of capacity constraints faced by miners, centralization in mining is lower than indicated by both public discourse and recent academic work. We show that advancements in hardware efficiency do not necessarily lead to larger miners increasing their advantage, but rather allow smaller miners to expand and new miners to enter the competition. Our calibrated model illustrates that hardware efficiency has a small impact on the cost of attacking a network, while the mining reward has a significant impact. This highlights the vulnerability of smaller and emerging cryptocurrencies, as well as of established cryptocurrencies transitioning to a fee-based mining reward scheme.
CrowdCog: A Cognitive Skill based System for Heterogeneous Task Assignment and Recommendation in Crowdsourcing
Authors: Danula Hettiachchi, Niels van Berkel, Vassilis Kostakos, Jorge Goncalves
Date: 2020-10-14 00:00:00
Proceeding: Proceedings of the ACM on Human-Computer Interaction 2020 10
Tasks: Sentiment Analysis

While crowd workers typically complete a variety of tasks in crowdsourcing platforms, there is no widely accepted method to successfully match workers to different types of tasks. Researchers have considered using worker demographics, behavioural traces, and prior task completion records to optimise task assignment. However, optimum task assignment remains a challenging research problem due to limitations of proposed approaches, which in turn can have a significant impact on the future of crowdsourcing. We present 'CrowdCog', an online dynamic system that performs both task assignment and task recommendations, by relying on fast-paced online cognitive tests to estimate worker performance across a variety of tasks. Our work extends prior work that highlights the effect of workers' cognitive ability on crowdsourcing task performance. Our study, deployed on Amazon Mechanical Turk, involved 574 workers and 983 HITs that span across four typical crowd tasks (Classification, Counting, Transcription, and Sentiment Analysis). Our results show that both our assignment method and recommendation method result in a significant performance increase (5% to 20%) as compared to a generic or random task assignment. Our findings pave the way for the use of quick cognitive tests to provide robust recommendations and assignments to crowd workers.
Combining Optimization Methods Using an Adaptive Meta Optimizer
Authors: Nicola Landro, Ignazio Gallo, Riccardo La Grassa
Date: 2021-06-19 00:00:00
Proceeding: Algorithsm MDPI 2021 6
Tasks: Articles

Optimization methods are of great importance for the efficient training of neural networks. There are many articles in the literature that propose particular variants of existing optimizers. In our article, we propose the use of the combination of two very different optimizers that, when used simultaneously, can exceed the performance of the single optimizers in very different problems. We propose a new optimizer called ATMO (AdapTive Meta Optimizers), which integrates two different optimizers simultaneously weighing the contributions of both. Rather than trying to improve each single one, we leverage both at the same time, as a meta-optimizer, by taking the best of both. We have conducted several experiments on the classification of images and text documents, using various types of deep neural models, and we have demonstrated through experiments that the proposed ATMO produces better performance than the single optimizers.
Distance-Enhanced Graph Neural Network for Link Prediction
Authors: Boling Li, Yingce Xia, Shufang Xie, Lijun Wu, Tao Qin
Date: 2021-05-20 00:00:00
Proceeding: NA 2021 5
Tasks: Graph Neural Network, Link Prediction, Link Property Prediction, Prediction

Link prediction, which is to predict the existence of a link/edge between two vertices in a graph, is a classical problem in machine learning. Intuitively, if it takes a long distance to walk from u to v along the existing edges, there should not be a link between them, and vice versa. This motivates us to explicitly combine the distance information with graph neural networks (GNNs) to improve link prediction. Calculating the distances between any two vertices (e.g., shortest path, expectation of random walk) in training is time consuming. To overcome this difficulty, we propose an anchorbased distance: First, we randomly select K anchor vertices from the graph and then calculate the shortest distances of all vertices in the graph to them. The distance between vertices u and v is estimated as the average of their distances to the K anchor vertices. After that, we feed the distance into the GNN module. Our method brings significant improvement for link prediction with few additional parameters. We achieved state-of-the-art result on the drug-drug-interaction (i.e., DDI) and protein-protein-association (i.e., PPA) tasks of OGB (Hu et al., 2020). Our code is available at https://github.com/lbn187/DLGNN.
Molecular Representation Learning by Leveraging Chemical Information
Authors: Weibin Li, Shanzhuo Zhang, Lihang Liu, Zhengjie Huang, Jieqiong Lei, Xiaomin Fang, Shikun Feng, Fan Wang
Date: 2021-03-15 00:00:00
Proceeding: NA 2021 3
Tasks: Drug Design, Graph Property Prediction, Molecular Property Prediction, molecular representation, Prediction, Property Prediction, Representation Learning

Molecular property prediction is of great importance in AI drug design due to its high experimental efficiency compared with biological experiments. As graph neural networks have achieved great success in many domains, some studies apply graph neural networks to molecular property prediction and regard each molecule as a graph. A molecule’s atom is regarded as a node of the graph, while its bond is regarded as an edge of the graph. However, most existing methods simply apply general graph neural networks without considering the domain knowledge. As chemical information is highly related to molecular functions, it is critical for accurate property prediction. Thus, we leverage chemical information to learn molecular representation by integrating molecular fingerprints, i.e., the presence or absence of particular chemical substructures. We compare our proposed method to several strong baselines, and our proposed method significantly surpasses other methods. Up to now, our method ranks first in the Open Graph Benchmark(OGB) leaderboard for ogbg-molhiv.
In the beginning was game semantics
Authors: Giorgi Japaridze
Date: 2005-07-18 00:00:00

This article presents an overview of computability logic -- the game-semantically constructed logic of interactive computational tasks and resources. There is only one non-overview, technical section in it, devoted to a proof of the soundness of affine logic with respect to the semantics of computability logic. A comprehensive online source on the subject can be found at http://www.cis.upenn.edu/~giorgi/cl.html
The role of temporal cortex in the control of attention
Authors: Hamidreza Ramezanpour, Mazyar Fallah
Date: 2021-06-18 00:00:00
Tasks: Object Recognition, STS

Attention is an indispensable component of active vision. Contrary to the widely accepted notion that temporal cortex processing primarily focusses on passive object recognition, a series of very recent studies emphasize the role of temporal cortex structures, specifically the superior temporal sulcus (STS) and inferotemporal (IT) cortex, in guiding attention and implementing cognitive programs relevant for behavioral tasks. The goal of this theoretical paper is to advance the hypothesis that the temporal cortex attention network (TAN) entails necessary components to actively participate in attentional control in a flexible task-dependent manner. First, we will briefly discuss the general architecture of the temporal cortex with a focus on the STS and IT cortex of monkeys and their modulation with attention. Then we will review evidence from behavioral and neurophysiological studies that support their guidance of attention in the presence of cognitive control signals. Next, we propose a mechanistic framework for executive control of attention in the temporal cortex. Finally, we summarize the role of temporal cortex in implementing cognitive programs and discuss how they contribute to the dynamic nature of visual attention to ensure flexible behavior.
MeshRIR: A Dataset of Room Impulse Responses on Meshed Grid Points For Evaluating Sound Field Analysis and Synthesis Methods
Authors: Shoichi Koyama, Tomoya Nishida, Keisuke Kimura, Takumi Abe, Natsuki Ueno, Jesper Brunnström
Date: 2021-06-21 00:00:00
Tasks: Distant Speech Recognition, Room Impulse Response (RIR), Speech Enhancement, speech-recognition, Speech Recognition

A new impulse response (IR) dataset called "MeshRIR" is introduced. Currently available datasets usually include IRs at an array of microphones from several source positions under various room conditions, which are basically designed for evaluating speech enhancement and distant speech recognition methods. On the other hand, methods of estimating or controlling spatial sound fields have been extensively investigated in recent years; however, the current IR datasets are not applicable to validating and comparing these methods because of the low spatial resolution of measurement points. MeshRIR consists of IRs measured at positions obtained by finely discretizing a spatial region. Two subdatasets are currently available: one consists of IRs in a three-dimensional cuboidal region from a single source, and the other consists of IRs in a two-dimensional square region from an array of 32 sources. Therefore, MeshRIR is suitable for evaluating sound field analysis and synthesis methods. This dataset is freely available at https://sh01k.github.io/MeshRIR/ with some codes of sample applications.
Plant Disease Detection Using Image Processing and Machine Learning
Authors: Pranesh Kulkarni, Atharva Karwande, Tejas Kolhe, Soham Kamble, Akshay Joshi, Medha Wyawahare
Date: 2021-06-20 00:00:00
Tasks: BIG-bench Machine Learning

One of the important and tedious task in agricultural practices is the detection of the disease on crops. It requires huge time as well as skilled labor. This paper proposes a smart and efficient technique for detection of crop disease which uses computer vision and machine learning techniques. The proposed system is able to detect 20 different diseases of 5 common plants with 93% accuracy.
GPLA-12: An Acoustic Signal Dataset of Gas Pipeline Leakage
Authors: Jie Li, Lizhong Yao
Date: 2021-06-19 00:00:00
Tasks: Fault Detection, Fault Diagnosis, Time Series, Time Series Analysis

In this paper, we introduce a new acoustic leakage dataset of gas pipelines, called as GPLA-12, which has 12 categories over 684 training/testing acoustic signals. Unlike massive image and voice datasets, there have relatively few acoustic signal datasets, especially for engineering fault detection. In order to enhance the development of fault diagnosis, we collect acoustic leakage signals on the basis of an intact gas pipe system with external artificial leakages, and then preprocess the collected data with structured tailoring which are turned into GPLA-12. GPLA-12 dedicates to serve as a feature learning dataset for time-series tasks and classifications. To further understand the dataset, we train both shadow and deep learning algorithms to observe the performance. The dataset as well as the pretrained models have been released at both www.daip.club and github.com/Deep-AI-Application-DAIP
One-to-many Approach for Improving Super-Resolution
Authors: Sieun Park, Eunho Lee
Date: 2021-06-19 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Image Super-Resolution, Super-Resolution

Recently, there has been discussions on the ill-posed nature of super-resolution that multiple possible reconstructions exist for a given low-resolution image. Using normalizing flows, SRflow[23] achieves state-of-the-art perceptual quality by learning the distribution of the output instead of a deterministic output to one estimate. In this paper, we adapt the concepts of SRFlow to improve GAN-based super-resolution by properly implementing the one-to-many property. We modify the generator to estimate a distribution as a mapping from random noise. We improve the content loss that hampers the perceptual training objectives. We also propose additional training techniques to further enhance the perceptual quality of generated images. Using our proposed methods, we were able to improve the performance of ESRGAN[1] in x4 perceptual SR and achieve the state-of-the-art LPIPS score in x16 perceptual extreme SR by applying our methods to RFB-ESRGAN[21].
Towards Long-Form Video Understanding
Authors: Chao-yuan Wu, Philipp Krähenbühl
Date: 2021-06-21 00:00:00
Proceeding: CVPR 2021 1
Tasks: Action Recognition, Form, Video Recognition, Video Understanding

Our world offers a never-ending stream of visual stimuli, yet today's vision systems only accurately recognize patterns within a few seconds. These systems understand the present, but fail to contextualize it in past or future events. In this paper, we study long-form video understanding. We introduce a framework for modeling long-form videos and develop evaluation protocols on large-scale datasets. We show that existing state-of-the-art short-term models are limited for long-form tasks. A novel object-centric transformer-based video recognition architecture performs significantly better on 7 diverse tasks. It also outperforms comparable state-of-the-art on the AVA dataset.
TS2Vec: Towards Universal Representation of Time Series
Authors: Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, Bixiong Xu
Date: 2021-06-19 00:00:00
Tasks: Anomaly Detection, Contrastive Learning, Time Series, Time Series Analysis, Time Series Classification, Time Series Forecasting, Unsupervised Anomaly Detection

This paper presents TS2Vec, a universal framework for learning representations of time series in an arbitrary semantic level. Unlike existing methods, TS2Vec performs contrastive learning in a hierarchical way over augmented context views, which enables a robust contextual representation for each timestamp. Furthermore, to obtain the representation of an arbitrary sub-sequence in the time series, we can apply a simple aggregation over the representations of corresponding timestamps. We conduct extensive experiments on time series classification tasks to evaluate the quality of time series representations. As a result, TS2Vec achieves significant improvement over existing SOTAs of unsupervised time series representation on 125 UCR datasets and 29 UEA datasets. The learned timestamp-level representations also achieve superior results in time series forecasting and anomaly detection tasks. A linear regression trained on top of the learned representations outperforms previous SOTAs of time series forecasting. Furthermore, we present a simple way to apply the learned representations for unsupervised anomaly detection, which establishes SOTA results in the literature. The source code is publicly available at https://github.com/yuezhihan/ts2vec.
How Do Adam and Training Strategies Help BNNs Optimization?
Authors: Zechun Liu, Zhiqiang Shen, Shichao Li, Koen Helwegen, Dong Huang, Kwang-Ting Cheng
Date: 2021-06-21 00:00:00

The best performing Binary Neural Networks (BNNs) are usually attained using Adam optimization and its multi-step training variants. However, to the best of our knowledge, few studies explore the fundamental reasons why Adam is superior to other optimizers like SGD for BNN optimization or provide analytical explanations that support specific training strategies. To address this, in this paper we first investigate the trajectories of gradients and weights in BNNs during the training process. We show the regularization effect of second-order momentum in Adam is crucial to revitalize the weights that are dead due to the activation saturation in BNNs. We find that Adam, through its adaptive learning rate strategy, is better equipped to handle the rugged loss surface of BNNs and reaches a better optimum with higher generalization ability. Furthermore, we inspect the intriguing role of the real-valued weights in binary networks, and reveal the effect of weight decay on the stability and sluggishness of BNN optimization. Through extensive experiments and analysis, we derive a simple training scheme, building on existing Adam-based optimization, which achieves 70.5% top-1 accuracy on the ImageNet dataset using the same architecture as the state-of-the-art ReActNet while achieving 1.1% higher accuracy. Code and models are available at https://github.com/liuzechun/AdamBNN.
Boundary Graph Neural Networks for 3D Simulations
Authors: Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter
Date: 2021-06-21 00:00:00
Tasks: Computational Efficiency

The abundance of data has given machine learning considerable momentum in natural sciences and engineering, though modeling of physical processes is often difficult. A particularly tough problem is the efficient representation of geometric boundaries. Triangularized geometric boundaries are well understood and ubiquitous in engineering applications. However, it is notoriously difficult to integrate them into machine learning approaches due to their heterogeneity with respect to size and orientation. In this work, we introduce an effective theory to model particle-boundary interactions, which leads to our new Boundary Graph Neural Networks (BGNNs) that dynamically modify graph structures to obey boundary conditions. The new BGNNs are tested on complex 3D granular flow processes of hoppers, rotating drums and mixers, which are all standard components of modern industrial machinery but still have complicated geometry. BGNNs are evaluated in terms of computational efficiency as well as prediction accuracy of particle flows and mixing entropies. BGNNs are able to accurately reproduce 3D granular flows within simulation uncertainties over hundreds of thousands of simulation timesteps. Most notably, in our experiments, particles stay within the geometric objects without using handcrafted conditions or restrictions.
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning
Authors: Hao Tan, Jie Lei, Thomas Wolf, Mohit Bansal
Date: 2021-06-21 00:00:00
Tasks: Action Classification, Action Recognition, Contrastive Learning, Video Understanding

Video understanding relies on perceiving the global content and modeling its internal connections (e.g., causality, movement, and spatio-temporal correspondence). To learn these interactions, we apply a mask-then-predict pre-training task on discretized video tokens generated via VQ-VAE. Unlike language, where the text tokens are more independent, neighboring video tokens typically have strong correlations (e.g., consecutive video frames usually look very similar), and hence uniformly masking individual tokens will make the task too trivial to learn useful representations. To deal with this issue, we propose a block-wise masking strategy where we mask neighboring video tokens in both spatial and temporal domains. We also add an augmentation-free contrastive learning method to further capture the global content by predicting whether the video clips are sampled from the same video. We pre-train our model on uncurated videos and show that our pre-trained model can reach state-of-the-art results on several video understanding datasets (e.g., SSV2, Diving48). Lastly, we provide detailed analyses on model scalability and pre-training method design. Code is released at https://github.com/airsplay/vimpac.
Can poachers find animals from public camera trap images?
Authors: Sara Beery, Elizabeth Bondi
Date: 2021-06-21 00:00:00

To protect the location of camera trap data containing sensitive, high-target species, many ecologists randomly obfuscate the latitude and longitude of the camera when publishing their data. For example, they may publish a random location within a 1km radius of the true camera location for each camera in their network. In this paper, we investigate the robustness of geo-obfuscation for maintaining camera trap location privacy, and show via a case study that a few simple, intuitive heuristics and publicly available satellite rasters can be used to reduce the area likely to contain the camera by 87% (assuming random obfuscation within 1km), demonstrating that geo-obfuscation may be less effective than previously believed.