pwc-archive/papers-with-abstracts huggingface dataset:
An adaptive graph learning method for automated molecular interactions and properties predictions
Authors: Yuquan Li, Chang-Yu Hsieh, Ruiqiang Lu, Xiaoqing Gong, Xiaorui Wang, Pengyong Li, Shuo Liu, Yanan Tian, Dejun Jiang, Jiaxian Yan, Qifeng Bai, Huanxiang Liu, Shengyu Zhang, Xiaojun Yao
Date: 2022-06-23 00:00:00
Proceeding: Nature Machine Intelligence 2022 6
Tasks: Drug Discovery, Graph Learning, Graph Representation Learning, Molecular Property Prediction

Improving drug discovery efficiency is a core and long-standing challenge in drug discovery. For this purpose, many graph learning methods have been developed to search potential drug candidates with fast speed and low cost. In fact, the pursuit of high prediction performance on a limited number of datasets has crystallized their architectures and hyperparameters, making them lose advantage in repurposing to new data generated in drug discovery. Here we propose a flexible method that can adapt to any dataset and make accurate predictions. The proposed method employs an adaptive pipeline to learn from a dataset and output a predictor. Without any manual intervention, the method achieves far better prediction performance on all tested datasets than traditional methods, which are based on hand-designed neural architectures and other fixed items. In addition, we found that the proposed method is more robust than traditional methods and can provide meaningful interpretability. Given the above, the proposed method can serve as a reliable method to predict molecular interactions and properties with high adaptability, performance, robustness and interpretability. This work takes a solid step forward to the purpose of aiding researchers to design better drugs with high efficiency.
TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
Authors: Lifan Chen, Xiaoqin Tan, Dingyan Wang, Feisheng Zhong, Xiaohong Liu, Tianbiao Yang, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, Mingyue Zheng
Date: 2020-05-19 00:00:00
Proceeding: Bioinformatics 2020 5
Tasks: Drug Discovery

Motivation
Identifying compound–protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional structure account for a large part of potential biological targets, which requires developing methods using only protein sequence information to predict CPI. However, sequence-based CPI models may face some specific pitfalls, including using inappropriate datasets, hidden ligand bias and splitting datasets inappropriately, resulting in overestimation of their prediction performance.

Results
To address these issues, we here constructed new datasets specific for CPI prediction, proposed a novel transformer neural network named TransformerCPI, and introduced a more rigorous label reversal experiment to test whether a model learns true interaction features. TransformerCPI achieved much improved performance on the new experiments, and it can be deconvolved to highlight important interacting regions of protein sequences and compound atoms, which may contribute chemical biology studies with useful guidance for further ligand structural optimization.
Drug–target affinity prediction using graph neural network and contact maps
Authors: Mingjian Jiang, Zhen Li, Shugang Zhang, Shuang Wang, XiaoFeng Wang, Qing Yuan, Zhiqiang Wei
Date: 2020-06-01 00:00:00
Proceeding: RSC advances 2020 6
Tasks: Drug Design, Drug Discovery, Graph Neural Network, Prediction

Computer-aided drug design uses high-performance computers to simulate the tasks in drug design, which is a promising research area. Drug–target affinity (DTA) prediction is the most important step of computer-aided drug design, which could speed up drug development and reduce resource consumption. With the development of deep learning, the introduction of deep learning to DTA prediction and improving the accuracy have become a focus of research. In this paper, utilizing the structural information of molecules and proteins, two graphs of drug molecules and proteins are built up respectively. Graph neural networks are introduced to obtain their representations, and a method called DGraphDTA is proposed for DTA prediction. Specifically, the protein graph is constructed based on the contact map output from the prediction method, which could predict the structural characteristics of the protein according to its sequence. It can be seen from the test of various metrics on benchmark datasets that the method proposed in this paper has strong robustness and generalizability.
Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations
Authors: Wan Xiang Shen, Xian Zeng, Feng Zhu, Ya li Wang, Chu Qin, Ying Tan, Yu Yang Jiang, Yu Zong Chen
Date: 2021-03-01 00:00:00
Proceeding: Nature Machine Intelligence 2021 3
Tasks: Deep Learning

Successful deep learning critically depends on the representation of the learned objects. Recent state-of-the-art pharmaceutical deep learning models successfully exploit graph-based de novo learning of molecular representations. Nonetheless, the combined potential of human expert knowledge of molecular representations and convolution neural networks has not been adequately explored for enhanced learning of pharmaceutical properties. Here we show that broader exploration of human-knowledge-based molecular representations enables more enhanced deep learning of pharmaceutical properties. By broad learning of 1,456 molecular descriptors and 16,204 fingerprint features of 8,506,205 molecules, a new feature-generation method MolMap was developed for mapping these molecular descriptors and fingerprint features into robust two-dimensional feature maps. Convolution-neural-network-based MolMapNet models were constructed for out-of-the-box deep learning of pharmaceutical properties, which outperformed the graph-based and other established models on most of the 26 pharmaceutically relevant benchmark datasets and a novel dataset. The MolMapNet learned important features that are consistent with the literature-reported molecular features.
Critical Role of Simultaneous Reduction of Atmospheric Odd Oxygen for Winter Haze Mitigation
Authors: Xiao-Feng Huang, Li-Ming Cao, Xu-Dong Tian, * Qiao Zhu, Eri Saikawa, Li-Liang Lin, Yong Cheng, Ling-Yan He, * Min Hu, Yuan-Hang Zhang, Ke-Ding Lu, Yu-Han Liu, Kaspar Daellenbach, Jay G. Slowik, Qian Tang, Qiao-Li Zou, Xin Sun, Bing-Ye Xu, Lan Jiang, Ye-Min Shen, Nga Lee Ng, and André S. H. Prévôt*
Date: 2021-09-07 00:00:00
Proceeding: Environmental Science & Technology 2021 9

The lockdown due to COVID-19 created a rare opportunity to examine the nonlinear responses of secondary aerosols, which are formed through atmospheric oxidation of gaseous precursors, to intensive precursor emission reductions. Based on unique observational data sets from six supersites in eastern China during 2019−2021, we found that the lockdown caused considerable decreases (32−61%) in different secondary aerosol components in the study region because of similar-degree precursor reductions. However, due to insufficient combustionrelated volatile organic compound (VOC) reduction, odd oxygen (Ox =O3 +NO2) concentration, an indicator of the extent of photochemical processing, showed little change and did not promote more decreases in secondary aerosols. We also found that the Chinese provinces and international cities that experienced reduced Ox during the lockdown usually gained a greater simultaneous PM2.5 decrease than other provinces and cities with an increased Ox. Therefore, we argue that strict VOC control in winter, which has been largely ignored so far, is critical in future policies to mitigate winter haze more efficiently by reducing Ox simultaneously.
COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation
Authors: Ali Aouf, Kahina Amara, Hoceine Kennouche
Date: 2022-05-15 00:00:00
Proceeding: Computers and Graphics 2022 2022 5
Tasks: 2D Semantic Segmentation, COVID-19 Diagnosis, Specificity

With the Coronavirus disease 2019 (COVID-19) spread, causing a world pandemic, and recently, the virus new variants continue to appear, making the situation more challenging and threatening, the visual assessment and quantification by expert radiologists have become costly and error-prone. Hence, there is a need to propose a model to predict the COVID-19 cases at the earliest possible to control the disease spread. In order to assist the medical professionals and reduce workload and the time the COVID-19 diagnosis cycle takes, this paper proposes a novel neural network architecture termed as O-Net to automatically segment chest Computerised Tomography Ct-scans infected by COVID-19 with optimised computing power and memory occupation. The O-Net consists of two convolutional autoencoders with an upsampling channel and a downsampling channel. Experimental tests show our proposal’s effectiveness and potential, with a dice score of 0.86, pixel accuracy, precision, specificity of 0.99, 0.99, 0.98, respectively. Performance on the external dataset illustrates generalisation and scalability capabilities of the O-Net model to Ct-scan obtained from different scanners with different sizes. The second objective of this work is to introduce our virtual reality platform, COVIR, that visualises and manipulates 3D reconstructed lungs and segmented infected lesions caused by COVID-19. COVIR platform acts as a reading and visualisation support for medical practitioners to diagnose COVID-19 lung infection. The COVIR platform could be used for medical education professional practice and training. It was tested by Thirteen participants (medical staff, researchers, and collaborators), they conclude that the 3D VR visualisation of segmented Ct-Scan provides an aid diagnosis tool for better interpretation.
Score-based Generative Models for Calorimeter Shower Simulation
Authors: Vinicius Mikuni, Benjamin Nachman
Date: 2022-06-17 00:00:00

Score-based generative models are a new class of generative algorithms that have been shown to produce realistic images even in high dimensional spaces, currently surpassing other state-of-the-art models for different benchmark categories and applications. In this work we introduce CaloScore, a score-based generative model for collider physics applied to calorimeter shower generation. Three different diffusion models are investigated using the Fast Calorimeter Simulation Challenge 2022 dataset. CaloScore is the first application of a score-based generative model in collider physics and is able to produce high-fidelity calorimeter images for all datasets, providing an alternative paradigm for calorimeter shower simulation.
SoccerTrack: A Dataset and Tracking Algorithm for Soccer With Fish-Eye and Drone Videos
Authors: Atom Scott, Ikuma Uchida, Masaki Onishi, Yoshinari Kameda, Kazuhiro Fukui, Keisuke Fujii
Date: 2022-06-20 00:00:00
Proceeding: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2022 6
Tasks: 4k, 8k, Camera Calibration, Multi-Object Tracking

Tracking devices that can track both players and balls are critical to the performance of sports teams. Recently, significant effort has been focused on building larger broadcast sports video datasets. However, broadcast videos do not show the entire pitch and only provides partial information about the game. On the other hand, other camera perspectives can capture the whole field in a single frame, such as fish-eye and bird-eye view (drone) cameras. Unfortunately, there has not been a dataset where such data has been publicly shared until now. This paper proposes SoccerTrack, a dataset set consisting of GNSS and bounding box tracking data annotated on video captured with a 8K-resolution fish-eye camera and a 4K-resolution drone camera. In addition to a benchmark tracking algorithm, we include code for camera calibration and other preprocessing. Finally, we evaluate the tracking accuracy among a GNSS, fish-eye camera and drone camera data. SoccerTrack is expected to provide a more robust foundation for designing MOT algorithms that are less reliant on visual cues and more reliant on motion analysis. The dataset and related project code are available at https://github.com/AtomScott/SoccerTrack.
On Certifying and Improving Generalization to Unseen Domains
Authors: Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
Date: 2022-06-24 00:00:00
Tasks: Domain Generalization

Domain Generalization (DG) aims to learn models whose performance remains high on unseen domains encountered at test-time by using data from multiple related source domains. Many existing DG algorithms reduce the divergence between source distributions in a representation space to potentially align the unseen domain close to the sources. This is motivated by the analysis that explains generalization to unseen domains using distributional distance (such as the Wasserstein distance) to the sources. However, due to the openness of the DG objective, it is challenging to evaluate DG algorithms comprehensively using a few benchmark datasets. In particular, we demonstrate that the accuracy of the models trained with DG methods varies significantly across unseen domains, generated from popular benchmark datasets. This highlights that the performance of DG methods on a few benchmark datasets may not be representative of their performance on unseen domains in the wild. To overcome this roadblock, we propose a universal certification framework based on distributionally robust optimization (DRO) that can efficiently certify the worst-case performance of any DG method. This enables a data-independent evaluation of a DG method complementary to the empirical evaluations on benchmark datasets. Furthermore, we propose a training algorithm that can be used with any DG method to provably improve their certified performance. Our empirical evaluation demonstrates the effectiveness of our method at significantly improving the worst-case loss (i.e., reducing the risk of failure of these models in the wild) without incurring a significant performance drop on benchmark datasets.
BYOL-S: Learning Self-supervised Speech Representations by Bootstrapping
Authors: Gasser Elbanna, Neil Scheidwasser-Clow, Mikolaj Kegler, Pierre Beckmann, Karl El Hajal, Milos Cernak
Date: 2022-06-24 00:00:00
Tasks: Scene Classification, Self-Supervised Learning

Methods for extracting audio and speech features have been studied since pioneering work on spectrum analysis decades ago. Recent efforts are guided by the ambition to develop general-purpose audio representations. For example, deep neural networks can extract optimal embeddings if they are trained on large audio datasets. This work extends existing methods based on self-supervised learning by bootstrapping, proposes various encoder architectures, and explores the effects of using different pre-training datasets. Lastly, we present a novel training framework to come up with a hybrid audio representation, which combines handcrafted and data-driven learned audio features. All the proposed representations were evaluated within the HEAR NeurIPS 2021 challenge for auditory scene classification and timestamp detection tasks. Our results indicate that the hybrid model with a convolutional transformer as the encoder yields superior performance in most HEAR challenge tasks.
A Test for Evaluating Performance in Human-Computer Systems
Authors: Andres Campero, Michelle Vaccaro, Jaeyoon Song, Haoran Wen, Abdullah Almaatouq, Thomas W. Malone
Date: 2022-06-24 00:00:00

The Turing test for comparing computer performance to that of humans is well known, but, surprisingly, there is no widely used test for comparing how much better human-computer systems perform relative to humans alone, computers alone, or other baselines. Here, we show how to perform such a test using the ratio of means as a measure of effect size. Then we demonstrate the use of this test in three ways. First, in an analysis of 79 recently published experimental results, we find that, surprisingly, over half of the studies find a decrease in performance, the mean and median ratios of performance improvement are both approximately 1 (corresponding to no improvement at all), and the maximum ratio is 1.36 (a 36% improvement). Second, we experimentally investigate whether a higher performance improvement ratio is obtained when 100 human programmers generate software using GPT-3, a massive, state-of-the-art AI system. In this case, we find a speed improvement ratio of 1.27 (a 27% improvement). Finally, we find that 50 human non-programmers using GPT-3 can perform the task about as well as--and less expensively than--the human programmers. In this case, neither the non-programmers nor the computer would have been able to perform the task alone, so this is an example of a very strong form of human-computer synergy.
Using BERT Embeddings to Model Word Importance in Conversational Transcripts for Deaf and Hard of Hearing Users
Authors: Akhter Al Amin, Saad Hassan, Cecilia O. Alm, Matt Huenerfauth
Date: 2022-06-24 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Word Embeddings

Deaf and hard of hearing individuals regularly rely on captioning while watching live TV. Live TV captioning is evaluated by regulatory agencies using various caption evaluation metrics. However, caption evaluation metrics are often not informed by preferences of DHH users or how meaningful the captions are. There is a need to construct caption evaluation metrics that take the relative importance of words in a transcript into account. We conducted correlation analysis between two types of word embeddings and human-annotated labeled word-importance scores in existing corpus. We found that normalized contextualized word embeddings generated using BERT correlated better with manually annotated importance scores than word2vec-based word embeddings. We make available a pairing of word embeddings and their human-annotated importance scores. We also provide proof-of-concept utility by training word importance models, achieving an F1-score of 0.57 in the 6-class word importance classification task.
Robustness to corruption in pre-trained Bayesian neural networks
Authors: Xi Wang, Laurence Aitchison
Date: 2022-06-24 00:00:00

We develop ShiftMatch, a new training-data-dependent likelihood for robustness to corruption in Bayesian neural networks (BNNs). ShiftMatch is inspired by the training-data-dependent "EmpCov" priors from Izmailov et al. (2021a), and efficiently matches test-time spatial correlations to those at training time. Critically, ShiftMatch is designed to leave the neural network's training time likelihood unchanged, allowing it to use publicly available samples from pre-trained BNNs. Using pre-trained HMC samples, ShiftMatch gives strong performance improvements on CIFAR-10-C, outperforms EmpCov priors (though ShiftMatch uses extra information from a minibatch of corrupted test points), and is perhaps the first Bayesian method capable of convincingly outperforming plain deep ensembles.
Achievement and Fragility of Long-term Equitability
Authors: Andrea Simonetto, Ivano Notarnicola
Date: 2022-06-24 00:00:00
Tasks: Decision Making, Fairness

Equipping current decision-making tools with notions of fairness, equitability, or other ethically motivated outcomes, is one of the top priorities in recent research efforts in machine learning, AI, and optimization. In this paper, we investigate how to allocate limited resources to {locally interacting} communities in a way to maximize a pertinent notion of equitability. In particular, we look at the dynamic setting where the allocation is repeated across multiple periods (e.g., yearly), the local communities evolve in the meantime (driven by the provided allocation), and the allocations are modulated by feedback coming from the communities themselves. We employ recent mathematical tools stemming from data-driven feedback online optimization, by which communities can learn their (possibly unknown) evolution, satisfaction, as well as they can share information with the deciding bodies. We design dynamic policies that converge to an allocation that maximize equitability in the long term. We further demonstrate our model and methodology with realistic examples of healthcare and education subsidies design in Sub-Saharian countries. One of the key empirical takeaways from our setting is that long-term equitability is fragile, in the sense that it can be easily lost when deciding bodies weigh in other factors (e.g., equality in allocation) in the allocation strategy. Moreover, a naive compromise, while not providing significant advantage to the communities, can promote inequality in social outcomes.
Toward multi-target self-organizing pursuit in a partially observable Markov game
Authors: Lijun Sun, Yu-Cheng Chang, Chao Lyu, Ye Shi, Yuhui Shi, Chin-Teng Lin
Date: 2022-06-24 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Multi-Agent Path Finding, Multi-agent Reinforcement Learning

The multiple-target self-organizing pursuit (SOP) problem has wide applications and has been considered a challenging self-organization game for distributed systems, in which intelligent agents cooperatively pursue multiple dynamic targets with partial observations. This work proposes a framework for decentralized multi-agent systems to improve the implicit coordination capabilities in search and pursuit. We model a self-organizing system as a partially observable Markov game (POMG) featured by large-scale, decentralization, partial observation, and noncommunication. The proposed distributed algorithm: fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit. FSC2 includes a coordinated multi-agent deep reinforcement learning (MARL) method that enables homogeneous agents to learn natural SOS patterns. Additionally, we propose a fuzzy-based distributed task allocation method, which locally decomposes multi-target SOP into several single-target pursuit problems. The cooperative coevolution principle is employed to coordinate distributed pursuers for each single-target pursuit problem. Therefore, the uncertainties of inherent partial observation and distributed decision-making in the POMG can be alleviated. The experimental results demonstrate that by decomposing the SOP task, FSC2 achieves superior performance compared with other implicit coordination policies fully trained by general MARL algorithms. The scalability of FSC2 is proved that up to 2048 FSC2 agents perform efficient multi-target SOP with almost 100 percent capture rates. Empirical analyses and ablation studies verify the interpretability, rationality, and effectiveness of component algorithms in FSC2.