pwc-archive/papers-with-abstracts huggingface dataset:
Structured Multi-task Learning for Molecular Property Prediction
Authors: Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang
Date: 2022-02-22 00:00:00
Tasks: Drug Discovery, Graph Neural Network, Molecular Property Prediction, Multi-Task Learning, Prediction, Property Prediction, Relation, Structured Prediction

Multi-task learning for molecular property prediction is becoming increasingly important in drug discovery. However, in contrast to other domains, the performance of multi-task learning in drug discovery is still not satisfying as the number of labeled data for each task is too limited, which calls for additional data to complement the data scarcity. In this paper, we study multi-task learning for molecular property prediction in a novel setting, where a relation graph between tasks is available. We first construct a dataset (ChEMBL-STRING) including around 400 tasks as well as a task relation graph. Then to better utilize such relation graph, we propose a method called SGNN-EBM to systematically investigate the structured task modeling from two perspectives. (1) In the \emph{latent} space, we model the task representations by applying a state graph neural network (SGNN) on the relation graph. (2) In the \emph{output} space, we employ structured prediction with the energy-based model (EBM), which can be efficiently trained through noise-contrastive estimation (NCE) approach. Empirical results justify the effectiveness of SGNN-EBM. Code is available on https://github.com/chao1224/SGNN-EBM.
Collective Variable for Metadynamics Derived from AlphaFold Output
Authors: Vojtěch Spiwok, Martin Kurečka, Aleš Křenek
Date: 2022-02-17 00:00:00
Tasks: Prediction, Protein Folding

AlphaFold is a neural-network-based tool for the prediction of 3D structures of protein. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, which makes it the best available structure prediction tool. One of the outputs of AlphaFold is the probability profile of residue-residue distances. This makes it possible to score any conformation of the studied protein to express its compliance with the AlphaFold model. Here we show how this score can be used to drive protein folding simulation by metadynamics and parallel tempering metadynamics. By parallel tempering metadynamics, we simulated folding of a mini-protein Trp-cage beta hairpin and predicted their folding equilibria. We see the potential of AlphaFold-based collective variable in applications beyond structure prediction, such as in structure refinement or prediction of the outcome of a mutation.
Correspondence on ACMG STATEMENT: ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG) by Miller et al
Authors: Kathryn A. McGurk, Sean L. Zheng, Albert Henry, Katherine Josephs, Matthew Edwards, Antonio de Marvao, Nicola Whiffin, Angharad Roberts, Thomas R. Lumbers, Declan P. O Regan, James S. Ware
Date: 2022-03-09 00:00:00
Tasks: Diagnostic, Medical Genetics

We were interested to read the recent update on recommendations for reporting of secondary findings in clinical sequencing1, and the accompanying updated list of genes in which secondary findings should be sought (ACMG SF v3.0)2. Though the authors discuss challenges around incomplete penetrance in considerable detail, we are concerned that the recommendations do not fully convey the degree of uncertainty regarding the penetrance of variants in genes associated with inherited cardiomyopathies, which make up almost a quarter of the list. Since penetrance is incomplete and age-related, individuals found to carry variants will often require surveillance, rather than a one-off definitive diagnostic assessment. There is a lack of evidence regarding benefits, harms, and healthcare costs associated with opportunistic screening.
Fluctuation-driven plasticity allows for flexible rewiring of neuronal assemblies
Authors: Federico Devalle, Alex Roxin
Date: 2022-03-09 00:00:00

Synaptic connections in neuronal circuits are modulated by pre- and post-synaptic spiking activity. Heuristic models of this process of synaptic plasticity can provide excellent fits to results from in-vitro experiments in which pre- and post-synaptic spiking is varied in a controlled fashion. However, the plasticity rules inferred from fitting such data are inevitably unstable, in that given constant pre- and post-synaptic activity the synapse will either fully potentiate or depress. This instability can be held in check by adding additional mechanisms, such as homeostasis. Here we consider an alternative scenario in which the plasticity rule itself is stable. When this is the case, net potentiation or depression only occur when pre- and post-synaptic activity vary in time, e.g. when driven by time-varying inputs. We study how the features of such inputs shape the recurrent synaptic connections in models of neuronal circuits. In the case of oscillatory inputs, the resulting structure is strongly affected by the phase relationship between drive to different neurons. In large networks, distributed phases tend to lead to hierarchical clustering. Our results may be of relevance for understanding the effect of sensory-driven inputs, which are by nature time-varying, on synaptic plasticity, and hence on learning and memory.
On the Fitness Landscapes of Interdependency Models in the Travelling Thief Problem
Authors: Mohamed El Yafrani, Marcella Scoczynski, Myriam Delgado, Ricardo Lüders, Peter Nielsen, Markus Wagner
Date: 2022-02-28 00:00:00

Since its inception in 2013, the Travelling Thief Problem (TTP) has been widely studied as an example of problems with multiple interconnected sub-problems. The dependency in this model arises when tying the travelling time of the "thief" to the weight of the knapsack. However, other forms of dependency as well as combinations of dependencies should be considered for investigation, as they are often found in complex real-world problems. Our goal is to study the impact of different forms of dependency in the TTP using a simple local search algorithm. To achieve this, we use Local Optima Networks, a technique for analysing the fitness landscape.
'Beach' to 'Bitch': Inadvertent Unsafe Transcription of Kids' Content on YouTube
Authors: Krithika Ramesh, Ashiqur R. KhudaBukhsh, Sumeet Kumar
Date: 2022-02-17 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Hallucination, speech-recognition, Speech Recognition

Over the last few years, YouTube Kids has emerged as one of the highly competitive alternatives to television for children's entertainment. Consequently, YouTube Kids' content should receive an additional level of scrutiny to ensure children's safety. While research on detecting offensive or inappropriate content for kids is gaining momentum, little or no current work exists that investigates to what extent AI applications can (accidentally) introduce content that is inappropriate for kids. In this paper, we present a novel (and troubling) finding that well-known automatic speech recognition (ASR) systems may produce text content highly inappropriate for kids while transcribing YouTube Kids' videos. We dub this phenomenon as \emph{inappropriate content hallucination}. Our analyses suggest that such hallucinations are far from occasional, and the ASR systems often produce them with high confidence. We release a first-of-its-kind data set of audios for which the existing state-of-the-art ASR systems hallucinate inappropriate content for kids. In addition, we demonstrate that some of these errors can be fixed using language models.
Deep Learning for Sleep Stages Classification: Modified Rectified Linear Unit Activation Function and Modified Orthogonal Weight Initialisation
Authors: Akriti Bhusal, Abeer Alsadoon, P. W. C. Prasad, Nada Alsalami, Tarik A. Rashid
Date: 2022-02-18 00:00:00
Tasks: Classification

Background and Aim: Each stage of sleep can affect human health, and not getting enough sleep at any stage may lead to sleep disorder like parasomnia, apnea, insomnia, etc. Sleep-related diseases could be diagnosed using Convolutional Neural Network Classifier. However, this classifier has not been successfully implemented into sleep stage classification systems due to high complexity and low accuracy of classification. The aim of this research is to increase the accuracy and reduce the learning time of Convolutional Neural Network Classifier. Methodology: The proposed system used a modified Orthogonal Convolutional Neural Network and a modified Adam optimisation technique to improve the sleep stage classification accuracy and reduce the gradient saturation problem that occurs due to sigmoid activation function. The proposed system uses Leaky Rectified Linear Unit (ReLU) instead of sigmoid activation function as an activation function. Results: The proposed system called Enhanced Sleep Stage Classification system (ESSC) used six different databases for training and testing the proposed model on the different sleep stages. These databases are University College Dublin database (UCD), Beth Israel Deaconess Medical Center MIT database (MIT-BIH), Sleep European Data Format (EDF), Sleep EDF Extended, Montreal Archive of Sleep Studies (MASS), and Sleep Heart Health Study (SHHS). Our results show that the gradient saturation problem does not exist anymore. The modified Adam optimiser helps to reduce the noise which in turn result in faster convergence time. Conclusion: The convergence speed of ESSC is increased along with better classification accuracy compared to the state of art solution.
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model
Authors: Tianyu Shi, Yifei Ai, Omar ElSamadisy, Baher Abdulhai
Date: 2022-03-03 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Deep Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In the coming years and decades, autonomous vehicles (AVs) will become increasingly prevalent, offering new opportunities for safer and more convenient travel and potentially smarter traffic control methods exploiting automation and connectivity. Car following is a prime function in autonomous driving. Car following based on reinforcement learning has received attention in recent years with the goal of learning and achieving performance levels comparable to humans. However, most existing RL methods model car following as a unilateral problem, sensing only the vehicle ahead. Recent literature, however, Wang and Horn [16] has shown that bilateral car following that considers the vehicle ahead and the vehicle behind exhibits better system stability. In this paper we hypothesize that this bilateral car following can be learned using RL, while learning other goals such as efficiency maximisation, jerk minimization, and safety rewards leading to a learned model that outperforms human driving. We propose and introduce a Deep Reinforcement Learning (DRL) framework for car following control by integrating bilateral information into both state and reward function based on the bilateral control model (BCM) for car following control. Furthermore, we use a decentralized multi-agent reinforcement learning framework to generate the corresponding control action for each agent. Our simulation results demonstrate that our learned policy is better than the human driving policy in terms of (a) inter-vehicle headways, (b) average speed, (c) jerk, (d) Time to Collision (TTC) and (e) string stability.
Restoring and attributing ancient texts using deep neural networks
Authors: Yannis Assael, Thea Sommerschield, Brendan Shillingford, Mahyar Bordbar, John Pavlopoulos, Marita Chatzipanagiotou, Ion Androutsopoulos, Jonathan Prag, Nando de Freitas
Date: 2022-03-09 00:00:00
Proceeding: Nature 2022 3
Tasks: Ancient Text Restoration, Attribute

Ancient history relies on disciplines such as epigraphy—the study of inscribed texts known as inscriptions—for evidence of the thought, language, society and history of past civilizations1. However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original location and their date of writing is steeped in uncertainty. Here we present Ithaca, a deep neural network for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian’s workflow. The architecture of Ithaca focuses on collaboration, decision support and interpretability. While Ithaca alone achieves 62% accuracy when restoring damaged texts, the use of Ithaca by historians improved their accuracy from 25% to 72%, confirming the synergistic effect of this research tool. Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history. This research shows how models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians, transformationally impacting the way that we study and write about one of the most important periods in human history.
AlignedReID++: Dynamically matching local information for person re-identification
Authors: HaoLuo，WeiJiangXuan，ZhangXingFan，JingjingQian，ChiZhang
Date: 2019-10-01 00:00:00
Proceeding: Pattern Recognition 2019 10
Tasks: Human Detection, Person Re-Identification, Pose Estimation

Person re-identification (ReID) is a challenging problem, where global features of person images are not enough to solve unaligned image pairs. Many previous works used human pose information to acquire aligned local features to boost the performance. However, those methods need extra labeled data to train an available human pose estimation model. In this paper, we propose a novel method named Dynamically Matching Local Information (DMLI) that could dynamically align local information without requiring extra supervision. DMLI could achieve better performance, especially when encountering the human pose misalignment caused by inaccurate person detection boxes. Then, we propose a deep model name AlignedReID++ which is jointly learned with global features and local feature based on DMLI. AlignedReID++ improves the performance of global features, and could use DMLI to further increase accuracy in the inference phase. Experiments show effectiveness of our proposed method in comparison with several state-of-the-art person ReID approaches. Additionally, it achieves rank-1 accuracy of 92.8% on Market1501 and 86.2% on DukeMTMCReID with ResNet50. The code and models have been released2
A Comparative Analysis of Machine Learning Approaches for Automated Face Mask Detection During COVID-19
Authors: Junaed Younus Khan, Md Abdullah Al Alamin
Date: 2021-12-15 00:00:00
Tasks: Transfer Learning

The World Health Organization (WHO) has recommended wearing face masks as one of the most effective measures to prevent COVID-19 transmission. In many countries, it is now mandatory to wear face masks, specially in public places. Since manual monitoring of face masks is often infeasible in the middle of the crowd, automatic detection can be beneficial. To facilitate that, we explored a number of deep learning models (i.e., VGG1, VGG19, ResNet50) for face-mask detection and evaluated them on two benchmark datasets. We also evaluated transfer learning (i.e., VGG19, ResNet50 pre-trained on ImageNet) in this context. We find that while the performances of all the models are quite good, transfer learning models achieve the best performance. Transfer learning improves the performance by 0.10\%--0.40\% with 30\% less training time. Our experiment also shows these high-performing models are not quite robust for real-world cases where the test dataset comes from a different distribution. Without any fine-tuning, the performance of these models drops by 47\% in cross-domain settings.
Distill and De-bias: Mitigating Bias in Face Verification using Knowledge Distillation
Authors: Prithviraj Dhar, Joshua Gleason, Aniket Roy, Carlos D. Castillo, P. Jonathon Phillips, Rama Chellappa
Date: 2021-12-17 00:00:00
Tasks: Attribute, Face Recognition, Face Verification, Knowledge Distillation

Face recognition networks generally demonstrate bias with respect to sensitive attributes like gender, skintone etc. For gender and skintone, we observe that the regions of the face that a network attends to vary by the category of an attribute. This might contribute to bias. Building on this intuition, we propose a novel distillation-based approach called Distill and De-bias (D&D) to enforce a network to attend to similar face regions, irrespective of the attribute category. In D&D, we train a teacher network on images from one category of an attribute; e.g. light skintone. Then distilling information from the teacher, we train a student network on images of the remaining category; e.g., dark skintone. A feature-level distillation loss constrains the student network to generate teacher-like representations. This allows the student network to attend to similar face regions for all attribute categories and enables it to reduce bias. We also propose a second distillation step on top of D&D, called D&D++. Here, we distill the `un-biasedness' of the D&D network into a new student network, the D&D++ network, while training this new network on all attribute categories; e.g., both light and dark skintones. This helps us train a network that is less biased for an attribute, while obtaining higher face verification performance than D&D. We show that D&D++ outperforms existing baselines in reducing gender and skintone bias on the IJB-C dataset, while obtaining higher face verification performance than existing adversarial de-biasing methods. We evaluate the effectiveness of our proposed methods on two state-of-the-art face recognition networks: ArcFace and Crystalface.
Face Deblurring Based on Separable Normalization and Adaptive Denormalization
Authors: Xian Zhang, Hao Zhang, Jiancheng Lv, Xiaojie Li
Date: 2021-12-18 00:00:00
Tasks: Deblurring, Face Parsing, SSIM

Face deblurring aims to restore a clear face image from a blurred input image with more explicit structure and facial details. However, most conventional image and face deblurring methods focus on the whole generated image resolution without consideration of special face part texture and generally produce unsufficient details. Considering that faces and backgrounds have different distribution information, in this study, we designed an effective face deblurring network based on separable normalization and adaptive denormalization (SNADNet). First, We fine-tuned the face parsing network to obtain an accurate face structure. Then, we divided the face parsing feature into face foreground and background. Moreover, we constructed a new feature adaptive denormalization to regularize fafcial structures as a condition of the auxiliary to generate more harmonious and undistorted face structure. In addition, we proposed a texture extractor and multi-patch discriminator to enhance the generated facial texture information. Experimental results on both CelebA and CelebA-HQ datasets demonstrate that the proposed face deblurring network restores face structure with more facial details and performs favorably against state-of-the-art methods in terms of structured similarity indexing method (SSIM), peak signal-to-noise ratio (PSNR), Frechet inception distance (FID) and L1, and qualitative comparisons.
High-Accuracy RGB-D Face Recognition via Segmentation-Aware Face Depth Estimation and Mask-Guided Attention Network
Authors: Meng-Tzu Chiu, Hsun-Ying Cheng, Chien-Yi Wang, Shang-Hong Lai
Date: 2021-12-22 00:00:00
Tasks: Depth Estimation, Face Recognition, Segmentation, Semantic Segmentation

Deep learning approaches have achieved highly accurate face recognition by training the models with very large face image datasets. Unlike the availability of large 2D face image datasets, there is a lack of large 3D face datasets available to the public. Existing public 3D face datasets were usually collected with few subjects, leading to the over-fitting problem. This paper proposes two CNN models to improve the RGB-D face recognition task. The first is a segmentation-aware depth estimation network, called DepthNet, which estimates depth maps from RGB face images by including semantic segmentation information for more accurate face region localization. The other is a novel mask-guided RGB-D face recognition model that contains an RGB recognition branch, a depth map recognition branch, and an auxiliary segmentation mask branch with a spatial attention module. Our DepthNet is used to augment a large 2D face image dataset to a large RGB-D face dataset, which is used for training an accurate RGB-D face recognition model. Furthermore, the proposed mask-guided RGB-D face recognition model can fully exploit the depth map and segmentation mask information and is more robust against pose variation than previous methods. Our experimental results show that DepthNet can produce more reliable depth maps from face images with the segmentation mask. Our mask-guided face recognition model outperforms state-of-the-art methods on several public 3D face datasets.
Improving Face-Based Age Estimation with Attention-Based Dynamic Patch Fusion
Authors: Haoyi Wang, Victor Sanchez, Chang-Tsun Li
Date: 2021-12-19 00:00:00
Tasks: Age Estimation

With the increasing popularity of convolutional neural networks (CNNs), recent works on face-based age estimation employ these networks as the backbone. However, state-of-the-art CNN-based methods treat each facial region equally, thus entirely ignoring the importance of some facial patches that may contain rich age-specific information. In this paper, we propose a face-based age estimation framework, called Attention-based Dynamic Patch Fusion (ADPF). In ADPF, two separate CNNs are implemented, namely the AttentionNet and the FusionNet. The AttentionNet dynamically locates and ranks age-specific patches by employing a novel Ranking-guided Multi-Head Hybrid Attention (RMHHA) mechanism. The FusionNet uses the discovered patches along with the facial image to predict the age of the subject. Since the proposed RMHHA mechanism ranks the discovered patches based on their importance, the length of the learning path of each patch in the FusionNet is proportional to the amount of information it carries (the longer, the more important). ADPF also introduces a novel diversity loss to guide the training of the AttentionNet and reduce the overlap among patches so that the diverse and important patches are discovered. Through extensive experiments, we show that our proposed framework outperforms state-of-the-art methods on several age estimation benchmark datasets.