pwc-archive/papers-with-abstracts huggingface dataset:
Competency of the Developmental Layer Alters Evolutionary Dynamics in an Artificial Embryogeny Model of Morphogenesis
Authors: Lakshwin Shreesha, Michael Levin
Date: 2022-07-29 00:00:00
Tasks: Anatomy

Biological genotypes do not code directly for phenotypes; developmental physiology is the control layer that separates genomes from capacities ascertained by selection. A key aspect is competency, as cells are not a passive material but descendants of unicellular organisms with complex context-sensitive capabilities. We used an evolutionary simulation in the context of minimal artificial embryogeny to probe the effects of different degrees of cellular competency on evolutionary dynamics. Virtual embryos consisted of a single axis of positional information values provided by cells' genomes, operated upon by an evolutionary cycle in which embryos' fitness was proportional to monotonicity of the axial gradient. Evolutionary dynamics were evaluated in two modes: hardwired "mosaic" development (genotype directly encodes phenotype), and a more realistic mode in which cells interact prior to evaluation by the fitness function ("regulative" development). Even minimal competency with respect to improving their position in the embryo results in better performance of the evolutionary search. Crucially, we observed that as competency of cells masks the raw fitness of the genomes, the phenotypic fitness gains are then mostly due to improvements of cells' developmental problem-solving capacities, not the structural genome. This suggests the existence of a powerful ratchet mechanism: evolution progressively becomes locked in to improvements in the intelligence of its agential substrate, with reduced pressure on the structural genome. A feedback loop in which evolution increasingly puts more effort into the developmental software than perfecting the hardware explains the very puzzling divergence of genome from anatomy in species like planaria, identifies a possible drive for scaling intelligence over time, and suggests strategies for engineering novel systems in silico and in bioengineering.
Benefits and limitations of a new hydraulic performance model
Authors: Fabian C. Weigend, Edward Gray, Oliver Obst, Jason Siegler
Date: 2022-07-28 00:00:00

Purpose: Performance models are important tools for coaches and athletes to optimise competition outcomes or training schedules. A recently published hydraulic performance model has been reported to outperform established work-balance models in predicting recovery during intermittent exercise. The new hydraulic model was optimised to predict exercise recovery dynamics. In this work, we hypothesised that the benefits of the model come at the cost of inaccurate predictions of metabolic responses to exercise such as $\dot{V}_{\mathrm{O}_2}$. Methods: Hydraulic model predictions were compared to breath-by-breath $\dot{V}_{\mathrm{O}_2}$ data from 25 constant high-intensity exercise tests of 5 participants (age $32\pm7.8$ years, weight $73.6 \pm 5.81$ kg, $\dot{V}_{\mathrm{O}_2\mathrm{max}} \; 3.59 \pm 0.62$ L/min). Each test was performed to volitional exhaustion on a cycle ergometer with a duration between 2 and 12 min. The comparison focuses on the onset of $\dot{V}_{\mathrm{O}_2}$ kinetics. Results: On average, the hydraulic model predicted peak $\dot{V}_{\mathrm{O}_2}$ during exercise $216\pm113$~s earlier than observed in the data. The new hydraulic model also did not predict the so-called $\dot{V}_{\mathrm{O}_2}$ slow component and made the unrealistic assumption that there is no $\dot{V}_{\mathrm{O}_2}$ at the onset of exercise. Conclusion: While the new hydraulic model may be a powerful tool for predicting energy recovery, it should not be used to predict metabolic responses during high-intensity exercise. The present study contributes towards a more holistic picture of the benefits and limitations of the new hydraulic model. Data and code are published as open source.
Analysis of VIX-linked fee incentives in variable annuities via continuous-time Markov chain approximation
Authors: Zhenyu Cui, Anne MacKay, Marie-Claude Vachon
Date: 2022-07-29 00:00:00

We consider the pricing of variable annuities (VAs) with general fee structures under popular stochastic volatility models such as Heston, Hull-White, Scott, $\alpha$-Hypergeometric, $3/2$, and $4/2$ models. In particular, we analyze the impact of different VIX-linked fee structures on the optimal surrender strategy of a VA contract with guaranteed minimum maturity benefit (GMMB). Under the assumption that the VA contract can be surrendered before maturity, the pricing of a VA contract corresponds to an optimal stopping problem with an unbounded, time-dependent, and discontinuous payoff function. We develop efficient algorithms for the pricing of VA contracts using a two-layer continuous-time Markov chain approximation for the fund value process. When the contract is kept until maturity and under a general fee structure, we show that the value of the contract can be approximated by a closed-form matrix expression. We also provide a quick and simple way to determine the value of early surrenders via a recursive algorithm and give an easy procedure to approximate the optimal surrender surface. We show numerically that the optimal surrender strategy is more robust to changes in the volatility of the account value when the fee is linked to the VIX index.
Replacing the Framingham-based equation for prediction of cardiovascular disease risk and adverse outcome by using artificial intelligence and retinal imaging
Authors: Ehsan Vaghefi, David Squirrell, Songyang An, Song Yang, John Marshall
Date: 2022-07-17 00:00:00

Purpose: To create and evaluate the accuracy of an artificial intelligence Deep learning platform (ORAiCLE) capable of using only retinal fundus images to predict both an individuals overall 5 year cardiovascular risk (CVD) and the relative contribution of the component risk factors that comprise this risk. Methods: We used 165,907 retinal images from a database of 47,236 patient visits. Initially, each image was paired with biometric data age, ethnicity, sex, presence and duration of diabetes a HDL/LDL ratios as well as any CVD event wtihin 5 years of the retinal image acquisition. A risk score based on Framingham equations was calculated. The real CVD event rate was also determined for the individuals and overall population. Finally, ORAiCLE was trained using only age, ethnicity, sex plus retinal images. Results: Compared to Framingham-based score, ORAiCLE was up to 12% more accurate in prediciting cardiovascular event in he next 5-years, especially for the highest risk group of people. The reliability and accuracy of each of the restrictive models was suboptimal to ORAiCLE performance ,indicating that it was using data from both sets of data to derive its final results. Conclusion: Retinal photography is inexpensive and only minimal training is required to acquire them as fully automated, inexpensive camera systems are now widely available. As such, AI-based CVD risk algorithms such as ORAiCLE promise to make CV health screening more accurate, more afforadable and more accessible for all. Furthermore, ORAiCLE unique ability to assess the relative contribution of the components that comprise an individuals overall risk would inform treatment decisions based on the specific needs of an individual, thereby increasing the likelihood of positive health outcomes.
Active Distribution System Coordinated Control Method via Artificial Intelligence
Authors: Matthew Lau, Kayla Thames, Sakis Meliopoulos
Date: 2022-07-12 00:00:00
Tasks: global-optimization

The increasing deployment of end use power resources in distribution systems created active distribution systems. Uncontrolled active distribution systems exhibit wide variations of voltage and loading throughout the day as some of these resources operate under max power tracking control of highly variable wind and solar irradiation while others exhibit random variations and/or dependency on weather conditions. It is necessary to control the system to provide power reliably and securely under normal voltages and frequency. Classical optimization approaches to control the system towards this goal suffer from the dimensionality of the problem and the need for a global optimization approach to coordinate a huge number of small resources. Artificial Intelligence (AI) methods offer an alternative that can provide a practical approach to this problem. We suggest that neural networks with self-attention mechanisms have the potential to aid in the optimization of the system. In this paper, we present this approach and provide promising preliminary results.
Viewstamped Replication Revisited
Authors: Barbara Liskov, James Cowling
Date: 2012-07-23 00:00:00
Proceeding: MIT-CSAIL-TR 2012 7

This paper presents an updated version of Viewstamped Replication, a replication technique that handles failures in which nodes crash. It describes how client requests are handled, how the group reorganizes when a replica fails, and how a failed replica is able to rejoin the group. The paper also describes a number of important optimizations and presents a protocol for handling reconfigurations that can change both the group membership and the number of failures the group is able to handle.
DAS: Densely-Anchored Sampling for Deep Metric Learning
Authors: Lizhao Liu, Shangxin Huang, Zhuangwei Zhuang, Ran Yang, Mingkui Tan, YaoWei Wang
Date: 2022-07-30 00:00:00
Tasks: Face Recognition, Image Retrieval, Metric Learning, Retrieval

Deep Metric Learning (DML) serves to learn an embedding function to project semantically similar data into nearby embedding space and plays a vital role in many applications, such as image retrieval and face recognition. However, the performance of DML methods often highly depends on sampling methods to choose effective data from the embedding space in the training. In practice, the embeddings in the embedding space are obtained by some deep models, where the embedding space is often with barren area due to the absence of training points, resulting in so called "missing embedding" issue. This issue may impair the sample quality, which leads to degenerated DML performance. In this work, we investigate how to alleviate the "missing embedding" issue to improve the sampling quality and achieve effective DML. To this end, we propose a Densely-Anchored Sampling (DAS) scheme that considers the embedding with corresponding data point as "anchor" and exploits the anchor's nearby embedding space to densely produce embeddings without data points. Specifically, we propose to exploit the embedding space around single anchor with Discriminative Feature Scaling (DFS) and multiple anchors with Memorized Transformation Shifting (MTS). In this way, by combing the embeddings with and without data points, we are able to provide more embeddings to facilitate the sampling process thus boosting the performance of DML. Our method is effortlessly integrated into existing DML frameworks and improves them without bells and whistles. Extensive experiments on three benchmark datasets demonstrate the superiority of our method.
A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance
Authors: Jae-Won Choi, Dae-Yong Hong, Chan Jung, Eugene Hwang, Sung-Hyuk Park, Seung-Young Roh
Date: 2022-07-30 00:00:00
Tasks: Data Augmentation, MULTI-VIEW LEARNING

The performances of commonly used electrocardiogram (ECG) diagnosis models have recently improved with the introduction of deep learning (DL). However, the impact of various combinations of multiple DL components and/or the role of data augmentation techniques on the diagnosis have not been sufficiently investigated. This study proposes an ensemble-based multi-view learning approach with an ECG augmentation technique to achieve a higher performance than traditional automatic 12-lead ECG diagnosis methods. The data analysis results show that the proposed model reports an F1 score of 0.840, which outperforms existing state-ofthe-art methods in the literature.
Multi-person 3D pose estimation from a single image captured by a fisheye camera
Authors: YaHui Zhang, ShaoDi You, Sezer Karaoglu, Theo Gevers
Date: 2022-07-09 00:00:00
Proceeding: Computer Vision and Image Understanding 2022 7
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation

Multi-person 3D pose estimation with absolute depths for a fisheye camera is a challenging task but with valuable applications in daily life, especially for video surveillance. However, to the best of our knowledge, such problem has not been explored so far, leaving a gap in practical applications. In this work, we first propose a method for multi-person 3D pose estimation from a single image taken by a fisheye camera. Our method consists of two branches to estimate absolute 3D human poses: (1) a 2D-to-3D lifting module to predict root-relative 3D human poses (HPoseNet); (2) a root regression module to estimate absolute root locations in the camera coordinate (HRootNet). Finally, we propose a fisheye re-projection module without using ground-truth camera parameters to connect two branches, alleviating the impact of image distortions on 3D pose estimation and further regularizing prediction absolute 3D poses. Experimental results demonstrate that our method achieves the state-of-the-art performance on two public multi-person 3D pose datasets with synthetic fisheye images and our newly collected dataset with real fisheye images. The code and new dataset will be made publicly available.
QuickPose: Real-time Multi-view Multi-person Pose Estimation in Crowded Scenes
Authors: Zhize Zhou, Qing Shuai, Yize Wang, Qi Fang, Xiaopeng Ji, Fashuai Li, Hujun Bao, Xiaowei Zhou
Date: 2022-07-22 00:00:00
Proceeding: SIGGRAPH 2022 7
Tasks: 2D Pose Estimation, 3D Multi-Person Pose Estimation, Multi-Person Pose Estimation, Pose Estimation

This work proposes a real-time algorithm for reconstructing 3D human poses in crowded scenes from multiple calibrated views. The key challenge of this problem is to efficiently match 2D observations across multiple views. Previous methods perform multi-view matching either at the full-body level, which is sensitive to 2D pose estimation error, or at the part level, which ignores 2D constraints between different types of body parts in the same view. Instead, our approach reasons about all plausible skeleton proposals during multi-view matching, where each skeleton may consist of an arbitrary number of parts instead of being a whole body or a single part. To this end, we formulate the multi-view matching problem as mode seeking in the space of skeleton proposals and develop an efficient algorithm named QuickPose to solve the problem, which enables real-time motion capture in crowded scenes. Experiments show that the proposed algorithm achieves the state-of-the-art performance in terms of both speed and accuracy on public datasets.
DRSOM: A Dimension Reduced Second-Order Method
Authors: Chuwen Zhang, Dongdong Ge, Chang He, Bo Jiang, Yuntian Jiang, Yinyu Ye
Date: 2022-07-30 00:00:00

In this paper, we propose a Dimension-Reduced Second-Order Method (DRSOM) for convex and nonconvex (unconstrained) optimization. Under a trust-region-like framework, our method preserves the convergence of the second-order method while using only curvature information in a few directions. Consequently, the computational overhead of our method remains comparable to the first-order such as the gradient descent method. Theoretically, we show that the method has a local quadratic convergence and a global convergence rate of $O(\epsilon^{-3/2})$ to satisfy the first-order and second-order conditions if the subspace satisfies a commonly adopted approximated Hessian assumption. We further show that this assumption can be removed if we perform a corrector step using a Krylov-like method periodically at the end stage of the algorithm. The applicability and performance of DRSOM are exhibited by various computational experiments, including $L_2 - L_p$ minimization, CUTEst problems, and sensor network localization.
Relay Hindsight Experience Replay: Self-Guided Continual Reinforcement Learning for Sequential Object Manipulation Tasks with Sparse Rewards
Authors: Yongle Luo, Yuxin Wang, Kun Dong, Qiang Zhang, Erkang Cheng, Zhiyong Sun, Bo Song
Date: 2022-08-01 00:00:00
Tasks: FetchPush-v1, Reinforcement Learning (RL), Robot Manipulation

Exploration with sparse rewards remains a challenging research problem in reinforcement learning (RL). Especially for sequential object manipulation tasks, the RL agent always receives negative rewards until completing all sub-tasks, which results in low exploration efficiency. To solve these tasks efficiently, we propose a novel self-guided continual RL framework, RelayHER (RHER). RHER first decomposes a sequential task into new sub-tasks with increasing complexity and ensures that the simplest sub-task can be learned quickly by utilizing Hindsight Experience Replay (HER). Secondly, we design a multi-goal & multi-task network to learn these sub-tasks simultaneously. Finally, we propose a Self-Guided Exploration Strategy (SGES). With SGES, the learned sub-task policy will guide the agent to the states that are helpful to learn more complex sub-task with HER. By this self-guided exploration and relay policy learning, RHER can solve these sequential tasks efficiently stage by stage. The experimental results show that RHER significantly outperforms vanilla-HER in sample-efficiency on five singleobject and five complex multi-object manipulation tasks (e.g., Push, Insert, ObstaclePush, Stack, TStack, etc.). The proposed RHER has also been applied to learn a contact-rich push task on a physical robot from scratch, and the success rate reached 10/10 with only 250 episodes.
Few-shot Adaptation Works with UnpredicTable Data
Authors: Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
Date: 2022-08-01 00:00:00
Tasks: Domain Adaptation, Few-Shot Learning

Prior work on language models (LMs) shows that training on a large number of diverse tasks improves few-shot learning (FSL) performance on new tasks. We take this to the extreme, automatically extracting 413,299 tasks from internet tables - orders of magnitude more than the next-largest public datasets. Finetuning on the resulting dataset leads to improved FSL performance on Natural Language Processing (NLP) tasks, but not proportionally to dataset scale. In fact, we find that narrow subsets of our dataset sometimes outperform more diverse datasets. For example, finetuning on software documentation from support.google.com raises FSL performance by a mean of +7.5% on 52 downstream tasks, which beats training on 40 human-curated NLP datasets (+6.7%). Finetuning on various narrow datasets leads to similar broad improvements across test tasks, suggesting that the gains are not from domain adaptation but adapting to FSL in general. We do not observe clear patterns between the datasets that lead to FSL gains, leaving open questions about why certain data helps with FSL.
Some Practice for Improving the Search Results of E-commerce
Authors: Fanyou Wu, Yang Liu, Rado Gazo, Benes Bedrich, Xiaobo Qu
Date: 2022-07-30 00:00:00

In the Amazon KDD Cup 2022, we aim to apply natural language processing methods to improve the quality of search results that can significantly enhance user experience and engagement with search engines for e-commerce. We discuss our practical solution for this competition, ranking 6th in task one, 2nd in task two, and 2nd in task 3. The code is available at https://github.com/wufanyou/KDD-Cup-2022-Amazon.
Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images
Authors: Nazanin Moradinasab, Yash Sharma, Laura S. Shankman, Gary K. Owens, Donald E. Brown
Date: 2022-07-29 00:00:00
Tasks: Weakly-supervised Learning

Two major causes of death in the United States and worldwide are stroke and myocardial infarction. The underlying cause of both is thrombi released from ruptured or eroded unstable atherosclerotic plaques that occlude vessels in the heart (myocardial infarction) or the brain (stroke). Clinical studies show that plaque composition plays a more important role than lesion size in plaque rupture or erosion events. To determine the plaque composition, various cell types in 3D cardiovascular immunofluorescent images of plaque lesions are counted. However, counting these cells manually is expensive, time-consuming, and prone to human error. These challenges of manual counting motivate the need for an automated approach to localize and count the cells in images. The purpose of this study is to develop an automatic approach to accurately detect and count cells in 3D immunofluorescent images with minimal annotation effort. In this study, we used a weakly supervised learning approach to train the HoVer-Net segmentation model using point annotations to detect nuclei in fluorescent images. The advantage of using point annotations is that they require less effort as opposed to pixel-wise annotation. To train the HoVer-Net model using point annotations, we adopted a popularly used cluster labeling approach to transform point annotations into accurate binary masks of cell nuclei. Traditionally, these approaches have generated binary masks from point annotations, leaving a region around the object unlabeled (which is typically ignored during model training). However, these areas may contain important information that helps determine the boundary between cells. Therefore, we used the entropy minimization loss function in these areas to encourage the model to output more confident predictions on the unlabeled areas. Our comparison studies indicate that the HoVer-Net model trained using our weakly ...
Robust Trajectory Prediction against Adversarial Attacks
Authors: Yulong Cao, Danfei Xu, Xinshuo Weng, Zhuoqing Mao, Anima Anandkumar, Chaowei Xiao, Marco Pavone
Date: 2022-07-29 00:00:00
Tasks: Autonomous Driving, Data Augmentation, Prediction, Trajectory Prediction

Trajectory prediction using deep neural networks (DNNs) is an essential component of autonomous driving (AD) systems. However, these methods are vulnerable to adversarial attacks, leading to serious consequences such as collisions. In this work, we identify two key ingredients to defend trajectory prediction models against adversarial attacks including (1) designing effective adversarial training methods and (2) adding domain-specific data augmentation to mitigate the performance degradation on clean data. We demonstrate that our method is able to improve the performance by 46% on adversarial data and at the cost of only 3% performance degradation on clean data, compared to the model trained with clean data. Additionally, compared to existing robust methods, our method can improve performance by 21% on adversarial examples and 9% on clean data. Our robust model is evaluated with a planner to study its downstream impacts. We demonstrate that our model can significantly reduce the severe accident rates (e.g., collisions and off-road driving).