pwc-archive/papers-with-abstracts huggingface dataset:
Penguins Search Optimization Algorithm (PeSOA
Authors: Youcef Gheraibia & Abdelouahab Moussaoui
Date: 2013-06-01 00:00:00
Proceeding: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems 2013 6
Tasks: global-optimization

In this paper we propose a new meta-heuristic algorithm called penguins Search Optimization Algorithm (PeSOA), based on collaborative hunting strategy of penguins. In recent years, various effective methods, inspired by nature and based on cooperative strategies, have been proposed to solve NP-hard problems in which, no solutions in polynomial time could be found. The global optimization process starts with individual search process of each penguin, who must communicate to his group its position and the number of fish found. This collaboration aims to synchronize dives in order to achieve a global solution (place with high amounts of food). The global solution is chosen by election of the best group of penguins who ate the maximum of fish. After describing the behavior of penguins, we present the formulation of the algorithm before presenting the various tests with popular benchmarks. Comparative studies with other meta-heuristics have proved that PeSOA performs better as far as new optimization strategy of collaborative and progressive research of the space solutions.
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
Authors: Tianxin Wei, Yuning You, Tianlong Chen, Yang shen, Jingrui He, Zhangyang Wang
Date: 2022-10-07 00:00:00
Tasks: Contrastive Learning, Fairness, Hypergraph Contrastive Learning, Representation Learning

This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL). We focus on the following question: How to construct contrastive views for hypergraphs via augmentations? We provide the solutions in two folds. First, guided by domain knowledge, we fabricate two schemes to augment hyperedges with higher-order relations encoded, and adopt three vertex augmentation strategies from graph-structured data. Second, in search of more effective views in a data-driven manner, we for the first time propose a hypergraph generative model to generate augmented views, and then an end-to-end differentiable pipeline to jointly learn hypergraph augmentations and model parameters. Our technical innovations are reflected in designing both fabricated and generative augmentations of hypergraphs. The experimental findings include: (i) Among fabricated augmentations in HyperGCL, augmenting hyperedges provides the most numerical gains, implying that higher-order information in structures is usually more downstream-relevant; (ii) Generative augmentations do better in preserving higher-order information to further benefit generalizability; (iii) HyperGCL also boosts robustness and fairness in hypergraph representation learning. Codes are released at https://github.com/weitianxin/HyperGCL.
LOCL: Learning Object-Attribute Composition using Localization
Authors: Satish Kumar, ASM Iftekhar, Ekta Prashnani, B. S. Manjunath
Date: 2022-10-07 00:00:00
Tasks: Attribute, Object, Zero-Shot Learning

This paper describes LOCL (Learning Object Attribute Composition using Localization) that generalizes composition zero shot learning to objects in cluttered and more realistic settings. The problem of unseen Object Attribute (OA) associations has been well studied in the field, however, the performance of existing methods is limited in challenging scenes. In this context, our key contribution is a modular approach to localizing objects and attributes of interest in a weakly supervised context that generalizes robustly to unseen configurations. Localization coupled with a composition classifier significantly outperforms state of the art (SOTA) methods, with an improvement of about 12% on currently available challenging datasets. Further, the modularity enables the use of localized feature extractor to be used with existing OA compositional learning methods to improve their overall performance.
(Fusionformer):Exploiting the Joint Motion Synergy with Fusion Network Based On Transformer for 3D Human Pose Estimation
Authors: Xinwei Yu, Xiaohua Zhang
Date: 2022-10-08 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

For the current 3D human pose estimation task, a group of methods mainly learn the rules of 2D-3D projection from spatial and temporal correlation. However, earlier methods model the global features of the entire body joint in the time domain, but ignore the motion trajectory of individual joint. The recent work [29] considers that there are differences in motion between different joints and deals with the temporal relationship of each joint separately. However, we found that different joints show the same movement trends under some specific actions. Therefore, our proposed Fusionformer method introduces a self-trajectory module and a mutual-trajectory module based on the spatio-temporal module .After that, the global spatio-temporal features and local joint trajectory features are fused through a linear network in a parallel manner. To eliminate the influence of bad 2D poses on 3D projections, finally we also introduce a pose refinement network to balance the consistency of 3D projections. In addition, we evaluate the proposed method on two benchmark datasets (Human3.6M, MPI-INF-3DHP). Comparing our method with the baseline method poseformer, the results show an improvement of 2.4% MPJPE and 4.3% P-MPJPE on the Human3.6M dataset, respectively.
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Authors: Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández
Date: 2022-10-02 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

Despite the proliferation of explainable AI (XAI) methods, little is understood about end-users' explainability needs and behaviors around XAI explanations. To address this gap and contribute to understanding how explainability can support human-AI interaction, we conducted a mixed-methods study with 20 end-users of a real-world AI application, the Merlin bird identification app, and inquired about their XAI needs, uses, and perceptions. We found that participants desire practically useful information that can improve their collaboration with the AI, more so than technical system details. Relatedly, participants intended to use XAI explanations for various purposes beyond understanding the AI's outputs: calibrating trust, improving their task skills, changing their behavior to supply better inputs to the AI, and giving constructive feedback to developers. Finally, among existing XAI approaches, participants preferred part-based explanations that resemble human reasoning and explanations. We discuss the implications of our findings and provide recommendations for future XAI design.
A solution for external costs beyond negotiation and taxation
Authors: Alexandre Magno de Melo Faria, Helde A. D. Hdom
Date: 2022-10-08 00:00:00

This article aims to launch light on the limitations of the Coase and Pigou approach in the solution of externalities. After contextualizing the need for integration of ecological and economic approaches, we are introducing a new conceptual proposal complementary to conventional economic approaches. Whose process is guaranteed by a set of diffuse agents in the economy that partially reverses entropy formation and marginal external costs generated by also diffuse agents? The approach differs in six fundamentals from traditional theory and proposes a new way of examining the actions of agents capable of reducing entropy and containing part of external costs in the market economy.
Predicting Blossom Date of Cherry Tree With Support Vector Machine and Recurrent Neural Network
Authors: Hongyi Zheng, Yanyu Chen, Zihan Zhang
Date: 2022-10-10 00:00:00

Our project probes the relationship between temperatures and the blossom date of cherry trees. Through modeling, future flowering will become predictive, helping the public plan travels and avoid pollen season. To predict the date when the cherry trees will blossom exactly could be viewed as a multiclass classification problem, so we applied the multi-class Support Vector Classifier (SVC) and Recurrent Neural Network (RNN), particularly Long Short-term Memory (LSTM), to formulate the problem. In the end, we evaluate and compare the performance of these approaches to find out which one might be more applicable in reality.
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning
Authors: Yao Mu, Yuzheng Zhuang, Fei Ni, Bin Wang, Jianyu Chen, Jianye Hao, Ping Luo
Date: 2022-10-09 00:00:00
Tasks: Decision Making, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Adapting to the changes in transition dynamics is essential in robotic applications. By learning a conditional policy with a compact context, context-aware meta-reinforcement learning provides a flexible way to adjust behavior according to dynamics changes. However, in real-world applications, the agent may encounter complex dynamics changes. Multiple confounders can influence the transition dynamics, making it challenging to infer accurate context for decision-making. This paper addresses such a challenge by Decomposed Mutual INformation Optimization (DOMINO) for context learning, which explicitly learns a disentangled context to maximize the mutual information between the context and historical trajectories, while minimizing the state transition prediction error. Our theoretical analysis shows that DOMINO can overcome the underestimation of the mutual information caused by multi-confounded challenges via learning disentangled context and reduce the demand for the number of samples collected in various environments. Extensive experiments show that the context learned by DOMINO benefits both model-based and model-free reinforcement learning algorithms for dynamics generalization in terms of sample efficiency and performance in unseen environments.
Kernel-based Substructure Exploration for Next POI Recommendation
Authors: Wei Ju, Yifang Qin, Ziyue Qiao, Xiao Luo, Yifan Wang, Yanjie Fu, Ming Zhang
Date: 2022-10-08 00:00:00
Tasks: Graph Neural Network, Recommendation Systems

Point-of-Interest (POI) recommendation, which benefits from the proliferation of GPS-enabled devices and location-based social networks (LBSNs), plays an increasingly important role in recommender systems. It aims to provide users with the convenience to discover their interested places to visit based on previous visits and current status. Most existing methods usually merely leverage recurrent neural networks (RNNs) to explore sequential influences for recommendation. Despite the effectiveness, these methods not only neglect topological geographical influences among POIs, but also fail to model high-order sequential substructures. To tackle the above issues, we propose a Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which combines the characteristics of both geographical and sequential influences in a collaborative way. KBGNN consists of a geographical module and a sequential module. On the one hand, we construct a geographical graph and leverage a message passing neural network to capture the topological geographical influences. On the other hand, we explore high-order sequential substructures in the user-aware sequential graph using a graph kernel neural network to capture user preferences. Finally, a consistency learning framework is introduced to jointly incorporate geographical and sequential information extracted from two separate graphs. In this way, the two modules effectively exchange knowledge to mutually enhance each other. Extensive experiments conducted on two real-world LBSN datasets demonstrate the superior performance of our proposed method over the state-of-the-arts. Our codes are available at https://github.com/Fang6ang/KBGNN.
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction
Authors: Miao Peng, Ben Liu, Qianqian Xie, Wenjie Xu, Hua Wang, Min Peng
Date: 2022-10-10 00:00:00
Tasks: Contrastive Learning, Knowledge Graphs, Link Prediction, Prediction

Link prediction is the task of inferring missing links between entities in knowledge graphs. Embedding-based methods have shown effectiveness in addressing this problem by modeling relational patterns in triples. However, the link prediction task often requires contextual information in entity neighborhoods, while most existing embedding-based methods fail to capture it. Additionally, little attention is paid to the diversity of entity representations in different contexts, which often leads to false prediction results. In this situation, we consider that the schema of knowledge graph contains the specific contextual information, and it is beneficial for preserving the consistency of entities across contexts. In this paper, we propose a novel Schema-augmented Multi-level contrastive LEarning framework (SMiLE) to conduct knowledge graph link prediction. Specifically, we first exploit network schema as the prior constraint to sample negatives and pre-train our model by employing a multi-level contrastive learning method to yield both prior schema and contextual information. Then we fine-tune our model under the supervision of individual triples to learn subtler representations for link prediction. Extensive experimental results on four knowledge graph datasets with thorough analysis of each component demonstrate the effectiveness of our proposed framework against state-of-the-art baselines. The implementation of SMiLE is available at https://github.com/GKNL/SMiLE.
CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification
Authors: Kewen Li, Wenlong Liu, YiMin Dou, Zhifeng Xu, Hongjie Duan, Ruilin Jing
Date: 2022-10-10 00:00:00
Tasks: Contrastive Learning, Facies Classification

Recently, seismic facies classification based on convolutional neural networks (CNN) has garnered significant research interest. However, existing CNN-based supervised learning approaches necessitate massive labeled data. Labeling is laborious and time-consuming, particularly for 3D seismic data volumes. To overcome this challenge, we propose a semi-supervised method based on pixel-level contrastive learning, termed CONSS, which can efficiently identify seismic facies using only 1% of the original annotations. Furthermore, the absence of a unified data division and standardized metrics hinders the fair comparison of various facies classification approaches. To this end, we develop an objective benchmark for the evaluation of semi-supervised methods, including self-training, consistency regularization, and the proposed CONSS. Our benchmark is publicly available to enable researchers to objectively compare different approaches. Experimental results demonstrate that our approach achieves state-of-the-art performance on the F3 survey.
Effect of Stress Ulcers Prophylaxis, Sedative and Statin on Ventilator-Associated Pneumonia: A Retrospective Analysis Based on MIMIC Database
Authors: Xuetao Kong1†, Yaozhou Wu2†, Bingqin Wen2, Dongmei Meng2, Li Wei2* and Pengjiu Yu2
Date: 2022-06-20 00:00:00
Proceeding: FRONTIERS IN PHARMACOLOGY 2022 6
Tasks: Prognosis, regression

The use of MV can easily lead to VAP especially in ICU patients. SUP, sedatives, statin and insulin have been proved to prevent VAP and improve the prognosis of patients. Our aim was to analyze the effects of SUP, sedative, statin, and insulin on patients with MV. Methods: The occurrence of VAP and death in MV patients and VAP patients were explored by multivariate logistic regression and Cox regression to analyze analyses. Results: Totally, 5277 cases who received MV in ICU from MIMIC IV database were included. There were 826 (15.7%) cases in VAP-group and 4451 (84.3%) cases in non-VAP group and there were 1914 (36.3%) cases in hospital mortalities altogether. No protective effect of drugs on VAP was found in MV patients. The risk of death was 1.43 times higher in MV patients taking midazolam than in propofol (aHR = 1.43 95% CI: 1.04,1.97). No protective effect of drugs on death was found in VAP patients. Conclusion: Compared with midazolam, propofol is more recommended as sedation regimen in ICU patients with MV. Further high-quality studies are needed to confirm this finding.
In-Hand Object Rotation via Rapid Motor Adaptation
Authors: Haozhi Qi, Ashish Kumar, Roberto Calandra, Yi Ma, Jitendra Malik
Date: 2022-10-10 00:00:00
Tasks: Object, Reinforcement Learning (RL)

Generalized in-hand manipulation has long been an unsolved challenge of robotics. As a small step towards this grand goal, we demonstrate how to design and learn a simple adaptive controller to achieve in-hand object rotation using only fingertips. The controller is trained entirely in simulation on only cylindrical objects, which then - without any fine-tuning - can be directly deployed to a real robot hand to rotate dozens of objects with diverse sizes, shapes, and weights over the z-axis. This is achieved via rapid online adaptation of the controller to the object properties using only proprioception history. Furthermore, natural and stable finger gaits automatically emerge from training the control policy via reinforcement learning. Code and more videos are available at https://haozhi.io/hora
SCAM! Transferring humans between images with Semantic Cross Attention Modulation
Authors: Nicolas Dufour, David Picard, Vicky Kalogeiton
Date: 2022-10-10 00:00:00
Tasks: Diversity, Image Generation, Pose Transfer, Reconstruction, Subject Transfer

A large body of recent work targets semantically conditioned image generation. Most such methods focus on the narrower task of pose transfer and ignore the more challenging task of subject transfer that consists in not only transferring the pose but also the appearance and background. In this work, we introduce SCAM (Semantic Cross Attention Modulation), a system that encodes rich and diverse information in each semantic region of the image (including foreground and background), thus achieving precise generation with emphasis on fine details. This is enabled by the Semantic Attention Transformer Encoder that extracts multiple latent vectors for each semantic region, and the corresponding generator that exploits these multiple latents by using semantic cross attention modulation. It is trained only using a reconstruction setup, while subject transfer is performed at test time. Our analysis shows that our proposed architecture is successful at encoding the diversity of appearance in each semantic region. Extensive experiments on the iDesigner and CelebAMask-HD datasets show that SCAM outperforms SEAN and SPADE; moreover, it sets the new state of the art on subject transfer.
Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsing
Authors: Francesco Cazzaro, Davide Locatelli, Ariadna Quattoni, Xavier Carreras
Date: 2022-10-10 00:00:00
Tasks: Semantic Parsing

Prior work in semantic parsing has shown that conventional seq2seq models fail at compositional generalization tasks. This limitation led to a resurgence of methods that model alignments between sentences and their corresponding meaning representations, either implicitly through latent variables or explicitly by taking advantage of alignment annotations. We take the second direction and propose TPOL, a two-step approach that first translates input sentences monotonically and then reorders them to obtain the correct output. This is achieved with a modular framework comprising a Translator and a Reorderer component. We test our approach on two popular semantic parsing datasets. Our experiments show that by means of the monotonic translations, TPOL can learn reliable lexico-logical patterns from aligned data, significantly improving compositional generalization both over conventional seq2seq models, as well as over other approaches that exploit gold alignments.
Tracking changes using Kullback-Leibler divergence for the continual learning
Authors: Sebastián Basterrech, Michal Woźniak
Date: 2022-10-10 00:00:00
Tasks: Continual Learning

Recently, continual learning has received a lot of attention. One of the significant problems is the occurrence of \emph{concept drift}, which consists of changing probabilistic characteristics of the incoming data. In the case of the classification task, this phenomenon destabilizes the model's performance and negatively affects the achieved prediction quality. Most current methods apply statistical learning and similarity analysis over the raw data. However, similarity analysis in streaming data remains a complex problem due to time limitation, non-precise values, fast decision speed, scalability, etc. This article introduces a novel method for monitoring changes in the probabilistic distribution of multi-dimensional data streams. As a measure of the rapidity of changes, we analyze the popular Kullback-Leibler divergence. During the experimental study, we show how to use this metric to predict the concept drift occurrence and understand its nature. The obtained results encourage further work on the proposed methods and its application in the real tasks where the prediction of the future appearance of concept drift plays a crucial role, such as predictive maintenance.
Transformer-based Localization from Embodied Dialog with Large-scale Pre-training
Authors: Meera Hahn, James M. Rehg
Date: 2022-10-10 00:00:00

We address the challenging task of Localization via Embodied Dialog (LED). Given a dialog from two agents, an Observer navigating through an unknown environment and a Locator who is attempting to identify the Observer's location, the goal is to predict the Observer's final location in a map. We develop a novel LED-Bert architecture and present an effective pretraining strategy. We show that a graph-based scene representation is more effective than the top-down 2D maps used in prior works. Our approach outperforms previous baselines.
Second-order regression models exhibit progressive sharpening to the edge of stability
Authors: Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington
Date: 2022-10-10 00:00:00
Tasks: regression

Recent studies of gradient descent with large step sizes have shown that there is often a regime with an initial increase in the largest eigenvalue of the loss Hessian (progressive sharpening), followed by a stabilization of the eigenvalue near the maximum value which allows convergence (edge of stability). These phenomena are intrinsically non-linear and do not happen for models in the constant Neural Tangent Kernel (NTK) regime, for which the predictive function is approximately linear in the parameters. As such, we consider the next simplest class of predictive models, namely those that are quadratic in the parameters, which we call second-order regression models. For quadratic objectives in two dimensions, we prove that this second-order regression model exhibits progressive sharpening of the NTK eigenvalue towards a value that differs slightly from the edge of stability, which we explicitly compute. In higher dimensions, the model generically shows similar behavior, even without the specific structure of a neural network, suggesting that progressive sharpening and edge-of-stability behavior aren't unique features of neural networks, and could be a more general property of discrete learning algorithms in high-dimensional non-linear models.