pwc-archive/papers-with-abstracts huggingface dataset:
Gender differences in the neural structures of (non) emotional perspective-taking: A tDCS study
Authors: Shahab Mahdian, Michael A. Nitsche, Vahid Nejati
Date: 2022-05-24 00:00:00

The perspective-taking in social cognition is an ability that makes third-person judgments about the intentions, beliefs and thoughts of others. We aimed to investigate gender differences in the neural structure differences in (non) emotional perspective-taking ability.Thirty healthy adults (15 females and 15 males) received anodal and sham transcranial direct current stimulation (tDCS) (2 mA, 15 min) over the right temporoparietal junction (rTPJ) and ventromedial prefrontal cortex (vmPFC).Participants underwent a pain visual analog scale, a visuospatial perspective-taking task (VPT) and a self-referential attribution task during stimulation.Findings revealed significant differences between vmPFC and rTPJ anodal tDCS stimulation in emotional perspective-taking.Primarily the vmPFC increased emotional perception in comparison with the rTPJ cite.This emotional perception pattern was significant only for men. The rTPJ enhanced the males' non-emotional perspective ability performance compared to their female contributors.The results indicated that both the rTPJ and vmPFC are associated with self-referential processing (SRP).The vmPFC increased Self Emotional bias negatively while rTPJ increased it positively.The vmPFC and rTPJ play a role in (non) emotional perspective-taking in men.While the vmPFC increased men's emotional perspective-taking compared to rTPJ.The rTPJ excitatory stimulation enhanced males' performance compared to females.Moreover, it indicated that the rTPJ posterior cluster activation in men is higher than in women.
Quiescence generates moving average in a stochastic epidemiological model with one host and two parasites
Authors: Usman Sanusi, Sona John, Johannes Mueller, Aurélien Tellier
Date: 2022-05-25 00:00:00
Tasks: Management

Mathematical modelling of epidemiological and coevolutionary dynamics is widely being used to improve disease management strategies of infectious diseases. Many diseases present some form of intra-host quiescent stage, also known as covert infection, while others exhibit dormant stages in the environment. As quiescent/dormant stages can be resistant to drug, antibiotics, fungicide treatments, it is of practical relevance to study the influence of these two life-history traits on the coevolutionary dynamics. We develop first a deterministic coevolutionary model with two parasite types infecting one host type and study analytically the stability of the dynamical system. We specifically derive a stability condition for a five-by-five system of equations with quiescence. Second, we develop a stochastic version of the model to study the influence of quiescence on stochasticity of the system dynamics. We compute the steady state distribution of the parasite types which follows a multivariate normal distribution. Furthermore, we obtain numerical solutions for the covariance matrix of the system under symmetric and asymmetric quiescence rates between parasite types. When parasite strains are identical, quiescence increases the variance of the number of infected individuals at high transmission rate and vice versa when the transmission rate is low. However, when there is competition between parasite strains with different quiescent rates, quiescence generates a moving average behaviour which dampen off stochasticity and decreases the variance of the number of infected hosts. The strain with the highest rate of entering quiescence determines the strength of the moving average and the magnitude of reduction of stochasticity. Thus, it is worth investigating simple models of multi-strain parasite under quiescence/dormancy to improve disease management strategies.
Pneumococcus and the stress-gradient hypothesis: a trade-off links $R_0$ and susceptibility to co-colonization across countries
Authors: Ermanda Dekaj, Erida Gjini
Date: 2022-05-25 00:00:00
Tasks: Diversity, Epidemiology

Modern molecular technologies have revolutionized our understanding of bacterial epidemiology, but reported data across different settings remain under-integrated in common theoretical frameworks. Pneumococcus serotype co-colonization, caused by the polymorphic bacteria Streptococcus pneumoniae, has been increasingly investigated in recent years. While the global genomic diversity and serotype distribution of S. pneumoniae are well-characterized, there is limited information on how co-colonization patterns vary globally, critical for understanding bacterial evolution and dynamics. Gathering a rich dataset of cross-sectional pneumococcal colonization studies in the literature, we quantified patterns of transmission intensity and co-colonization prevalence in children populations across 17 geographic locations. Fitting these data to an SIS model with co-colonization under the assumption of similarity among interacting strains, our analysis reveals strong patterns of negative co-variation between transmission intensity ($R_0$) and susceptibility to co-colonization ($k$). In support of the stress-gradient hypothesis in ecology (SGH), pneumococcus serotypes appear to compete more in high-transmission settings and less in low-transmission settings, a trade-off which ultimately leads to a conserved ratio of single to co-colonization $\mu=1/(R_0-1)k$. Within our mathematical model, such conservation suggests preservation of 'stability-diversity-complexity' regimes in multi-strain coexistence. We find no major study differences in serotype composition, pointing to underlying adaptation of the same set of serotypes across environments. Our work highlights that understanding pneumococcus transmission patterns from global epidemiological data can benefit from simple analytical approaches that account for quasi-neutrality among strains, co-colonization, as well as variable environmental adaptation.
A Mathematical Analysis and Evaluation of Varroa mite Control Methods for a Mite-infested Honey bee Colony
Authors: A. Ssenoga, H. Ddumba, J. Y. T. Mugisha
Date: 2022-05-24 00:00:00
Tasks: Unity

In this paper, we present a mathematical model for the interaction between honey bees and mites. The dynamics of a mite-infested honey bee colony and the evaluation of the commonly used mite-control strategies (traditional, mechanical and chemical) are studied. The mite-free and mite reproduction numbers are derived using the next generation operator approach. The mathematical analysis of the model reveals that in the absence of mites, the colony survives if the mite-free reproduction number is greater than unity otherwise it goes extinct. Stability and sensitivity analyses of the model reveal that the egg laying rate of the queen bee is key in regulating mite reproduction. Adult bee grooming and hygienic behavior of worker bees have also been shown to play a vital role in reducing parasitism. Using the Volterra-Lyapunov stable matrix approach, the mite-infested equilibrium is confirmed to be globally asymptotically stable when the mite-free reproduction number is greater than unity and the mite reproduction number is greater than unity. It is also shown that varroa mite control strategies that focus on limiting mite-reproduction such as caging the queen bee and using a young queen bee quickly reduces the mite reproduction number to a value less than unity compared to those that are intended to kill the mites.
Estimating Waning of Vaccine Effectiveness: a Simulation Study
Authors: Ariel Nikas, Hasan Ahmed, Veronika I. Zarnitsyna
Date: 2022-05-24 00:00:00

Developing accurate and reliable methods to estimate vaccine protection is a key goal in immunology and public health. While several statistical methods have been proposed, their potential inaccuracy in capturing fast intra-seasonal waning of vaccine-induced protection needs to be rigorously investigated. To compare statistical methods for vaccine effectiveness (VE) estimation, we generated simulated data using a multiscale agent-based model of an epidemic with an acute viral infection and differing extents of VE waning. We extended the previously proposed framework for VE measures based on the observational data richness to assess changes of vaccine-induced protection with time. While VE measures based on hard-to-collect information (e.g. exact timing of exposures) were accurate, usually VE studies rely on time-to-infection data and the Cox proportional hazard model. We found that its extension utilizing scaled Schoenfeld residuals, previously proposed for capturing VE waning, was unreliable in capturing both the degree of waning and its functional form and identified the mathematical factors contributing to this unreliability. We showed that partitioning time and including a time-vaccine interaction term in the Cox model significantly improved estimation of VE waning, even in the case of dramatic, rapid waning. We also proposed how to optimize the partitioning scheme. Using simulated data, we compared different measures of VE for capturing the intra-seasonal waning of vaccine-induced protection. We propose an extension of the Cox model based on including a time-vaccine interaction term with further optimization of partitioning time. These findings may guide future analysis of VE waning in observational data.
Action Recognition for American Sign Language
Authors: Nguyen Huu Phong, Bernardete Ribeiro
Date: 2022-05-20 00:00:00
Tasks: Action Recognition, Transfer Learning

In this research, we present our findings to recognize American Sign Language from series of hand gestures. While most researches in literature focus only on static handshapes, our work target dynamic hand gestures. Since dynamic signs dataset are very few, we collect an initial dataset of 150 videos for 10 signs and an extension of 225 videos for 15 signs. We apply transfer learning models in combination with deep neural networks and background subtraction for videos in different temporal settings. Our primarily results show that we can get an accuracy of $0.86$ and $0.71$ using DenseNet201, LSTM with video sequence of 12 frames accordingly.
Regression Approach for Modeling COVID-19 Spread and its Impact On Stock Market
Authors: Bohdan M. Pavlyshenko
Date: 2020-04-02 00:00:00
Tasks: Bayesian Inference, regression

The paper studies different regression approaches for modeling COVID-19 spread and its impact on the stock market. The logistic curve model was used with Bayesian regression for predictive analytics of the coronavirus spread. The impact of COVID-19 was studied using regression approach and compared to other crises influence. In practical analytics, it is important to find the maximum of coronavirus cases per day, this point means the estimated half time of coronavirus spread in the region under investigation. The obtained results show that different crises with different reasons have different impact on the same stocks. It is important to analyze their impact separately. Bayesian inference makes it possible to analyze the uncertainty of crisis impacts.
Attention-based residual autoencoder for video anomaly detection
Authors: Viet-Tuan Le, Yong-Guk Kim
Date: 2022-05-25 00:00:00
Proceeding: Applied Intelligence 2022 5
Tasks: Anomaly Detection, Anomaly Detection In Surveillance Videos, Decoder, Unsupervised Anomaly Detection, Video Anomaly Detection

Automatic anomaly detection is a crucial task in video surveillance system intensively used for public safety and others. The present system adopts a spatial branch and a temporal branch in a unified network that exploits both spatial and temporal information effectively. The network has a residual autoencoder architecture, consisting of a deep convolutional neural network-based encoder and a multi-stage channel attention-based decoder, trained in an unsupervised manner. The temporal shift method is used for exploiting the temporal feature, whereas the contextual dependency is extracted by channel attention modules. System performance is evaluated using three standard benchmark datasets. Result suggests that our network outperforms the state-of-the-art methods, achieving 97.4% for UCSD Ped2, 86.7% for CUHK Avenue, and 73.6% for ShanghaiTech dataset in term of Area Under Curve, respectively.
Dual Quaternion based modal kinematics for multisection continuum arms.
Authors: Godage, I. S., & Walker, I. D. (2015, May).
Date: 2015-05-26 00:00:00
Proceeding: 2015 IEEE International Conference on Robotics and Automation (ICRA) 2015 5
Tasks: Position

This paper presents general modal dual Quaternion (DQ) kinematics for multisection continuum arms. DQ's produce higher accuracy than homogeneous transformation matrices (HTM) when transformed to modal shape functions (MSF) of similar order and are numerically stable. Thus, the model is compact, more accurate and computationally efficient than the modal kinematics proposed by the author based on HTM's. Also, DQ kinematics does not suffer from singularity related limitations of Euler angle based inverse orientation kinematics. Recursive schemes for deriving DQ's and DQ Jacobians are also presented and can be extended arbitrarily. Both modal HTM and modal DQ kinematics are then applied to solve illustrative spatial inverse position and orientation tracking problems. Based on the results, this paper quantitatively compares both methods and highlights the advantages of modal DQ kinematics. The proposed DQ kinematics are easily extensible to variable length multisection continuum arm with general actuator configurations.
Rapid Single Image-Based DTM Estimation from ExoMars TGO CaSSIS Images Using Generative Adversarial U-Nets
Authors: Yu Tao, Siting Xiong, Susan J. Conway, Jan-Peter Muller, Anthony Guimpier, Peter Fawdon, Nicolas Thomas, Gabriele Cremonese
Date: 2021-07-21 00:00:00
Proceeding: Remote Sensing 2021 7

The lack of adequate stereo coverage and where available, lengthy processing time, various artefacts, and unsatisfactory quality and complexity of automating the selection of the best set of processing parameters, have long been big barriers for large-area planetary 3D mapping. In this paper, we propose a deep learning-based solution, called MADNet (Multi-scale generative Adversarial u-net with Dense convolutional and up-projection blocks), that avoids or resolves all of the above issues. We demonstrate the wide applicability of this technique with the ExoMars Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) 4.6 m/pixel images on Mars. Only a single input image and a coarse global 3D reference are required, without knowing any camera models or imaging parameters, to produce high-quality and high-resolution full-strip Digital Terrain Models (DTMs) in a few seconds. In this paper, we discuss technical details of the MADNet system and provide detailed comparisons and assessments of the results. The resultant MADNet 8 m/pixel CaSSIS DTMs are qualitatively very similar to the 1 m/pixel HiRISE DTMs. The resultant MADNet CaSSIS DTMs display excellent agreement with nested Mars Reconnaissance Orbiter Context Camera (CTX), Mars Express’s High-Resolution Stereo Camera (HRSC), and Mars Orbiter Laser Altimeter (MOLA) DTMs at large-scale, and meanwhile, show fairly good correlation with the High-Resolution Imaging Science Experiment (HiRISE) DTMs for fine-scale details. In addition, we show how MADNet outperforms traditional photogrammetric methods, both on speed and quality, for other datasets like HRSC, CTX, and HiRISE, without any parameter tuning or re-training of the model. We demonstrate the results for Oxia Planum (the landing site of the European Space Agency’s Rosalind Franklin ExoMars rover 2023) and a couple of sites of high scientific interest.
Nonmyopic Informative Path Planning Based on Global Kriging Variance Minimization
Authors: Chenxi Xiao, Juan Wachs
Date: 2022-04-01 00:00:00
Proceeding: IEEE ROBOTICS AND AUTOMATION LETTERS 2022 4

Remote, hazardous, and extreme exploration missions require robots to be equipped with on-board sensors for rich and heterogeneous information during deployment. In such tasks, path planning can directly affect the quality and quantity of the observations obtained under temporal and energetic constraints. While most informative path planners can only plan for a short horizon ahead of time (referred as to myopic planners), we propose a novel planner that is capable of planning global paths in which the nonmyopic exploration efficiency is optimized. To achieve this, a novel sampling algorithm named MPE is proposed to adaptively sample landmarks that are associated with high information capacity, in order to minimize the global Kriging variance. The traverse path for the landmarks is then obtained by the IPP-MPE algorithm for minimizing the overall traveling cost. The algorithm is flexible enough to be applied to various information acquisition tasks. The tractable computational cost allows the horizon to be long enough for scene coverage. The algorithm was deployed on a real robot for accomplishing tactile based object searching tasks, which shows superior efficiency compared to the myopic planner baseline. Last, the complexity and other theoretical analysis of the algorithm is provided.
Revealing the Dark Secrets of Masked Image Modeling
Authors: Zhenda Xie, Zigang Geng, Jingcheng Hu, Zheng Zhang, Han Hu, Yue Cao
Date: 2022-05-26 00:00:00
Proceeding: CVPR 2023 1
Tasks: Depth Estimation, Diversity, Inductive Bias, Monocular Depth Estimation, Object Tracking, Pose Estimation, Video Object Tracking, Visual Object Tracking

Masked image modeling (MIM) as pre-training is shown to be effective for numerous vision downstream tasks, but how and where MIM works remain unclear. In this paper, we compare MIM with the long-dominant supervised pre-trained models from two perspectives, the visualizations and the experiments, to uncover their key representational differences. From the visualizations, we find that MIM brings locality inductive bias to all layers of the trained models, but supervised models tend to focus locally at lower layers but more globally at higher layers. That may be the reason why MIM helps Vision Transformers that have a very large receptive field to optimize. Using MIM, the model can maintain a large diversity on attention heads in all layers. But for supervised models, the diversity on attention heads almost disappears from the last three layers and less diversity harms the fine-tuning performance. From the experiments, we find that MIM models can perform significantly better on geometric and motion tasks with weak semantics or fine-grained classification tasks, than their supervised counterparts. Without bells and whistles, a standard MIM pre-trained SwinV2-L could achieve state-of-the-art performance on pose estimation (78.9 AP on COCO test-dev and 78.0 AP on CrowdPose), depth estimation (0.287 RMSE on NYUv2 and 1.966 RMSE on KITTI), and video object tracking (70.7 SUC on LaSOT). For the semantic understanding datasets where the categories are sufficiently covered by the supervised pre-training, MIM models can still achieve highly competitive transfer performance. With a deeper understanding of MIM, we hope that our work can inspire new and solid research in this direction.
Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality
Authors: Tom Zahavy, Yannick Schroecker, Feryal Behbahani, Kate Baumli, Sebastian Flennerhag, Shaobo Hou, Satinder Singh
Date: 2022-05-26 00:00:00
Tasks: Diversity

Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations. In reinforcement learning, a set of diverse policies can be useful for exploration, transfer, hierarchy, and robustness. We propose DOMiNO, a method for Diversity Optimization Maintaining Near Optimality. We formalize the problem as a Constrained Markov Decision Process where the objective is to find diverse policies, measured by the distance between the state occupancies of the policies in the set, while remaining near-optimal with respect to the extrinsic reward. We demonstrate that the method can discover diverse and meaningful behaviors in various domains, such as different locomotion patterns in the DeepMind Control Suite. We perform extensive analysis of our approach, compare it with other multi-objective baselines, demonstrate that we can control both the quality and the diversity of the set via interpretable hyperparameters, and show that the discovered set is robust to perturbations.
Are Transformers Effective for Time Series Forecasting?
Authors: Ailing Zeng, Muxi Chen, Lei Zhang, Qiang Xu
Date: 2022-05-26 00:00:00
Tasks: Anomaly Detection, Relation Extraction, Temporal Relation Extraction, Time Series, Time Series Analysis, Time Series Forecasting

Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time series modeling, we are to extract the temporal relations in an ordered set of continuous points. While employing positional encoding and using tokens to embed sub-series in Transformers facilitate preserving some ordering information, the nature of the \emph{permutation-invariant} self-attention mechanism inevitably results in temporal information loss. To validate our claim, we introduce a set of embarrassingly simple one-layer linear models named LTSF-Linear for comparison. Experimental results on nine real-life datasets show that LTSF-Linear surprisingly outperforms existing sophisticated Transformer-based LTSF models in all cases, and often by a large margin. Moreover, we conduct comprehensive empirical studies to explore the impacts of various design elements of LTSF models on their temporal relation extraction capability. We hope this surprising finding opens up new research directions for the LTSF task. We also advocate revisiting the validity of Transformer-based solutions for other time series analysis tasks (e.g., anomaly detection) in the future. Code is available at: \url{https://github.com/cure-lab/LTSF-Linear}.
An Analytic Framework for Robust Training of Artificial Neural Networks
Authors: Ramin Barati, Reza Safabakhsh, Mohammad Rahmati
Date: 2022-05-26 00:00:00
Tasks: Learning Theory

The reliability of a learning model is key to the successful deployment of machine learning in various industries. Creating a robust model, particularly one unaffected by adversarial attacks, requires a comprehensive understanding of the adversarial examples phenomenon. However, it is difficult to describe the phenomenon due to the complicated nature of the problems in machine learning. Consequently, many studies investigate the phenomenon by proposing a simplified model of how adversarial examples occur and validate it by predicting some aspect of the phenomenon. While these studies cover many different characteristics of the adversarial examples, they have not reached a holistic approach to the geometric and analytic modeling of the phenomenon. This paper propose a formal framework to study the phenomenon in learning theory and make use of complex analysis and holomorphicity to offer a robust learning rule for artificial neural networks. With the help of complex analysis, we can effortlessly move between geometric and analytic perspectives of the phenomenon and offer further insights on the phenomenon by revealing its connection with harmonic functions. Using our model, we can explain some of the most intriguing characteristics of adversarial examples, including transferability of adversarial examples, and pave the way for novel approaches to mitigate the effects of the phenomenon.