Predicting Motivations of Actions by Leveraging Text

Understanding human actions is a key problem in computer vision. However, recognizing actions is only the first step of understanding what a person is doing. In this paper, we introduce the problem of predicting why a person has performed an action in images. This problem has many applications in human activity understanding, such as anticipating or explaining an action. To study this problem, we introduce a new dataset of people performing actions annotated with likely motivations. However, the information in an image alone may not be sufficient to automatically solve this task. Since humans can rely on their lifetime of experiences to infer motivation, we propose to give computer vision systems access to some of these experiences by using recently developed natural language models to mine knowledge stored in massive amounts of text. While we are still far away from fully understanding motivation, our results suggest that transferring knowledge from language into vision can help machines understand why people in images might be performing an action.Comment: CVPR 201

A rapid and automated computational approach to the design of multistable soft actuators

We develop an automated computational modeling framework for rapid gradient-based design of multistable soft mechanical structures composed of non-identical bistable unit cells with appropriate geometric parameterization. This framework includes a custom isogeometric analysis-based continuum mechanics solver that is robust and end-to-end differentiable, which enables geometric and material optimization to achieve a desired multistability pattern. We apply this numerical modeling approach in two dimensions to design a variety of multistable structures, accounting for various geometric and material constraints. Our framework demonstrates consistent agreement with experimental results, and robust performance in designing for multistability, which facilities soft actuator design with high precision and reliability

Preferences for Doors of Vernacular Structures: The Case Study of Kaleici

This study aims to find choices and a significant indicator which changes preferences of the doors of traditional Structures. Within the scope of this study, we investigated how the preference of entries, which is a transition interface between the urban space and structures, is affected by determined variables. As a result of the regression analysis, the results showed that the critical variables that are preferred, invite a degree of mystery by the existing literature. However, unlike the research literature, the result shows that complexity is not adversely predictive or useful in liking in the selected case study. It has been found that the preferred doors and entrance interfaces have natural materials, harmonious colors, vernacular architectural features, and common structural elements such as steps and eaves.Keywords: Doors, Vernacular Structures, KaleicieISSN: 2398-4287 © 2019. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/e-bpj.v4i11.1656             

JAX FDM: A differentiable solver for inverse form-finding

We introduce JAX FDM, a differentiable solver to design mechanically efficient shapes for 3D structures conditioned on target architectural, fabrication and structural properties. Examples of such structures are domes, cable nets and towers. JAX FDM solves these inverse form-finding problems by combining the force density method, differentiable sparsity and gradient-based optimization. Our solver can be paired with other libraries in the JAX ecosystem to facilitate the integration of form-finding simulations with neural networks. We showcase the features of JAX FDM with two design examples. JAX FDM is available as an open-source library at this URL: https://github.com/arpastrana/jax_fdm.Comment: https://github.com/arpastrana/jax_fd

Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh

Partial differential equations (PDEs) are often computationally challenging to solve, and in many settings many related PDEs must be be solved either at every timestep or for a variety of candidate boundary conditions, parameters, or geometric domains. We present a meta-learning based method which learns to rapidly solve problems from a distribution of related PDEs. We use meta-learning (MAML and LEAP) to identify initializations for a neural network representation of the PDE solution such that a residual of the PDE can be quickly minimized on a novel task. We apply our meta-solving approach to a nonlinear Poisson's equation, 1D Burgers' equation, and hyperelasticity equations with varying parameters, geometries, and boundary conditions. The resulting Meta-PDE method finds qualitatively accurate solutions to most problems within a few gradient steps; for the nonlinear Poisson and hyper-elasticity equation this results in an intermediate accuracy approximation up to an order of magnitude faster than a baseline finite element analysis (FEA) solver with equivalent accuracy. In comparison to other learned solvers and surrogate models, this meta-learning approach can be trained without supervision from expensive ground-truth data, does not require a mesh, and can even be used when the geometry and topology varies between tasks

A method for capturing dynamic spectral coupling in resting fMRI reveals domain-specific patterns in schizophrenia

IntroductionResting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool for assessing functional brain connectivity. Recent studies have focused on shorter-term connectivity and dynamics in the resting state. However, most of the prior work evaluates changes in time-series correlations. In this study, we propose a framework that focuses on time-resolved spectral coupling (assessed via the correlation between power spectra of the windowed time courses) among different brain circuits determined via independent component analysis (ICA).MethodsMotivated by earlier work suggesting significant spectral differences in people with schizophrenia, we developed an approach to evaluate time-resolved spectral coupling (trSC). To do this, we first calculated the correlation between the power spectra of windowed time-courses pairs of brain components. Then, we subgrouped each correlation map into four subgroups based on the connectivity strength utilizing quartiles and clustering techniques. Lastly, we examined clinical group differences by regression analysis for each averaged count and average cluster size matrices in each quartile. We evaluated the method by applying it to resting-state data collected from 151 (114 males, 37 females) people with schizophrenia (SZ) and 163 (117 males, 46 females) healthy controls (HC).ResultsOur proposed approach enables us to observe the change of connectivity strength within each quartile for different subgroups. People with schizophrenia showed highly modularized and significant differences in multiple network domains, whereas males and females showed less modular differences. Both cell count and average cluster size analysis for subgroups indicate a higher connectivity rate in the fourth quartile for the visual network in the control group. This indicates increased trSC in visual networks in the controls. In other words, this shows that the visual networks in people with schizophrenia have less mutually consistent spectra. It is also the case that the visual networks are less spectrally correlated on short timescales with networks of all other functional domains.ConclusionsThe results of this study reveal significant differences in the degree to which spectral power profiles are coupled over time. Importantly, there are significant but distinct differences both between males and females and between people with schizophrenia and controls. We observed a more significant coupling rate in the visual network for the healthy controls and males in the upper quartile. Fluctuations over time are complex, and focusing on only time-resolved coupling among time-courses is likely to miss important information. Also, people with schizophrenia are known to have impairments in visual processing but the underlying reasons for the impairment are still unknown. Therefore, the trSC approach can be a useful tool to explore the reasons for the impairments

Evaluation of anxiety in doctors working in new type 2019 COVID and non-COVID services

Amaç: Bu çalışma İstanbul’da bir vakıf üniversitesi hastaneler kompleksinde yeni tip 2019 koronavirüs hastalığı (COVID) ve COVID dışı servislerde çalışan hekimlerde anksiyetenin değerlendirilmesi amacıyla yapılmış tanımlayıcı bir çalışmadır. Gereç ve Yöntem: Çalışma kapsamına, pandemi servislerinde çalışan 50, pandemi dışı servislerde çalışan 52 hekim alınmıştır. Veri toplama aracı olarak hekimlerin sosyo-demografik ve mesleki bazı özelliklerini içeren anket formu ve durumluk-süreklilik kaygı ölçeği kullanılmıştır. Veriler online anket uygulaması yoluyla toplanmıştır. Verilerin değerlendirilmesinde verilerin normal dağılım gösterip göstermediğine Shapiro-Wilk normallik testi ile bakılmıştır. Verilerin normal dağılım göstermediği için iki grup karşılaştırmalarında Mann-Whitney U testi, ikiden fazla grup karşılaştırmalarında ise Kruskal-Wallis testi kullanılmıştır. Korelasyon analizinde ise Pearson korelasyon analizi yapılmıştır. Bulgular: Çalışmamızda pandemi servislerinde çalışan hekimlerin durumluk kaygı puan ortalamalarının, pandemi dışı servislerde çalışan hekimlerin kaygı puan ortalamalarından daha yüksek olduğu ve aradaki farkın istatistiksel olarak anlamlı olduğu saptanmıştır (p<0,05). Cinsiyete göre pandemi servisinde çalışan kadın hekimlerin durumluk kaygı puan ortalamalarının erkek hekimlerden daha yüksek ve farkın istatistiksel olarak önemli olduğu belirlenmiştir (p<0,05). Yaş gruplarına göre pandemi servislerinde çalışan 43 yaş ve üzerindeki hekimlerin süreklilik kaygı puan ortalamalarının diğer yaş gruplarındaki hekimlerden daha düşük ve farkın istatistiksel olarak önemli olduğu saptanmıştır (p<0,05). Hem pandemi servislerinde çalışan hekimlerin hem de pandemi servisleri dışında çalışan hekimlerin durumluk ve süreklilik kaygı ölçeği puan ortalamaları arasında pozitif yönlü kuvvetli ilişki saptanmıştır (p<0,05). Yani durumluk kaygı arttıkça süreklilik kaygı, süreklilik kaygı arttıkça durumluk kaygı da artmaktadır. Sonuç: Çalışmamızda pandemi servislerinde çalışan hekimlerin durumluk kaygısının diğer servislerde çalışan hekimlerden daha fazla olduğu ve süreklilik kaygısı arasında bir fark bulunamaması pandemi servisinde çalışmanın anksiyeteye neden olduğunu göstermektedir.Objective: This descriptive study was conducted in a foundation university hospital complex in Istanbul and aimed to evaluate the anxiety in physicians who provide new type 2019 coronavirus disease (COVID) related and non-COVID-19-related services. Materials and Methods: This study included 50 physicians who provide COVID-19-related services and 52 physicians with non-COVID-19-related services. A questionnaire that contains sociodemographic and occupational characteristics of physicians and a state-trait anxiety scale were used as data collection tools. Data were collected through an online survey application. Data analysis checked the variable distribution using the Shapiro-Wilk normality test. Since no normal distribution was found, the Mann-Whitney U test was used for comparisons of two groups, and the Kruskal-Wallis test was used for comparisons of more than two groups. The Pearson correlation analysis was performed for correlation analysis. Results: Our study determined significantly higher mean state anxiety scores of physicians who provide COVID-19-related services than that of the other group (p<0.05). According to age groups, the mean trait anxiety scores of physicians aged 43 years and over who provide COVID-19-related services were significantly lower than that in physicians who provide non-COVID-19-related services (p<0.05). A strong positive correlation was found in the state and trait anxiety scale mean scores between both groups (p<0.05). Therefore, state and trait anxiety increase in correlation. Conclusion: Our study revealed higher state anxiety of physicians who provide COVID-19-related services than that of physicians who provide non-COVID-19-related services. Additionally, no difference was found in the trait anxiety, which indicates that working in the pandemic services causes anxiety