Hasil untuk "Art"

The CMS collaboration, A. Tumasyan, W. Adam et al.

Ana Nieto-Carracedo, Consuelo Gómez-Iñiguez, Leticia A. Tamayo et al.

Emotional intelligence has been associated with academic achievement, which entails that improving emotional intelligence could lead to better academic achievement. However, the mechanisms of this relationship are not well known. This paper focuses on assessing relevant factors associated with academic achievement (emotional well-being, motivation, and learning strategies) as potential mediators of this link. A cross-sectional study with a sample of 96 high school students was conducted. They were assessed using the Mayer-Salovey-Caruso Emotional Intelligence Test, the Psychological Well-being Questionnaire, the Learning Strategies and Motivation Questionnaire, and their final grades. Results of a serial mediation analysis revealed that emotional intelligence is not directly associated with academic achievement but through mediating factors. Serial indirect effects show that emotionally intelligent students have higher levels of emotional well-being, which predicts better learning strategies and is, in turn, associated with academic achievement. Emotional intelligence also predicts greater motivation and better learning strategies (without the mediation of emotional well-being), which is ultimately also associated with academic achievement. Theoretical and instructional implications are discussed.

Irfan Kareem, Syed Farooq Ali, Muhammad Bilal et al.

Over the last decade, there have been a lot of advances in the area of human posture recognition. Among multiple approaches proposed to solve this problem, those based on deep learning have shown promising results. Taking another step in this direction, this paper analyzes the performance of deep learning-based hybrid architecture for fall detection, In this regard, the fusion of the residual network (ResNet-50) deep features with support vector machine (SVM) at the classification layer has been considered. The proposed approach outperforms the existing methods yielding an accuracy of 98.82%, 97.95%, and 99.98% on three datasets i.e. Multi-Camera Fall (MCF) using four postures, UR Fall detection (URFD) using four postures, and UP-Fall detection (UPFD) using four postures respectively. It is important to mention that the existing methods achieve accuracies of 97.9%, 97.33%, and 95.64% on the MCF, URDF and UPFD datasets, respectively. Moreover, we achieved 100% accuracy on the UPFD two-posture task. The URFD and MCF datasets have been utilized to assess the fall detection performance of our method under a realistic environment (e.g. camouflage, occlusion, and variation in lighting conditions due to day/night lighting variation). For comparison purposes, we have also performed experiments using six state-of-the-art deep learning networks, namely; ResNet-50, ResNet-101, VGG-19, InceptionV3, MobileNet, and Xception. The results demonstrate that the proposed approach outperforms other network models both in terms of accuracy and time efficiency. We also compared the performance of SVM with Naive Bayes, Decision Tree, Random Forest, KNN, AdaBoost, and MLP used at the classifier layer and found that SVM outperforms or is on par with other classifiers.

Humam M. Abdulsahib, Foad Ghaderi

Profit maximization and risk mitigation require good financial market predictions. Financial markets have a correlated nature, which means that there are some shared patterns between them; therefore, learning about one market might help understand the behavior of others. End-to-end training techniques have proven successful in financial markets, but they have flaws, such as picking up noise and failing to account for the complicated relationships across markets. We present a promising model for predicting financial markets using the correlation between the two markets, which draws inspiration from the recent progress in disentanglement learning. This model learns to disentangle representations of features shared between markets from specific representations, and removes features that cause interference. We utilized a dilated convolutional neural network as an encoder to extract features while using self-attention and cross-attention to capture specifics and shared patterns. Our model uses Dynamic Time Warping (DTW) to minimize the similarity between specific and shared patterns. It also combines DTW’s alignment-based similarity with the Mean Square Error (MSE) to determine the optimal balance between alignment and prediction accuracy. We conducted our experiments using datasets that included the closing prices of Apple, Samsung, Bitcoin, Ethereum, Meta platforms, and the X platform. Spearman’s rank correlation coefficient was used to evaluate the disentanglement by describing the relationship between the extracted representations. The findings confirm that our model surpasses state-of-the-art approaches in prediction error, financial risk assessment, correlation evolution, and prediction net curves, thereby giving market participants more trust in their decisions.

Bereket A. Yilma, Chan Mi Kim, Gerald C. Cupchik et al.

Staying in the intensive care unit (ICU) is often traumatic, leading to post-intensive care syndrome (PICS), which encompasses physical, psychological, and cognitive impairments. Currently, there are limited interventions available for PICS. Studies indicate that exposure to visual art may help address the psychological aspects of PICS and be more effective if it is personalized. We develop Machine Learning-based Visual Art Recommendation Systems (VA RecSys) to enable personalized therapeutic visual art experiences for post-ICU patients. We investigate four state-of-the-art VA RecSys engines, evaluating the relevance of their recommendations for therapeutic purposes compared to expert-curated recommendations. We conduct an expert pilot test and a large-scale user study (n=150) to assess the appropriateness and effectiveness of these recommendations. Our results suggest all recommendations enhance temporal affective states. Visual and multimodal VA RecSys engines compare favourably with expert-curated recommendations, indicating their potential to support the delivery of personalized art therapy for PICS prevention and treatment.

Yi Rou Yap, Yun Li Lee

This paper presents a Virtual Reality (VR) art therapy known as "Break Times" which aims to enhance students' mental well-being and foster creative expression. The proposed "Break Times" application mimics the art therapy sessions in the VR environment design. Pilot user acceptance test with 10 participants showed a notable reduction in stress levels, with 50% reporting normal stress levels post-intervention, compared to 20% pre-intervention. Participants praised the "Break Times" therapy's functionality and engagement features and suggested improvements such as saving creations, incorporating 3D painting, and expanding the artmaking scene variety. The study highlights that VR art therapy has potential as an effective tool for stress management, emphasizing the need for continued refinement to maximize its therapeutic benefits.

Azzouz Elhamma

Research Question: Does Covid-19 crisis moderate significantly the relationship between mandatory International Financial Reporting Standards (IFRS) adoption and economic growth in developing countries, especially in the MENA (Middle East and North Africa) region and SSA (Sub-Saharan Africa) countries? Motivation: Two sources of motivation are behind this study. First, research works on the impact of mandatory IFRS adoption on macroeconomic indicators such as economic growth are still scarce. Second, studying the impact of mandatory IFRS adoption on economic growth before and during the Covid-19 crisis allows to better understand this relationship in times of crisis. Idea: This article aims to investigate the moderating role of Covid-19 crisis in the relationship between mandatory IFRS adoption and economic growth in developing countries. Tools: The study was conducted based on panel data from 30 developing countries (15 MENA countries and 15 SSA countries) during the period 2017–2020. Collected data were analysed by using the Generalized Least Squares (EGLS/weighted cross-section) with fixed effect estimation technique. Findings: The main results of the study show that mandatory IFRS adoption has a positive impact on economic growth of the full sample, and that this positive impact is reduced during Covid-19 crisis. Contribution: The study results are very useful to policymakers and regulators in developing countries, especially in crisis periods.

M. S. Clark, J. I. Hoffman, L. S. Peck et al.

Abstract Polar ecosystems are experiencing amongst the most rapid rates of regional warming on Earth. Here, we discuss ‘omics’ approaches to investigate polar biodiversity, including the current state of the art, future perspectives and recommendations. We propose a community road map to generate and more fully exploit multi-omics data from polar organisms. These data are needed for the comprehensive evaluation of polar biodiversity and to reveal how life evolved and adapted to permanently cold environments with extreme seasonality. We argue that concerted action is required to mitigate the impact of warming on polar ecosystems via conservation efforts, to sustainably manage these unique habitats and their ecosystem services, and for the sustainable bioprospecting of novel genes and compounds for societal gain.

Mark Rego

Henri Bergson’s philosophy and Japanese cultural, aesthetic, and architectural traditions share an affinity regarding their view of reality as continuous change and the awareness of the vital impulses of life in all things. For example, these ideas are central to Buddhist and Shinto philosophies, for whom impermanence is an essential feature of the world. There are strong sympathies with Bergson’s duration, which has facilitated his reception in Japan. An understanding of Japanese aesthetics and philosophy of art can help illustrate and comprehend Bergson’s philosophy, and, in return, Bergson’s philosophy can reveal deeper understandings of the world and the environment we live in. This paper proposes to reflect on how duration manifests in Japanese aesthetics, particularly focusing on the philosophy of architecture and the built environment. I propose to analyze how architectural traditions in Japan interconnect with Bergson’s philosophy and how they might contribute to an architectural ontology of duration.

Basudev Saha, Bidyut Das, Mukta Majumder

Over the past two decades, digital microfluidic biochips have been in much demand for safety-critical and biomedical applications and increasingly important in point-of-care analysis, drug discovery, and immunoassays, among other areas. However, for complex bioassays, finding routes for the transportation of droplets in an electrowetting-on-dielectric digital biochip while maintaining their discreteness is a challenging task. In this study, we propose a deep reinforcement learning-based droplet routing technique for digital microfluidic biochips. The technique is implemented on a distributed architecture to optimize the possible paths for predefined source–target pairs of droplets. The actors of the technique calculate the possible routes of the source–target pairs and store the experience in a replay buffer, and the learner fetches the experiences and updates the routing paths. The proposed algorithm was applied to benchmark suites I and III as two different test benches, and it achieved significant improvements over state-of-the-art techniques.

Boxiang Wang, Yunan Wu, Chenglong Ye

Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources. In this work, we propose Adaptive Robust Transfer Learning (ART), a flexible pipeline of performing transfer learning with generic machine learning algorithms. We establish the non-asymptotic learning theory of ART, providing a provable theoretical guarantee for achieving adaptive transfer while preventing negative transfer. Additionally, we introduce an ART-integrated-aggregating machine that produces a single final model when multiple candidate algorithms are considered. We demonstrate the promising performance of ART through extensive empirical studies on regression, classification, and sparse learning. We further present a real-data analysis for a mortality study.

Moumen El-Melegy, Rasha Kamel, Mohamed Abou El-Ghar et al.

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has great potential in the diagnosis, therapy, and follow-up of patients with chronic kidney disease (CKD). Towards that end, precise kidney segmentation from DCE-MRI data becomes a prerequisite processing step. Exploiting the useful information about the kidney’s shape in this step mandates a registration operation beforehand to relate the shape model coordinates to those of the image to be segmented. Imprecise alignment of the shape model induces errors in the segmentation results. In this paper, we propose a new variational formulation to jointly segment and register DCE-MRI kidney images based on fuzzy c-means clustering embedded within a level-set (LSet) method. The image pixels’ fuzzy memberships and the spatial registration parameters are simultaneously updated in each evolution step to direct the LSet contour toward the target kidney. Results on real medical datasets of 45 subjects demonstrate the superior performance of the proposed approach, reporting a Dice similarity coefficient of 0.94 ± 0.03, Intersection-over-Union of 0.89 ± 0.05, and 2.2 ± 2.3 in 95-percentile of Hausdorff distance. Extensive experiments show that our approach outperforms several state-of-the-art LSet-based methods as well as two UNet-based deep neural models trained for the same task in terms of accuracy and consistency.

Hailu Gemechu Benti, Abraham Debebe Woldeyohannes, Belete Sirahbizu Yigezu

The challenge of enhancing cutting tool life has been dealt with by many research studies. However, this challenge seems endless with growing technological advancement which brings about incremental improvement in tool life. The objective of this review paper is focused at assessing filtered cathodic vacuum arc deposition techniques applied on cutting tools and their effect on tool efficiency. The paper particularly picks filtered cathodic vacuum arc deposition (FCVAD) among other well-identified methods of coating like the Chemical Vapor Deposition (CVD) and Physical Vapor Deposition (PVD). Filtered Cathodic Vacuum Arc Deposition is the state of art in the coating technology finding wide application in the electronics industry and medical industry in addition to the machining industry, which is the concern of this review paper. This review is made in order to summarize and present the various techniques of FCVAD coatings and their applications, as investigated by various researches in the area.

Naofumi Hama, Masayoshi Mase, Art B. Owen

A model-agnostic variable importance method can be used with arbitrary prediction functions. Here we present some model-free methods that do not require access to the prediction function. This is useful when that function is proprietary and not available, or just extremely expensive. It is also useful when studying residuals from a model. The cohort Shapley (CS) method is model-free but has exponential cost in the dimension of the input space. A supervised on-manifold Shapley method from Frye et al. (2020) is also model free but requires as input a second black box model that has to be trained for the Shapley value problem. We introduce an integrated gradient (IG) version of cohort Shapley, called IGCS, with cost $\mathcal{O}(nd)$. We show that over the vast majority of the relevant unit cube that the IGCS value function is close to a multilinear function for which IGCS matches CS. Another benefit of IGCS is that is allows IG methods to be used with binary predictors. We use some area between curves (ABC) measures to quantify the performance of IGCS. On a problem from high energy physics we verify that IGCS has nearly the same ABCs as CS does. We also use it on a problem from computational chemistry in 1024 variables. We see there that IGCS attains much higher ABCs than we get from Monte Carlo sampling. The code is publicly available at https://github.com/cohortshapley/cohortintgrad

Naofumi Hama, Masayoshi Mase, Art B. Owen

A basic task in explainable AI (XAI) is to identify the most important features behind a prediction made by a black box function $f$. The insertion and deletion tests of Petsiuk et al. (2018) can be used to judge the quality of algorithms that rank pixels from most to least important for a classification. Motivated by regression problems we establish a formula for their area under the curve (AUC) criteria in terms of certain main effects and interactions in an anchored decomposition of $f$. We find an expression for the expected value of the AUC under a random ordering of inputs to $f$ and propose an alternative area above a straight line for the regression setting. We use this criterion to compare feature importances computed by integrated gradients (IG) to those computed by Kernel SHAP (KS) as well as LIME, DeepLIFT, vanilla gradient and input$\times$gradient methods. KS has the best overall performance in two datasets we consider but it is very expensive to compute. We find that IG is nearly as good as KS while being much faster. Our comparison problems include some binary inputs that pose a challenge to IG because it must use values between the possible variable levels and so we consider ways to handle binary variables in IG. We show that sorting variables by their Shapley value does not necessarily give the optimal ordering for an insertion-deletion test. It will however do that for monotone functions of additive models, such as logistic regression.

Masayoshi Mase, Art B. Owen, Benjamin B. Seiler

The most popular methods for measuring importance of the variables in a black box prediction algorithm make use of synthetic inputs that combine predictor variables from multiple subjects. These inputs can be unlikely, physically impossible, or even logically impossible. As a result, the predictions for such cases can be based on data very unlike any the black box was trained on. We think that users cannot trust an explanation of the decision of a prediction algorithm when the explanation uses such values. Instead we advocate a method called Cohort Shapley that is grounded in economic game theory and unlike most other game theoretic methods, it uses only actually observed data to quantify variable importance. Cohort Shapley works by narrowing the cohort of subjects judged to be similar to a target subject on one or more features. We illustrate it on an algorithmic fairness problem where it is essential to attribute importance to protected variables that the model was not trained on.

Jack Stade, Jamie Tucker-Foltz

We prove that any compact semi-algebraic set is homeomorphic to the solution space of some art gallery problem. Previous works have established similar universality theorems, but holding only up to homotopy equivalence, rather than homeomorphism, and prior to this work, the existence of art galleries even for simple spaces such as the Möbius strip or the three-holed torus were unknown. Our construction relies on an elegant and versatile gadget to copy guard positions with minimal overhead. It is simpler than previous constructions, consisting of a single rectangular room with convex slits cut out from the edges. We show that both the orientable and non-orientable surfaces of genus $n$ admit galleries with only $O(n)$ vertices.

Natabhona M. Mabachi, Melinda Brown, Catherine Wexler et al.

Abstract Background Prevention of mother-to-child HIV transmission (PMTCT) services in Kenya can be strengthened through the delivery of relevant and culturally appropriate SMS messages. Methods This study reports on the results of focus groups conducted with pre and postnatal women living with HIV (5 groups, n = 40) and their male partners (3 groups, n = 33) to elicit feedback and develop messages to support HIV+ women’s adherence to ART medication, ANC appointments and a facility-based birth. The principles of message design informed message development. Results Respondents wanted ART adherence messages that were low in verbal immediacy (ambiguous), came from an anonymous source, and were customized in timing and frequency. Unlike other studies, low message immediacy was prioritized over customization of message content. For retention, participants preferred messages with high verbal immediacy—direct appointment reminders and references to the baby—sent infrequently from a clinical source. Conclusion Overall, participants favored content that was brief, cheerful, and emotionally appealing.

Erik J. Hasenoehrl, Thomas J. Wiggins, Michael Berney

Development of novel anti-tuberculosis combination regimens that increase efficacy and reduce treatment timelines will improve patient compliance, limit side-effects, reduce costs, and enhance cure rates. Such advancements would significantly improve the global TB burden and reduce drug resistance acquisition. Bioenergetics has received considerable attention in recent years as a fertile area for anti-tuberculosis drug discovery. Targeting the electron transport chain (ETC) and oxidative phosphorylation machinery promises not only to kill growing cells but also metabolically dormant bacilli that are inherently more drug tolerant. Over the last two decades, a broad array of drugs targeting various ETC components have been developed. Here, we provide a focused review of the current state of art of bioenergetic inhibitors of Mtb with an in-depth analysis of the metabolic and bioenergetic disruptions caused by specific target inhibition as well as their synergistic and antagonistic interactions with other drugs. This foundation is then used to explore the reigning theories on the mechanisms of antibiotic-induced cell death and we discuss how bioenergetic inhibitors in particular fail to be adequately described by these models. These discussions lead us to develop a clear roadmap for new lines of investigation to better understand the mechanisms of action of these drugs with complex mechanisms as well as how to leverage that knowledge for the development of novel, rationally-designed combination therapies to cure TB.

Bernice Rogowitz, Laura J. Perovich, Yuke Li et al.

It is human to want to touch artworks, to feel their surface curvature and texture, their shapes and structures, and to feel the hand of the artist. Museum guards need to be constantly vigilant to protect art objects from adoring and exploring touches by visitors. This paper introduces a novel technique for capturing where and how art objects are touched. In this method, the users' touch either adds, or subtracts, microscopic fluorescent particles from a three-dimensional art object. Viewing the object under ultraviolet light reveals their touch traces and gestures. We present human touch behavior for a three-dimensional stylized landscape, and for two abstract and two representational art objects. We also present the results of video recordings of real-time behavior and user interviews. The resulting data show the kinds of touches, and where they are directed, and also reveal important individual differences. We feel this method opens the door to studying art perception through touch, and also enables new kinds of studies into touch behavior in other applications, including visualization, embodied cognition, and design.