Miracle-dz/UniFAR-FTUs huggingface dataset:
What Makes Indian Consumers to Trust the Faceless? Exploration of Antecedents and Influence of Trust in Indian b2c E-Commerce

: Trust is one of the most fundamental aspects which play a crucial role before adopting electronic commerce services. Generally, consumers don’t want to provide sensitive information during e-commerce transactions and they are afraid of fraudsters and fake information over online shopping websites. Hence, the main idea of this study is to explore what makes Indian online consumers to trust e-tailers which are faceless? And what is the influence of e-trust? Measurement items for such necessities created in different countries may not be suitable for use in Indian e-commerce. Hence authors have identified five element scales comprising of Website Reputation, Information Quality, and Privacy as precursors of E-Trust and Purchase Intention as outcome variable to validate the measurement items in India context. Study reveals that Website reputation, information quality and privacy are important variables that influences positively on electronic trust and e-trust has strong and positive influence on purchase intention in electronic transactions. Authors have discussed theoretical and managerial implications at the end.
Inverter based implementation of maximum power point techniques

The utilization of renewable energy sources is being pushed by both greater environmental consciousness and expanding demand. Recently, solar photovoltaic technology has found increased use for a broader range of applications. This may be ascribed to solar energy's extensive availability as well as its long-term viability and low cost. According to the global photovoltaic (PV) industry, 594 gigawatts (GW) of PV capacity were installed in 2019, with the objective of replacing conventional source-based generating facilities. The major problem in PV production, however, is identifying the maximum power point tracking (MPPT) systems that are currently in use to compute peak output. For 1240 W PV power plants, this article compares perturb & observe MPPT approaches with incremental conductance MPPT techniques. The MATLAB Simulink program was utilized to conduct the study, which was based on many factors such as voltage, current, and output power under various weather conditions. When these MPPT algorithms are applied to solar trackers, the efficiency, reaction time, and steady-state oscillations all improve.
CMAR_A_370961 2673..2680

1Department of Biostatistics, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan; 2Cancer Biostatistics Laboratory, Clinical Research Institute, National Hospital Organization Kyushu Cancer Center, Fukuoka, Japan; 3Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; 4Department of Surgery, Saiseikai Fukuoka General Hospital, Fukuoka, Japan; 5Department of Surgery, National Hospital Organization Fukuoka National Hospital, Fukuoka, Japan; 6Department of Thoracic Oncology, National Hospital Organization Kyushu Cancer Center, Fukuoka, Japan; 7Center for Clinical Research, Yamaguchi University Hospital, Yamaguchi, Japan; 8Department of Pharmaceutical and Health Care Management, Faculty of Pharmaceutical Sciences, Fukuoka University, Fukuoka, Japan
Model simplification using image and geometry-based metrics

This thesis describes methods for simplifying complex polygonal surfaces and optimizing their approximations. Such densely sampled surfaces often arise from range scanning, isosurface extraction, and approximation of smooth curved surfaces. Because of their complexity, these models must often be reduced by orders of magnitude using simplification to allow interactive display, efficient storage and transmission, and faster processing. In this thesis, I describe three different simplification algorithms, each with a different application domain. The first method, memoryless simplification, uses simple geometric heuristics based on changes in volume and area for ordering a sequence of edge collapses and optimizing the positions of the resulting vertices. In order to handle extremely large models that are too complex to fit in main memory, I propose a method for out-of-core simplification. This method is able to process an arbitrarily large mesh in a single pass, while building up an in-core representation of a greatly simplified version. For many computer graphics applications, preserving the visual appearance of the original model during simplification is the most important goal. I present a method for image-driven simplification that uses rendered images of an object and an image metric to directly capture and preserve the visual appearance, which allows the effects of surface attributes, visibility, and rendering parameters and viewing conditions to be explicitly accounted for during simplification. This method produces models of higher visual quality than existing geometry-based algorithms. Based on the image-driven method for simplification, I have developed a new method for mesh optimization which accepts an already simplified mesh and attempts to find a mesh with the same number of vertices that is more globally optimal with respect to an image metric. This method performs multidimensional simultaneous optimization of vertex positions and surface attributes, while also making local connectivity changes to the mesh where appropriate. Finally, I describe a new image metric for image-driven simplification and optimization that is motivated by human visual perception. This metric draws upon previous work in vision and psychology, but has been designed mainly for use in polygonal simplification. (Abstract shortened by UMI.)
Fano Configurations in Subregular Planes

In this article a wide variety of odd-order translation planes, constructedfrom Desarguesian planes by multiple derivation, are shown to admit Fanoconfigurations. For example, we show that subregular planes of odd order obtained by the replacement of at most disjoint nets admitFano configurations. Further, for odd and greater than 1, any subregularplane of order can be embedded into a subregular plane of order that contains the original plane as a subplane and admits Fano configurations.
Real Time Breast Histology Image Classification with a Mobile Phone

Background: Deep learning, specifically convolutional neural network, has made a breakthrough in the complex task of computer image recognition. In this study, depthwise separable convolutional neural network (DS-CNN), MobileNets v1, was used in classifying breast cancer histology images on the computer and then on a mobile/smart phone, in real time. This study propose that DS-CNN can be applied for histological image analysis and its network can be transferred to a commercially available mobile phone for real-time histological image analysis captured through the mobile phone camera. Method: This study utilizes the DS-CNN on breast cancer histology images downloaded from publicly available repository: https://rdm.inesctec. pt/dataset/nis-2017-003. Training set images are augmented by rotation and mirroring the images. DS-CNN is trained to classify breast tissue images between 4 categories: i) normal, ii) benign, iii) carcinoma in-situ, and iv) invasive carcinoma. Finally, the trained DS-CNN is deployed on to a mobile phone to classify the images captured through the mobile phone camera in real-time. The output on the mobile phone screen is the real-time image from the camera and its probability of it being one of the 4 categories (from high to low confidence). Accuracy of DS-CNN is assessed, both on the computer and on mobile phone, by whether its prediction with highest confidence matches the true class in the test dataset. Secondary results of sensitivity and specificities were calculated. Results: The trained DS-CNN accuracy on the computer reached as high as 86% in 4 class classification. On the mobile phone, accuracy reached 67% in 4 class classification and 78% in 2 class classification (normal or benign vs. in situ or invasive). Training time took less than 30 min on 1.4 GHz Intel Core i5 dual-core CPU. Latency for evaluation on mobile phone was less than 1 second. Demo video of real time histology image analysis with mobile phone can be found here: https://www.youtube. com/watch? v=qx2CdrSuazg Conclusion: DS-CNN is a fast and efficient neural network architecture that can learn to distinguish histological images even with limited sample size and computational power. The architecture can be deployed onto a mobile phone and maintain relatively good accuracy through the phone camera. It is likely that the accuracy can be increased by expanding the dataset and with updated CNN that are optimized for mobile phones
Quantization for Decentralized Learning Under Subspace Constraints

In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained formulation includes consensus or single-task optimization as special cases, and allows for more general task relatedness models such as multitask smoothness and coupled optimization. In order to cope with communication constraints, we propose and study an adaptive decentralized strategy where the agents employ <italic>differential randomized quantizers</italic> to compress their estimates before communicating with their neighbors. The analysis shows that, under some general conditions on the quantization noise, and for sufficiently small step-sizes <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>, the strategy is stable both in terms of mean-square error and average bit rate: by reducing <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>, it is possible to keep the <italic>estimation errors small (on the order of <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>) without increasing indefinitely the bit rate as <inline-formula><tex-math notation="LaTeX">$\mu \rightarrow 0$</tex-math></inline-formula> when variable-rate quantizers are used. </italic> Simulations illustrate the theoretical findings and the effectiveness of the proposed approach, revealing that decentralized learning is achievable at the expense of only a few bits.
Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations

Multimodal demonstrations provide robots with an abundance of information to make sense of the world. However, such abundance may not always lead to good performance when it comes to learning sensorimotor control policies from human demonstrations. Extraneous data modalities can lead to state over-specification, where the state contains modalities that are not only useless for decision-making but also can change data distribution across environments. State over-specification leads to issues such as the learned policy not generalizing outside of the training data distribution. In this work, we propose Masked Imitation Learning (MIL) to address state over-specification by selectively using informative modalities. Specifically, we design a masked policy network with a binary mask to block certain modalities. We develop a bi-level optimization algorithm that learns this mask to accurately filter over-specified modalities. We demonstrate empirically that MIL outperforms baseline algorithms in simulated domains and effectively recovers the environment-invariant modalities on a multimodal dataset collected on a real robot. Videos and supplemental details are at: https://tinyurl.com/masked-il
Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics.
Context-Aware Query Rewriting for Improving Users’ Search Experience on E-commerce Websites

E-commerce queries are often short and ambiguous. Consequently, query understanding often uses query rewriting to disambiguate user-input queries. While using e-commerce search tools, users tend to enter multiple searches, which we call context, before purchasing. These history searches contain contextual insights about users’ true shopping intents. Therefore, modeling such contextual information is critical to a better query rewriting model. However, existing query rewriting models ignore users’ history behaviors and consider only the instant search query, which is often a short string offering limited information about the true shopping intent.We propose an end-to-end context-aware query rewriting model to bridge this gap, which takes the search context into account. Specifically, our model builds a session graph using the history search queries and their contained words. We then employ a graph attention mechanism that models cross-query relations and computes contextual information of the session. The model subsequently calculates session representations by combining the contextual information with the instant search query using an aggregation network. The session representations are then decoded to generate rewritten queries. Empirically, we demonstrate the superiority of our method to state-of-the-art approaches under various metrics.
3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs

Numerous applications require robots to operate in environments shared with other agents, such as humans or other robots. However, such shared scenes are typically subject to different kinds of long-term semantic scene changes. The ability to model and predict such changes is thus crucial for robot autonomy. In this work, we formalize the task of semantic scene variability estimation and identify three main varieties of semantic scene change: changes in the position of an object, its semantic state, or the composition of a scene as a whole. To represent this variability, we propose the Variable Scene Graph (VSG), which augments existing 3D Scene Graph (SG) representations with the variability attribute, representing the likelihood of discrete long-term change events. We present a novel method, DeltaVSG, to estimate the variability of VSGs in a supervised fashion. We evaluate our method on the 3RScan long-term dataset, showing notable improvements in this novel task over existing approaches. Our method DeltaVsgachieves an accuracy of 77.1% and a recall of 72.3%, often mimicking human intuition about how indoor scenes change over time. We further show the utility of VSG prediction in the task of active robotic change detection, speeding up task completion by 66.0% compared to a scene-change-unaware planner. We make our code available as open-source.
Neuro-evolutionary evidence for a universal fractal primate brain shape

The cerebral cortex displays a bewildering diversity of shapes and sizes across and within species. Despite this diversity, we present a universal multi-scale description of primate cortices. We show that all cortical shapes can be described as a set of nested folds of different sizes. As neighbouring folds are gradually merged, the cortices of 11 primate species follow a common scale-free morphometric trajectory, that also overlaps with over 70 other mammalian species. Our results indicate that all cerebral cortices are approximations of the same archetypal fractal shape with a fractal dimension of d_f=2.5. Importantly, this new understanding enables a more precise quantification of brain morphology as a function of scale. To demonstrate the importance of this new understanding, we show a scale-dependent effect of ageing on brain morphology. We observe a more than four-fold increase in effect size (from 2 standard deviations to 8 standard deviations) at a spatial scale of approximately 2~mm compared to standard morphological analyses. Our new understanding may therefore generate superior biomarkers for a range of conditions in the future.
Identities of the multi-variate independence polynomials from heaps theory

We study and derive identities for the multi-variate independence polynomials from the perspective of heaps theory. Using the inversion formula and the combinatorics of partially commutative algebras we show how the multi-variate version of Godsil type identity as well as the fundamental identity can be obtained from weight preserving bijections. Finally, we obtain a new multi-variate identity involving connected bipartite subgraphs similar to the Christoﬀel-Darboux type identities obtained by Bencs.
Comments on "Iteratively Re-weighted Algorithm for Fuzzy c-Means"

In this comment, we present a simple alternate derivation to the IRW-FCM algorithm presented in"Iteratively Re-weighted Algorithm for Fuzzy c-Means"for Fuzzy c-Means problem. We show that the iterative steps derived for IRW-FCM algorithm are nothing but steps of the popular Majorization Minimization (MM) algorithm. The derivation presented in this note is much simpler and straightforward and, unlike the derivation of IRW-FCM, the derivation here does not involve introduction of any auxiliary variable. Moreover, by showing the steps of IRW-FCM as the MM algorithm, the inner loop of the IRW-FCM algorithm can be eliminated and the algorithm can be effectively run as a"single loop"algorithm. More precisely, the new MM-based derivation deduces that a single inner loop of IRW-FCM is sufficient to decrease the Fuzzy c-means objective function, which speeds up the IRW-FCM algorithm.
Autoregressive transformers for data-driven spatiotemporal learning of turbulent flows

A convolutional encoder–decoder-based transformer model is proposed for autoregressively training on spatiotemporal data of turbulent flows. The prediction of future fluid flow fields is based on the previously predicted fluid flow field to ensure long-term predictions without diverging. A combination of convolutional neural networks and transformer architecture is utilized to handle both the spatial and temporal dimensions of the data. To assess the performance of the model, a priori assessments are conducted, and significant agreements are found with the ground truth data. The a posteriori predictions, which are generated after a considerable number of simulation steps, exhibit predicted variances. The autoregressive training and prediction of a posteriori states are deemed crucial steps toward the development of more complex data-driven turbulence models and simulations. The highly nonlinear and chaotic dynamics of turbulent flows can be handled by the proposed model, and accurate predictions over long time horizons can be generated. Overall, the potential of using deep learning techniques to improve the accuracy and efficiency of turbulence modeling and simulation is demonstrated by this approach. The proposed model can be further optimized and extended to incorporate additional physics and boundary conditions, paving the way for more realistic simulations of complex fluid dynamics.
Bflier's: A Novel Butterfly Inspired Multi-robotic Model in Search of Signal Sources

The diversified ecology in nature had various forms of swarm behaviors in many species. The butterfly species is one of the prominent and a bit insightful in their random flights and converting that into an artificial metaphor would lead to enormous possibilities. This paper considers one such metaphor known as Butterfly Mating Optimization (BMO). In BMO, the Bfly follows the patrolling mating phenomena and simultaneously captures all the local optima of multimodal functions. To imitate this algorithm, a mobile robot (Bflybot) was designed to meet the features of the Bfly in the BMO algorithm. Also, the multi-Bflybot swarm is designed to act like butterflies in nature and follow the algorithm's rules. The real-time experiments were performed on the BMO algorithm in the multi-robotic arena and considered the signal source as the light source. The experimental results show that the BMO algorithm is applicable to detect multiple signal sources with significant variations in their movements i.e., static and dynamic. In the case of static signal sources, with varying initial locations of Bflybots, the convergence is affected in terms of time and smoothness. Whereas the experiments with varying step-size leads to their variation in the execution time and speed of the bots. In this work, experiments were performed in a dynamic environment where the movement of the signal source in both maneuvering and non-maneuvering scenarios. The Bflybot swarm is able to detect the single and multi-signal sources, moving linearly in between two fixed points, in circular, up and down movements.To evaluate the BMO phenomenon, various ongoing and prospective works such as mid-sea ship detection, aerial search applications, and earthquake prediction were discussed.
Optimal Filter Selection for Multispectral Object Classification Using Fast Binary Search

When designing multispectral imaging systems for classifying different spectra it is necessary to choose a small number of filters from a set with several hundred different ones. Tackling this problem by full search leads to a tremendous number of possibilities to check and is NP-hard. In this paper we introduce a novel fast binary search for optimal filter selection that guarantees a minimum distance metric between the different spectra to classify. In our experiments, this procedure reaches the same optimal solution as with full search at much lower complexity. The desired number of filters influences the full search in factorial order while the fast binary search stays constant. Thus, fast binary search allows to find the optimal solution of all combinations in an adequate amount of time and avoids prevailing heuristics. Moreover, our fast binary search algorithm outperforms other filter selection techniques in terms of misclassified spectra in a real-world classification problem.
On the Robustness of Graph Neural Diffusion to Topology Perturbations

Neural diffusion on graphs is a novel class of graph neural networks that has attracted increasing attention recently. The capability of graph neural partial differential equations (PDEs) in addressing common hurdles of graph neural networks (GNNs), such as the problems of over-smoothing and bottlenecks, has been investigated but not their robustness to adversarial attacks. In this work, we explore the robustness properties of graph neural PDEs. We empirically demonstrate that graph neural PDEs are intrinsically more robust against topology perturbation as compared to other GNNs. We provide insights into this phenomenon by exploiting the stability of the heat semigroup under graph topology perturbations. We discuss various graph diffusion operators and relate them to existing graph neural PDEs. Furthermore, we propose a general graph neural PDE framework based on which a new class of robust GNNs can be defined. We verify that the new model achieves comparable state-of-the-art performance on several benchmark datasets.