Big Objects Research Articles

On the category 𝒪 of a hybrid quantum group

We study the representation theory of a hybrid quantum group at root of unity ζ \zeta introduced by Gaitsgory. After discussing some basic properties of its category O \mathcal {O} , we study deformations of the category O \mathcal {O} . For subgeneric deformations, we construct the endomorphism algebra of big projective object and compute it explicitly. Our main result is an algebra isomorphism between the center of deformed category O \mathcal {O} and the equivariant cohomology of ζ \zeta -fixed locus on the affine Grassmannian attached to the Langlands dual group.

Real-Time Automatic M-Mode Echocardiography Measurement With Panel Attention.

Motion mode (M-mode) echocardiography is essential for measuring cardiac dimension and ejection fraction. However, the current diagnosis is time-consuming and suffers from diagnosis accuracy variance. This work resorts to building an automatic scheme through well-designed and well-trained deep learning to conquer the situation. That is, we proposed RAMEM, an automatic scheme of real-time M-mode echocardiography, which contributes three aspects to address the challenges: 1) provide MEIS, the first dataset of M-mode echocardiograms, to enable consistent results and support developing an automatic scheme; For detecting objects accurately in echocardiograms, it requires big receptive field for covering long-range diastole to systole cycle. However, the limited receptive field in the typical backbone of convolutional neural networks (CNN) and the losing information risk in non-local block (NL) equipped CNN risk the accuracy requirement. Therefore, we 2) propose panel attention embedding with updated UPANets V2, a convolutional backbone network, in a real-time instance segmentation (RIS) scheme for boosting big object detection performance; 3) introduce AMEM, an efficient algorithm of automatic M-mode echocardiography measurement, for automatic diagnosis; The experimental results show that RAMEM surpasses existing RIS schemes (CNNs with NL & Transformers as the backbone) in PASCAL 2012 SBD and human performances in MEIS.

A novel system for the classification of zinc-plated components by benchmarking deep neural networks

Artificial intelligence (AI) has become the new technique for solving the most complicated challenges related to big data, image recognition, object detection, and prediction issues due to the fourth industrial revolution. This article introduces the development and implementation of a zinc-plated component recognition system within a manufacturing process using deep learning (DL) techniques. This paper aims to train and evaluate different DL algorithms to recognize five zinc-plated components and one assembly tray under different ambient light conditions and finishing. The proposed method begins with creating a custom dataset of 5.5K images, with six classes that match the assembly components. However, before the training stage starts, a subset between 5% and 10% is created. Eight deep neural networks have been benchmarked: Swing Transformer, Convnextv2, Xception41, Data-efficient image Transformer, Inception_v4, ViT, Resnet50 and Efficientnet_b0. The training stage is performed with the k-fold method using k = 6 and a progressive data size between different runs with an average of data utilized during training of 8%. In this study, convnextv2_tiny exhibits the highest F1-Score with 99.177%, followed by swin_tiny_patch4_window7_224 with 99.175%, while resnet50 achieved the lowest F1-Score at 95.886%. Therefore, the new generation of convolutional architectures is still better for classifying images. Thus, these algorithms can be adopted as good classifier models for zinc-plated component classification in the industrial manufacturing environment.

Contextual expectations shape the motor coding of movement kinematics during the prediction of observed actions: A TMS study

Contextual information may shape motor resonance and support intention understanding during observation of incomplete, ambiguous actions. It is unclear, however, whether this effect is contingent upon kinematics ambiguity or contextual information is continuously integrated with kinematics to predict the overarching action intention. Moreover, a differentiation between the motor mapping of the intention suggested by context or kinematics has not been clearly demonstrated. In a first action execution phase, 29 participants were asked to perform reaching-to-grasp movements towards big or small food objects with the intention to eat or to move; electromyography from the First Dorsal Interosseous (FDI) and Abductor Digiti Minimi (ADM) was recorded. Depending on object size, the intentions to eat or to move were differently implemented by a whole-hand or a precision grip kinematics, thus qualifying an action-muscle dissociation. Then, in a following action prediction task, the same participants were asked to observe an actor performing the same actions and to predict his/her intention while motor resonance was assessed for the same muscles. Of note, videos were interrupted at early or late action phases, and actions were embedded in contexts pointing toward an eating or a moving intention, congruently or incongruently with kinematics. We found greater involvement of the FDI or ADM in the execution of precision or whole-hand grips, respectively. Crucially, this pattern of activation was mirrored during observation of the same actions in congruent contexts, but it was cancelled out or reversed in the incongruent ones, either when videos were interrupted at either early or long phases of action deployment. Our results extend previous evidence by showing that contextual information shapes motor resonance not only under conditions of perceptual uncertainty but also when more informative kinematics is available.

A Study of Global Trade War and Its Impact on Indian Economy

A Study of Global Trade War and Its Impact on Indian Economy

Motor Development of Children in the Kurdistan Region of Iraq: Parent Survey.

The actual literature highlights the importance of the socio-cultural context in the development of children. However, there is a lack of specific evidence about the middle East, especially regarding the development of Kurdish children who are living in a post-war scenario, in a country which is experiencing continuous instability due to the different crises. The main aim of this study is to identify the features of the motor development of Kurdish children according to parents' opinion. A comparison with Italian children is provided as a Western example, which reflects data from the literature. In the study, 331 parents of Kurdish and Italian children aged between 3 and 7 years were involved. Parents filled the questionnaire at kindergartens, after providing consent. The questionnaire was conceptualized, designed, tested and provided ad hoc for this study; it focused on the timing of development, concerning major milestones like head control, sitting and standing-up. The questionnaire consists of 15 questions and has not been standardized yet. A logistic regression showed several differences between Kurdish and Italian children, like head control (p = 0.007) or the manipulation of big objects (p < 0.0001). These results identify the effect of the socio-cultural context and the impact of the growing environment of the child. Moreover, the results of this survey show the need for introducing different adapted, translated and validated assessment tools for motor development, considering differences related to the socio-cultural context.

Palm Gripper Measurement Device for Post-Stroke Rehabilitation Progressive Tracking

Various devices are used in the medical world to measure grip force. However, there is no well-defined method being used to quantify the distribution of grip forces applied by post-stroke patients. It is important to track the patient's progress in neurofeedback training throughout rehabilitation with quantitative evaluation. A palm gripper measurement device is being developed, equipped with Force Sensing Resistors (FSRs) (RP-S40-ST model) to capture grip force. The device provides valuable insights into rehabilitation progress by assessing the grip force. The analogue value from FSRs is linearly interpolated from the input range to the output range using the map function; 'map(avg_force, 0, 1023, 0, 15)' to scale the 'fsrReading' from the input range of 0 to 1023. The input range is converted into a score 0 to 15 scale of a bar graph to indicate the amount of force measured. The accuracy showed by Pearson’s r shows a correlation trend between the analogue value and length of the bar graph with 0.97651 and 0.98083, respectively. A matrix was plotted for three subjects with different object sizes for device testing which shows the adjusted R2 is 0.955 highest for big objects and the lowest adjusted R2 is 0.63672 for small objects.

The unique floor of Juling crater on Ceres

Results of NASA's Dawn mission indicate that Ceres, the biggest object in the main asteroid belt, may be geologically active because it shows changes in its morphology that might have happened in geologically recent times. Juling is a ∼2.5 Ma old, 20-km diameter impact crater on this dwarf planet, which has an extensive ejecta blanket of spectrally bright bluish material and water ice exposures on its steep northern inner wall. The crater floor is dominated by a spectrally reddish material, which is distinct from other surface types in this region. No other crater of Juling's size on Ceres, shows such a reddish floor. In addition, the floor has a complex morphology characterized by lobate flows and indications of a north-south directed mass wasting possibly leading to the elongated, ∼16 km long and several hundred-meter-high central structure. Here we describe the characteristics of the material that constitutes the floor, and we present a geological map of the crater, using the Framing Camera (FC) imagery. From the analysis of data acquired by the Visible and Infrared Spectrometer (VIR), we did not find evidence for the presence of organic rich materials in Juling at the available data resolution. The spectrum of the floor material seems to be a combination of mineralogy and physical properties of the regolith. Our findings suggest that the processes leading to the reddish material and the peculiar morphology of the crater floor, must have occurred after the formation of Juling crater.

Lift at low Reynolds number.

Lift forces are widespread in hydrodynamics. These are typically observed for big and fast objects and are often associated with a combination of fluid inertia (i.e. large Reynolds numbers) and specific symmetry-breaking mechanisms. In contrast, the properties of viscosity-dominated (i.e. low Reynolds numbers) flows make it more difficult for such lift forces to emerge. However, the inclusion of boundary effects qualitatively changes this picture. Indeed, in the context of soft and biological matter, recent studies have revealed the emergence of novel lift forces generated by boundary softness, flow gradients and/or surface charges. The aim of the present review is to gather and analyse this corpus of literature, in order to identify and unify the questioning within the associated communities, and pave the way towards future research.

A Comprehensive Study of Deep Learning and Performance Comparison of Deep Neural Network Models (YOLO, RetinaNet)

This paper presents the latest advances in machine learning techniques and highlights deep learning (DL) methods in recent studies. This technology has recently received great attention as it can solve complex problems. This paper focuses on covering one of the deep learning algorithms (deep neural network) and learning about its types such as convolutional neural network (CNN), Recurrent Neural Networks (RNN), etc. We have discussed recent changes, such as advanced DL technologies. Next, we continue analyzing and discussing the challenges and possible solutions of machine learning, such as big data and object detection, studying more papers in deep learning, and knowing the main experiments and future directions. In addition, this review also identifies some successful deep learning applications in recent years. Moreover, in this paper, one of the deep learning methods, convolutional neural networks, is applied to detect objects in images by using the You Only Look One model and comparing it with RetinaNet using pre-trained models. The results found that CNN (using YOLOv3) is a more accurate model than RetinaNet.

Pedestrian Detection System using YOLOv5 for Advanced Driver Assistance System (ADAS)

The technology in transportation is continuously developing due to reaching the self-driving vehicle. The need of detecting the situation around vehicles is a must to prevent accidents. It is not only limited to the conventional vehicle in which accident commonly happens, but also to the autonomous vehicle. In this paper, we proposed a detection system for recognizing pedestrians using a camera and minicomputer. The approach of pedestrian detection is applied using object detection method (YOLOv5) which is based on the Convolutional Neural Network. The model that we proposed in this paper is trained using numerous epochs to find the optimum training configuration for detecting pedestrians. The lowest value of object and bounding box loss is found when it is trained using 2000 epochs, but it needs at least 3 hours to build the model. Meanwhile, the optimum model’s configuration is trained using 1000 epochs which has the biggest object (1.49 points) and moderate bounding box (1.5 points) loss reduction compared to the other number of epochs. This proposed system is implemented using Raspberry Pi4 and a monocular camera and it is only able to detect objects for 0.9 frames for each second. As further development, an advanced computing device is needed due to reach real-time pedestrian detection. &#x0D;

An improved Representative Particle Monte Carlo method for the simulation of particle growth

Context. A rocky planet is formed out of the agglomeration of around 1040 cosmic dust particles. As dust aggregates grow by coagulation, their number decreases. But until they have grown to hundreds of kilometres, their number still remains well above the number of particles a computer model can handle directly. The growth from micrometres to planetesimal-sized objects therefore has to be modelled using statistical methods, often using size distribution functions or Monte Carlo methods. However, when the particles reach planetary masses, they must be treated individually. This can be done by defining two classes of objects: a class of many small bodies or dust particles treated in a statistical way, and a class of individual bodies such as one or more planets. This introduces a separation between small and big objects, but it leaves open how to transition from small to big objects, and how to treat objects of intermediate sizes. Aims. We aim to improve the Representative Particle Monte Carlo (RPMC) method, which is often used for the study of dust coagulation, to be able to smoothly transition from the many-particle limit into the single-particle limit. Results. Our new version of the RPMC method allows for variable swarm masses, making it possible to refine the mass resolution where needed. It allows swarms to consist of few numbers of particles, and it includes a treatment of the transition from swarm to individual particles. The correctness of the method for a simplified two-component test case is validated with an analytical argument. The method is found to retain statistical balance and to accurately describe runaway growth, as is confirmed with the standard constant kernel, linear kernel, and product kernel tests as well as by comparison with a fiducial non-representative Monte Carlo simulation.

Influence of the charge compensating agent on tunable emission of the Ba2MgWO6:Eu3+ double perovskite

Double perovskites have recently received extensive attention due to their attractive photoluminescent properties. Herein, we report how the charge compensating agent influences the structural and spectroscopic properties of investigated ceramic powder. Ba2MgWO6:Eu3+ samples were co-doped with K+ and Li+ ions and obtained by the mechanochemical route. SEM images of the representative sample showed big objects with visible grain boundaries, sticking together. The charge compensator does not affect the character of Eu3+ ions emission which is, in both cases, the emission is composed of magnetic dipole emission from the 5D0 excited level to the 7F1 ground state. However, the experimental results showed that in the sample co-doped with K+ ions a higher number of irregular WO6 groups were created and that they influence the shape and position of the host emission. The mechanism of emission generation from regular and irregular WO6 groups and the interaction between them are discussed in detail. The experimental data are supported by the DFT calculation of the density of states of the samples with Na/K+ co-dopant.

Moonlight

&#x0D; &#x0D; &#x0D; The moon is the biggest object in the night sky. Its light has always fascinated. That special quality of light invites all kinds of superstition, from the healing power of the waning moon to good luck during full moon, from disabled children to pots of gold. Sometimes the moon appears blue, white, yellow or red. In the middle of the night, its light turns everything silver. How can we make an analogue print on paper and capture this ever- changing companion of our lives? How can we show the fickle quality of moonlight? In this paper we explain why the moon has so many colours but is mostly seen as silver to the human eye and we chart the progress of our attempts to translate this special optical feature into print by exploring different ink formulations, and their influence on the optical appearance of a wood engraving of the moon.&#x0D; &#x0D; &#x0D;

INVESTIGATION OF THE HEMPEL PARADOX WITH CLASSES IN BIG DATA OF SOCIO-ECONOMIC SYSTEMS

The methods of self-learning and learning with an artificial intelligence teacher are being investigated. The research is carried out in the interests of solving the problems of countering money laundering and terrorist financing on incomplete, heterogeneous big data of socio-economic systems. The N-scheme of the Markov algorithm with a quantitative measure of induction is synthesized. A method is proposed for recording the number of relationships (interactions) of big data objects in the database of artificial intelligence. The theoretical provisions of Hempel’s paradox with classes are considered. The Hempel paradox is evaluated in heterogeneous big data. The Markov occurrence diagram for Hempel’s paradox with classes is synthesized. The Hempel paradox with classes is investigated under conditions of incompleteness and heterogeneity of the data sample. A method of inductive self-learning of artificial intelligence in the N-scheme of the Markov algorithm with a quantitative measure of induction is proposed. The method of using the associator m for training artificial intelligence in the N-scheme of the Markov algorithm is presented. A method of category, predetermined, inductive, teacher-directed self-study is proposed. It is concluded that self-learning methods have a limited potential for mathematical expressiveness. The reason for the low expressiveness is the inability to correctly operate with classes and categories, as well as the limitations of the possibility of logical predestination of truth, taking into account the heterogeneous, anthropogenic nature of objects and morphisms. The study of methods of teaching without a teacher revealed the scientific task of overcoming the Hempel paradox for classes of objects. It is indicated that the proposed method of categorically predetermined, inductive, teacher-directed self-learning actually shows that artificial intelligence a priori must be trained by a teacher, in other words, it cannot correctly self-study without it.

Experimental fragments distributions for thin aluminium plates subjected to hypervelocity impacts

Hypervelocity impacts involving big objects in space are expected to occur more frequently in the next years, generating a significant number of new debris. In this context, understanding the physical mechanisms involved in orbital collisions and modelling space debris production after fragmentation are fundamental prerequisites to predict the long-term evolution of the space debris environment. In this work we present impact experiments on aluminium thin plates with the purpose to evaluate the effect of impact velocity and target thickness on the fragments generation. Results are presented as cumulative characteristic length distributions and shapes distributions. It is shown that characteristic length distributions can be modelled by a power law similar to the NASA SBM and that shape distributions are independent from target thickness and impact velocity.

Towards efficient and robust intelligent mobile vision system via small object aware parallel offloading

As mobile devices continuously generate streams of images and videos, intelligent mobile vision applications are rapidly emerging. An ideal object detection system for mobile vision applications should be accurate and real-time. Nevertheless, it is non-trivial to achieve these goals utilizing resource-constrained mobile devices. In this work, we propose an efficient and robust intelligent mobile vision system AREdge via small object aware parallel offloading. We find that the detection performance of small objects is a core factor that affects detection accuracy. To tackle this, we design a local lightweight DNN model that runs on mobile devices to detect big objects fast and identify the regions of interest (RoIs) that may have small objects. These areas are then cropped and offloaded to multiple edge servers for more accurate detection based on complex and large-scale DNN models. To further improve the performance, we propose a dynamic area-aware parallel offloading scheme for fine-grained parallel execution on multiple edge servers. Experimental results show that the accuracy of AREdge is 214.27% higher than that of the local detection in small objects. It also reduces the detection latency by 20.68% on average over the offloading method based on full images and well-used object detection models.

Word order preference in sign influences speech in hearing bimodal bilinguals but not vice versa: Evidence from behavior and eye-gaze

AbstractWe investigated cross-modal influences between speech and sign in hearing bimodal bilinguals, proficient in a spoken and a sign language, and its consequences on visual attention during message preparation using eye-tracking. We focused on spatial expressions in which sign languages, unlike spoken languages, have a modality-driven preference to mention grounds (big objects) prior to figures (smaller objects). We compared hearing bimodal bilinguals’ spatial expressions and visual attention in Dutch and Dutch Sign Language (N = 18) to those of their hearing non-signing (N= 20) and deaf signing peers (N= 18). In speech, hearing bimodal bilinguals expressed more ground-first descriptions and fixated grounds more than hearing non-signers, showing influence from sign. In sign, they used as many ground-first descriptions as deaf signers and fixated grounds equally often, demonstrating no influence from speech. Cross-linguistic influence of word order preference and visual attention in hearing bimodal bilinguals appears to be one-directional modulated by modality-driven differences.

How big should this object be? Perceptual influences on viewing-size preferences

When viewing objects depicted in a frame, observers prefer to view large objects like cars in larger sizes and smaller objects like cups in smaller sizes. That is, the visual size of an object that “looks best” is linked to its typical physical size in the world. Why is this the case? One intuitive possibility is that these preferences are driven by semantic knowledge: For example, when we recognize a sofa, we access our knowledge about its real-world size, and this influences what size we prefer to view the sofa within a frame. However, might visual processing play a role in this phenomenon—that is, do visual features that are related to big and small objects look better at big and small visual sizes, respectively, even when observers do not have explicit access to semantic knowledge about the objects? To test this possibility, we used “texform” images, which are synthesized versions of recognizable objects, which critically retain local perceptual texture and coarse contour information, but are no longer explicitly recognizable. To test for visual size preferences, we first used a size adjustment task, and the results were equivocal. However, clear results were obtained using a two-interval forced choice task, in which each texform was presented at the preferred visual size of its corresponding original image, and a visual size slightly bigger or smaller. Observers consistently selected the texform presented at the canonical visual size as the more aesthetically pleasing one. An additional control experiment ruled out alternative explanations related to size priming effects. These results suggest that the preferred visual size of an object depends not only on explicit knowledge of its real-world size, but also can be evoked by mid-level visual features that systematically covary with an object's real-world size.

Exact categories, big Cohen-Macaulay modules and finite representation type

Abstract One of the first remarkable results in the representation theory of artin algebras , due to Auslander and Ringel-Tachikawa, is the characterisation of when an artin algebra is representation-finite. In this paper, we investigate aspects of representation-finiteness in the general context of exact categories in the sense of Quillen. In this framework, we introduce “big objects” and prove an Auslander-type “splitting-big-objects” theorem. Our approach generalises and unifies the known results from the literature. As a further application of our methods, we extend the theorems of Auslander and Ringel-Tachikawa to arbitrary dimension, i.e. we characterise when a Cohen-Macaulay order over a complete regular local ring is of finite representation type.