Final Object Research Articles

Pineline: Industrialization of high-energy theory predictions

We present a collection of tools efficiently automating the computation of large sets of theory predictions for high-energy physics. Calculating predictions for different processes often require dedicated programs. These programs, however, accept inputs and produce outputs that are usually very different from each other. The industrialization of theory predictions is achieved by a framework which harmonizes inputs (runcard, parameter settings), standardizes outputs (in the form of grids), produces reusable intermediate objects, and carefully tracks all meta data required to reproduce the computation. Parameter searches and fitting of non-perturbative objects are exemplary use cases that require a full or partial re-computation of theory predictions and will thus benefit of such a toolset. As an example application we present a study of the impact of replacing NNLO QCD K-factors with the exact NNLO predictions in a PDF fit. Program summaryProgram Title:pinelineCPC Library link to program files:https://doi.org/10.17632/dyvns7gnwy.1Developer's repository link:https://nnpdf.github.io/pineline/Licensing provisions: GPLv3Programming language: Python, RustNature of the problem: The computation of theoretical quantities in particle physics often involves computationally-intensive tasks such as the calculation of differential cross sections in a systematic and reproducible way. Different groups often use different conventions and choices which makes tasks such as the fitting of physical parameters or quantities very computationally challenging and hard to reliably reproduce.Solution method: We create a pipeline of tools such that a user can define an observable and a theory framework and obtain a final object, containing all relevant theoretical information. Such objects can be then used in a variety of interchangeable ways (fitting, analysis, experimental comparisons).

The Threads That Weave Me

The Threads That Weave Me

A Multi-Object Tracking Approach Combining Contextual Features and Trajectory Prediction

Aiming to solve the problem of the identity switching of objects with similar appearances in real scenarios, a multi-object tracking approach combining contextual features and trajectory prediction is proposed. This approach integrates the motion and appearance features of objects. The motion features are mainly used for trajectory prediction, and the appearance features are divided into contextual features and individual features, which are mainly used for trajectory matching. In order to accurately distinguish the identities of objects with similar appearances, a context graph is constructed by taking the specified object as the master node and its neighboring objects as the branch nodes. A preprocessing module is applied to exclude unnecessary connections in the graph model based on the speed of the historical trajectory of the object, and to distinguish the features of objects with similar appearances. Feature matching is performed using the Hungarian algorithm, based on the similarity matrix obtained from the features. Post-processing is performed for the temporarily unmatched frames to obtain the final object matching results. The experimental results show that the approach proposed in this paper can achieve the highest MOTA.

Efficient Object Detection Using Semantic Region of Interest Generation with Light-Weighted LiDAR Clustering in Embedded Processors.

Many fields are currently investigating the use of convolutional neural networks to detect specific objects in three-dimensional data. While algorithms based on three-dimensional data are more stable and insensitive to lighting conditions than algorithms based on two-dimensional image data, they require more computation than two-dimensional data, making it difficult to drive CNN algorithms using three-dimensional data in lightweight embedded systems. In this paper, we propose a method to process three-dimensional data through a simple algorithm instead of complex operations such as convolution in CNN, and utilize its physical characteristics to generate ROIs to perform a CNN object detection algorithm based on two-dimensional image data. After preprocessing the LiDAR point cloud data, it is separated into individual objects through clustering, and semantic detection is performed through a classifier trained based on machine learning by extracting physical characteristics that can be utilized for semantic detection. The final object recognition is performed through a 2D-based object detection algorithm that bypasses the process of tracking bounding boxes by generating individual 2D image regions from the location and size of objects initially detected by semantic detection. This allows us to utilize the physical characteristics of 3D data to improve the accuracy of 2D image-based object detection algorithms, even in environments where it is difficult to collect data from camera sensors, resulting in a lighter system than 3D data-based object detection algorithms. The proposed model achieved an accuracy of 81.84% on the YOLO v5 algorithm on an embedded board, which is 1.92% higher than the typical model. The proposed model achieves 47.41% accuracy in an environment with 40% higher brightness and 54.12% accuracy in an environment with 40% lower brightness, which is 8.97% and 13.58% higher than the general model, respectively, and can achieve high accuracy even in non-optimal brightness environments. The proposed technique also has the advantage of reducing the execution time depending on the operating environment of the detection model.

Simulation of temperature profile in fused filament fabrication 3D printing method

PurposeTemperature is a critical factor in the fused filament fabrication (FFF) process, which affects the flow behavior and adhesion of the melted filament and the mechanical properties of the final object. Therefore, modeling and predicting temperature in FFF is crucial for achieving high-quality prints, repeatability, process control and failure prediction. This study aims to investigate the melt deposition and temperature profile in FFF both numerically and experimentally using different Acrylonitrile Butadiene Styrene single-strand specimens. The process parameters, including layer thickness, nozzle temperature and build platform temperature, were varied.Design/methodology/approachCOMSOL Multiphysics software was used to perform numerical simulations of fluid flow and heat transfer for the printed strands. The polymer melt/air interface was tracked using the coupling of continuity equation, equation of motion and the level set equation, and the heat transfer equation was used to simulate the temperature distribution in the deposited strand.FindingsThe numerical results show that increasing the nozzle temperature or layer thickness leads to an increase in temperature at points close to the nozzle, but the bed temperature is the main determinant of the overall layer temperature in low-thickness strands. The experimental temperature profile of the deposited strand was measured using an infrared (IR) thermal imager to validate the numerical results. The comparison between simulation and observed temperature at different points showed that the numerical model accurately predicts heat transfer in the three-dimensional (3D) printing of a single-strand under different conditions. Finally, a parametric analysis was performed to investigate the effect of selected parameters on the thermal history of the printed strand.Originality/valueThe numerical results show that increasing the nozzle temperature or layer thickness leads to an increase in temperature at points close to the nozzle, but the bed temperature is the main determinant of the overall layer temperature in low-thickness strands. The experimental temperature profile of the deposited strand was measured using an IR thermal imager to validate the numerical results. The comparison between simulation and observed temperature at different points showed that the numerical model accurately predicts heat transfer in the 3D printing of a single-strand under different conditions. Finally, a parametric analysis was performed to investigate the effect of selected parameters on the thermal history of the printed strand.

A preliminary investigation of gyroids made of W-Cu composite materials produced by investment casting into SiO2-based molds 3D-printed by stereolithography

In this work, a new indirect additive manufacturing technique to produce tungsten-copper composites (W-Cu) triply periodic minimal surface (TPMS) geometries by investment casting is proposed. The process consists in the combination of stereolithography (SLA) and molten copper casting to realize complex architectures (i.e., gyroids) and to control the volume fractions of W and Cu, tailoring the composite properties.SiO2-based tubular molds were produced by SLA to reproduce the negative of the object to be casted. The positive shape of the final object was obtained by filling the cavity of the SiO2 mold with a mixture of tungsten and corn starch (CS), which was used as space holder. After a pyrolysis stage at high temperature, the W porous structure was infiltrated by melted copper under vacuum. The leaching of the SiO2 mold using a basic solution allowed obtaining the final geometry. W-based particles of different diameter (i.e., d50 = 10, 22, and 150 µm) were used, and CS content was changed between 5 and 30 %vol. (with respect to W) to adjust the copper content in the composite. In all cases, produced samples retained the geometric properties of the CAD design.

High‐resolution 3D printing resin reinforced by polyurethane filler particles; physical and mechanical properties

AbstractIn 3D printing technology, unwanted resin properties such as low mechanical, thermal, electrical, and biocompatibility can be enhanced by using proper fillers. In this study, sucrose urethane microparticles, acrylic modified are used to improve the properties of the final object without losing other properties such as printing resolution. Modification is confirmed by FTIR spectroscopy and hydrogen bonding index enhancement after modification of the urethane filler. Filler particles are reduced in sizes by modification followed by ball‐milling as is confirmed by scanning electron microscopy. When the filler content is 2.5 wt%, mechanical tensile and flexural modulus increase compared to the neat resin however, increases the brittleness of the photo‐cured samples. Thermal degradation temperature is lifted up for 2.5–5.0 wt% of the filler loading in the resin. Improved dimensional accuracy and detailed printing in higher resolution, due to the physical effect of the filler particles, can also be achieved in real 3D‐printed object as is explored.Highlights High‐resolution 3D printing, by random geometry of filler particles in the resin. Scattering from polyurethane foam microparticles lead to high‐resolution printing. Acrylate‐modified polyurethane filler particles provide resin compatibility. Polyurethane filler leads to higher resolution with enhanced mechanical properties.

4-Dimensional printing: exploring current and future capabilities in biomedical and healthcare systems-a Concise review.

4-Dimensional Printing (4DP) is the latest concept in the pharmacy and biomedical segment with enormous potential in dosage from personalization and medication designing, which adopts time as the fourth dimension, giving printed structures the flexibility to modify their morphology. It can be defined as the fabrication in morphology with the help of smart/intelligent materials like polymers that permit the final object to alter its properties, shape, or function in response to external stimuli such as heat, light, pH, and moisture. The applications of 4DP in biomedicines and healthcare are explored with a focus on tissue engineering, artificial organs, drug delivery, pharmaceutical and biomedical field, etc. In the medical treatments and pharmaceutical field 4DP is paving the way with unlimited potential applications; however, its mainstream use in healthcare and medical treatments is highly dependent on future developments and thorough research findings. Therefore, previous innovations with smart materials are likely to act as precursors of 4DP in many industries. This review highlights the most recent applications of 4DP technology and smart materials in biomedical and healthcare fields which can show a better perspective of 4DP applications in the future. However, in view of the existing limitations, major challenges of this technology must be addressed along with some suggestions for future research. We believe that the application of proper regulatory constraints with 4DP technology would pave the way for the next technological revolution in the biomedical and healthcare sectors.

A Point Cloud Classification Method and Its Applications Based on Multi-Head Self-Attention

<p>In the monitoring the safety status of hazardous chemical warehouses by three-dimensional re-construction of deep camera point clouds, there are classification difficulties such as large space, sparse distribution of point clouds in cargo images, and similar distribution in low dimensions. Based on the above problem, a point cloud recognition method based on multi-head attention mechanism is proposed. The algorithm first normalizes the distribution of the point cloud data set through the affine transformation algorithm to solve the problem of sparse distribution. Then, the high-dimensional feature map is obtained by fusing the data down-sampling and curve feature aggregation algorithms to solve the problem of low-dimensional distribution approximation. The feature map is then encoded using a multi-head self-attention encoder to obtain features under different heads, which are then merged into a feature map. Finally, a multi-layer fully connected neural network is used as the decoder to decode the feature map into the final object classification. Comparative experiments were performed on the ModelNet40 dataset and the self-built dataset of warehouse goods, and the results showed that the accuracy of this paper was improved by 0.5% to 7.8% compared with that of other classification algorithms.</p> <p> </p>

Parton labeling without matching: unveiling emergent labelling capabilities in regression models

Parton labeling methods are widely used when reconstructing collider events with top quarks or other massive particles. State-of-the-art techniques are based on machine learning and require training data with events that have been matched using simulations with truth information. In nature, there is no unique matching between partons and final state objects due to the properties of the strong force and due to acceptance effects. We propose a new approach to parton labeling that circumvents these challenges by recycling regression models. The final state objects that are most relevant for a regression model to predict the properties of a particular top quark are assigned to said parent particle without having any parton-matched training data. This approach is demonstrated using simulated events with top quarks and outperforms the widely-used chi ^2 method.

Describing the neolithic cord production process: Raw materials, techniques and experimental archaeology in La Draga (Girona, Spain; 5207–4862 cal BC)

Cordage is related to daily activities and as such has been a basic element for self-suffiency since Prehistory. Due to the lack of their preservation in most archaeological contexts, the knowledge of these technologies in the past is limited. This paper analyses the production process through an experimental program of the cordage assemblage from La Draga (Girona, Spain; 5207–4862 cal BC). The site is an Early Neolithic site from north-eastern Iberia, where a wide vegetal set of fibre-based craft was recovered in waterlogged preservation conditions. First, a systematic identification of raw materials used for cord production is done. Subsequently an experimental approach to these cords, considering both the raw materials used and their properties, has been developed. The study aims to infer raw material selection for producing cords, as well as the importance of raw material and group standards involved in the final objects. The results showed a preferable selection of lime tree bark (Tilia sp.) and nettle bast fibre (Urtica sp.), which may be obtained from the surroundings of the site. Both were used to produce different types of cords, twisting preferred above braiding, which should be related to cultural standards and traditional learning processes.

ПОТЕНЦИАЛ КОМПАРАТИВНОГО АНАЛИЗА ТЕНДЕНЦИЙ РАЗВИТИЯ ПРОМЫШЛЕННОГО КОМПЛЕКСА ФЕДЕРАЛЬНЫХ ОКРУГОВ

This article is devoted to the analysis of the development of the industrial complex of the Southern Federal District in order to identify the main vectors of development and determine the final object of the industrial complex of the region. The dynamics of development over several years makes it possible to determine the general trends and changes that have occurred in industrial production, to identify the subjects of the region with the greatest potential and the subjects of the regions that require additional support. This analysis determines the relevance and necessity of maintaining the existing high growth potential of the industry of the Southern Federal District in the face of external changes.

Penyusunan Laporan Keuangan pada Yayasan Bundo Saiyo

The final report is to comprise the financial report of the Bundo Saiyo Foundation according to the general standard of financial accounting. The creation of financial statements is set by Statement of Financial Accounting no. 1 (PSAK 1) and for non-profit entities set in the Standard Interpretation of Financial Accounting no. 35 (ISAK 35). The foundation is one form of a nonprofit entity that is primarily moving toward humanity with the primary goal of not making profit. The final research object for this project is the Bundo Saiyo Foundation, which is located at the Balai Gadang, Koto Tangah district, Padang City, West Sumatra. The Bundo Saiyo Foundation has not yet compiled a financial report and has only a record of cash in and cash out, so the true value of assets, liabilities and assets is unknown. The study results in a financial report composed of comprehensive income statements, statement of changes inn equity, financial position statements and cash flow statements. With the financial report expected transparency and accountability to the Bundo Saiyo foundation.

Certified grasping

This paper studies the robustness of grasping in the frictionless plane from a geometric perspective. By treating grasping as a process that shapes the free-space object over time, we define three types of certificates to guarantee success of a grasp: (a) invariance under an initial set, (b) convergence toward a goal grasp, and (c) observability over the final object pose. We develop convex-combinatorial models for each of these certificates, which can be expressed as simple semi-algebraic relations under mild-modeling assumptions, such as point-fingers and frictionless contact. By leveraging these models to synthesize certificates, we optimize certifiable grasps of planar objects composed as a union of convex polygons, using manipulators described as point-fingers. We validate this approach in simulations by grasping random polygons, and with real sensorless grasps of several objects.

Global–local–global context-aware network for salient object detection in optical remote sensing images

For the salient object detection in optical remote sensing images (ORSI-SOD), many existing methods are trapped in a local–global mode, i.e., CNN-based encoder binds with a specific global context-aware module, struggling to deal with the challenging ORSIs with complex background and scale-variant objects. To solve this issue, we explore the synergy of the global-context-aware and local-context-aware modeling and construct a preferable global–local–global context-aware network (GLGCNet). In the GLGCNet, a transformer-based encoder is adopted to extract global representations, combining with local-context-aware features gathered from three saliency-up modules for comprehensive saliency modeling, and an edge assignment module is additionally employed to refine the preliminary detection. Specifically, the saliency-up module involves two components, one for global–local context-aware transfer towards pixel-wise dynamic convolution parameters prediction, the other for dynamically local-context aware modeling. The corresponding position-sensitive filter is aware of its previous global-wise focus, thus enhancing the spatial compactness of salient objects and encouraging the feature upsampling achievement for multi-scale feature combinations. The edge assignment module enhances the robustness of preliminary saliency prediction and assigns the semantic attributes of preliminary saliency cues to the shallow-level edge feature to obtain final complete salient objects in a spatially and semantically global manner. Extensive experiments demonstrate that the proposed GLGCNet surpasses 23 state-of-the-art methods on three popular datasets.

EGA-Net: Edge feature enhancement and global information attention network for RGB-D salient object detection

With the supplement of texture and geometry cues in depth maps, salient object detection (SOD) shifts from 2D to 3D, aiming to detect the most attractive object in a pair of color and depth images. Previous work primarily focused on regional integrity. Few methods are used to improve the edge quality of prediction results, resulting in a final prediction with a complete structure but blurred edges. Moreover, due to the complexity of real-life scenarios, the problem of effectively separating the salient object from complex background has become a hot potato. Aiming to address these issues, we propose a novel network, EGA-Net, to improve the edge quality and highlight the main features of the salient object. Specifically, in the EGA-Net, we propose a feature interaction (FI) module and an edge feature enhancement (EFE) module, respectively. Among them, the FI module is used to remove unimodal feature redundancy, capture multi-modal feature complementarity, and reduce the contamination of low-quality depth maps. The EFE is used to improve the edge quality of the final salient object prediction results. Furthermore, a Global Information Guide Integration (GIGI) module has been proposed to suppress the background noise and effectively highlight the salient objects’ main features. It uses interleaving and fusion methods to automatically select and enhance the vital information in the original input features under the guidance of global features. We put the training of EGA-Net under the supervision of a new hybrid loss function that can simultaneously take global pixel point, foreground, and depth map quality into account. Quantitative and qualitative experiment results demonstrate that our method outperforms the 19 advanced methods on eight publicly available RGB-D salient object detection datasets with five evaluation metrics. You can find the code and results of our method athttps://github.com/guanyuzong/EGA-Net.

Engineering In vitro Models: Bioprinting of Organoids with Artificial Intelligence

In the last decade, organoids have gained popularity for developing mini-organs to support advancements in the study of organogenesis, disease modeling, and drug screening and, subsequently, in the development of new therapies. To date, such cultures have been used to replicate the composition and functionality of organs such as the kidney, liver, brain, and pancreas. However, depending on the experimenter, the culture environment and cell conditions may slightly vary, resulting in different organoids; this factor significantly affects their application in new drug development, especially during quantification. Standardization in this context can be achieved using bioprinting technology-an advanced technology that can print various cells and biomaterials at desired locations. This technology offers numerous advantages, including the manufacturing of complex three-dimensional biological structures. Therefore, in addition to the standardization of organoids, bioprinting technology in organoid engineering can facilitate automation in the fabrication process as well as a closer mimicry of native organs. Further, artificial intelligence (AI) has currently emerged as an effective tool to monitor and control the quality of final developed objects. Thus, organoids, bioprinting technology, and AI can be combined to obtain high-quality in vitro models for multiple applications.

Multi-Scale Vision Transformer for Defect Object Detection

Defect object detection for printed circuit boards (PCBs) is one of the most important tasks in the field of industrial inspections. For this task, deep learning-based object detection algorithms are generally used. Regardless of whether one-stage or two-stage object detection algorithms are used, image features are extracted using multi-layer convolutional neural networks, which makes it hard to focus on global features. Few methods use Transformer-based models to extract features because of their computational complexity, which is proportional to the square of the length of the input, and they also lack the ability to extract multi-scale features. In our paper, we design a variant of the Vision Transformer (ViT) to extract features at multiple scales. We use a multi-stage ViT to extract image features, and then 1 × 1 convolution is used to connect the adjacent stages to enhance the feature dimensionality. Although the conventional ViT does not have the ability to extract multi-scale features, we use convolution to downsample the output of each stage of the ViT to obtain multi-scale features of the image. The size and stride of the convolution kernel used to downsample also change as the feature dimensionality changes. In addition, to reduce the computational complexity, we introduce window attention into each stage of the ViT, and as the feature dimensionality changes, the window size also changes. We construct a feature pyramid network and a path aggregation network based on the multi-scale features obtained as described above, and we adopt the detection head from YOLOx to implement the final multi-scale ViT-based object detection algorithm. Experimental results show that our improved model achieves a mean average precision of 69.3% on the MSCOCO 2017 test dataset, 86.0% on the PASCAL VOC 2007 test dataset, and 99.9% on our PCB ultrasonic image dataset.

Construcción de una escala para evaluar tipos de elogios otorgados por cuidadores primarios a infantes de 0 a 3 años

Praise is defined as positive feedback given to people about their attributes, their performance, the objects or products made by them. The different types of praise that primary caregivers or parents use with infants when they carry out an activity will impact on their development, beliefs or perception of themselves or types of learning, and motivation toward the tasks that the children perform. One type of praise is the praise directed towards an individual, which compliments their own attributes such as her intelligence. Another one is the product praise, which is directed towards the final objects or actions that the infant performs, such as a drawing or physical activities. Finally, a praise directed at the process aims to congratulate the infant during the task as a process, either for making an effort or for the strategies that they use to achieve something. This last kind of praise favors perseverance in the face of new tasks to a greater extent, while praise directed at the person could generate more frustration when the infant experiences failure. Therefore, the way caregivers interact with infants is relevant, since it modulates motivation and the development of different skills. For this reason, the main objective of this study was the creation and validation of a self-report technique to assess the praise that Argentinean caregivers give to infants from 0 to 3 years of age. The sample was made up of 128 primary caregivers ( N = 128) who were recruited through the internet. They completed a Likert scale questionnaire of approximately 5 minutes of duration. The data was collected during preventive social isolation due to COVID-19. This could lead to changes in parental behavior, while it also provides ecological value for understanding the ways to praise in this context, where caregivers spend a lot of time with infants; however, it would be important to conduct future research outside of this context. An exploratory factor analysis was performed, which led to an adjustment in the technique by eliminating four items in order to improve the psychometric characteristics of the instrument. By this analysis, the formation of the three dimensions was justified by the types of praise: person, process, and product praise.The final instrument consisted of 13 phrases that represent possible compliments used by caregivers. Participants indicated how often they usually use those compliments on a scale from 1 ( never ) to 5 ( very often ). The items are divided by types of praise: six were assigned to the product dimension, three to the process dimension and four to the person dimension. The internal consistencies of the dimensions were process (α = .91), product (α = .74) and person (α = .73). This self-report technique for primary caregivers of infants would allow a short and simple evaluation of the praise used. In future research, this technique would allow an assessment of praise for the realization of studies that seek to expand the knowledge about how they affect child development. Likewise, it would contribute to the development of interventions with caregivers aimed to promote praise that benefits an adaptive development of infants. However, more studies are needed to investigate possible changes in the inventory, such as the inclusion of neutral praise or negative feedback. Furthermore, a larger sample size would be necessary to carry out a more exhaustive validation, performing a confirmatory factor analysis, convergent variance and factor invariance. https://doi.org/10.16888/interd.2023.40.1.9

RGB-D salient object ranking based on depth stack and truth stack for complex indoor scenes

RGB-D salient object detection has achieved a great development in recent years due to its extensive applications. Previous studies mainly focus on simple scene images with one single object. These models usually become overwhelmed by complex scenes with multiple objects. Moreover, these methods model salient object detection as a binary segmentation problem. However, psychology studies show that humans shift their visual attention from one object to another and rank salient objects, especially in complex indoor scenes. Following the psychological studies, we propose to rank salient objects in RGB-D images of complex indoor scenes. Due to the lack of such data, we first construct a RGB-D salient object ranking dataset containing complex indoor scenes with multiple objects. The saliency ranking of different objects is defined based on the order that an observer notices these objects. The final salient object ranking result is an average across the saliency rankings of 13 observers. This RGB-D salient object ranking dataset is also analyzed with current mainstream RGB-D salient object detection dataset for comparison. Since location information provided by depth images can help to determine the saliency ranking of objects, we further propose an end-to-end network exploiting depth stack and ground truth stack to predict the order of salient objects in complex scenes. The quantitative and qualitative comparisons demonstrate the effectiveness of the proposed method.