Complex Objects Research Articles

Representation alignment contrastive regularisation for multi‐object tracking

AbstractAchieving high‐performance in multi‐object tracking algorithms heavily relies on modelling spatial‐temporal relationships during the data association stage. Mainstream approaches encompass rule‐based and deep learning‐based methods for spatial‐temporal relationship modelling. While the former relies on physical motion laws, offering wider applicability but yielding suboptimal results for complex object movements, the latter, though achieving high‐performance, lacks interpretability and involves complex module designs. This work aims to simplify deep learning‐based spatial‐temporal relationship models and introduce interpretability into features for data association. Specifically, a lightweight single‐layer transformer encoder is utilised to model spatial‐temporal relationships. To make features more interpretative, two contrastive regularisation losses based on representation alignment are proposed, derived from spatial‐temporal consistency rules. By applying weighted summation to affinity matrices, the aligned features can seamlessly integrate into the data association stage of the original tracking workflow. Experimental results showcase that our model enhances the majority of existing tracking networks' performance without excessive complexity, with minimal increase in training overhead and nearly negligible computational and storage costs.

Use of two-dimensional correlation spectroscopy in chemical analysis

An overview of the main applications of generalized two-dimensional correlation spectroscopy (2D-COS) in analytical chemistry is presented. 2D-COS is a method used to analyze datasets obtained from spectroscopic measurements. This approach is based on the use of two-dimensional correlation maps to identify and analyze correlations between different regions of the spectrum or data from two measurement methods. The purpose of using 2D-COS is to increase the amount of analytical information by revealing hidden data correlations. Analyzing such correlations for series of spectral data obtained for a certain range of analyte concentrations, pH, or component ratios of a mixture, as well as changes in temperature or other external factors, allows researchers to investigate and identify chemical processes and interactions that cannot be directly obtained from the spectra. Compared to one-dimensional spectra, 2D-COS offers significant analytical information for complex mixtures, particularly in identifying components and determining composition. Additionally, 2D-COS can be used to monitor changes in a sample over time, making it a valuable tool for studying dynamically changing systems. Overall, 2D-COS is a highly versatile approach that can be used in conjunction with a large number of methods for most analytical tasks and complex objects, including those without sample preparation. The review presents advancements in the application of 2D-COS as of early September 2023.

SS3DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion

Learning object shapes from a single image is challenging due to variations in scene content, geometric structures, and environmental factors, which create significant disparities between 2D image features and their corresponding 3D representations, hindering the effective training of deep learning models. Existing learning-based approaches can be divided into two-stage and single-stage methods, each with limitations. Two-stage methods often rely on generating intermediate proposals by searching for similar structures across the entire dataset, a process that is computationally expensive due to the large search space and high-dimensional feature-matching requirements, further limiting flexibility to predefined object categories. In contrast, single-stage methods directly reconstruct 3D shapes from images without intermediate steps, but they struggle to capture complex object geometries due to high feature loss between image features and 3D shapes and limit their ability to represent intricate details. To address these challenges, this paper introduces SS3DNet-AF, a single-stage, single-view 3D reconstruction network with an attention-based fusion (AF) mechanism to enhance focus on relevant image features, effectively capturing geometric details and generalizing across diverse object categories. The proposed method is quantitatively evaluated using the ShapeNet dataset, demonstrating its effectiveness in achieving accurate 3D reconstructions while overcoming the computational challenges associated with traditional approaches.

Problems of Public Law Regulation of the Digital Platforms Regime

The article discusses the issues of legal regulation of digital platforms as a complex object of information relations. Topical problems of public law regulation of the regime of digital platforms regarding the use of unfair practices in the design of the platform’s web interface are identified. Attention is paid to the consolidation of the concept of dark patterns in the legislation of foreign countries. Particular attention is paid to the protection of personal data subjects when using dark patterns in digital platforms. The relevant areas of scientific research in the field of information law and also for the development of recommendations for improving law enforcement practice are highlighted.

Statistical Analysis of Complex Shape Graphs.

This paper provides developments in statistical shape analysis of shape graphs, and demonstrates them using such complex objects as Retinal Blood Vessel (RBV) networks and neurons. The shape graphs are represented by sets of nodes and edges (articulated curves) connecting some nodes. The goals are to utilize nodes (locations, connectivity) and edges (edge weights and shapes) to: (1) characterize shapes, (2) quantify shape differences, and (3) model statistical variability. We develop a mathematical representation, elastic Riemannian metrics, and associated tools for shape graphs. Specifically, we derive tools for shape graph registration, geodesics, statistical summaries, shape modeling, and shape synthesis. Geodesics are convenient for visualizing optimal deformations, and PCA helps in dimension reduction and statistical modeling. One key challenge lies in comparing shape graphs with vastly different complexities (in number of nodes and edges). This paper introduces a novel multi-scale representation to handle this challenge. Using the notions of (1) "effective resistance" to cluster nodes and (2) elastic shape averaging of edge curves, it reduces graph complexity while retaining overall structures. This allows shape comparisons by bringing graphs to similar complexities. We demonstrate these ideas on 2D RBV networks from the STARE and DRIVE databases and 3D neurons from the NeuroMorpho database.

Structure-Guided Image Completion With Image-Level and Object-Level Semantic Discriminators.

Structure-guided image completion aims to inpaint a local region of an image according to an input guidance map from users. While such a task enables many practical applications for interactive editing, existing methods often struggle to hallucinate realistic object instances in complex natural scenes. Such a limitation is partially due to the lack of semantic-level constraints inside the hole region as well as the lack of a mechanism to enforce realistic object generation. In this work, we propose a learning paradigm that consists of semantic discriminators and object-level discriminators for improving the generation of complex semantics and objects. Specifically, the semantic discriminators leverage pretrained visual features to improve the realism of the generated visual concepts. Moreover, the object-level discriminators take aligned instances as inputs to enforce the realism of individual objects. Our proposed scheme significantly improves the generation quality and achieves state-of-the-art results on various tasks, including segmentation-guided completion, edge-guided manipulation and panoptically-guided manipulation on Places2 datasets. Furthermore, our trained model is flexible and can support multiple editing use cases, such as object insertion, replacement, removal and standard inpainting. In particular, our trained model combined with a novel automatic image completion pipeline achieves state-of-the-art results on the standard inpainting task.

ПЕРСПЕКТИВИ ОНТОЛОГІЧНОГО МОДЕЛЮВАННЯ ЯК ЗАСОБУ ВЕРИФІКАЦІЇ РЕЗУЛЬТАТІВ ПСИХОЛОГІЧНОГО ДОСЛІДЖЕННЯ (НА ПРИКЛАДІ ВИВЧЕННЯ ЯВИЩ ГРИ)

Purpose. Methodological differences in the verification standards for the results of scientific knowledge between individual psychological branches represented in the domestic and global scientific space, the peculiarities of extremely complex psychological knowledge objects indicates the need to modernize the relevant standards and choose the optimal methodological platform for the verification of the results of modern psychological research. This paper discusses the feasibility of choosing such a ontological modeling platform, which is a special descriptive methodology and an alternative to the naturalistic experimental approach in verifying the results of psychological research. Ontological modeling provides the possibility for a more objective verification of the achievements for the study of intelligible objects, which have a distinct cultural and historical meaning, extremely complex structure and functionality, an ancient and diverse history of their own empirical representations. The purpose of the article is a theoretical study of ways to modernize the standards of verification of the results of modern psychological research using the example of the verification of the promising theory of game modeling of human vital activity in uncertainty conditions. Methods.The research uses the methods of analysis and systematization for psychological and interdisciplinary approaches in the study of verification patterns for the psychological knowledge results, comparison and optimization for the content of its standards on the study example of game modeling of human vital activity in uncertainty conditions. Results. According to the results of the study, the expediency of choosing ontological modeling (the names "conceptual modeling", "formal ontology", "formal ontology" and "ontological engineering" are also used synonymously) as a perspective basis for the verification of the psychological research results, a new universal methodological tool of Ukrainian psychology, has been proven using the example for game modeling of human vital activity in uncertainty. The existing scientific approaches that argue for the choice of ontological modeling as the basis for the verification of new psychological knowledge are specified. The methodological perspective of the modernization for the verification norms of psychological theories and concepts based on the ontological approach has been updated and substantiated. Modern scientific ideas are generalized and fundamental approaches to the verification of the results of knowledge of unlimited generality objects, i.e. ultra-complex simultaneously intelligible and empirical objects, are deepened. It has been established that ontological modeling has a universal scientific purpose and is able to overcome the shortcomings of the naturalistic experimental method for verification of research products, to be the optimal methodological platform for the verification of the modern psychological research results. Conclusions. The results of the study allow us to conclude about the relevance of the above issues and the expediency of choosing ontological modeling as a prospective basis for the verification of the psychological research results using the example for game modeling of human vital activity in uncertainty. A well-founded description of the standards and the process for verification of the psychological research results for game modeling of human vital activity in uncertainty conditions is considered promising.

Research Status and Prospect of the Key Technologies for Environment Perception of Intelligent Excavators

With the urgent need of the industry and the continuous development of artificial intelligence, research into intelligent excavators has achieved certain progress. However, intelligent excavators often face strong vibrations, dense dust, and complex objectives. These have brought severe challenges to environmental perception, and are important research difficulties that must be overcome in realizing the practical engineering applications of intelligent excavators. Many researchers have studied these problems in reducing vibration and dust noise for light detection and ranging (LiDAR) scanners, multi-sensor information fusion, and the segmentation and recognition of 3D scenes. This paper reviews the research status of these key technologies and discusses their development trends.

Magnetic manipulation of unknown and complex conductive nonmagnetic objects with application in the remediation of space debris

This article extends recent work in magnetic manipulation of conductive, nonmagnetic objects using rotating magnetic dipole fields. Eddy-current-based manipulation provides a contact-free way to manipulate metallic objects. We are particularly motivated by the large amount of aluminum in space debris. We previously demonstrated dexterous manipulation of solid spheres with all object parameters known a priori. This work expands the previous model, which contained three discrete modes, to a continuous model that covers all possible relative positions of the manipulated spherical object with respect to the magnetic field source. We further leverage this new model to examine manipulation of spherical objects with unknown physical parameters by applying techniques from the online-optimization and adaptive-control literature. Our experimental results validate our new dynamics model, showing that we get improved performance compared to the previously proposed model, while also solving a simpler optimization problem for control. We further demonstrate the first physical magnetic manipulation of aluminum spheres, as previous controllers were only physically validated on copper spheres. We show that our adaptive control framework can quickly acquire useful object parameters when weakly initialized. Finally, we demonstrate that the spherical-object model can be used as an approximate model for adaptive control of nonspherical objects by performing magnetic manipulation of a variety of objects for which a spherical model is not an obvious approximation.

Detection of volatile organic compounds in soils (literature review)

Volatile organic compounds (VOC) are major environmental pollutants. Due to their high mobility, they penetrate into all environmental objects, pose an environmental threat and health risks. Getting into the soil, they deteriorate its quality. VOC content requires reliable control. There is presented a review of the literature, including methods of the US Environmental Protection Agency, and regulatory and methodological documents of the Russian Federation regulating methods of selection, storage, preparation and analysis of soil samples for VOC content. The dominant place among methods for monitoring for VOCs belongs to gas chromatography with various types of detectors. For multicomponent analysis of complex objects, gas chromatography with mass selective detection is used due to the wide capabilities provided by the mass detector. A universal mass spectrometry method used in analytical laboratories is electron impact ionization mass spectrometry. For research purposes, modern highly sensitive methods are used – mass spectrometry based on the proton transfer reaction PTR-MS, ion trap mass spectrometry PIT-MS, negative ion ionization mass spectrometry NI-PT-CIMS, time-of-flight mass spectrometry of the transfer reaction proton PTR-TOF-MS. The collection and storage of samples for VOC analysis requires compliance with regulations to prevent both loss of analytes and sample contamination. Sample preparation includes methods such as vacuum and azeotropic distillation, thermal desorption, liquid extraction, various options for static and dynamic headspace analysis.

Multi-level Partition of Unity on Differentiable Moving Particles

We introduce a differentiable moving particle representation based on the multi-level partition of unity (MPU) to represent dynamic implicit geometries. At the core of our representation are two groups of particles, named feature particles and sample particles, which can move in space and produce dynamic surfaces according to external velocity fields or optimization gradients. These two particle groups iteratively guide and correct each other by alternating their roles as inputs and outputs. Each feature particle carries a set of coefficients for a local quadratic patch. These particle patches are assembled with partition-of-unity weights to derive a continuous implicit global shape. Each sampling particle carries its position and orientation, serving as dense surface samples for optimization tasks. Based on these moving particles, we develop a fully differentiable framework to infer and evolve highly detailed implicit geometries, enhanced by a multi-level background grid for particle adaptivity, across different inverse tasks. We demonstrated the efficacy of our representation through various benchmark comparisons with state-of-the-art neural representations, achieving lower memory consumption, fewer training iterations, and orders of magnitude higher accuracy in handling topologically complex objects and dynamic tracking tasks.

Inverse Rendering for Tomographic Volumetric Additive Manufacturing

Tomographic Volumetric Additive Manufacturing (TVAM) is an emerging 3D printing technology that can create complex objects in under a minute. The key idea is to project intense light patterns onto a rotating vial of photo-sensitive resin, causing polymerization where the cumulative dose of these patterns reaches the polymerization threshold. We formulate the pattern calculation as an inverse light transport problem and solve it via physically based differentiable rendering. In doing so, we address longstanding limitations of prior work by accurately modeling and correcting for scattering in composite resins, printing in non-symmetric vials, and supporting unusual printing geometries. We also introduce an improved discretization scheme that exploits the ray tracing operation to mitigate resolution-related artifacts in prints. We demonstrate the benefits of our method in real-world experiments, where our computed patterns produce prints with an improved fidelity.

Simplified internal models in human control of complex objects.

Humans are skillful at manipulating objects that possess nonlinear underactuated dynamics, such as clothes or containers filled with liquids. Several studies suggested that humans implement a predictive model-based strategy to control such objects. However, these studies only considered unconstrained reaching without any object involved or, at most, linear mass-spring systems with relatively simple dynamics. It is not clear what internal model humans develop of more complex objects, and what level of granularity is represented. To answer these questions, this study examined a task where participants physically interacted with a nonlinear underactuated system mimicking a cup of sloshing coffee: a cup with a ball rolling inside. The cup and ball were simulated in a virtual environment and subjects interacted with the system via a haptic robotic interface. Participants were instructed to move the system and arrive at a target region with both cup and ball at rest, 'zeroing out' residual oscillations of the ball. This challenging task affords a solution known as 'input shaping', whereby a series of pulses moves the dynamic object to the target leaving no residual oscillations. Since the timing and amplitude of these pulses depend on the controller's internal model of the object, input shaping served as a tool to identify the subjects' internal representation of the cup-and-ball. Five simulations with different internal models were compared against the human data. Results showed that the features in the data were correctly predicted by a simple internal model that represented the cup-and-ball as a single rigid mass coupled to the hand impedance. These findings provide evidence that humans use simplified internal models along with mechanical impedance to manipulate complex objects.

Developing WasteSAM: A novel approach for accurate construction waste image segmentation to facilitate efficient recycling

The escalating volume of construction activities and resultant waste generation underscores the imperative for developing sophisticated segmentation models to facilitate efficient sorting and recycling processes. This study introduces WasteSAM, an enhanced iteration of the segment anything model (SAM), specifically tailored to address the intricate complexities inherent in construction waste imagery. Drawing upon a comprehensive dataset comprising over 15,000 masks representing five distinct categories of construction materials, WasteSAM exhibits notably superior segmentation capabilities. Quantitative analysis demonstrates significant performance improvements, with WasteSAM outperforming the original SAM model by an average of 23.9% in dice similarity coefficient and 30.0% in normalized surface distance metrics. The integration of stereo-image techniques in refining the training dataset has facilitated WasteSAM in more accurately discerning the three-dimensional structure of waste materials, thereby augmenting the precision of waste classification. Noteworthy is the model’s adeptness in handling intricate textures and patterns across diverse imaging modalities, including varying lighting conditions and complex object interactions. While showing promising results, this study also highlights the need for high-quality, diverse datasets that reflect real-world construction site complexities, rather than merely larger datasets.

A Refined and Efficient CNN Algorithm for Remote Sensing Object Detection.

Remote sensing object detection (RSOD) plays a crucial role in resource utilization, geological disaster risk assessment and urban planning. Deep learning-based object-detection algorithms have proven effective in remote sensing image studies. However, accurate detection of objects with small size, dense distribution and complex object arrangement remains a significant challenge in the remote sensing field. To address this, a refined and efficient object-detection algorithm (RE-YOLO) has been proposed in this paper for remote sensing images. Initially, a refined and efficient module (REM) was designed to balance computational complexity and feature-extraction capabilities, which serves as a key component of the RE_CSP block. RE_CSP block efficiently extracts multi-scale information, overcoming challenges posed by complex backgrounds. Moreover, the spatial extracted attention module (SEAM) has been proposed in the bottleneck of backbone to promote representative feature learning and enhance the semantic information capture. In addition, a three-branch path aggregation network (TBPAN) has been constructed as the neck network, which facilitates comprehensive fusion of shallow positional information and deep semantic information across different channels, enabling the network with a robust ability to capture contextual information. Extensive experiments conducted on two large-scale remote sensing datasets, DOTA-v1.0 and SCERL, demonstrate that the proposed RE-YOLO outperforms state-of-the-art other object-detection approaches and exhibits a significant improvement in generalization ability.

Phoneme-by-Phoneme Speech Recognition as a Classification of Series on a Set of Sequences of Elements of Complex Objects Using an Improved Trie-Tree

Sequences, including vector sequences, are applicable in any subject domains. Sequences of scalar values or vectors (series) can be produced by higher-order sequences, for example: a series of states, or elements of complex objects. This academic paper is devoted to the application of an improved trie-tree in the classification of series on a set of sequences of elements of complex objects using the dynamic programming method. The implementation areas of dynamic programming have been considered. It has been shown that dynamic programming is adapted to multi-step operations of calculating additive (multiplicative) similarity/difference measures. It is argued that the improved trie-tree is applicable in the problem of classifying a series on a set of sequences of elements of complex objects using such similarity/difference measures. An analysis of hierarchical representations of sets of sequences has been performed. The advantages of the improved trie-tree over traditional representations of other highly branching trees have been described. A formal description of the improved trie-tree has been developed. An explanation has been given to the previously obtained data on a significant speed gain for operations of adding and deleting sequences in the improved trie-tree relative to the use of an array with an index table (24 and 380 times, respectively). The problem of phoneme-by-phoneme recognition of speech commands has been formulated as a problem of classifying series on a set of sequences of elements of complex objects and a method for its solving has been presented. A method for classifying a series on a set of sequences of elements of complex objects using the improved trie-tree is developed. The method has been studied using the example of phoneme-by-phoneme recognition with a hierarchical representation of the dictionary of speech command classes. In this method, recognition of speech commands is executed traversing the improved trie-tree that stores a set of transcriptions of speech commands – sequences of transcription symbols that denote classes of sounds. Numerical studies have shown that classifying a series as sequences of elements of complex objects increases the frequency of correct classification compared to classifying a series on a set of series, and using the improved trie-tree reduces the time spent on classification.

Knowledge-oriented management systems for complex objects

The article examines the issue of improving the efficiency of complex systems management through the use of knowledge-based methods and technologies. The article emphasizes the need to revise traditional approaches to scientific research and knowledge management by introducing a transdisciplinary paradigm and artificial intelligence to achieve sustainable development and knowledge evolution. The authors try to solve the problem of management in the face of uncertainty and incomplete information, which is typical for such systems. The methods of integrating knowledge from different fields to create an integrated management system are considered. The basis of management is the holarchic structure, which organizes the system in the form of a hierarchy of holons. Each holon has a specific role and knowledge, which ensures the flexibility and adaptability of the system. The study also uses self-organization and learning methods to ensure continuous improvement of the systemʼs functioning. The article discusses the requirements for knowledge-based management systems, such as flexibility, adaptability, data and knowledge integration, learning and self-organization, transparency of decisions, and resilience to errors and failures. The findings of the study emphasize the importance of implementing knowledge-based approaches in the management system of complex facilities. This allows to increase the level of adaptability and stability of management, which is critical in todayʼs environment of dynamic development of technology and knowledge. The proposed methods can be applied in various fields, which makes them universal and useful for a wide range of scientific and practical tasks, providing a high level of adaptability, efficiency and stability of complex objects management, taking into account modern requirements and challenges.

Contribution of transient heat transfer to overpressure protection in a passive Boiling Water Reactor

The BWRX-300 reactor is a small modular reactor with 300 MW nominal electric power output. As the abbreviation indicates, the reactor is a boiling type reactor using water as the coolant. The main operating principle of the reactor model is the use of natural circulation of the coolant for both steady-state operation and emergency cooling of the reactor. Isolation Condenser Systems (ICS) have been used in early BWRs and were reintroduced in the 1990s to implement passive safety.Modern technologies enable effective simulation of objects of various complexity. In this study, the thermal-hydraulic system code TRACE v.5 is used to simulate BWRX-300 reactor plant operation. The software enables calculation of a system consisting of the reactor pressure vessel and ICS in both steady-state and transient operation.The ICS is an emergency cooling system borrowed from the previous generation of GE Hitachi BWRs, the ESBWRs, and it consists of a vertical tube bundle heat exchanger immersed in a water tank. Although this system design remains unchanged, its purpose is different. The BWRX-300 design completely eliminates safety and relief valves. This is why the ICS must perform both overpressure protection and long term heat dissipation functions.Three reactor plant operation states were simulated: normal operation with nominal parameters; reactor isolation; and the transient process into the cooling down mode by reactor shutdown and ICS activation. The results demonstrate that the ICS is capable to mitigate overpressure transients. Upon ICS startup, its metallic structures absorb heat well in excess of its nominal steady-state rating. Initial thermal inertia of the system is important for overpressure mitigation in transients.

Different responses of MVL neurons when pigeons attend to local versus global information during object classification

Most prior studies have indicated that pigeons have a tendency to rely on local information for target categorization, yet there is a lack of electrophysiological evidence to support this claim. The mesopallium ventrolaterale (MVL) is believed to play a role in processing both local and global information during visual cognition. The difference between responses of MVL neurons when pigeons are focusing on local versus global information during visual object categorization remain unknown. In this study, pigeons were trained to categorize hierarchical stimuli that maintained consistency in local and global information. Subsequently, stimuli with different local and global components were presented to examine the pigeons' behavioral preferences. Not surprisingly, the behavioral findings revealed that pigeons predominantly attended to the local elements when performing categorization tasks. Moreover, MVL neurons exhibited significantly distinct responses when pigeons prioritized local versus global information. Specifically, most recording sites showed heightened gamma band power and increased nonlinear entropy values, indicating strong neural responses and rich information when pigeons concentrated on the local components of an object. Furthermore, neural population functional connectivity was weaker when the pigeons focused on local elements, suggesting that individual neurons operated more independently and effectively when focusing on local features. These findings offer electrophysiological evidence supporting the notion of pigeons displaying a behavioral preference for local information. The study provides valuable insight into the understanding of cognitive processes of pigeons when presented with complex objects, and further sheds light on the neural mechanisms underlying pigeons' behavioral preference for attending to local information.

Potential of Low-Cost UAV Photogrammetry for Documenting Hard-to-Access Interior Spaces Through Building Openings

Unmanned aerial vehicles (UAVs) are primarily used in the field of cultural heritage for mapping the exteriors of larger objects and documenting the roofs and façades of tall structures that cannot be efficiently or feasibly measured using conventional terrestrial technologies and methods. However, due to the considerable diversity of cultural heritage, there are practical demands for the measurement of complex and inaccessible objects in interior spaces. This article focuses on the use of two different off-the-shelf UAVs for partial photogrammetric reconstruction of the attic of a mining house, which was only visible through a window in the gable wall. Data from both UAVs were compared with each other and with terrestrial laser scanning. Despite the lower quality of the results from the DJI Mini 4 Pro compared to the DJI Mavic 3 Enterprise, the results from both UAVs would still be suitable for documenting the interior attic spaces. However, a detailed analysis of the photogrammetric data indicates that, when selecting a UAV for this purpose, it is necessary to consider the limitations of the camera system, which may lead to a reduction in the geometric accuracy and completeness of the point clouds.