Properties Of Objects Research Articles

Concept model and realization of object orientied system with metaprogramming

The purpose of the research is to develop a conceptual model of an object oriented system and implement it using a functional language with metaprogramming support. Methods. A conceptual model of an object oriented system was developed based on a dynamic object system with message passing and dynamic dispatch. The developed object oriented system includes functions for creating and deleting classes, creating and deleting object instances of a class, functions for working with object properties, functions for adding methods to a class and passing messages. Inheritance is also possible, when a child class will have the properties of the parent class and its own properties. Results. The object oriented system was implemented using a set of macros and helper functions. The basic data structure was a hash table with key access capabilities, which was also implemented using macros. When a class is created, an instance constructor is automatically generated, setting the initial values of the object’s fields. Inheritance is implemented as the inclusion of fields of a parent class in a child object. Polymorphism is implemented as dynamic dispatch: when processing a message, a handler method is searched. The resulting object oriented system, together with the hash table module, occupies 97 lines. Conclusion. As a result of the work, a conceptual model of an object oriented system was created and implemented, which includes the main functions of the object oriented programming paradigm. It has been shown that macros can be used to create a compact object oriented system without the need to add any new functions or capabilities to the functional language interpreter.

Shape and texture biases in dogs' generalization of trained objects.

Shape bias, the tendency to link the meaning of words to the shape of objects, is a widely investigated phenomenon, but the extent to which it is linked to vocabulary acquisition and/or to tool using is controversial. Understanding how non-human animals generalize the properties of objects can provide insights into the evolutionary processes and cognitive mechanisms that influence this bias. We investigated object generalization in dogs, focusing on their tendency to attend to shape or texture in a two-way choice task. We analyzed data of thirty-five dogs that were successfully trained to discriminate a target object among distractors and retrieve it to their owner. In subsequent testing, dogs chose between objects similar to the target in either shape or texture. Results showed that dogs first approached objects of similar shape, but then predominantly chose objects of similar texture. This suggests a reliance on visual cues for initial assessment and tactile cues for final discrimination. Our findings highlight the influence of perceptual modalities on object generalization in dogs, showing that, before object manipulation, they seem to show a shape bias but, once they make physical contact with the objects, they rather rely on texture to generalize. This study contributes to our understanding of generalization in a non-verbal and non-tool using species and opens avenues for comparative investigations into the relationship between vocabulary acquisition, tool using and biases in object generalization.

The Development of Intellect in Emerging Adults: Predictors of Longitudinal Trajectories

Intellect is an important personality trait, especially with regard to the prediction and explanation of intellectual performance, such as occupational or academic success. However, much less is known about the development of Intellect. I present results from a longitudinal study spanning eight years to investigate changes in Intellect during a critical period: the transition from school to vocation. The study is based on a large and heterogeneous sample with up to 1964 participants. Using a facet approach, I investigate predictors of longitudinal trajectories theoretically derived from construct definition, including subjective and objective attributes of education and profession; attitudes regarding the malleability of personality traits; as well as personality traits beyond Intellect, especially intelligence. Results reveal some support for the social investment principle according to neo-socioanalytic theory, as epistemic job demands and epistemic leisure activities predicted the increase in Intellect over time. The study contributes to our understanding of the development of personality traits related to intellectual achievement, including important internal and external predictors of longitudinal trajectories.

Correlation Analysis of Subjective and Objective Texture Properties: Color and Heterocyclic Amine Content of Grilled Chicken Breast Fillet

The present study discusses the technofunctional properties (color, texture) of grilled chicken with both objective (colorimeter, texture profile) analysis and subjective (sensory) analysis. Besides them, the total content of potentially carcinogenic heterocyclic amines (HCAs) was also monitored by the applied grilling time–temperature combinations. The same samples were analyzed during the heat treatment of chicken breast samples for 5–15 min at 150–230 °C on an electric contact grill. The grilling variables included the effect of skin cover. Among the structural parameters, hardness exhibited a significant difference between the groups of skin-covered and not-covered ones, and in this case, a linear connection was also found with the time and temperature values, respectively. The structural parameters obtained by sensory and instrumental analysis were compared to each other. In addition, the structural parameters were compared to the color ones (sensory, color according to the CIELAB system: brightness, redness, and yellowness) and to the total HCA content. In the case of closed grilling, sensory-evaluated parameters such as juiciness and tenderness showed a strong negative correlation to instrumentally measured hardness (R = −0.879; −0.749) and chewiness (R = −0.872; −0.718) due to the water loss from the increase in grilling temperature and time. The total HCA content positively correlated to chewiness (R = 0.789). The sensory color parameter and the brightness had a strong connection with juiciness (R = 0.738; 0.723), hardness (R = −0.795; −0.706), and chewiness (R = −0.843; −0.818); redness showed positive correlation to cohesiveness (R = 0.764) and chewiness (R = 0.829). Accordingly, juiciness and chewiness showed a connection to the total HCA levels, which makes it possible to carry out further research on instrumental and sensory parameters that may predict the formation of hazardous amounts of carcinogenic heterocyclic amines.

Data, not documents: Moving beyond theories of information‐seeking behavior to advance data discovery

AbstractMany theories of human information behavior (HIB) assume that information objects are in text document format. This paper argues four important HIB theories are insufficient for describing users' search strategies for data because of assumptions about the attributes of objects that users seek. We first review and compare four HIB theories: Bates' berrypicking, Marchionni's electronic information search, Dervin's sense‐making, and Meho and Tibbo's social scientist information‐seeking. All four theories assume that information‐seekers search for text documents. Next, we compare these theories to search behavior by analyzing Google Analytics data from the Inter‐university Consortium for Political and Social Research (ICPSR). Users took direct, scenic, and orienting paths when searching for data. We also interviewed ICPSR users (n = 20), and they said they needed dataset documentation and contextual information to find data. However, Dervin's sense‐making alone cannot explain the information‐seeking behaviors that we observed. Instead, what mattered most were object attributes determined by the type of information that users sought (i.e., data, not documents). We conclude by suggesting an alternative frame for building user‐centered data discovery tools.

IDEOGRAPHIC MODELLING OF THE EMOTIONAL PICTURE OF THE WORLD IN ENGLISH PHRASEOLOGY

Emotion is a mental reflection in the form of a direct biased experience of the content of life phenomena and situations, caused by the relation of their objective properties to the needs of the subject. Emotions are essential components of life activity, a powerful means of activating the sensory and perceptual activity of the individual. In the theory of activity, they are defined as a reflection of the relationship between the result of an activity and its motive. If the activity is successful in terms of the motive, positive emotions (interest, satisfaction) arise, and if it is unsuccessful, negative emotions arise. Emotions arise only in connection with events or results of actions that are related to motives. The highest product of the development of human emotions is strong feelings for objects that meet one’s highest needs. A strong, absolutely dominant feeling is called passion. Events signalling possible changes in a person’s life, along with specific emotions, can cause changes in the general emotional background – mood. The attitude to the reflected phenomena as the main property of emotions is presented: 1) in their qualitative characteristics, which include: a) sign – positive, negative; b) modality – anger, contempt, embarrassment, guilt, interest, sadness, surprise, disgust, pleasure, fear, shame; 2) in the dynamics of the course of emotions themselves – duration, intensity, etc.; 3) in the dynamics of external expression of emotions – emotional expression – in facial expressions, speech, pantomime.

Acoustic feature extraction of radiation pressure signal induced by bubble oscillation under dual-frequency acoustic excitation based on CEEMDAN and bubble entropy

Acoustic feature extraction of radiation pressure signals (RPSs) induced by bubble oscillations is a crucial task in the characterization of the properties of underwater objects. In this article, to improve the extraction accuracy, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bubble entropy (BE) algorithms are combined to extract the effective acoustic components of the RPS. For verification, the proposed extraction scheme is applied to a typical simulated RPS under dual-frequency acoustic excitation. Compared with other extraction methods, CEEMDAN can extract richer acoustic feature information from the RPS, including accurate values for the amplitude and period of oscillation. Furthermore, when the components of the simulated RPS become more complex, the CEEMDAN–BE scheme gives better evaluation results than other schemes in terms of three evaluation indices. Under complex conditions, the signal extraction performances of singular value decomposition and ensemble empirical mode decomposition decrease greatly, but CEEMDAN retains its high signal extraction efficiency, which further confirms the effectiveness of the proposed signal extraction scheme.

Breaking dimensional barriers in hyperspectral target detection: Atrous convolution with Gramian Angular field representations

Hyperspectral images contain extensive spectral bands with rich spectral information that reflects object properties. Leveraging state-of-the-art deep learning techniques has proven to be effective in hyperspectral target detection. However, compared to two-dimensional matrix data, the one-dimensional nature of spectral sequence limits the information that can be extracted, posing a challenge for deep learning-based hyperspectral target detection methodologies. To address this issue, a novel hyperspectral target detection method employing atrous convolution with gramian angular field representations is proposed in this paper. This approach breaks the barrier between one-dimensional vector and two-dimensional matrix by gramian angular field, transforming the spectral sequences from one-dimensional vectors into two-dimensional matrices, enabling the exploration of multidimensional relationships within spectral band relations through an atrous convolution-based spectral feature extraction network. The proposed model transcends the traditional one-dimensional spectral target detection limitations, offering a new perspective for spectral-based hyperspectral target detection. Experimental results on four real-world hyperspectral datasets demonstrate that the proposed method significantly outperforms existing state-of-the-art methods in detection performance, showcasing its potential for advancing hyperspectral target detection.

Mental simulation of colour properties during language comprehension: influence of context and comprehension stages.

Many studies have shown that mental simulation may occur during language comprehension. Supporting evidence is derived from the matching effects in the sentence-picture verification (SPV) task often used to assess mental simulations of object properties, such as size, orientation, and shape. However, mixed results have been obtained regarding object colour, with researchers reporting matching or mismatching effects. This study investigated the impact of colour information clarity within sentences on the process of mental simulation during language comprehension. Employing the SPV task and using novel objects, we examined whether there is a mental simulation of colour after excluding typical/atypical colour bias and how varying levels of colour information clarity in sentences influence the emergence of matching effects at different stages of comprehension. To address these issues, we conducted two experiments. In Experiment 1, the participants read normal sentences and subsequently engaged in picture verification with a novel object after a 500ms delay. In Experiment 2, the participants encountered sentences containing both clear and unclear colour information and, after either a 0ms or 1500ms interval, completed picture verification tasks with a novel object. Null effects were found in the 500ms condition for normal sentences and the 0ms condition for unclear colour information sentences. A mismatching effect appeared in the 0ms condition after clear colour information sentences, and a matching effect appeared in the 1500ms condition for all sentences. The results indicated that after excluding colour bias, the participants still formed mental simulations of colour during language comprehension. Our results also indicated that ongoing colour simulation with time pressure impacted the participant responses. The participants ignored unclear colour information under time pressure, but without time pressure, they constructed simulations that were as detailed as possible, regardless of whether the implicit colour information in the sentence was clear.

Design and Optimization of an Origami Gripper for Versatile Grasping and Manipulation

Robotic grasping and manipulation demand the ability to handle a multitude of objects with different shapes, sizes, quantities, surface smoothness, vulnerability, and stiffness, which is challenging without prior knowledge about object properties. Herein, a novel origami‐inspired gripper for universal grasping is presented. The innovative structure seamlessly transforms a simple uniaxial pulling motion into a flexible and robust envelope or pinch grasp, enabling it to tackle various scenarios. The origami gripper offers distinctive advantages, including scalable and optimizable design, grasping compliance and robustness, providing material flexibility, and providing solutions to challenging manipulation tasks. The working principles of the origami gripper are characterized and analyzed. An optimization‐based inverse design method is presented to adjust gripper properties for various scenarios. Through comprehensive experimentation and evaluation, the gripper's capabilities to grasp various objects with a wide range of distinctive properties, including ultrasoft, slippery, granular, and multiple objects, which is a challenge for the existing robotic grippers, are demonstrated. The research holds promise for transformative applications in areas such as the food industry, waste handling, fine and fragile objects grasping, and environmental sampling.

Motor-related oscillations reveal the involvement of sensorimotor processes during recognition memory

Certain object properties may render an item as more memorable than others. One such property is manipulability, or the extent to which an object can be interacted with using our hands. This study sought to determine if the manipulability of an item modulates memory task performance on both a behavioural and neural level. We recorded electroencephalography (EEG) from a large sample of right-handed individuals (N = 53) during a visual item recognition memory task. The task contained stimuli of both high and low manipulability. Analysis focused on activity in the theta rhythm (3.5–7 Hz), which has been implicated in sensorimotor integration, and the mu rhythm (8–14 Hz), the primary oscillation associated with sensorimotor related behaviours. At both encoding and retrieval, theta oscillations were greater over the left motor region for high manipulability stimuli, suggesting that an item’s sensorimotor properties are assessed immediately upon presentation. Manipulability did not affect activity in the mu rhythm. However, mu oscillations over the left motor region were lower during the retrieval of old versus new items and response time was faster for old items, aligning with the cortical reinstatement hypothesis. These results collectively reveal an association between motor oscillations and memory processes, highlight the involvement of sensorimotor processing at both encoding and retrieval.

Explaining wine scores through stochastic frontier analysis. An intent to measure sensory non-observable attributes

ABSTRACT Experts give scores to wines, which are quality proxies for marketers and buyers. The production of scores proxying wine quality is explained by a set of observable objective attributes, plus another set of unobservable and subjective (sensory) features, and randomness. Is it possible to use a Stochastic Frontier Approach (SFA) to understand whether objective and subjective (sensory) characteristics of wines explain the differences in ‘producing’ wine scores? We estimate a wine quality stochastic frontier production function, using a database of 1800 top-scored wines, in 18 years-window encompassing objective determinants that largely explain the scores (price, production, year, grape, country, etcetera), being aware that sensory aspects related to wine grading are unobservable. We find that the observable variables explain part of the ‘efficiency’ in attaining scores and suggest that ‘inefficiency’ in score generation can be attributed to unobservable (sensory) variables.

Real-world estimation taps into basic numeric abilities.

Accurately estimating and assessing real-world quantities (e.g., how long it will take to get to the train station; the calorie content of a meal) is a central skill for adaptive cognition. To date, theoretical and empirical work on the mental resources recruited by real-world estimation has focused primarily on the role of domain knowledge (e.g., knowledge of the metric and distributional properties of objects in a domain). Here we examined the role of basic numeric abilities - specifically, symbolic-number mapping - in real-world estimation. In Experiment 1 ( ) and Experiment 2 ( ), participants first completed a country-population estimation task (a task domain commonly used to study real-world estimation) and then completed a number-line task (an approach commonly used to measure symbolic-number mapping). In both experiments, participants with better performance in the number-line task made more accurate estimates in the estimation task. Moreover, Experiment 2 showed that performance in the number-line task predicts estimation accuracy independently of domain knowledge. Further, in Experiment 2 the association between estimation accuracy and symbolic-number mapping did not depend on whether the number-line task involved small numbers (up to 1000) or large numbers that matched the range of the numbers in the estimation task (up to 100,000,000). Our results show for the first time that basic numeric abilities contribute to the estimation of real-world quantities. We discuss implications for theories of real-world estimation and for interventions aiming to improve people's ability to estimate real-world quantities.

The problem of knowing future phenomena

The paper is devoted to the problem of epistemic conditions of cognition of future phenomena.The problem is that the cognizable aspects of information are based on the evolutionary mechanisms of adaptation of the body, which are reduced in humans, and on the rational principles of human cognition, which are mixed with irrational prophecies. The solution is to separate the heuristic possibilities of theory from empirical guesses and consider the structures of theoretical knowledge that process information about possible and impossible facts.The aim of the paper is to discover a mental act directed at the future, which cannot be reduced to extrapolating the past and varying alternatives. The conditions of the possibility of knowing about the reality of the future as a mode of time, and the idea of a specific event in the future are split.The following hypothesis is considered: cognition of the future is based on thinking about the possible and necessary relations of an object to an integral system including a subject.The attitude of the mind towards the future is not a cognitive act like receiving information, the source of which is problematic. This is not animal foresight, not divination, not planning. Cognition is understood not as a perception of the objective properties of a phenomenon, but as its construction according to the rules of the organization of consciousness. In general, the future is not knowable, but there are types of phenomena other than the causal concept of cognition that justify information about the future. Theoretical knowledge of the predispositions of a situation involving an object is proactive. The subject of prediction is not only the truth about the future, but also a judgment about what exactly will not happen. This limitation narrows the scope of possible alternatives. The elementary acts of realizing that an event is not yet happening have been investigated by the phenomenology of Buddhism. They are directed at the stages of the same process. The conclusion from content of one temporal interval to content of another interval is based on theoretical constructions that do not have spatiotemporal characteristics. Information about the future is limited by theoretical knowledge of the holistic situation and the properties of the object that arise as a result of its inclusion in this situation.

What do we see behind an occluder? Amodal completion of statistical properties in complex objects.

When a spiky object is occluded, we expect its spiky features tocontinue behind the occluder. Although many real-world objects contain complex features, it is unclear how more complex features are amodally completed and whether this process is automatic. To investigate this issue, we created pairs of displays with identical contour edges up to the point of occlusion, but with occluded portions exchanged. We then asked participants to search for oddball targets among distractors and asked whether relations between searches involving occluded displays would match better with relations between searches involving completions that are either globally consistent or inconsistent with the visible portions of these displays. Across two experiments involving simple and complex shapes, search times involving occluded displays matched better with those involving globally consistent compared with inconsistent displays. Analogous analyses on deep networks pretrained for object categorization revealed a similar pattern of results for simple but not complex shapes. Thus, deep networks seem to extrapolate simple occluded contours but not more complex contours. Taken together, our results show that amodal completion in humans is sophisticated and can be based on extrapolating global statistical properties.

End-to-end multiple object tracking in high-resolution optical sensors of drones with transformer models

Drone aerial imaging has become increasingly important across numerous fields as drone optical sensor technology continues to advance. One critical challenge in this domain is achieving both accurate and efficient multi-object tracking. Traditional deep learning methods often separate object identification from tracking, leading to increased complexity and potential performance degradation. Conventional approaches rely heavily on manual feature engineering and intricate algorithms, which can further limit efficiency. To overcome these limitations, we propose a novel Transformer-based end-to-end multi-object tracking framework. This innovative method leverages self-attention mechanisms to capture complex inter-object relationships, seamlessly integrating object detection and tracking into a unified process. By utilizing end-to-end training, our approach simplifies the tracking pipeline, leading to significant performance improvements. A key innovation in our system is the introduction of a trajectory detection label matching technique. This technique assigns labels based on a comprehensive assessment of object appearance, spatial characteristics, and Gaussian features, ensuring more precise and logical label assignments. Additionally, we incorporate cross-frame self-attention mechanisms to extract long-term object properties, providing robust information for stable and consistent tracking. We further enhance tracking performance through a newly developed self-characteristics module, which extracts semantic features from trajectory information across both current and previous frames. This module ensures that the long-term interaction modules maintain semantic consistency, allowing for more accurate and continuous tracking over time. The refined data and stored trajectories are then used as input for subsequent frame processing, creating a feedback loop that sustains tracking accuracy. Extensive experiments conducted on the VisDrone and UAVDT datasets demonstrate the superior performance of our approach in drone-based multi-object tracking.

An Approach to Realize Generalized Optimal Motion Primitives Using Physics Informed Neural Networks

Abstract Autonomous manipulation is a challenging problem in field robotics due to uncertainty in object properties, constraints, and coupling phenomenon with robot control systems. Humans learn motion primitives over time to effectively interact with the environment. We postulate that autonomous manipulation can be enabled by basic sets of motion primitives as well, but do not necessitate mimicking human motion primitives. This work presents an approach to generalized optimal motion primitives using physics-informed neural networks. Our simulated and experimental results demonstrate that optimality is notionally maintained where the mean maximum observed final position percent error was 0.564% and the average mean error for all the trajectories was 1.53%. These results indicate that notional generalization is attained using a physics-informed neural network approach that enables near optimal real-time adaptation of primitive motion profiles.

Physical properties of trans-Neptunian object (143707) 2003 UY117 derived from stellar occultation and photometric observations

Context. Trans-Neptunian objects (TNOs) are considered to be among the most primitive objects in our Solar System. Knowledge of their primary physical properties is essential for understanding their origin and the evolution of the outer Solar System. In this context, stellar occultations are a powerful and sensitive technique for studying these distant and faint objects. Aims. We aim to obtain the size, shape, absolute magnitude, and geometric albedo for TNO (143707) 2003 UY117. Methods. We predicted a stellar occultation by this TNO for 2020 October 23 UT and ran a specific campaign to investigate this event. We derived the projected profile shape and size from the occultation observations by means of an elliptical fit to the occultation chords. We also performed photometric observations of (143707) 2003 UY117 to obtain the absolute magnitude and the rotational period from the observed rotational light curve. Finally, we combined these results to derive the three-dimensional shape, volume-equivalent diameter, and geometric albedo for this TNO. Results. From the stellar occultation, we obtained a projected ellipse with axes of (282 ± 18) × (184 ± 32) km. The area-equivalent diameter for this ellipse is Deq,A = 228 ± 21 km. From our photometric R band observations, we derived an absolute magnitude of HV = 5.97 ± 0.07 mag using V − R = 0.46 ± 0.07 mag, which was derived from a V band subset of these data. The rotational light curve has a peak-to-valley amplitude of ∆m = 0.36 ± 0.13 mag. We find the most likely rotation period to be P = 12.376 ± 0.0033 hours. By combining the occultation with the rotational light curve results and assuming a triaxial ellipsoid, we derived axes of a × b × c = (332 ± 24) km × (216 ± 24) km × (180−24+28) km for this ellipsoid, and therefore a volume-equivalent diameter of Deq,V = 235 ± 25 km. Finally, the values for the absolute magnitude and for the area-equivalent diameter yield a geometric albedo of pV = 0.139 ± 0.027.

Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning

Eight-band one-shot MSFA (multispectral filter array) cameras are innovative technologies used to capture multispectral images by capturing multiple spectral bands simultaneously. They thus make it possible to collect detailed information on the spectral properties of the observed scenes economically. These cameras are widely used for object detection, material analysis, and agronomy. The evolution of one-shot MSFA cameras from 8 to 32 bands makes obtaining much more detailed spectral data possible, which is crucial for applications requiring delicate and precise analysis of the spectral properties of the observed scenes. Our study aims to develop models based on deep learning to estimate the spectral response of this type of camera and provide images close to the spectral properties of objects. First, we prepare our experiment data by projecting them to reflect the characteristics of our camera. Next, we harness the power of deep super-resolution neural networks, such as very deep super-resolution (VDSR), Laplacian pyramid super-resolution networks (LapSRN), and deeply recursive convolutional networks (DRCN), which we adapt to approximate the spectral response. These models learn the complex relationship between 8-band multispectral data from the camera and 31-band multispectral data from the multi-object database, enabling accurate and efficient conversion. Finally, we evaluate the images’ quality using metrics such as loss function, PSNR, and SSIM. The model evaluation revealed that DRCN outperforms others in crucial performance. DRCN achieved the lowest loss with 0.0047 and stood out in image quality metrics, with a PSNR of 25.5059, SSIM of 0.8355, and SAM of 0.13215, indicating better preservation of details and textures. Additionally, DRCN showed the lowest RMSE 0.05849 and MAE 0.0415 values, confirming its ability to minimize reconstruction errors more effectively than VDSR and LapSRN.

An end-to-end model of active electrosensation.

1Weakly electric fish localize and identify objects by sensing distortions in a self-generated electric field. Fish can determine the resistance and capacitance of an object, for example, even though the field distortions being sensed are small and highly-dependent on object distance and size. Here we construct a model of the responses of the fish's electroreceptors on the basis of experimental data, and we develop a model of the electric fields generated by the fish and the distortions due to objects of different resistances and capacitances. This provides us with an accurate and efficient method for generating large artificial data sets simulating fish interacting with a wide variety of objects. Using these sets, we train an artificial neural network (ANN), representing brain areas downstream of electroreceptors, to extract the 3D location, size, and electrical properties of objects. The model performs best if the ANN operates in two stages: first estimating object distance and size and then using this information to extract electrical properties. This suggests a specific form of modularity in the electrosensory system that can be tested experimentally and highlights the potential of end-to-end modeling for studies of sensory processing.