Properties Of Objects Research Articles

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.

Freezing of movements and its correspondence with MLG1 neurons response to looming stimuli in the crab Neohelice.

Upon visually detecting a moving predator animals often freeze, i.e. stop moving, to minimize being uncovered and to gather detailed information of the object's movements and properties. In certain conditions the freezing behavior can be enough to avoid a predatory menace but, when the risk is high or increases to a higher level, animals switch strategy and engage in an escape response. The neural bases underlying escape responses to visual stimuli are extensively investigated both in vertebrates and arthropods. However, those involved in freezing behaviors are much less studied. Here, we investigated the freezing behavior displayed by the crab Neohelice granulata when confronted with a variety of looming stimuli simulating objects of distinct sizes approaching on a collision course at different speeds. The experiments were performed in a treadmill-like device. Animals engaged in exploratory walks respond to the looming stimulus with freezing followed by escaping. The analysis of the stimulus optical variables shows that regardless of the looming dynamic, the freezing decision is made when the angular size of the object increases by 1.4°. In vivo intracellular recording responses of Monostratified Lobula Giant Neurons (MLG1) to the same looming stimuli show that the freezing times correlate with the times predicted by a hypothetical spike counter of this neuron.

A multiwavelength study of the most distant gamma-ray detected BL Lacertae object 4FGL J1219.0+3653 (z = 3.59)

BL Lac objects are a class of jetted active galactic nuclei that do not exhibit or have weak emission lines in their optical spectra. Recently, the first γ-ray emitting BL Lac beyond z=3, 4FGL J1219.0 +3653 (hereafter J1219), was identified, i.e., within the first two billion years of the age of the universe. Here we report the results obtained from a detailed broadband study of this peculiar source by analyzing the new ∼58 ksec XMM-Newton and archival observations and reproducing the multiwavelength spectral energy distribution with the conventional one-zone leptonic radiative model. The XMM-Newton data revealed that J1219 is a faint X-ray emitter (F0.3−10 keV=1.02−0.24+0.47×10−15ergcm−2s−1) and exhibits a soft spectrum (0.3−10 keV photon index=2.28−0.46+0.58). By comparing the broadband physical properties of J1219 with z>3γ-ray detected flat spectrum radio quasars (FSRQs), we have found that it has a relatively low jet power and, similar to FSRQs, the jet power is larger than the accretion disk luminosity. We conclude that deeper multiwavelength observations will be needed to fully explore the physical properties of this unique high-redshift BL Lac object.

Ontology construction of traditional Chinese medicine diagnosis and treatment for myopia

Objective: To keep up with the progress of treatment for myopia in traditional Chinese medicine (TCM) through literature research, so as to determine data source of TCM diagnosis and treatment for myopia, from which entities are extracted for integration of relevant knowledge, and further formal expression and visualization of knowledge. Methods: Literature research and comparative analysis were used to determine the data source, and the seven-step method of constructing ontology was referred to for extraction of TCM diagnosis and treatment entities, which then was terminalized for development of classes and class hierarchy, and class properties, as well as creation of instances before consistency test of the ontology. Results: The ontology of TCM diagnosis and treatment for myopia includes 26 classes and 124 instances, 9 object properties and 1 data property. Conclusion: The combination of ontology technology and TCM diagnosis and treatment knowledge of myopia contributes to the construction of knowledge base of TCM diagnosis and treatment ontology for myopia, which realizes formal expression and visualization of relevant TCM knowledge, lays a foundation for the construction of TCM top-level ontology and ophthalmology ontology, and facilitates knowledge reusing and sharing.

A Material Ecology of Toulouse’s Observatories in the 18th Century

Abstract Based on the concept of “material ecology”, which designates the structural properties of space and objects in the constitution of places of scientific practice, this article proposes a socio-historical analysis of the materiality of Toulouse observatories in the 18th century. Toulouse observatories reveal the importance of the material dimension in the organization of scientific work throughout the 18th century. First, the article demonstrates how the material infrastructure of the observatory of the short-lived Academy of Sciences of Toulouse contributed to creating and consolidating the local astronomical community. Secondly, it shows that the architectural characteristics of the observatories built by the most active astronomers in Toulouse served as a model to design later scholarly institutions. The patrimonialization of the old observatories at the beginning of the 19th century is finally discussed. By resorting to the notion of material ecology concerning the places of astronomical observation, the article brings to light the socio-epistemic dimension that underpins scientific practice.

Checking out the unexplained: With age, children become increasingly skeptical of surprising claims.

When presented with surprising claims, older children investigate such claims more often than younger children. The present study tests whether older children (6-7-year-olds) are more skeptical than younger children (4-5-year-olds) about surprising claims that lack supporting evidence because they expect informants to provide evidence for them. To test this hypothesis, we presented 140 4-7-year-old children (47-96 months, 46.4% girls, 53.6% boys, 86.4% with at least one parent who completed a BA degree, 50% parents with income above median) with a series of vignettes. In each vignette, the protagonist wanted to accomplish a task and needed to select the most appropriate object for that task. Before deciding which object to use, the protagonist heard a surprising claim about one of the object's properties, presented with or without supporting evidence. For example, in the supporting explanation condition, the informant stated that the smallest object was the heaviest and that they knew because they had lifted the objects. Children were then asked whether the protagonist knew which object to use and why. Contrary to expectation, children across all ages typically indicated that the protagonist had sufficient knowledge, regardless of whether an informant provided supporting evidence or not. However, with increasing age, children became more skeptical of both supported and unsupported surprising claims and increasingly stated that the protagonist should not select the object suggested by the informant. Finally, when asked to justify this judgment, older children were more likely than younger to express skepticism toward the claims, especially when presented without supporting evidence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

SonoPrint: Acoustically Assisted Volumetric 3D Printing for Composites.

Advances in additive manufacturing in composites have transformed aerospace, medical devices, tissue engineering, and electronics. A key aspect of enhancing properties of 3D-printed objects involves fine-tuning the material by embedding and orienting reinforcement within the structure. Existing methods for orienting these reinforcements are limited by pattern types, alignment, and particle characteristics. Acoustics offers a versatile method to control the particles independent of their size, geometry, and charge, enabling intricate pattern formations. However, integrating acoustics into 3D printing has been challenging due to the scattering of the acoustic field between polymerized layers and unpolymerized resin, resulting in unwanted patterns. To address this challenge, SonoPrint, an innovative acoustically assisted volumetric 3D printer is developed that enables simultaneous reinforcement patterning and printing of the entire structure. SonoPrint generates mechanically tunable composite geometries by embedding reinforcement particles, such as microscopic glass, metal, and polystyrene, within the fabricated structure. This printer employs a standing wave field to create targeted particle motifs-including parallel lines, radial lines, circles, rhombuses, hexagons, and polygons-directly in the photosensitive resin, completing the print in just a few minutes. SonoPrint enhances structural properties and promises to advance volumetric printing, unlocking applications in tissue engineering, biohybrid robots, and composite fabrication.

Evidence for Intrinsic X-Ray Weakness among Red Quasars at Cosmic Noon

Quasar feedback is a key ingredient in shaping galaxy evolution. A rare population of extremely red quasars (ERQs) at z = 2−3 are often associated with high-velocity [O iii]λ5008 outflows and may represent sites of strong feedback. In this paper, we present an X-ray study of 50 ERQs to investigate the link between the X-ray and outflow properties of these intriguing objects. Using hardness ratio analysis, we confirm that the ERQs are heavily obscured systems with gas column density reaching N H = 1023−24 cm−2. We identify 20 X-ray-nondetected ERQs at high mid-infrared (MIR) luminosities of ν L ν,6 μm ≳ 3 × 1046 erg s−1. By stacking the X-ray observations, we find that the nondetected ERQs are on average underluminous in X-rays by a factor of ∼10 for their MIR luminosities. We consider such X-ray weakness to be due to both heavy gas absorption and intrinsic factors. Moreover, we find that the X-ray-weak sources also display higher-velocity outflows. One option to explain this trend is that weaker X-rays facilitate more vigorous line-driven winds, which then accelerate the [O iii]-emitting gas to kiloparsec scales. Alternatively, super-Eddington accretion could also lead to intrinsic X-ray weakness and more powerful continuum-driven outflow.

Selective laser melting of partially amorphous regolith analog for ISRU lunar applications

As the idea of crewed outposts on the Moon gains momentum, In-Situ Resource Utilization (ISRU) technologies tend to become imperative to fulfill astronauts' needs. This article explores a way to use the lunar regolith as a source material for the additive manufacturing of complex objects, based on the selective laser melting (SLM) technique. A lunar regolith analog, Basalt of Pic d’Ysson (BPY), is used as a starting point for this study, to investigate the now demonstrated impact of amorphous analog content in the powder bed, substrate type, and post-SLM annealing treatments on the mechanical properties of 3D-printed objects. Improvements to the manufacturing and sample extraction stages are proposed to systematically reproduce the high compressive strength values obtained, thus contributing to the robustness and reliability of the process.

Young sanctuary-living chimpanzees produce more communicative expressions with artificial objects than with natural objects

In humans, interactions with objects are often embedded in communicative exchanges. Objects offer unique affordances to explore, carry functions and hold cultural relevance, which can shape children’s interactions and communication. Research indicates that the use of artificial objects, such as certain toys, helps promote pre-linguistic communication, consequently impacting language development. Given that chimpanzees use objects extensively compared to other great apes, and considering the differences between chimpanzees and bonobos in intrinsic motivation for tool use and the extended developmental period during which they learn to use objects, it is reasonable to expect that objects may influence chimpanzees’ communication. Here, we examined interactions of 31 immature sanctuary-living chimpanzees with non-novel artificial and natural objects and tested their vocal and facial expressions, applying methods previously designed for children. Our results showed an increase in these expressions associated with artificial objects. These findings provide the first empirical evidence that chimpanzee communicative expressions may be influenced by inherent properties of objects, potentially promoting varied communication, comparable to the impact distinctive objects have on pre-linguistic children. By exploring this connection between object-centric interactions and communication, this study reveals deep phylogenetic roots where objects may have shaped great ape communication and possibly evolutionary foundations of language.

All-optical platform for ultrasound transmission matrix measurements

Piezoelectric ultrasound transducers are constrained by size, bandwidth, and angular response, limiting their ability to fully characterize the acoustic properties of objects. In this study, we introduce a novel modular all-optical platform for ultrasound generation and detection to overcome these limitations, demonstrating wideband operation (>50 MHz), omnidirectional response, and high signal fidelity. Ultrasound generation is performed via the optoacoustic effect by illuminating an optically absorbing coating with spatially modulated pulsed light, and ultrasound detection is carried out using a silicon-photonic acoustic detector. By illuminating patterns that span a basis and scanning the detector, the full transmission matrix is measured, consisting of the acoustic waveforms for all the transmitter–receiver pairs in the measurement geometry. Our method is experimentally demonstrated in transmission mode for beam steering, beam focusing, and imaging, achieving excellent agreement with the theory.

Discrete diffusion models with Refined Language-Image Pre-trained representations for remote sensing image captioning

RS image captioning (RSIC) utilizes natural language to provide a description of image content, assisting in the comprehension of object properties and relationships. Nonetheless, RS images are characterized by variations in object scales, distributions, and quantities, which make it challenging to obtain global semantic information and object connections. To enhance the accuracy of captions produced from RS images, this paper proposes a novel method referred to as Discrete Diffusion Models with Refined Language-Image Pre-trained representations (DDM-RLIP), leveraging an advanced discrete diffusion model (DDM) for nosing and denoising text tokens. DDM-RLIP is based on an advanced DDM-based method designed for natural pictures. The primary approach for refining image representations involves fine-tuning a CLIP image encoder on RS images, followed by adapting the transformer with an additional attention module to focus on crucial image regions and relevant words. Furthermore, experiments were conducted on three datasets, Sydney-Captions, UCM-Captions, and NWPU-Captions, and the results demonstrated the superior performance of the proposed method compared to conventional autoregressive models. On the NWPU-Captions dataset, the CIDEr score improved from 116.4 to 197.7, further validating the efficacy and potential of DDM-RLIP. The implementation codes for our approach DDM-RLIP are available at https://github.com/Leng-bingo/DDM-RLIP.

Accurate Robotic Pushing Manipulation Through Online Model Estimation Under Uncertain Object Properties

Accurate Robotic Pushing Manipulation Through Online Model Estimation Under Uncertain Object Properties