Similar Objects Research Articles

Mapping and Integrating Security and Risk Standards: a Systematic Literature Review

Organizations are under increasing pressure to comply with various rules, standards, and policies in today’s regulatory environment. Compliance controls are put in place to avoid legal or regulatory violations, which could lead to severe penalties, loss of reputation, and financial damages. However, these controls may have similar scopes and objectives, resulting in duplicated work and unnecessary costs for the organizations. To address this issue, researchers carry out the mapping and integration of these standards to avoid duplication, streamline compliance efforts, and identify best practices. Our work aims to improve the State-of-the-Art by exploring the main benefits and problems resulting from these processes, as well as identifying methods or artifacts that can be reused in the future. We focus on the fields of Risk, Security, and Business Continuity, as these are critical areas where compliance is crucial for organizations. Through our research, we have found that current methods of generating mapping artifacts are not only cumbersome to execute but also ineffective, as they output a single artifact without the reasoning behind it.

A Double Deep Q-Network framework for a flexible job shop scheduling problem with dynamic job arrivals and urgent job insertions

In the semiconductor manufacturing industry, the Dynamic Flexible Job Shop Scheduling Problem is regarded as one of the most complex and significant scheduling problems. Existing studies consider the dynamic arrival of jobs, however, the insertion of urgent jobs such as testing chips poses a challenge to the production model, and there is an urgent need for new scheduling methods to improve the dynamic response and self-adjustment of the shop floor. In this work, deep reinforcement learning is utilized to address the dynamic flexible job shop scheduling problem and facilitate near-real-time shop floor decision-making. We extracted eight state features, including machine utilization, operation completion rate, etc., to reflect real-time shop floor production data. After examining machine availability time, the machine's earliest available time is redefined and incorporated into the design of compound scheduling rules. Eight compound scheduling rules have been developed for job selection and machine allocation. By using the state features as inputs to the Double Deep Q-Network, it is possible to acquire the state action values (Q-values) of each compound scheduling rule, and the intelligent agent can learn a reasonable optimization strategy through training. Simulation studies show that the proposed Double Deep Q-Network algorithm outperforms other heuristics and well-known scheduling rules by generating excellent solutions quickly. In most scenarios, the Double Deep Q-Network algorithm outperforms the Deep Q-Network, Q-Learning, and State-Action-Reward-State-Action (SARSA) frameworks. Moreover, the intelligent agent has good generalization ability in terms of optimization for similar objectives.

Application of Web 2.0 to Reduce Failure in English

The purpose of the intervention was to reduce the failure rate in English with the help of a website built to support the learning experience. Action research was employed in a mixed-methods approach with an exploratory sequential design. Techniques such as documentary analysis, semistructured interviews, and participant observation were employed with 255 high schoolers. Findings revealed that the website, together with follow-up from the teacher-researchers, helped most students achieve a passing grade, empowered the learning experience, and enhanced communicative competence and linguistic awareness. Additionally, the website’s user-friendly nature and well-suited interface simplified the learning dynamic. Furthermore, according to website data, this digital space was also used in other settings with similar objectives. Even so, apathy remained a problem as some learners chose not to take part in the strategy and did not pass. We believe that the research contributes to the state of the art and generates knowledge, especially given the lack of research addressing failure in language education.

Differences and similarities of the preliminary ruling procedure of the European Court of Justice, and the advisory opinion of the Permanent Tribunal of Revision of the Mercosur.

The purpose of this paper is to present the reader with a comparative analysis of two judicial tools with similar objectives, under two dissimilar regional integration systems. In order to understand the origin of the differences and similarities of the institute under analysis, we will dive into the cultural, historical, economic and political circumstances, that will ultimately make clear the reasons that explain why the integration outcomes in the Southern Common Market differs from the European one. The final aim is to prove the statement that integration outcomes are highly dependent on regional endogenous properties. The concrete institutions under analysis will present the reader with a concrete case, where the different evolution paths, result in judicial tools of different levels of effectiveness in their respective integration processes.

Tidal Disruption of Planetesimals from an Eccentric Debris Disk Following a White Dwarf Natal Kick

The surfaces of many white dwarfs are polluted by metals, implying a recent accretion event. The tidal disruption of planetesimals is a viable source of white dwarf pollution and offers a unique window into the composition of exoplanet systems. The question of how planetary material enters the tidal disruption radius of the white dwarf is currently unresolved. Using a series of N-body simulations, we explore the response of the surrounding planetesimal debris disk as the white dwarf receives a natal kick caused by anisotropic mass loss on the asymptotic giant branch. We find that the kick can form an apse-aligned, eccentric debris disk in the range 30–240 au, which corresponds to the orbits of Neptune, the Kuiper Belt, and the scattered disk in our solar system. In addition, many planetesimals beyond 240 au flip to counterrotating orbits. Assuming an isotropic distribution of kicks, we predict that approximately 80% of white dwarf debris disks should exhibit significant apsidal alignment and a fraction of counterrotating orbits. The eccentric disk is able to efficiently and continuously torque planetesimals onto radial, star-grazing orbits. We show that the kick causes both an initial burst in tidal disruption events as well as an extended period of 100 Myr where tidal disruption rates are consistent with observed mass accretion rates on polluted white dwarfs.

A Multilevel Approach for Direct Core Calculation Schemes

A method of dynamic homogenization was recently proposed as an alternative technique for three-dimensional (3D) core calculations using a direct approach. This technique allows for producing homogenization parameters for a subdomain within the core at its actual state, and offers the advantage of avoiding off-line calculations, cross-section interpolations, and expensive 3D transport calculations. The methodology has shown a remarkable reduction in computational cost compared to 3D transport. However, the application of direct calculation schemes in multiphysics and multicycle problems still demands an extensive use of computational resources. Within the framework of traditional two-step calculations, several multilevel schemes have been developed to accelerate lattice calculations for the generation of the multiparameter cross-section libraries. With a similar objective, this work investigates how the multilevel approach can be applied within the framework of direct calculation schemes and discusses how depletion calculations may be performed. The objective is to enhance their performance and reduce memory requirements in favor of a high-fidelity model of the matter behavior in the reactor. We have then identified suitable flux solvers for each level and explored various homogenization options. We tested the methodology in a 3D pressurized water reactor core problem inspired by the TVA Watts Bar Unit 1 Multi-Physics Multi-Cycle OECD/NEA Benchmark, and performed a comparative analysis to assess the accuracy and computational efficiency of these new calculation schemes against direct single-level calculations and a two-step calculation based on pin-by-pin homogenization, as well as Monte Carlo simulations. Our analysis proves that the computational resources required to solve the full-core problem using a multilevel scheme are significantly reduced, and the best computational features are provided by multilevel dynamic homogenization. Furthermore, we observed that the multilevel approach not only allows for speedup, but can also be advantageously applied in order to improve the accuracy of the final solution of a calculation scheme when the core solver relies on an approximate transport operator.

Adaptive and Anti-Drift Motion Constraints for Object Tracking in Satellite Videos

Object tracking in satellite videos has garnered significant attention due to its increasing importance. However, several challenging attributes, such as the presence of tiny objects, occlusions, similar objects, and background clutter interference, make it a difficult task. Many recent tracking algorithms have been developed to tackle these challenges in tracking a single interested object, but they still have some limitations in addressing them effectively. This paper introduces a novel correlation filter-based tracker, which uniquely integrates attention-enhanced bounding box regression and motion constraints for improved single-object tracking performance. Initially, we address the regression-related interference issue by implementing a spatial and channel dual-attention mechanism within the search area’s region of interest. This enhancement not only boosts the network’s perception of the target but also improves corner localization. Furthermore, recognizing the limitations in small size and low resolution of target appearance features in satellite videos, we integrate motion features into our model. A long short-term memory (LSTM) network is utilized to create a motion model that can adaptively learn and predict the target’s future trajectory based on its historical movement patterns. To further refine tracking accuracy, especially in complex environments, an anti-drift module incorporating motion constraints is introduced. This module significantly boosts the tracker’s robustness. Experimental evaluations on the SatSOT and SatVideoDT datasets demonstrate that our proposed tracker exhibits significant advantages in satellite video scenes compared to other recent trackers for common scenes or satellite scenes.

The matching problem between functional shapes via a $BV$ penalty term: A $\Gamma$-convergence result

The matching problem often arises in image processing and involves finding a correspondence between similar objects. In particular, variational matching models optimize suitable energies that evaluate the dissimilarity between the current shape and the relative template. A penalty term often appears in the energy to constrain the regularity of the solution. To perform numerical computation, a discrete version of the energy is defined. Then, the question of consistency between the continuous and discrete solutions arises. This paper proves a \Gamma -convergence result for the discrete energy to the continuous one. In particular, we highlight some geometric properties that must be guaranteed in the discretization process to ensure the convergence of minimizers. We prove the result in the framework introduced in the 2017 paper of Charlier et al., which studies the matching problem between geometric structures carrying on a signal (fshapes). The matching energy is defined for L^{2} signals and evaluates the difference between fshapes in terms of the varifold norm. This paper maintains a dual attachment term, but we consider a BV penalty term in place of the original L^{2} norm.

Multiband task related components enhance rapid cognition decoding for both small and similar objects

The cortically-coupled target recognition system based on rapid serial visual presentation (RSVP) has a wide range of applications in brain computer interface (BCI) fields such as medical and military. However, in the complex natural environment backgrounds, the identification of event-related potentials (ERP) of both small and similar objects that are quickly presented is a research challenge. Therefore, we designed corresponding experimental paradigms and proposed a multi-band task related components matching (MTRCM) method to improve the rapid cognitive decoding of both small and similar objects. We compared the areas under the receiver operating characteristic curve (AUC) between MTRCM and other 9 methods under different numbers of training sample using RSVP-ERP data from 50 subjects. The results showed that MTRCM maintained an overall superiority and achieved the highest average AUC (0.6562 ± 0.0091). We also optimized the frequency band and the time parameters of the method. The verification on public data sets further showed the necessity of designing MTRCM method. The MTRCM method provides a new approach for neural decoding of both small and similar RSVP objects, which is conducive to promote the further development of RSVP-BCI.

Deformation measurement by digital speckle pattern interferometry using holographically recorded object in LiNbO3 as a reference

A new method, to the best of our knowledge, in digital speckle pattern interferometry is introduced. It depends on extending the applicability of using LiNbO3 crystal as a holographic recording medium for the evaluation of the difference in displacement between two similar objects (master and test), and displaying the comparison result in the form of an interference pattern. The method is a two-stage process. In the first stage, two states (un-displaced and displaced states) of the master object are recorded in a LiNbO3 crystal using the usual holographic arrangement. In the second stage, various phase stepping algorithms are applied, using the reconstructed wavefronts of the master object as a holographic optical element to produce a reference wave field in the comparison process. Recording and analysis of the difference correlation fringes are performed using the phase wrapping algorithm and real experiment.

Feature Engineering Versus Deep Learning for Scene Recognition: A Brief Survey

Scene recognition is an important computer vision task that has evolved from the study of the biological visual system. Its applications range from video surveillance, autopilot systems, to robotics. The early works were based on feature engineering that involved the computation and aggregation of global and local image descriptors. Several popular image features such as SIFT, SURF, HOG, ORB, LBP, KAZE, etc. have been proposed and applied to the task with successful results. Features can be either computed from the entire image on a global scale, or extracted from local sub-regions and aggregated across the image. Suitable classifier models are deployed that learn to classify these features. This review paper analyzes several of these handcrafted features that have been applied to the scene recognition task over the past decades, and tracks the transition from the traditional feature engineering to deep learning which forms the current state of the art in computer vision. Deep learning is now deemed to have overtaken feature engineering in several computer vision applications. Deep convolutional neural networks and vision transformers are the current state of the art for object recognition. However, scenes from urban landscapes are bound to contain similar objects posing a challenge to deep learning solutions for scene recognition. In our study, a critical analysis of feature engineering and deep learning methodologies for scene recognition is provided, and results on benchmark scene datasets are presented, concluding with a discussion on challenges and possible solutions that may facilitate more accurate scene recognition.

MASNet: mixed attention Siamese network for visual object tracking

Most Siamese-based trackers adopt correlation operation to perform similarity matching on feature fusion of template branch and search branch. However, the correlation operation directly uses the template feature to slide the window on the search area feature, and it is difficult to distinguish the target and background information when encountering similar target interference and background clutter, which can easily lead to tracking failure. In this paper, a mixed attention Siamese Network (MASNet) is proposed, which neatly integrates the modelling of global information and mutual information into the Siamese tracking framework. Specifically, through the learnable attention module, the local and global dependencies are adaptively integrated, and the rich context information is captured to improve the discrimination of the network for similar targets. After that, the correlation of the context information of the two branches is aggregated and adaptively encoded into the two branches, which makes up for the shortcomings of single correlation operation. In addition, the framework adopts a three-layer feature fusion method, which enables the tracker to better adapt to object deformation and similar object interference. Extensive experiments on OTB100, UAV123, GOT-10k and LaSOT show that the proposed tracking method achieves comparable performance to the advanced trackers.

FLEX: A fast and light-weight learned index for kNN search in high-dimensional space

The k Nearest Neighbors (kNN) search in high-dimensional space is a fundamental problem with various applications. In this paper, we try to solve this problem by using deep neural networks (DNNs). We apply DNNs to represent complex correlations between high-dimensional objects, enabling us to project similar objects into the same class, thus reducing the search cost. Based on DNNs, we propose two novel techniques to improve query efficiency while keeping accuracy. First, traditional DNNs typically demand extensive training data for achieving high accuracy. To decrease the training size, we design a multi-module DNN framework comprising several small modules. Each module learns to capture part of knowledge for the given query. The collective output of these sub-modules is then seamlessly integrated to form the final result. Second, with machine learning models, the size of candidates are unbounded. Thus, we design a linear-time data layout refinement algorithm, aiming to limit the number of candidates to a small constant. Empirically we find that our approach significantly outperforms the state-of-the-art methods in terms of both time efficiency and space efficiency while still attaining comparable or better accuracy.

What role does Skill Centre based consultancy serve in mathematics and statistics teaching communities of practice?

Mathematics and Statistics Teaching (MAST) is a staff facing service set up by the University of Bath Mathematics Resource Centre in September 2021. The service was created to collaborate with academics involved in mathematics and statistics teaching in their academic departments. In September 2023, MAST ended its pilot phase and entered a new growth phase fine-tuning existing structures and practices. During the pilot phase, MAST worked with academics consulting on enriching their programme of study and/or teaching practices. Specifically, the key principle that guides MAST is engaging in long-term sustainable collaborations with academic departments. As a means to address the key principle, we explore the potential of building communities of practice and supporting a wider network across the range of disciplines who encounter similar objectives. Facilitating a wider community of practice could be challenging, especially considering the tight schedule of the academics involved. We discuss our plans to cultivate communities of practice within departments and how we are working towards meeting our goal of establishing best practice and collecting evidence to support a wider community.

The visible and thermal light curve of the large Kuiper belt object (50000) Quaoar

Recent stellar occultations have allowed accurate instantaneous size and apparent shape determinations of the large Kuiper belt object (50000) Quaoar and the detection of two rings with spatially variable optical depths. In this paper we present new visible range light curve data of Quaoar from the Kepler/K2 mission, and thermal light curves at 100 and 160 µm obtained with Herschel/PACS. The K2 data provide a single-peaked period of 8.88 h, very close to the previously determined 8.84 h, and it favours an asymmetric double-peaked light curve with a 17.76 h period. We clearly detected a thermal light curve with relative amplitudes of ~ 10% at 100 and at 160 µm. A detailed thermophysical modelling of the system shows that the measurements can be best fit with a triaxial ellipsoid shape, a volume-equivalent diameter of 1090 km, and axis ratios of a/b = 1.19 and b/c = 1.16. This shape matches the published occultation shape, as well as visual and thermal light curve data. The radiometric size uncertainty remains relatively large (±40 km) as the ring and satellite contributions to the system-integrated flux densities are unknown. In the less likely case of negligible ring or satellite contributions, Quaoar would have a size above 1100 km and a thermal inertia ≤ 10 J m−2K−1s−1/2. A large and dark Weywot in combination with a possible ring contribution would lead to a size below 1080 km in combination with a thermal inertia ≳10 J m−2K−1s−1/2, notably higher than that of smaller Kuiper belt objects with similar albedo and colours. We find that Quaoar’s density is in the range 1.67–1.77 g cm−3, significantly lower than previous estimates. This density value closely matches the relationship observed between the size and density of the largest Kuiper belt objects.

JWST Near-infrared Spectroscopy of the Lucy Jupiter Trojan Flyby Targets: Evidence for OH Absorption, Aliphatic Organics, and CO2

We present observations obtained with the Near Infrared Spectrograph on JWST of the five Jupiter Trojans that will be visited by the Lucy spacecraft—the Patroclus–Menoetius binary, Eurybates, Orus, Leucus, and Polymele. The measured 1.7–5.3 μm reflectance spectra, which provide increased wavelength coverage, spatial resolution, and signal-to-noise ratio over previous ground-based spectroscopy, reveal several distinct absorption features. We detect a broad OH band centered at 3 μm that is most prominent on the less-red objects Eurybates, Patroclus–Menoetius, and Polymele. An additional absorption feature at 3.3–3.6 μm, indicative of aliphatic organics, is systematically deeper on the red objects Orus and Leucus. The collisional fragment Eurybates is unique in displaying an absorption band at 4.25 μm that we attribute to bound or trapped CO2. Comparisons with other solar system small bodies reveal broad similarities in the 2.7–3.6 μm bands with analogous features on Centaurs, Kuiper Belt objects (KBOs), and the active asteroid 238P. In the context of recent solar system evolution models, which posit that the Trojans initially formed in the outer solar system, the significant attenuation of the 2.7–3.6 μm absorption features on Trojans relative to KBOs may be the result of secondary thermal processing of the Trojans’ surfaces at the higher temperatures of the Jupiter region. The CO2 band manifested on the surface of Eurybates suggests that CO2 may be a major constituent in the bulk composition of Trojans, but resides in the subsurface or deeper interior and is largely obscured by refractory material that formed from the thermophysical processes that were activated during their inward migration.

Rotational Study of 5:3 and 7:4 Resonant Objects within the Main Classical Trans-Neptunian Belt

The 5:3 and 7:4 mean motion resonances of Neptune are at 42.3 and 43.7 au, respectively, and overlap with objects in the classical trans-Neptunian belt (Kuiper Belt). We report the complete/partial lightcurves of 13 and 14 trans-Neptunian objects (TNOs) in the 5:3 and 7:4 resonances, respectively. We report a most likely contact binary in the 7:4 resonance, 2013 FR28, with a periodicity of 13.97 ± 0.04 hr and a lightcurve amplitude of 0.94 ± 0.02 mag. With a V-/U-shaped lightcurve, 2013 FR28 has one of the largest well-sampled TNO amplitudes observed with ground-based observations, comparable to the well-determined contact binary 2001 QG298. 2013 FR28 has a mass ratio q ∼ 1 with a density ρ ∼ 1 g cm−3. We find several objects with large amplitudes and classify 2004 SC60, 2006 CJ69, and 2013 BN82 as likely contact binaries and 2001 QF331, 2003 YW179, and 2015 FP345 as likely elongated objects. We observe the 17:9 resonant or classical object 2003 SP317 that we classify as a likely contact binary. A lower estimate of 10%–50% and 20%–55% for the fraction of (nearly) equal-sized contact binaries is calculated in the 5:3 and 7:4 resonances, respectively. Surface colors of 2004 SC60, 2013 BN82, 2014 OL394, and 2015 FP345 have been obtained. Including these colors with ones from the literature reveals that elongated objects and contact binaries share the same ultrared surface color, except Manwë–Thorondor and 2004 SC60. Not only are the colors of the 7:4 and 5:3 TNOs similar to the cold classicals, but we demonstrate that the rotational properties of the 5:3 and 7:4 resonants are similar to those of the cold classicals, inferring a clear link between these subpopulations.

Digital technologies and mathematics teaching: An analysis of teacher professional knowledge

Professional teaching knowledge interferes with the planning and development of classes, which also relates to integrating digital technologies in the classroom. This study aimed to understand and discuss teachers’ professional knowledge for teaching mathematics with digital technologies. The methodology of this systematic literature review was composed of selecting 42 scientific articles in the ERIC, Latindex, Scopus, Scielo, and Web of Science databases and vertical and horizontal analyses of the indicated studies. The results suggest that the studies have similar structures with similar theoretical frameworks, objectives, and methodological procedures, identifying and classifying teacher professional knowledge based on knowledge achieved or that still needs to be developed. The teachers mobilize their knowledge by recognizing the potential of digital technologies for teaching mathematics critically and reflectively.

The Bombardment History of the Giant Planet Satellites

The origins of the giant planet satellites are debated, with scenarios including formation from a protoplanetary disk, sequential assembly from massive rings, and recent accretion after major satellite–satellite collisions. Here, we test their predictions by simulating outer solar system bombardment and calculating the oldest surface ages on each moon. Our crater production model assumes the projectiles originated from a massive primordial Kuiper Belt (PKB) that experienced substantial changes from collisional evolution, which transformed its size frequency distribution into a wavy shape, and Neptune’s outward migration, which ejected most PKB objects onto destabilized orbits. The latter event also triggered an instability among the giant planets some tens of Myr after the solar nebula dispersed. We find all giant planet satellites are missing their earliest crater histories, with the likely source being impact resetting events. Iapetus, Hyperion, Phoebe, and Oberon have surface ages that are a few Myr to a few tens of Myr younger than when Neptune entered the PKB (i.e., they are 4.52–4.53 Gyr old). The remaining midsized satellites of Saturn and Uranus, as well as the small satellites located between Saturn’s rings and Dione, have surfaces that are younger still by many tens to many hundreds of Myr (4.1–4.5 Gyr old). A much wider range of surface ages are found for the large moons Callisto, Ganymede, Titan, and Europa (4.1, 3.4, 1.8, and 0.18 Gyr old, respectively). At present, we favor the midsized and larger moons forming within protoplanetary disks, with the other scenarios having several challenges to overcome.

SiamATTRPN: Enhance Visual Tracking With Channel and Spatial Attention

Visual tracking is an important research topic in the field of computer vision. The current Siamese tracker based on the region proposal network (SiamRPN) has achieved promising tracking results in terms of efficiency and performance. However, through our empirical study, we have observed that deep features learned by SiamRPN are of substandard quality, as the salient regions within the deep features fail to correspond accurately with meaningful objects. To address this limitation, we propose an approach to enhance the quality of the learned deep features through the incorporation of an attention mechanism. Attention mechanisms have been shown to be effective in distinguishing similar objects, as they suppress background objects while highlighting target information that is most relevant. As a result, a new tracking method with channel and spatial attention termed SiamATTRPN is explored. To verify the effectiveness of SiamATTRPN, experiments on benchmark datasets demonstrate that our proposed tracker outperforms the baseline tracker significantly.