Miracle-dz/UniFAR-FTUs huggingface dataset:
SD-Pose: Structural Discrepancy Aware Category-Level 6D Object Pose Estimation

Category-level 6D object pose estimation aims to predict the full pose and size information for previously unseen instances from known categories, which is an essential portion of robot grasping and augmented reality. However, the core challenge of this task still is the enormous shape variation within each category. With regard to the challenge, we propose a novel framework SD-Pose, which utilizes the instance-category structural discrepancy and the potential geometric-semantic association to enhance the exploration of the intra-class shape information. Specifically, an information exchange augmentation (IEA) module is introduced to supplement the instance-category structural information by their structural discrepancy, thus facilitating the enhanced geometric information to contain both the character of instance shape and the commonality of category structure. For complementing the deficiencies of structural information adaptively, a semantic dynamic fusion (SDF) module is further designed to fuse semantic and geometric features. Finally, the proposed SD-Pose framework equipped with the IEA and SDF modules hierarchically supplements instance-category structural information in a stacked manner and achieves state-of-the-art performance on the CAMERA25 and REAL275 datasets.
Examining the contribution of motivation in the job search of youth with developmental language disorder

Background & aims Youth with developmental language disorder (DLD) are at risk of experiencing challenges during their job search. It is thus crucial to promote efficient job search behaviors in terms of effort, intensity, and self-regulation. Based on self-determination theory (SDT), we verified the role of autonomous and controlled motivations in enhancing these behaviors. Methods Study 1 included 37 young adults with DLD who have finished school, and Study 2 included 52 youth with DLD transitioning from school to work. They completed a questionnaire examining their job search behaviors and their motivation toward their job search. Results Autonomous motivation positively predicted job search effort, intensity, and self-regulation. Small to moderate relations were observed in Study 1, and moderate to strong ones in Study 2. Controlled motivation was unrelated to the three behaviors. Conclusions In line with SDT, autonomous motivation is an important foundation for positive job search behaviors among youth with DLD. Implications Supporting the development of autonomous motivation is thus encouraged in transition services for this population.
Numerical Modeling of Thermoacoustic No-Stack Heat Pumps and Prime Movers

Abstract As opposed to conventional thermoacoustic systems, no-stack setups do not include porous material as a medium for storing and transferring heat. In such systems, heat is transported by gas parcels directly between heat exchangers. A simplified Lagrangian model has been applied to analyze no-stack standing-wave thermoacoustic systems with the aim of estimating beneficial geometrical parameters and drive ratio values. Promising configurations provided by the simplified model were also studied with computational fluid dynamics simulations. Using CFD code FLUENT, potentially more efficient setups were found with the second-law efficiency of 40% for a prime mover and 55% for a heat pump. The dependence of system performance is reported for variable system parameters. A good agreement between the simplified model and CFD results was achieved in some cases. Heat exchangers with fmite-thickness plates were additionally investigated in high-performance setups. For porosities below 25%, the thickness effect appeared to be insignificant.
Modal time history analysis of non‐classically damped structures for seismic motions

The step-by-step modal time history integration methods are developed for dynamic analysis of non-classically damped linear structures subjected to earthquake-induced ground motions. Both the mode displacement and mode acceleration-based algorithms are presented for the calculation of member and acceleration responses. The complex-valued eigenvectors are used to effect the modal decoupling of the equations of motion. However, the recursive step-by-step algorithms are still in terms of real quantities. The numerical results for the acceleration response and floor response spectra, obtained with these approaches, are presented. The mode acceleration approach is observed to be decidedly better than the mode displacement approach in as much as it alleviates the so-called missing mass effect, caused by the truncation of modes, very effectively. The utilization of the mode acceleration-based algorithms is, thus, recommended in all dynamic analyses for earthquake-induced ground motions.
Detection and Mitigation of IoT-Based Attacks Using SNMP and Moving Target Defense Techniques

This paper proposes a solution for ensuring the security of IoT devices in the cloud environment by protecting against distributed denial-of-service (DDoS) and false data injection attacks. The proposed solution is based on the integration of simple network management protocol (SNMP), Kullback–Leibler distance (KLD), access control rules (ACL), and moving target defense (MTD) techniques. The SNMP and KLD techniques are used to detect DDoS and false data sharing attacks, while the ACL and MTD techniques are applied to mitigate these attacks by hardening the target and reducing the attack surface. The effectiveness of the proposed framework is validated through experimental simulations on the Amazon Web Service (AWS) platform, which shows a significant reduction in attack probabilities and delays. The integration of IoT and cloud technologies is a powerful combination that can deliver customized and critical solutions to major business vendors. However, ensuring the confidentiality and security of data among IoT devices, storage, and access to the cloud is crucial to maintaining trust among internet users. This paper demonstrates the importance of implementing robust security measures to protect IoT devices in the cloud environment and highlights the potential of the proposed solution in protecting against DDoS and false data injection attacks.
Advances in the Synthesis and Analysis of Biologically Active Phosphometabolites

Phosphorus-containing metabolites cover a large molecular diversity and represent an important domain of small molecules which are highly relevant for life and represent essential interfaces between biology and chemistry, between the biological and abiotic world. The large but not unlimited amount of phosphate minerals on our planet is a key resource for living organisms on our planet, while the accumulation of phosphorus-containing waste is associated with negative effects on ecosystems. Therefore, resource-efficient and circular processes receive increasing attention from different perspectives, from local and regional levels to national and global levels. The molecular and sustainability aspects of a global phosphorus cycle have become of much interest for addressing the phosphorus biochemical flow as a high-risk planetary boundary. Knowledge of balancing the natural phosphorus cycle and the further elucidation of metabolic pathways involving phosphorus is crucial. This requires not only the development of effective new methods for practical discovery, identification, and high-information content analysis, but also for practical synthesis of phosphorus-containing metabolites, for example as standards, as substrates or products of enzymatic reactions, or for discovering novel biological functions. The purpose of this article is to review the advances which have been achieved in the synthesis and analysis of phosphorus-containing metabolites which are biologically active.
Directional terahertz beams realized by depth-modulated metallic surface grating structures

We propose a subwavelength metal slit with surrounding depth-modulated surface grating structures to realize directional beams in the terahertz regime. The surface gratings consist of two sets of grooves of different depths. The shallow grooves are designed to support the spoof surface plasmons, and the deep grooves are utilized to diffract the terahertz surface wave into free space. Theoretical analysis and numerical simulations based on the finite element method confirm that various beaming effects including splitting, on-axis beaming, and off-axis beaming can be realized by controlling the distributions of the deep grooves.
Off-Grid DOA Estimation for Noncircular Signals via Block Sparse Representation Using Extended Transformed Nested Array

An off-grid direction-of-arrival (DOA) estimation method based on block sparse representation is proposed to localize the strictly noncircular (NC) sources utilizing an extended transformed nested array (ETNA). This novel off-grid DOA estimation algorithm effectively promotes spatial distribution information mining. Furthermore, it is conducive to providing stable signal recovery, which refines the DOA estimation precision with interpolation over a coarse grid. We then combine the above algorithm with the designed ETNA to improve the detection performance. The ETNA is an optimal displacement on the existing TNA, which enlarges the degree of freedom (DOF) and lengthens the maximum contiguous segment from the derived virtual array. Simulation results demonstrate its superiority in estimation performance and DOF.
Double spectral representations of single loop amplitudes with k vertices: k ≥ 4

A method developed in several previous papers is combined with the method of induction to derive double dispersion relations, with Mandelstam boundary, for the class of single loop amplitudes with four or more vertices. The spectral functions are expressed as integral representations and restrictions on the masses and kinematic invariants for which dispersion relations are valid are found. It is also discussed how representations for the low order single loop amplitudes can be obtained for wider ranges of these variables.
What is the global glacier ice volume outside the ice sheets?

Abstract A recent study (Millan and others, 2022a, Nature Geoscience 15(2), 124–129) claims that ice volume contained in all glaciers outside the ice sheets and its potential contribution to sea level is 20% less than previously estimated. However, the apparent decrease is largely due to differences in choice of domain, as the study excludes 80% of the glacier area in the Antarctic periphery that was included in previous global glacier volume estimates. The issue highlights the difficulty in separating glaciers from the ice-sheet proper, especially in Antarctica, and the need for both the glacier and ice-sheet communities to develop standards and protocols to avoid double-counting in global ice volume and mass-change assessments and projections. Process-based inversion models have replaced earlier scaling methods, but large uncertainties in global glacier volume estimation remain due to the ill-posed nature of the inversion problem and poorly constrained parameters emphasizing the need for more direct ice thickness observations.
MEDICAL INSTRUMENT FOR ROBOTIC ASSISTED RADIOFREQUENCY LIVER ABLATION

The paper presents the design of a medical instrument proposed for the robotic assisted radio frequency ablation procedure using the PARABRACHYROB parallel robotic system. Following the medical procedure requirements, a protocol is established for the robotic assisted radiofrequency liver ablation to serve as design considerations for the medical instrument. The design of the experimental model and its integration on PARA-BRACHYROB are presented.
Enzymatic and Non-Enzymatic Antioxidant Responses of Young Tomato Plants (cv. Micro-Tom) to Single and Combined Mild Nitrogen and Water Deficit: Not the Sum of the Parts

This study aims to perform a broad analysis of the antioxidant (AOX) responses of young tomato plants exposed to single and combined mild nitrogen (N) and water deficits through the evaluation of oxidative biomarkers, non-enzymatic and enzymatic AOX components. ‘Micro-Tom’ seedlings were subjected to four treatments: control (CTR; 100%N + 100%W), N deficit (N; 50%N), water deficit (W; 50%W), and combined deficits (N + W; 50%N + 50%W). An enhancement of several non-enzymatic and enzymatic components was found in plants subjected to N + W deficit, which presented higher anthocyanins accumulation (up to 103%) as well as higher levels of superoxide dismutase (SOD) transcripts at root level and of ascorbate peroxidase (APX) and catalase (CAT) transcripts at shoot level. This increase in the gene expression was also translated in augmented SOD (up to 202%), APX (up to 155%) and CAT (up to 108%) activity compared to CTR plants and the single deficits. Overall, tomato plants were able to employ defense strategies to cope with this combined deficit, as demonstrated by the higher total AOX capacity (up to 87%) compared to the single deficits, which contributed to the maintenance of their redox homeostasis, with unchanged values of lipid peroxidation and hydrogen peroxide compared with CTR plants.
Multivariate Probabilistic Monocular 3D Object Detection

In autonomous driving, monocular 3D object detection is an important but challenging task. Towards accurate monocular 3D object detection, some recent methods recover the distance of objects from the physical height and visual height of objects. Such decomposition framework can introduce explicit constraints on the distance prediction, thus improving its accuracy and robustness. However, the inaccurate physical height and visual height prediction still may exacerbate the inaccuracy of the distance prediction. In this paper, we improve the framework by multivariate probabilistic modeling. We explicitly model the joint probability distribution of the physical height and visual height. This is achieved by learning a full covariance matrix of the physical height and visual height during training, with the guide of a multivariate likelihood. Such explicit joint probability distribution modeling not only leads to robust distance prediction when both the predicted physical height and visual height are inaccurate, but also brings learned covariance matrices with expected behaviors. The experimental results on the challenging Waymo Open and KITTI datasets show the effectiveness of our framework1.
The depletion of interstellar elements and the interaction between gas and dust in space

Recent data obtained with Copernicus, combined with new results from the literature, indicate that the depletions of interstellar elements may depend on cloud density in a simple way. This is expected if the depletions are due to accretion of gas particles onto grains under presently existing conditions, but is not expected if the depletions take place during the grain formation process, before mixing into the interstellar medium. The suggestion that depletion occurs via accretion may be supported by the existence of a good correlation between depletions and first ionization potentials of the elements, since the latter quantity determines to a great extent the chemical and physical properties, and hence possibly the sticking coefficient, of each species. If the grains do not carry large positive charges, then ion-grain encounters may be important not only in creating the depletions, but also in determining ionization equilibrium, particularly if a large population of very small grains is present. (AIP)
A technique of synthesizing the excitation currents of planar arrays or apertures

A technique of synthesizing or reconstructing the excitation currents of a planar array of aperture-type antennas from the known near-field patterns of the radiating source is presented. This technique uses an exact solution to the fields radiated by the aperture antenna without disregarding the source currents. Typical numerical computations have been carried out to validate the analytical technique developed. Sensitivity and stability of the numerical computations performed have been studied. The available iterative bandlimited signal extrapolation technique is used to reconstruct the aperture excitation currents only if the far-field patterns of the radiating source are known. Far-field patterns of aperture antennas measured in the laboratory were also used to reconstruct the aperture electric field distribution in the principal plane. >
An Asynchronously Alternative Stochastic Gradient Descent Algorithm for Efficiently Parallel Latent Feature Analysis on Shared-Memory

A latent feature analysis (LFA) model is highly efficient in performing representation learning to high-dimensional incomplete (HDI) data like user-item interactions data from a recommender system. Stochastic gradient descent (SGD) is frequently adopted as the learning algorithm by an LFA model owing to its low computational complexity. However, a standard SGD algorithm is in nature a serial algorithm, which affects the resultant LFA model's scalability on mass HDI data. On the other hand, existing parallel SGD algorithms commonly suffer from low speedup when building an LFA model due to their frequent synchronizations during the training process. Motivated by this discovery, this paper proposes an Asynchronously Alternative Stochastic Gradient Descent (A2SGD) algorithm to achieve an efficiently parallelized LFA model on shared-memory with two fold-ideas: a) adopting the principle of an alternative stochastic gradient descent algorithm to decouple the LFA process, thereby achieving two parallelizable subtasks with minimal learning information loss; b) designing a novel parallelization scheme by eliminating synchronizations from both the subtask and the thread perspectives, i.e., both sub-tasks are taken simultaneously, and their affiliated threads also executes without synchronizations. Rigorously theoretical convergence proof illustrates that the newly-proposed parallelization scheme guarantees the convergence of a resultant LFA model. Detailed experimental results on four real HDI datasets indicate that an A2SGD-based LFA model outperforms several state-of-the-art parallel SGD-based LFA models in terms of both missing data estimation accuracy and parallelization speedup.
Effect of Musical Stimulation on Placental Programming and Neurodevelopment Outcome of Preterm Infants: A Systematic Review

Background: The fetal environment is modulated by the placenta, which integrates and transduces information from the maternal environment to the fetal developmental program and adapts rapidly to changes through epigenetic mechanisms that respond to internal (hereditary) and external (environmental and social) signals. Consequently, the fetus corrects the trajectory of own development. During the last trimester of gestation, plasticity shapes the fetal brain, and prematurity can alter the typical developmental trajectories. In this period, prevention through activity-inducing (e.g., music stimulation) interventions are currently tested. The purpose of this review is to describe the potentialities of music exposure on fetus, and on preterm newborns in the Neonatal Intensive Care Unit evaluating its influence on neurobehavioral development. Methods: Databases were searched from 2010 to 2022 for studies investigating mechanisms of placental epigenetic regulation and effects of music exposure on the fetus and pre-term neonates. Results: In this case, 28 selected papers were distributed into three research lines: studies on placental epigenetic regulation (13 papers), experimental studies of music stimulation on fetus or newborns (6 papers), and clinical studies on premature babies (9 papers). Placental epigenetic changes of the genes involved in the cortisol and serotonin response resulted associated with different neurobehavioral phenotypes in newborns. Prenatal music stimulation had positive effects on fetus, newborn, and pregnant mother while post-natal exposure affected the neurodevelopment of the preterm infants and parental interaction. Conclusions: The results testify the relevance of environmental stimuli for brain development during the pre- and perinatal periods and the beneficial effects of musical stimulation that can handle the fetal programming and the main neurobehavioral disorders.
Misconceptions of Mexican Teachers in The Solution of Simple Pendulum

Solving the position of a simple pendulum at any time is apparently one of the most simple and basic problems to solve -in high school and college physics courses. However, because of this apparent simplicity, teachers and physics texts often assume that the solution is immediate -without pausing to reflect on the problem formulation or verifying that the solution obtained is indeed correct. This process causes conceptual errors to be carried out with them -students and even worse teachers. This paper presents some of the misconceptions found in teachers solving simple pendulum problems, moreover it presents proposals made in the texts, which generate the creation of such misconceptions in both teachers and students, and finally, it presents the proposal of a solution to correct this problem.
A Novel Approach to Parameter Determination of the Continuous Spontaneous Localization Collapse Model

Models of dynamical wave function collapse consistently describe the breakdown of the quantum superposition with the growing mass of the system by introducing non-linear and stochastic modifications to the standard Schrödinger dynamics. Among them, Continuous Spontaneous Localization (CSL) was extensively investigated both theoretically and experimentally. Measurable consequences of the collapse phenomenon depend on different combinations of the phenomenological parameters of the model—the strength λ and the correlation length rC—and have led, so far, to the exclusion of regions of the admissible (λ−rC) parameters space. We developed a novel approach to disentangle the λ and rC probability density functions, which discloses a more profound statistical insight.
Virtual mentoring and coaching through virtual professional leadership learning communities for school leaders: a mixed-methods study

ABSTRACT The empirical research examining the effects of virtual mentoring and coaching (VMC) on principal leadership development is limited. Furthermore, little is known about school leaders who engage in virtual professional leadership learning communities (VPLCs) and how much school leaders grow with VPLCs who are also receiving VMC at the same time. The purpose of this study was to examine the effect of VMC on school leaders’ professional growth and to determine how school leaders perceived the effectiveness of the VMC while being engaged in VPLCs. The findings showed positive effects of VMC on school leaders’ professional leadership development, and the younger practicing leaders tended to benefit more from the VPLCs.
Distilling Business Value from COVID - 19 Public Media Dataset with Machine Learning and Natural Language Processing

While manual analysis of news coverage is difficult and time consuming, methods in natural language processing can be used to uncover otherwise hidden semantics. This work analyses more than 370,000 news articles to explore connections and trends in business decisions and their financial impact during the COVID-19 pandemic. Topic modelling, sentiment analysis and named entity recognition methods are used to identify connections between the articles and the financial performance of selected companies or industries. This report sets out the results of the individual natural language processing methods and the resulting analysis with financial data. Interesting contrasting topics in the media can be filtered out that are associated with the companies with the highest or lowest positive sentiment. This information could be useful to companies to gain an understanding of topics that are currently treated favourably or unfavourably by the media and hence assist with communication strategies and competitive intelligence.