Neighbor Pairs Research Articles

Whole Even Sum Domination in Graphs

In this paper, a new concept of domination which is called whole even sum domination is presented, many bounded, properties, remarks, examples, are introduced. Furthermore, some operations as join and corona graphs of sum graphs are discussed. Finally, determined this number of the graph obtained from a graph G by the removal of all edges between any pair of neighbours of a vertex.

Reconstruction Attacks on Aggressive Relaxations of Differential Privacy

Differential privacy is a widely accepted formal privacy definition that allows aggregate information about a dataset to be released while controlling privacy leakage for individuals whose records appear in the data. Due to the unavoidable tension between privacy and utility, there have been many works trying to relax the requirements of differential privacy to achieve greater utility.One class of relaxation, which is gaining support outside the privacy community is embodied by the definitions of individual differential privacy (IDP) and bootstrap differential privacy (BDP). Classical differential privacy defines a set of neighboring database pairs and achieves its privacy guarantees by requiring that each pair of neighbors should be nearly indistinguishable to an attacker. The privacy definitions we study, however, aggressively reduce the set of neighboring pairs that are protected.To a non-expert, IDP and BDP can seem very appealing as they echo the same types of privacy explanations that are associated with differential privacy, and also experimentally achieve dramatically better utility. However, we show that they allow a significant portion of the dataset to be reconstructed using algorithms that have arbitrarily low privacy loss under their privacy accounting rules.With the non-expert in mind, we demonstrate these attacks using the preferred mechanisms of these privacy definitions. In particular, we design a set of queries that, when protected by these mechanisms with high noise settings (i.e., with claims of very low privacy loss), yield more precise information about the dataset than if they were not protected at all. The specific attacks here can be defeated and we give examples of countermeasures. However, the defenses are either equivalent to using differential privacy or to ad-hoc methods tailored specifically to the attack (with no guarantee that they protect against other attacks). Thus, the defenses emphasize the deficiencies of these privacy definitions.

The Conundrum of "Pair Sites" in Langmuir-Hinshelwood Reaction Kinetics in Heterogeneous Catalysis.

Understanding reaction kinetics is crucial for designing and applying heterogeneous catalytic processes in chemical and energy conversion. Here, we revisit the Langmuir-Hinshelwood (L-H) kinetic model for bimolecular surface reactions, originally formulated for metal catalysts, assuming immobile adsorbates on neighboring pair sites, with the rate varying linearly with the density of surface sites (sites per unit area); r ∝ [*]o 1. Supported metal oxide catalysts, however, offer systematic control over [*]o through variation of the active two-dimensional metal oxide loading in the submonolayer region. Various reactions catalyzed by supported metal oxides are analyzed, such as supported VO x catalysts, including methanol oxidation, oxidative dehydrogenation of propane and ethane, SO2 oxidation to SO3, propene oxidation to acrolein, n-butane oxidation to maleic anhydride, and selective catalytic reduction of nitric oxide with ammonia. The analysis reveals diverse dependencies of reaction rate on [*]o for these surface reactions, with r ∝ [*]o n , where n equals 1 for reactions with a unimolecular rate-determining step and 2 for those with a bimolecular rate-limiting step or exchange of more than 2 electrons. We propose refraining from a priori assumptions about the nature and density of surface sites or adsorbate behavior, advocating instead for data-driven elucidation of kinetics based on the density of surface sites, adsorbate coverage, etc. Additionally, recent studies on catalytic surface mechanisms have shed light on nonadjacent catalytic sites catalyzing surface reactions in contrast to the traditional requirement of adjacent/pair sites. These findings underscore the need for a more nuanced approach in modeling heterogeneous catalysis, especially supported metal oxide catalysts, encouraging reliance on experimental data over idealized assumptions that are often difficult to justify.

A Hybrid Improved SAC-IA with a KD-ICP Algorithm for Local Point Cloud Alignment Optimization

To overcome incomplete point cloud data obtained from laser scanners scanning complex surfaces, multi-viewpoint cloud data needs to be aligned for use. A hybrid improved SAC-IA with a KD-ICP algorithm is proposed for local point cloud alignment optimization. The scanned point cloud data is preprocessed with statistical filtering, as well as uniform down-sampling. The sampling consistency initial alignment (SAC-IA) algorithm is improved by introducing a dissimilarity vector for point cloud initial alignment. In addition, the iterative closest point (ICP) algorithm is improved by incorporating bidirectional KD-tree to form the KD-ICP algorithm for fine point cloud alignment. Finally, the algorithms are compared in terms of runtime and alignment accuracy. The implementation of the algorithms is based on the Visual Studio 2013 software configurating point cloud library environment for testing experiments and practical experiments. The overall alignment method can be 40%~50% faster in terms of running speed. The improved SAC-IA algorithm provides better transformed poses, combined with the KD-ICP algorithm to select the corresponding nearest neighbor pairs, which improves the accuracy, as well as the applicability of the alignment.

Intrinsic structure exploitation with dual alignment for universal visual recognition

This research explores the complex yet applicable domain of universal visual recognition, where unlabeled datasets may encompass additional, as-of-yet unidentified classes and present a skewed feature distribution within labeled datasets. The primary challenge lies in concurrently distinguishing instances of known classes and uncovering new classes within the unlabeled data. This difficulty is compounded by two factors: category shift and distribution shift. Current methodologies typically overlook the potential of harnessing the inherent structural relationship between labeled and unlabeled datasets, and they fail to effectively isolate the semantic variability of the newly identified classes from the established ones. To overcome these hurdles, we introduce an innovative framework, which we have termed MESA (short for Manifold structure Exploitation with double Spaces dual Alignment), to advance the field of universal visual recognition. Our approach begins by identifying geometrically and semantically related pairs of nearest neighbors between labeled and unlabeled data, which serve as anchor points to foster an intrinsic match between the two datasets. By integrating an affinity score for similarity, we establish a novel cross-view, anchor-guided, self-supervised learning paradigm. This model is designed to spread the learned labels’ information and to conjoin novel semantic insights within the predictive space. To intensify the matching accuracy for the known classes and enhance the differentiation of novel classes, we further implement a confident-prototype-driven, self-supervised learning paradigm in a cross-view manner. This strategy is aimed at revealing the macroscopic category configuration within the embedding space. MESA employs a pioneering bidirectional dual-alignment mechanism that operates simultaneously in the embedding and prediction spaces, hence providing a more robust approach to confronting challenges brought about by distribution and category shifts. Our exhaustive evaluation across several extensive image recognition benchmarks substantiates MESA’s superior performance over the latest cutting-edge methods, marking a significant leap forward in the sphere of universal visual recognition. Finally, MESA is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/aimeeyaoyao/MESA.

Spin Hall Effect of Two-Index Paraxial Vector Propagation-Invariant Beams

We investigate a simple paraxial vector beam, which is a coaxial superposition of two single-ringed Laguerre–Gaussian (LG) beams, linearly polarized along the horizontal axis, with topological charges (TC) n and −n, and of two LG beams, linearly polarized along the vertical axis, with the TCs m and −m. In the initial plane, such a vector beam has zero spin angular momentum (SAM). Upon propagation in free space, such a propagation-invariant beam has still zero SAM at several distances from the waist plane (initial plane). However, we show that at all other distances, the SAM becomes nonzero. The intensity distribution in the cross-section of such a beam has 2m (if m > n) lobes, the maxima of which reside on a circle of a certain radius. The SAM distribution has also several lobes, from 2m till 2(m + n), the centers of which reside on a circle with a radius smaller than that of the maximal-intensity circle. The SAM sign alternates differently: one lobe has a positive SAM, while two neighbor lobes on the circle have a negative SAM, or two neighbor pairs of lobes can have a positive and negative SAM. When passing through a plane with zero SAM, positive and negative SAM lobes are swapped. The maximal SAM value is achieved at a distance smaller than or equal to the Rayleigh distance.

Closest Pairs Search Over Data Stream

k-closest pair (KCP for short) search is a fundamental problem in database research. Given a set of d-dimensional streaming data S, KCP search aims to retrieve k pairs with the shortest distances between them. While existing works have studied continuous 1-closest pair query (i.e., k=1) over dynamic data environments, which allow for object insertions/deletions, they require high computational costs and cannot easily support KCP search with k>1. This paper investigates the problem of KCP search over data stream, aiming to incrementally maintain as few pairs as possible to support KCP search with arbitrarily k. To achieve this, we introduce the concept of NNS (short for N earest N eighbour pair- S et), which consists of all the nearest neighbour pairs and allows us to support KCP search via only accessing O(k) objects. We further observe that in most cases, we only need to use a small portion of NNS to answer KCP search as typically kłl n. Based on this observation, we propose TNNS (short for T hreshold-based NN pair S et), which contains a small number of high-quality NN pairs, and a partition named τ-DLBP (short for τ- D istance L ower- B ound based P artition) to organize objects, with τ being an integer significantly smaller than n. τ-DLBP organizes objects using up to O(łog n / τ) partitions and is able to support the construction and update of TNNS efficiently.

Far-field hydrodynamic interaction in a group of swimmers

Three-dimensional schools of hydrodynamically axisymmetric swimmers self-propelling at a constant velocity are studied. We introduce a low-order model for the induced velocity based on the far-field approximation. We inquire if, by holding suitable relative positions in the three-dimensional space, the swimmers can reduce the overall energy consumption of the school in comparison with the same number of isolated individuals at the same velocity. We find a considerable (several per cent) energy saving achievable by chain formations. The benefit increases asymptotically with the number of individuals, towards a finite limit that is a function of the minimum allowed spacing between each pair of neighbours.

Broadband EPR Spectroscopy of the Triplet State: Multi-Frequency Analysis of Copper Acetate Monohydrate.

Electron paramagnetic resonance spectroscopy is a long-standing method for the exploration of electronic structures of transition ion complexes. The difficulty of its analysis varies considerably, not only with the nature of the spin system, but more so with the relative magnitudes of the magnetic interactions to which the spin is subject, where particularly challenging cases ensue when two interactions are of comparable magnitude. A case in point is the triplet system S = 1 of coordination complexes with two unpaired electrons when the electronic Zeeman interaction and the electronic zero-field interaction are similar in strength. This situation occurs in the X-band spectra of the thermally excited triplet state of dinuclear copper(II) complexes, exemplified by copper acetate monohydrate. In this study, applicability of the recently developed low-frequency broadband EPR spectrometer to S = 1 systems is investigated on the analysis of multi-frequency, 0.5-16 GHz, data from [Cu(CH3COO)2H2O]2. Global fitting affords the spin Hamiltonian parameters gz = 2.365 ± 0.008; gy = 2.055 ± 0.010; gx = 2.077 ± 0.005; Az = 64 gauss; D = 0.335 ± 0.002 cm-1; E = 0.0105 ± 0.0003 cm-1. The latter two define zero-field absorptions at ca. 630, 7730, and 10,360 MHz, which show up in the spectra as one half of a sharpened symmetrical line. Overall, the EPR line shape is Lorentzian, reflecting spin-lattice relaxation, which is a combination of an unusual, essentially temperature-independent, inverted Orbach process via the S = 0 ground state, and a Raman process proportional to T2. Other broadening mechanisms are limited to at best minor contributions from a distribution in E values, and from dipolar interaction with neighboring copper pairs. Monitoring of a first-order double-quantum transition between 8 and 35 GHz shows a previously unnoticed very complex line shape behavior, which should be the subject of future research.

RNA polymerase sliding on DNA can couple the transcription of nearby bacterial operons

DNA transcription initiates after an RNA polymerase (RNAP) molecule binds to the promoter of a gene. In bacteria, the canonical picture is that RNAP comes from the cytoplasmic pool of freely diffusing RNAP molecules. Recent experiments suggest the possible existence of a separate pool of polymerases, competent for initiation, which freely slide on the DNA after having terminated one round of transcription. Promoter-dependent transcription reinitiation from this pool of posttermination RNAP may lead to coupled initiation at nearby operons, but it is unclear whether this can occur over the distance and timescales needed for it to function widely on a bacterial genome in vivo. Here, we mathematically model the hypothesized reinitiation mechanism as a diffusion-to-capture process and compute the distances over which significant interoperon coupling can occur and the time required. These quantities depend on molecular association and dissociation rate constants between DNA, RNAP, and the transcription initiation factor σ70; we measure these rate constants using single-molecule experiments in vitro. Our combined theory/experimental results demonstrate that efficient coupling can occur at physiologically relevant σ70 concentrations and on timescales appropriate for transcript synthesis. Coupling is efficient over terminator-promoter distances up to ∼1,000 bp, which includes the majority of terminator-promoter nearest neighbor pairs in the Escherichia coli genome. The results suggest a generalized mechanism that couples the transcription of nearby operons and breaks the paradigm that each binding of RNAP to DNA can produce at most one messenger RNA.

Self-supervised pretraining improves the performance of classification of task functional magnetic resonance imaging.

Decoding brain activities is one of the most popular topics in neuroscience in recent years. And deep learning has shown high performance in fMRI data classification and regression, but its requirement for large amounts of data conflicts with the high cost of acquiring fMRI data. In this study, we propose an end-to-end temporal contrastive self-supervised learning algorithm, which learns internal spatiotemporal patterns within fMRI and allows the model to transfer learning to datasets of small size. For a given fMRI signal, we segmented it into three sections: the beginning, middle, and end. We then utilized contrastive learning by taking the end-middle (i.e., neighboring) pair as the positive pair, and the beginning-end (i.e., distant) pair as the negative pair. We pretrained the model on 5 out of 7 tasks from the Human Connectome Project (HCP) and applied it in a downstream classification of the remaining two tasks. The pretrained model converged on data from 12 subjects, while a randomly initialized model required 100 subjects. We then transferred the pretrained model to a dataset containing unpreprocessed whole-brain fMRI from 30 participants, achieving an accuracy of 80.2 ± 4.7%, while the randomly initialized model failed to converge. We further validated the model's performance on the Multiple Domain Task Dataset (MDTB), which contains fMRI data of 26 tasks from 24 participants. Thirteen tasks of fMRI were selected as inputs, and the results showed that the pre-trained model succeeded in classifying 11 of the 13 tasks. When using the 7 brain networks as input, variations of the performance were observed, with the visual network performed as well as whole brain inputs, while the limbic network almost failed in all 13 tasks. Our results demonstrated the potential of self-supervised learning for fMRI analysis with small datasets and unpreprocessed data, and for analysis of the correlation between regional fMRI activity and cognitive tasks.

Simultaneous single-qubit driving of semiconductor spin qubits at the fault-tolerant threshold

Practical Quantum computing hinges on the ability to control large numbers of qubits with high fidelity. Quantum dots define a promising platform due to their compatibility with semiconductor manufacturing. Moreover, high-fidelity operations above 99.9% have been realized with individual qubits, though their performance has been limited to 98.67% when driving two qubits simultaneously. Here we present single-qubit randomized benchmarking in a two-dimensional array of spin qubits, finding native gate fidelities as high as 99.992(1)%. Furthermore, we benchmark single qubit gate performance while simultaneously driving two and four qubits, utilizing a novel benchmarking technique called N-copy randomized benchmarking, designed for simple experimental implementation and accurate simultaneous gate fidelity estimation. We find two- and four-copy randomized benchmarking fidelities of 99.905(8)% and 99.34(4)% respectively, and that next-nearest neighbor pairs are highly robust to cross-talk errors. These characterizations of single-qubit gate quality are crucial for scaling up quantum information technology.

Modeling multi-attribute and implicit relationship factors with self-supervised learning for recommender systems

Interactions of users and items can be naturally modeled as a user-item bipartite graph in recommender systems, and emerging research is devoted to exploring user-item graphs for collaborative filtering methods. In reality, user-item interaction usually stems from more complex underlying factors, such as the users’ specific preferences. A user-item bipartite graph could be used to understand the differences in motivation. However, existing research has not clearly proposed and modeled the factors that affect the differences, ignoring the similarities between user pairs and item pairs, preventing them from capturing fine-grained user preferences more effectively. In addition to the two points mentioned above, most GNN-based models for recommendation have the following two limitations: First, the model’s accuracy depends on the number of observed interactions in the dataset. Secondly, node representations are vulnerable to noisy interactions. This work has developed a novel recommendation model called “Multi-Attribute and Implicit Relationship Factors With Self-Supervised Learning for Collaborative filtering” (MIS-CF), which explicitly proposes and models multi-attribute and implicit relationship factors for collaborative filtering recommendation. Meanwhile, an auxiliary self-supervised learning task is designed to help the downstream task optimize the node representation. MIS-CF aggregates multi-attribute spaces through the user-item bipartite graph and establishes user-user and item-item graphs to model the similar relationship information of neighbor pairs through a memory model. The self-supervised learning task generates contrastive learning via self-discrimination, thus mining the rich auxiliary signals within the data, improving the accuracy and robustness of our model. Moreover, the sparse regularizer is utilized to alleviate the overfitting problem. Extensive experimental results on three public datasets not only show the significant performance and robustness gain of the proposed model but also prove the effectiveness and interpretability of fine-grained implicit factors modeling.

Local structures and undercooling ability of Zr–Ti melts

We developed an accurate model of Zr–Ti melts for classical molecular-dynamics simulations via regauging the existing embedded-atom method (EAM) potential, using particularly the diffusion coefficient data obtained from quasielastic neutron scattering. This EAM potential well reproduces the properties of the melts observed in experiments, including mass density, transport coefficients, melting temperatures, and static structure factors. The latter are also obtained from ab initio simulations. Using the developed EAM potentials, we analyzed the short-range order of the liquids and found a chemically preferred formation of Zr–Zr and Zr–Ti nearest neighbor pairs. When decreasing the temperature the number of icosahedral-like aggregates increases on the expense of more crystal-like structures. This is most pronounced at intermediate alloy compositions. At intermediate compositions also the lowest thermodynamic driving force for crystallization is found, resulting in the largest undercooling ability of the alloys.

MACHINE LEARNING DECISION SUPPORT SYSTEMS FOR ADAPTATION OF EDUCATIONAL CONTENT TO THE LABOR MARKET REQUIREMENTS

Context. The urgent task of increasing the functional efficiency of machine learning of decision support system (DSS) for assessing compliance with content modern requirements of the educational disciplines of the graduation department based on the results of the employer survey has been solved.
 Objective. Increasing the functional efficiency of machine learning of DSS for assessing compliance with modern requirements of the educational disciplines content of the first (bachelor’s) level specialty educational and professional program based on machine learning and pattern recognition.
 Method. The method of machine learning of DSS is proposed for adapting the educational content of the graduation department to the labor market requirements. The idea of the method is to maximize the information capacity of the DSS in the machine learning process, which allows in the monitoring mode to guarantee a high full probability of making the correct classification decisions. The method was developed as part of a functional approach to modeling cognitive processes of natural intelligence, which makes it possible to provide DSS with flexibility when retraining the system due to increasing the power of the recognition classes alphabet. The method is based on the principle of maximizing the amount of information in the machine learning process. The modified Kullback information measure, which is a functional of the accuracy characteristics of classification solutions, is considered as a criterion for optimizing machine learning parameters. According to the proposed functional category model, an information-extreme machine learning algorithm was developed based on the hierarchical data structure in the form of a binary decursive tree. The use of such a data structure allows you to automatically divide a large number of recognition classes into pairs of nearest neighbors, for which optimization of machine learning parameters is carried out according to a linear algorithm of the required depth. The geometric parameters of hyperspherical containers of recognition classes were considered as optimization parameters, which were restored in the radial basis of the binary space of Hamming features in the machine learning process. At the same time, the input traning matrix was transformed into a working binary training matrix, which was changed in the machine learning process through admissible transformations in order to adapt the input information description of the DSS to the maximum reliability of classification decisions.
 Results. The informational, algorithmic, and software of the DSS was developed to assess the educational content quality based on the machine analysis results of respondents’ answers. Within the framework of the geometric approach, based on the informationextreme machine learning results, highly reliable decisive rules, practically invariant to the multidimensionality of the recognition features space, were constructed based on the hierarchical data structure in the form of a binary decursive tree. The influence of machine learning parameters on the functional effectiveness of machine learning of the DSS was studied on the evaluation example of the educational content of the educational and professional bachelor’s program of the specialty 122 Computer Science.
 Conclusions. The computer modeling results confirm the high functional efficiency of the proposed method of informationextreme hierarchical machine learning and can be recommended for practical use in institutions of higher education to assess compliance with modern requirements of the educational content of graduation departments.

Simulations of sea surface reflection for V-band O2 differential absorption radar barometry

This study simulates V-band sea surface reflectance and normalized radar cross-section (NRCS) for sea surface air pressure barometry using a differential absorption radar operating at three spectrally even spaced close frequency bands (65.5, 67.75 and 70.0 GHz) with ± 15° cross-track scanning angle. The reflectance ratios of two neighboring frequency pairs and the ratio of the two ratios or three-channel approach are the focus of this study. Impacts of major sea surface geophysical variables such as sea surface temperature, wind, salinity, whitecap, and incidence angle on these reflection properties are analyzed. The reflection simulation is essentially based on geometric optics of rough sea surface. Simulation shows that NRCS values are sufficiently strong within the scanning angle and sea surface salinity would only introduce minimal variations in the surface reflection. The impact of sea surface reflection variations with sea surface temperature, wind, and whitecaps on sea surface barometry are mitigated when the ratios of frequency-paired radar signals are used. Furthermore, the ratios of a three-channel approach are very close to unity and calibration or compensation for the reflectance ratios may not be needed for sea level pressure retrievals. These results improve our understanding of sea surface reflection variations and would help the system design and development.

ZIF-derived non-bonding Co/Zn coordinated hollow carbon nitride for enhanced removal of antibiotic contaminants by peroxymonosulfate activation: Performance and mechanism

Co–Zn–N–C catalyst (MCZC) with neighboring Co and Zn pairs anchored on hollow carbon nitride was constructed by the direct etching of zeolitic imidazolate frameworks assembled with Co and Zn. Adequate characterizations and density functional theory (DFT) studies confirmed the successful construction of non-bonding Co and Zn pairs with enhanced electron transfer on Co sites by adjacent Zn sites. Based on the synergy of adjacent Co and Zn atom pairs, the MCZC/Perxymosulfate (PMS) system achieved 99.6% Tetracycline (TC) degradation in 24 min with a mineralization rate of 55.8% and PMS decomposition efficiency of 73.1%. DFT calculation based on the Fukui index identified the sites susceptible to attack by the active species. TC degradation pathways could be inferred, and reduced toxicity of intermediates was observed. This work provides new insights into the design of MOF-derived bimetallic catalysts and the importance of the interaction between adjacent metal active sites to catalytic performance.

Strategy to probe multimodal light emissions from Eu3+/Yb3+ activated garnet nanophosphors for LED devices and solar cell applications.

This study investigates multimodal light emission from an Eu3+/Yb3+ activated Y3Ga5O12 (YGG) nanophosphor synthesized using a low temperature solution combustion method. The prepared sample possesses a cubic phase and an Ia3̄d space group and this is confirmed with X-ray diffraction and Rietveld refinement analysis. The synthesized sample shows orange-red emission bands because of the f-f transitions of Eu3+ under UV (393 nm) and NIR (980 nm) excitations via downshifting (DS) and upconversion (UC) processes, respectively. Upon UV (393 nm) excitation of the sample, the Eu3+ ions absorb this energy and then transfer it to a neighboring pair of Yb3+ ions giving NIR emission (900-1100 nm) corresponding to the 2F5/2 → 2F7/2 transition of Yb3+. The energy transfer from a single Eu3+ ion to a pair of Yb3+ ions is possible because of the quantum cutting (QC) process and this energy transfer efficiency is found to increase with the increasing concentration of the Yb3+. The quantitative estimation of energy transfer and internal quantum cutting efficiency is determined by measuring the decay kinetics. An activation energy of 0.25 eV indicates the good thermal stability of the sample. Furthermore, samples are suitable for use in practical applications in lighting devices by combining them with the near-ultraviolet (NUV; InGaN) chip. The fabricated LED device shows stability with the driving current flow values. Studies indicate that the present nanophosphor could be useful for display devices, and in enhancing the spectral conversion efficiency of the solar cells.

Fast Octree Neighborhood Search for SPH Simulations

We present a new octree-based neighborhood search method for SPH simulation. A speedup of up to 1.9x is observed in comparison to state-of-the-art methods which rely on uniform grids. While our method focuses on maximizing performance in fixed-radius SPH simulations, we show that it can also be used in scenarios where the particle support radius is not constant thanks to the adaptive nature of the octree acceleration structure. Neighborhood search methods typically consist of an acceleration structure that prunes the space of possible particle neighbor pairs, followed by direct distance comparisons between the remaining particle pairs. Previous works have focused on minimizing the number of comparisons. However, in an effort to minimize the actual computation time, we find that distance comparisons exhibit very high throughput on modern CPUs. By permitting more comparisons than strictly necessary, the time spent on preparing and searching the acceleration structure can be reduced, yielding a net positive speedup. The choice of an octree acceleration structure, instead of the uniform grid typically used in fixed-radius methods, ensures balanced computational tasks. This benefits both parallelism and provides consistently high computational intensity for the distance comparisons. We present a detailed account of high-level considerations that, together with low-level decisions, enable high throughput for performance-critical parts of the algorithm. Finally, we demonstrate the high performance of our algorithm on a number of large-scale fixed-radius SPH benchmarks and show in experiments with a support radius ratio up to 3 that our method is also effective in multi-resolution SPH simulations.

The involvement of inhibition in word and sentence reading.

Individual differences in reading performance between children appear from the onset of literacy acquisition. One possible explanation for this variability is the influence of inhibition in reading ability, a topic that has received very little research attention. Nevertheless, children often make guessing errors characterized by replacing a word with an orthographic neighbor, possibly linked to failing inhibition. The present study aims to evaluate the role of inhibition during word and sentence reading and compare its effects in spoken and motor tasks. Participants comprised 25 children in Grades 2 and 3 (Mage = 8; 2). The children performed five inhibition tasks in reading (words, sentences), spoken (words, sentences) and motor modalities. Within the two reading tasks, inhibition demands were assessed using pairs of orthographic neighbors for which the frequency was manipulated. Accuracy, types of errors, latency, and response times were measured. GLMM analyses demonstrated that children were sensitive to the inhibitory demands of both spoken tasks and of the sentence reading task regarding accuracy, latency, and response times. Indeed, children made more mistakes and were slower when inhibitory demands were augmented. They also made more guessing errors in the word reading task. No such inhibitory effect was found in the motor task. Moreover, correlational analyses revealed that children who showed better inhibitory skills were able to read words and texts more accurately. These findings suggest that children need to utilize inhibitory resources when processing words or sentences and that these inhibitory skills are involved in overall reading ability.