Pair Distance Research Articles

Amphiphilic Polymer Co‐Network: A Versatile Matrix for Tailoring the Photonic Energy Transfer in Wearable Energy Harvesting Devices

AbstractIn recent years, Förster resonance energy transfer (FRET) and related topics have received marked attention both as a subject of scientific investigation and due to its many potential applications. However, the state‐of‐the‐art matrix materials for the FRET need to be improved in terms of universal loading for all types of luminescent moieties and the matrix integrability with real‐life devices, but without sacrificing the FRET efficiency, i.e., maintaining the proximity of the embedded donors and acceptors. Amphiphilic polymer co‐networks (APCNs) are investigated as versatile matrix materials for hosting luminescent materials and realizing highly efficient FRET between hydrophobic inorganic donors (CsPbBr3 nanocrystals) and hydrophilic organic acceptors (Rhodamine B). APCNs are advantageous owing to the unique properties of their hydrophilic and hydrophobic biphasic nature and the uniformly distributed nano‐domains. The energy transfer rate can be tailored in a straightforward way by manipulating the nano‐domain sizes and volumetric distribution, so steering donor–acceptor pair loading and distances. Consequently, APCNs are used as luminescent solar concentrators for fiber solar cells, demonstrating the ability to enhance existing solar‐energy harvesting electronics via photonic energy transfer steering. APCN is demonstrated as a powerful matrix for future photonic applications in the field of energy harvesting and energy generation.

A Boosting-Based Deep Distance Metric Learning Method.

By leveraging neural networks, deep distance metric learning has yielded impressive results in computer vision applications. However, the existing approaches mostly focus a single deep distance metric based on pairs or triplets of samples. It is difficult for them to handle heterogeneous data and avoid overfitting. This study proposes a boosting-based learning method of multiple deep distance metrics, which generates the final distance metric through iterative training of multiple weak distance metrics. Firstly, the distance of sample pairs was mapped by a convolution neural network (CNN) and evaluated by a piecewise linear function. Secondly, the evaluation function was added as a weak learner to the boosting algorithm to generate a strong learner. Each weak learner targets the difficult samples different from the samples of previous learners. Next, an alternating optimization method was employed to train the network and loss function. Finally, the effectiveness of our method was demonstrated in contrast to state of the arts on retrieving the images from the CUB-200-2011, Cars-196, and Stanford Online Products (SOP) datasets.

Synergistic Role of Model‐Independent and Model‐Dependent Approaches for Determining Size and Shape of Au Nanostructures Using SAXS

AbstractThe main objective of this study is to showcase the use of synergistic utilization of model‐independent and model‐dependent approaches for analyzing small angle X‐ray scattering (SAXS) data to determine the shape and size of different nanostructures viz. spherical, nanorod, and core‐shell. Herein, different gold nanostructures viz. spherical, nanorod, and core‐shell are synthesized and analyzed using SAXS. First, the SAXS data are analyzed using Guinier law, PDDF (pair–distance distribution function) plots, double logarithmic plots, and electron density curves to get primary information about the size and shape of nanostructures. This information is utilized for choosing an appropriate model and initial input parameters for fitting the SAXS data. The observations obtained from SAXS studies are compared with transmission electron microscopy and UV–vis spectroscopy. It is believed that this study will help in understanding the analysis of SAXS data using a systematic and synergistic approach of utilizing the two methods. It is believed that the study will further add to the understanding of the technique as an alternative approach for transmission electron microscopy.

Genetic structure analysis in several populations of cattle using SNP genotypes

Parameters such as effective population size (Ne), runs of homozygosity (ROH), and inbreeding based on ROH (FROH) can give new insight into the level of genetic diversity for the population under selection. This research aimed to measure the extent of linkage disequilibrium (LD), effective population size (Ne), Haplotype Block Structure, and runs of homozygosity (ROHs) in several populations of cattle using SNP genotypes. In this study, that the average r 2 decreased with the increasing distance of SNP pairs. A general decrease in Ne can be seen for all four populations, indicating a loss of genetic diversity. The Iranian Holstein had the lowest level of genomic inbreeding at an ROH of 1, 5, 10 Mb, while the French Holstein had the highest. The maximum number of ROH is seen at a distance of less than 1 Mb, and the lowest number of ROH is seen at a distance of 10 Mb. The number of ROH decreases with increasing distance due to the increased recombination rate. This is a concern as an increase in inbreeding leads to a reduction in the effective population size, which was also evident in the study populations.

Atomic structure evolution related to the Invar effect in Fe-based bulk metallic glasses

The Invar effect is universally observed in Fe-based bulk metallic glasses. However, there is limited understanding on how this effect manifests at the atomic scale. Here, we use in-situ synchrotron-based high-energy X-ray diffraction to study the structural transformations of (Fe71.2B24Y4.8)96Nb4 and (Fe73.2B22Y4.8)95Mo5 bulk metallic glasses around the Curie temperature to understand the Invar effect they exhibit. The first two diffraction peaks shift in accordance with the macroscopically measured thermal expansion, which reveals the Invar effect. Additionally, the nearest-neighbor Fe–Fe pair distance correlates well with the macroscopic thermal expansion. In-situ X-ray diffraction is thus able to elucidate the Invar effect in Fe-based metallic glasses at the atomic scale. Here, we find that the Invar effect is not just a macroscopic effect but has a clear atomistic equivalent in the average Fe–Fe pair distance and also shows itself in higher-order atomic shells composed of multiple atom species.

NaMn0.2Fe0.2Co0.2Ni0.2Ti0.2O2 high-entropy layered oxide – experimental and theoretical evidence of high electrochemical performance in sodium batteries

Li-ion batteries, widely used in portable electronics, electric vehicles, and energy storage systems, are an integral element of our daily life. However, the limitation of lithium sources, which leads to high prices, prompts the search for alternatives. Recently there has been noticed a rapid interest in Na-ion batteries technology. Especially, suitable cathode structures are investigated to accumulate larger sodium ions. In this paper, the high entropy layered oxide NaMn0.2Fe0.2Co0.2Ni0.2Ti0.2O2 is presented which achieves superior electrochemical properties with a stable capacity of ca. 180 mAh g−1. The understanding of its high performance is based on a complex study of the multiphase intercalation mechanism. The combination of advanced structural analysis by XAS, in situ XRD, TEM, and computational DFT modelling gives a new concept on the nature of O3-P3 structure reorganization. The presented experimental and theoretical evidence indicates that the P3 phase of layered oxides is energetically favourable for a lower sodium content for specific transition metal-oxide pair distance. Fundamental understanding of the nature of phase transformation is crucial for tailoring structural composition, where the desirable O3-P3 reorganization will occur, resulting in achieving high-performance cathodes.

Synchrotron x-ray total scattering and modeling study of high-pressure-induced inhomogeneous atom reconfiguration in an equiatomic Zr50Cu50 metallic glassy alloy

We studied in situ the local atomic structure evolution of an equiatomic ${\mathrm{Zr}}_{50}{\mathrm{Cu}}_{50}$ metallic glassy alloy under high pressure compression inside a diamond anvil cell using synchrotron x-ray total scattering. The empirical potential structure refinement method was used to reconstruct the three-dimensional atomic models at each pressure step, and to analyze the spatially averaged local atomic structure configurations. The interatomic distances of different atomic pairs are reduced at different rates with increasing pressure and the Cu-Cu pairs exhibit the highest percentage reduction. Between ambient pressure and 36.85 GPa, the atomic separation of the Cu-Cu pairs is reduced by \ensuremath{\sim}12% compared to \ensuremath{\sim}5% for Zr-Zr and Zr-Cu pairs. Such disproportional decrease in interatomic distance results in inhomogeneous atom reconfiguration in the short atomic range. With the increase of pressure, the Zr atoms move preferentially towards the Zr-Zr pairs, while the Cu atoms move preferentially towards the Cu-Cu pairs, creating inhomogeneous atom reconfiguration with positive short-range order coefficients of 0.0309 and 0.0464 for Zr-Zr and Cu-Cu respectively, but a negative value of \ensuremath{-}0.0464 for Zr-Cu pairs. Voronoi tessellation method was also used to study the evolution of the short-range atom packing versus pressure, elucidating the cause for the bimodal distribution of the bond angle distributions. The research sheds light on understanding of the atomic reconfiguration of equiatomic alloys under high pressure.

Refining pseudo labels for unsupervised Domain Adaptive Re-Identification

Our paper focuses on the topic of unsupervised domain adaptation for person re-identification (UDA re-ID) in an end-to-end framework. Currently, most existing methods tackle the problem by mining pseudo labels, and they rely heavily on the reliability of these pseudo labels. However, the generation of these labels mainly depends on clustering-based algorithms, which may inevitably introduce noise into the framework. Such noise substantially hinders the capability of the model to further improve feature representations in the target domain. We called these clustering-based labels HARD pseudo labels. To decrease the impacts of these noisy hard labels, in this paper, we directly consider the relative relationships of sample pair distances as another kind of pseudo label instead of clustering results, called SOFT pseudo labels. Furthermore, considering that the constructed relative relationships are not always correct and the wrong relationships may cause negative effects, it is not appropriate to assign the same weight to each sample pair. To alleviate the negative influence caused by tiny distances and strengthen the positive effect due to large distances, we assign different weights according to the different distances to adjust these relationships. By integrating the HARD labels and SOFT labels, the proposed method achieves considerable improvements on several popular person re-ID datasets, e.g, Market1501-to-Duke, Duke-to-Market1501, Market-to-MSMT17 and Duke-to-MSMT17 UDA tasks.

Top k Optimal Sequenced Route Query with POI Preferences

The optimal sequenced route (OSR) query, as a popular problem in route planning for smart cities, searches for a minimum-distance route passing through several POIs in a specific order from a starting position. In reality, POIs are usually rated, which helps users in making decisions. Existing OSR queries neglect the fact that the POIs in the same category could have different scores, which may affect users’ route choices. In this paper, we study a novel variant of OSR query, namely Rating Constrained Optimal Sequenced Route query (RCOSR), in which the rating score of each POI in the optimal sequenced route should exceed the query threshold. To efficiently process RCOSR queries, we first extend the existing TD-OSR algorithm to propose a baseline method, called MTDOSR. To tackle the shortcomings of MTDOSR, we try to design a new RCOSR algorithm, namely Optimal Subroute Expansion (OSE) Algorithm. To enhance the OSE algorithm, we propose a Reference Node Inverted Index (RNII) to accelerate the distance computation of POI pairs in OSE and quickly retrieve the POIs of each category. To make full use of the OSE and RNII, we further propose a new efficient RCOSR algorithm, called Recurrent Optimal Subroute Expansion (ROSE), which recurrently utilizes OSE to compute the current optimal route as the guiding path and update the distance of POI pairs to guide the expansion. Then, we extend our techniques to handle a variation of RCOSR query, namely RCkOSR query. The experimental results demonstrate that the proposed algorithm significantly outperforms the existing approaches.

Application of hyperbolic geometry of multiplex networks under layer link-based attacks.

At present, network science can be considered one of the prosperous scientific fields. The multi-layered network approach is a recent development in this area and focuses on identifying the interactions of several interconnected networks. In this paper, we propose a new method for predicting redundant links for multiplex networks using the similarity criterion based on the hyperbolic distance of the node pairs. We retrieve lost links found on various attack strategies in multiplex networks by predicting redundant links in these networks using the proffered method. We applied the recommended algorithm to real-world multiplex networks, and the numerical simulations show its superiority over other advanced algorithms. During the studies and numerical simulations, the power of the hyperbolic geometry criterion over different standard and current methods based on link prediction used for network retrieval is evident, especially in the case of attacks based on the edge betweenness and random strategies illustrated in the results.

Similar Pair-free Partial Label Metric Learning

Partial label learning is a new weak- ly supervised learning framework. In this frame- work, the real category label of a training sample is usually concealed in a set of candidate labels, which will lead to lower accuracy of learning al- gorithms compared with traditional strong super- vised cases. Recently, it has been found that met- ric learning technology can be used to improve the accuracy of partial label learning algorithm- s. However, because it is difficult to ascertain similar pairs from training samples, at present there are few metric learning algorithms for par- tial label learning framework. In view of this, this paper proposes a similar pair-free partial la- bel metric learning algorithm. The main idea of the algorithm is to define two probability distri- butions on the training samples, i.e., the proba- bility distribution determined by the distance of sample pairs and the probability distribution de- termined by the similarity of candidate label set of sample pairs, and then the metric matrix is ob- tained via minimizing the KL divergence of the two probability distributions. The experimental results on several real-world partial label dataset- s show that the proposed algorithm can improve the accuracy of k-nearest neighbor partial label learning algorithm (PL-KNN) better than the ex- isting partial label metric learning algorithms, up to 8 percentage points.

Boundary-Aware Hashing for Hamming Space Retrieval

Hamming space retrieval is a hot area of research in deep hashing because it is effective for large-scale image retrieval. Existing hashing algorithms have not fully used the absolute boundary to discriminate the data inside and outside the Hamming ball, and the performance is not satisfying. In this paper, a boundary-aware contrastive loss is designed. It involves an exponential function with absolute boundary (i.e., Hamming radius) information for dissimilar pairs and a logarithmic function to encourage small distance for similar pairs. It achieves a push that is bigger than the pull inside the Hamming ball, and the pull is bigger than the push outside the ball. Furthermore, a novel Boundary-Aware Hashing (BAH) architecture is proposed. It discriminatively penalizes the dissimilar data inside and outside the Hamming ball. BAH enables the influence of extremely imbalanced data to be reduced without up-weight to similar pairs or other optimization strategies because its exponential function rapidly converges outside the absolute boundary, making a huge contrast difference between the gradients of the logarithmic and exponential functions. Extensive experiments conducted on four benchmark datasets show that the proposed BAH obtains higher performance for different code lengths, and it has the advantage of handling extremely imbalanced data.

Fractal Features of the Catalytic Performances of Bimodal Mesoporous Silica‐Supported Organocatalysts Derived from Bipyridine‐Proline for Asymmetric Aldol Reaction

AbstractThe three bipyridine‐proline chiral ligands (Z) with different structural units as highly active species were successfully introduced on Zn‐modified bimodal mesoporous silicas (BMMs). The representative heterogeneous catalysts (ZZnBMMs‐n) containing different molar ratios of Z/Zn (5, 20, 50, 100, 200%) were prepared, and their catalytic performances were evaluated in the asymmetric aldol reactions between p‐nitrobenzaldehyde and cyclohexanone, showing the satisfactory activities (yields) and good stereoselectivity (dr and ee). Meanwhile, their microstructural features and physicochemical properties were characterized by various methods. Particularly, the fractal evolution, the pair distance distribution function, and the Porod plots of the ZZnBMMs catalysts were demonstrated by SAXS analysis. The results revealed that the interfacial layer thickness and their mass fractal (Dm) value gradually increased with the enhanced molar ratios of Z/Zn, along with the decrease of pore size and densification of volume, suggesting the successful Z‐grafting into the mesoporous channels of Zn‐modified matrixes. The essential relationships between the fractal features and their catalytic performance (yield, dr and ee) were explored, showing the larger the Dm values, the better catalytic performance. Besides, the uniform distributions of Zn and S elements in ZZnBMMs catalysts and the formation of enamine‐imine intermediates during the asymmetric aldol reactions were confirmed.

The repetitive local sampling and the local distribution theory

AbstractMolecular simulation is a mature and versatile tool set widely utilized in many subjects. However, its methodology development has been struggling with a tradeoff between accuracy and speed, significant improvement of both is necessary to reliably substitute many expensive and laborious experiments in molecular biology and nanotechnology. Previously, the ubiquitous severe wasting of computational resources in molecular simulations due to repetitive local sampling was raised, and the local free energy landscape approach was proposed to address it. The core idea is to first learn local distributions, and followed by dynamic assembly of which to infer global joint distribution of a target molecular system. When compared with conventional explicit solvent molecular dynamics simulations, a simple and approximate implementation of this theory in protein structural refinement harvested acceleration of about six orders of magnitude without loss of accuracy. While this initial test revealed tremendous benefits for addressing repetitive local sampling, there are some implicit assumptions need to be articulated. Here, I present a more thorough discussion of repetitive local sampling; potential options for learning local distributions; a more general formulation with potential extension to simulation of near equilibrium molecular systems; the prospect of developing computation driven molecular science; the connection to mainstream residue pair distance distribution based protein structure studies; and the fundamental difference of the averaging strategy from potential of mean force. This more general development is termed the local distribution theory to release the limitation of strict thermodynamic equilibrium in its potential wide application in general soft condensed molecular systems.This article is categorized under: Molecular and Statistical Mechanics > Free Energy Methods Data Science > Artificial Intelligence/Machine Learning

Predicting the matching probability and the expected ride/shared distance for each dynamic ridepooling order: A mathematical modeling approach

The popularity of smartphones and the advent of GPS positioning and wireless communication technologies in the recent decade have facilitated large-scale implementations of dynamic ridepooling services, such as Uber Pool, Lyft Line, and Didi Pinche. As in such services trips usually start before the appearance of pooling partners, knowing the probability of getting matching with another order (i.e., matching probability), the expected detour distance, and the expected shared distance before the start of each trip is essential for passengers to evaluate their willingness to pool and for ridepooling platforms to offer attractive discounts. In this paper, assuming that every ridepooling passenger shares vehicle space with at most one another during the entire trip, and ridepooling orders in each (origin-destination) OD pair appear following a Poisson process with a given rate, we propose a mathematical modeling approach to predict the matching probability, the expected ride distance, and the expected shared distance of each order under a first-come-first-serve strategy in dynamic ridepooling service. The method defines unmatched passengers at different locations along the exclusive-riding path of each OD pair into different seeker- and taker-states, formulates the complex interdependency of the matching probabilities, matching rates and arrival rates of (unmatched) passengers in different states into a system of nonlinear equations, and generates the matching probabilities and expected ride/shared distances of all OD pairs simultaneously. Under the same first-come-first-serve strategy, we simulated the occurrence, movements and state transitions of ridepooling orders based on a 30*30 grid network and the real network of Haikou City in China. In comparison with simulation results, we show that the method proposed in this paper can generate fairly satisfactory predictions under diverse matching conditions and demand intensities.

Deep Spatial-Spectral Subspace Clustering for Hyperspectral Images Based on Contrastive Learning

Hyperspectral image (HSI) clustering is a major challenge due to the redundant spectral information in HSIs. In this paper, we propose a novel deep subspace clustering method that extracts spatial–spectral features via contrastive learning. First, we construct positive and negative sample pairs through data augmentation. Then, the data pairs are projected into feature space using a CNN model. Contrastive learning is conducted by minimizing the distances of positive pairs and maximizing those of negative pairs. Finally, based on their features, spectral clustering is employed to obtain the final result. Experimental results gained over three HSI datasets demonstrate that our proposed method is superior to other state-of-the-art methods.

Small-Angle Scattering from Fractional Brownian Surfaces

Recent developments in nanotechnology have allowed the fabrication of a new generation of advanced materials with various fractal-like geometries. Fractional Brownian surfaces (fBs) are often used as models to simulate and characterize these complex geometries, such as the surface of particles in dilute particulate systems (e.g., colloids) or the interfaces in non-particulate two-phase systems (e.g., semicrystalline polymers with crystalline and amorphous phases). However, for such systems, a realistic simulation involves parameters averaged over a macroscopic volume. Here, a method based on small-angle scattering technique is proposed to extract the main structural parameters of surfaces/interfaces from experimental data. It involves the analysis of scattering intensities and the corresponding pair distance distribution functions. This allows the extraction of information with respect to the overall size, fractal dimension, Hurst and spectral exponents. The method is applied to several classes of fBs, and it is shown that the obtained numerical values of the structural parameters are in very good agreement with theoretical ones.

MD-SPKM: A set pair k-modes clustering algorithm for incomplete categorical matrix data

In order to solve the clustering problem with incomplete and categorical matrix data sets, and considering the uncertain relationship between samples and clusters, a set pair k-modes clustering algorithm is proposed (MD-SPKM). Firstly, the correlation theory of set pair information granule is introduced into k-modes clustering. By improving the distance formula of traditional k-modes algorithm, a set pair distance measurement method between incomplete matrix samples is defined. Secondly, considering the uncertain relationship between the sample and the cluster, the definition of the intra-cluster average distance and the threshold calculation formula to determine whether the sample belongs to multiple clusters is given, and then the result of set pair clustering is formed, which includes positive region, boundary region and negative region. Finally, through the selected three data sets and four contrast algorithms for experimental evaluation, the experimental results show that the set pair k-modes clustering algorithm can effectively handle incomplete categorical matrix data sets, and has good clustering performance in Accuracy, Recall, ARI and NMI.

Real-Time Motion Tracking and Correction During Tomotherapy for Prostate Cancer: Impact of Fiducial Migration

A real-time motion tracking technique has been recently implemented in our clinic to manage intrafraction prostate motion (IPM) during helical tomotherapy for prostate cancer. It involves detecting motion of multiple fiducials implanted in prostate based on real-time x-ray radiographs acquired at various beam angles and correcting the motion using dynamic jaw and MLC. The purpose of this study was to study interfractional fiducial migration (IFM) and its impact on intrafraction motion tracking.Daily MVCT and radiographs acquired during tomotherapy with real-time motion tracking for 5 prostate cancer patients were analyzed to quantify IFM and IPM. All patients were treated with either 70 Gy in 28 or 75.6 Gy in 42 fractions based on plans generated on planning CTs acquired 14-70 days after the implantation of 4 fiducials for each patient. Time intervals between the first treatment and the planning CT acquisition ranged 4-16 days. The IPM was measured based on the maximum target position difference between the radiographs. Daily migration of each fiducial was obtained by (1) rigidly registering the daily MVCT with the planning CT based on prostate matching, and (2) calculating coordinate differences between the fiducial positions on the daily and plan images. To investigate the impact of fiducial migration on motion tracking that uses fiducial positions on the planning CT as baseline, we compared the maximum fiducial pair distance change (RigidBody, a tracking parameter) calculated from the planning CT to radiographs (default) to the value calculated from daily MVCT to radiographs. The daily target offsets (another tracking parameter) determined between the planning CT and radiographs were assessed if the fiducial migrations were removed.The average daily IPM was 3.2 ± 2.9 (up to 5.8 ± 4.0 for a patient) mm. The average IFM was 2.0 ± 1.1 mm (ranging 1.5-2.5 mm among the patients) with median and interquartile range (IQR) values of 1.8 and 1.2 mm, respectively. The fiducials migrated differently in the longitudinal (std/IQR = 1.8/2.2 mm), vertical (1.2/1.6 mm), and lateral (0.7/0.8 mm) directions. The daily fiducial COM deviated from its plan position by 1.6 ± 0.8 mm (ranging 1.3-2.0 mm among the patients) with median/IQR = 1.5/1.0 mm. The overall average of RigidBody reduced from 2.6 ± 0.7 mm (default) to 1.3 ± 0.4 mm if from daily MVCT. The average daily target offsets decreased from 3.2 ± 1.5 to 2.2 ± 1.0 mm if the fiducial migrations were removed.The fiducial-based real-time prostate motion tracking during tomotherapy is advantageous, even in the presence of the measurable IFMs. Our results indicate that, to reduce/eliminate the effect of the fiducial migration on motion tracking stability and accuracy, the fiducial positions on the daily MVCT, instead of the planning CT in the current setting, should be used as the baseline.G. Chen: North Central Chapter of AAPM, Medical College of Wisconsin. A. Tai: None. E.A. Omari: None. M. Bedi: None. C.A. Lawton: None. A. Li: Research Grant; Elekta AB, Accuray Inc, Siemens Healthineers, Manteia Med. Honoraria; Elekta AB, Accuray Inc. Patent/License Fees/Copyright; Manteia Med.

Synchronous inhibitory synaptic inputs to layer II/III pyramidal neurons in the murine barrel cortex

The barrel cortex exhibits obvious columnar organization. Although GABAergic inhibition plays a critical role in regulating neural excitation in response to mechanical stimuli applied to whiskers, the profiles of synchronous events for inhibitory synaptic transmission in intracolumnar and transcolumnar pyramidal neurons remain unknown. To explore a functional mechanism of synchronous inhibition of pyramidal neurons, we performed paired whole-cell patch-clamp recordings and recorded spontaneous inhibitory postsynaptic currents (sIPSCs) from layer II/III pyramidal neurons. A cross-correlogram of sIPSCs (1 ms bin) was used to detect synchronous sIPSCs. Synchronous neuron pairs were defined as those whose peak number of sIPSCs between −3 and 3 ms exceeded the mean + 2 SD of the number of sIPSCs in the period of −50 to 50 ms minus the number in that of −3 to 3 ms period. In the recording of pyramidal neurons located in the same column (intracolumn), 61.5% of neuron pairs were classified as synchronous neuron pairs, while 52.6% of pyramidal neuron pairs in adjacent columns (transcolumn) were defined as synchronous neuron pairs. The amplitude of synchronous sIPSCs was comparable to that of asynchronous sIPSCs in asynchronous neuron pairs, whereas that of synchronous sIPSCs was larger than that of asynchronous sIPSCs in synchronous neuron pairs. Synchronicity of sIPSCs did not depend on the distance of neuron pairs. These results suggest that layer II/III pyramidal neurons receive synchronous inhibitory synaptic inputs generated by a certain type of GABAergic interneuron that induces large IPSCs in pyramidal neurons, likely to be fast-spiking cells.