Guangxi Normal University

nearest neighbor (kNN) method is a popular classification method in data mining and statistics because of its simple implementation and significant classification performance. However, it is impractical for traditional kNN methods to assign a fixed value (even though set by experts) to all test samples. Previous solutions assign different values to different test samples by the cross validation method but are usually time-consuming. This paper proposes a kTree method to learn different optimal values for different test/new samples, by involving a training stage in the kNN classification. Specifically, in the training stage, kTree method first learns optimal values for all training samples by a new sparse reconstruction model, and then constructs a decision tree (namely, kTree) using training samples and the learned optimal values. In the test stage, the kTree fast outputs the optimal value for each test sample, and then, the kNN classification can be conducted using the learned optimal value and all training samples. As a result, the proposed kTree method has a similar running cost but higher classification accuracy, compared with traditional kNN methods, which assign a fixed value to all test samples. Moreover, the proposed kTree method needs less running cost but achieves similar classification accuracy, compared with the newly kNN methods, which assign different values to different test samples. This paper further proposes an improvement version of kTree method (namely, k*Tree method) to speed its test stage by extra storing the information of the training samples in the leaf nodes of kTree, such as the training samples located in the leaf nodes, their kNNs, and the nearest neighbor of these kNNs. We call the resulting decision tree as k*Tree, which enables to conduct kNN classification using a subset of the training samples in the leaf nodes rather than all training samples used in the newly kNN methods. This actually reduces running cost of test stage. Finally, the experimental results on 20 real data sets showed that our proposed methods (i.e., kTree and k*Tree) are much more efficient than the compared methods in terms of classification tasks.

We study the process ${e}^{+}{e}^{\ensuremath{-}}\ensuremath{\rightarrow}{\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}J/\ensuremath{\psi}$ at a center-of-mass energy of 4.260 GeV using a $525\text{ }\text{ }{\mathrm{pb}}^{\ensuremath{-}1}$ data sample collected with the BESIII detector operating at the Beijing Electron Positron Collider. The Born cross section is measured to be $(62.9\ifmmode\pm\else\textpm\fi{}1.9\ifmmode\pm\else\textpm\fi{}3.7)\text{ }\text{ }\mathrm{pb}$, consistent with the production of the $Y(4260)$. We observe a structure at around $3.9\text{ }\text{ }\mathrm{GeV}/{c}^{2}$ in the ${\ensuremath{\pi}}^{\ifmmode\pm\else\textpm\fi{}}J/\ensuremath{\psi}$ mass spectrum, which we refer to as the ${Z}_{c}(3900)$. If interpreted as a new particle, it is unusual in that it carries an electric charge and couples to charmonium. A fit to the ${\ensuremath{\pi}}^{\ifmmode\pm\else\textpm\fi{}}J/\ensuremath{\psi}$ invariant mass spectrum, neglecting interference, results in a mass of $(3899.0\ifmmode\pm\else\textpm\fi{}3.6\ifmmode\pm\else\textpm\fi{}4.9)\text{ }\text{ }\mathrm{MeV}/{c}^{2}$ and a width of $(46\ifmmode\pm\else\textpm\fi{}10\ifmmode\pm\else\textpm\fi{}20)\text{ }\text{ }\mathrm{MeV}$. Its production ratio is measured to be $R=(\ensuremath{\sigma}\mathbf{(}{e}^{+}{e}^{\ensuremath{-}}\ensuremath{\rightarrow}{\ensuremath{\pi}}^{\ifmmode\pm\else\textpm\fi{}}{Z}_{c}(3900{)}^{\ensuremath{\mp}}\ensuremath{\rightarrow}{\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}J/\ensuremath{\psi}\mathbf{)}/\ensuremath{\sigma}({e}^{+}{e}^{\ensuremath{-}}\ensuremath{\rightarrow}{\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}J/\ensuremath{\psi}))=(21.5\ifmmode\pm\else\textpm\fi{}3.3\ifmmode\pm\else\textpm\fi{}7.5)%$. In all measurements the first errors are statistical and the second are systematic.

By taking into account the surface diffuseness correction for unstable nuclei, the accuracy of the macroscopic–microscopic mass formula is further improved. The rms deviation with respect to essentially all the available mass data falls to 298 keV, crossing the 0.3 MeV accuracy threshold for the first time within the mean-field framework. Considering the surface effect of the symmetry potential which plays an important role in the evolution of the “neutron skin” toward the “neutron halo” of nuclei approaching the neutron drip line, we obtain an optimal value of the symmetry energy coefficient J=30.16 MeV. With an accuracy of 258 keV for all the available neutron separation energies and of 237 keV for the α-decay Q-values of super-heavy nuclei, the proposed mass formula is particularly important not only for the reliable description of the r process of nucleosynthesis but also for the study of the synthesis of super-heavy nuclei.

The K Nearest Neighbor (kNN) method has widely been used in the applications of data mining and machine learning due to its simple implementation and distinguished performance. However, setting all test data with the same k value in the previous kNN methods has been proven to make these methods impractical in real applications. This article proposes to learn a correlation matrix to reconstruct test data points by training data to assign different k values to different test data points, referred to as the Correlation Matrix kNN (CM-kNN for short) classification. Specifically, the least-squares loss function is employed to minimize the reconstruction error to reconstruct each test data point by all training data points. Then, a graph Laplacian regularizer is advocated to preserve the local structure of the data in the reconstruction process. Moreover, an ℓ 1 -norm regularizer and an ℓ 2, 1 -norm regularizer are applied to learn different k values for different test data and to result in low sparsity to remove the redundant/noisy feature from the reconstruction process, respectively. Besides for classification tasks, the kNN methods (including our proposed CM-kNN method) are further utilized to regression and missing data imputation. We conducted sets of experiments for illustrating the efficiency, and experimental results showed that the proposed method was more accurate and efficient than existing kNN methods in data-mining applications, such as classification, regression, and missing data imputation.

We study ${e}^{+}{e}^{\ensuremath{-}}\ensuremath{\rightarrow}{\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}{h}_{c}$ at center-of-mass energies from 3.90 to 4.42 GeV by using data samples collected with the BESIII detector operating at the Beijing Electron Positron Collider. The Born cross sections are measured at 13 energies and are found to be of the same order of magnitude as those of ${e}^{+}{e}^{\ensuremath{-}}\ensuremath{\rightarrow}{\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}J/\ensuremath{\psi}$ but with a different line shape. In the ${\ensuremath{\pi}}^{\ifmmode\pm\else\textpm\fi{}}{h}_{c}$ mass spectrum, a distinct structure, referred to as ${Z}_{c}(4020)$, is observed at $4.02\text{ }\text{ }\mathrm{GeV}/{c}^{2}$. The ${Z}_{c}(4020)$ carries an electric charge and couples to charmonium. A fit to the ${\ensuremath{\pi}}^{\ifmmode\pm\else\textpm\fi{}}{h}_{c}$ invariant mass spectrum, neglecting possible interferences, results in a mass of $(4022.9\ifmmode\pm\else\textpm\fi{}0.8\ifmmode\pm\else\textpm\fi{}2.7)\text{ }\text{ }\mathrm{MeV}/{c}^{2}$ and a width of $(7.9\ifmmode\pm\else\textpm\fi{}2.7\ifmmode\pm\else\textpm\fi{}2.6)\text{ }\text{ }\mathrm{MeV}$ for the ${Z}_{c}(4020)$, where the first errors are statistical and the second systematic. The difference between the parameters of this structure and the ${Z}_{c}(4025)$ observed in the ${D}^{*}{\overline{D}}^{*}$ final state is within $1.5\ensuremath{\sigma}$, but whether they are the same state needs further investigation. No significant ${Z}_{c}(3900)$ signal is observed, and upper limits on the ${Z}_{c}(3900)$ production cross sections in ${\ensuremath{\pi}}^{\ifmmode\pm\else\textpm\fi{}}{h}_{c}$ at center-of-mass energies of 4.23 and 4.26 GeV are set.

{[Cu6(pybz)8(OH)2]·I5(–)·I7(–)}n (1), obtained hydrothermally by using iodine molecules as a versatile precursor template, consists of a cationic framework with two types of zigzag channels, which segregate I5(–) and I7(–) anions. The framework exhibits the first observed bipillared-bilayer structure featuring both interdigitation and interpenetration. 1 displays high framework stability in both acidic (HCl) and alkaline (NaOH) solutions. 1 slowly releases iodine in dry methanol to give [Cu6(pybz)8(OH)2](I–)2·3.5CH3OH (1′) and partially recovers iodine from cyclohexane to form [Cu6(pybz)8(OH)2](I–)2·xI2 (1″). Differences of up to 100 times in electrical conductivity and of 4 times in nonlinear optical activity (NLO) have been measured between 1 and 1′. This compound is one of few displaying multifunctionality, electrical conductivity, NLO, and crystal–crystal stability upon release and recovery of iodine. It is also unique in the iodine release from polyiodide anions in a metal–organic framework.

We present the syntheses, structural characterization, gas sorption, I2 uptake, and magnetic properties of a double-walled porous metal-organic framework, [Co(II)3(lac)2(pybz)2]·3DMF (1·3DMF, purple, where pybz = 4-pyridyl benzoate, lac = d- and l-lactate) and of its post-synthetic modified (PSM) congeners, [Co(II)3(lac)2(pybz)2]·xGuest (xGuest = 6MeOH, purple; 4.5EtOH, purple; 3PrOH, purple; 2C6H6, purple; 2.7I2, black), [Co(II)3(lac)2(pybz)2] (1, purple), [Co(II)3(pybz)2(lac)2(H2O)2]·7H2O (1a·7H2O, green), and [Co(III)Co(II)2(pybz)2(lac)2(H2O)2]I·2H2O·1.5DMSO (1b·I(-)·2H2O·1.5DMSO, yellow, DMSO = dimethyl sulfoxide). Crystallography shows that the framework is not altered by the replacement of DMF by different solvents or by the removal of the solvent molecules during the single-crystal to single-crystal (SC-SC) transformations, while upon exchange with H2O or partial oxidation by molecular iodine, the crystallinity is affected. 1 absorbs N2, H2, CH4, CH3OH, C2H5OH, PrOH, C6H6, and I2, but once it is in contact with H2O the absorption efficiency is drastically reduced. Upon PSM, the magnetism is transformed from a canted antiferromagnet (1·3DMF and 1·xGuest) to single-chain magnet (1), to a ferrimagnet (1a·7H2O), and to a ferromagnet (1b·I(-)·2H2O·1.5DMSO). Raman spectroscopy suggests the color change (purple to green 1a·7H2O or yellow 1b·I(-)·2H2O·1.5DMSO) is associated with a change of geometry from a strained octahedron due to the very acute chelating angle (∼60°) of the lactate of a cobalt center to a regular octahedron with a monodentate carboxylate and one H2O. The magnetic transformation is explained by the different interchain exchanges (J'), antiferromagnetic for 1·3DMF and 1·xSolvent (J' < 0), SCM for 1 (J' verge to 0), and ferromagnetic for 1a·7H2O (J' > 0), between homometal topological ferrimagnetic chains (two octahedral and one tetrahedral Co(II) ions) connected by the double walls of pybz at 13.3 Å (shortest Co···Co). For 1b·I(-)·2H2O·1.5DMSO the moment of the tetrahedral site is turned off, thus stabilizing a ferromagnetic state (J' > 0). The present stabilization of four magnetic ground states is unique in the field of metal-organic frameworks as well as the electrical conductivity of 1·2.7I2.

The widespread application of sewage sludge produced from wastewater treatment plants for agricultural use has been regarded as a primary source of microplastics (MPs) into soils. However, little is known regarding MPs in sludge-based fertilizers and their relevant fate in soils as being applied in agriculture. We comprehensively investigated the abundance, polymer size, type, and morphology of MPs in dewatered sludge, sewage sludge composts, sludge-based fertilizer-amended soils, and earthworms by stereoscopy and micro Fourier transform infrared (μ-FTIR) spectrometry methods. The results clearly showed that the quantity of MPs in soils exhibited a close correlation with the application rate of sludge-based fertilizers. The total abundances of MPs were 545.9 and 87.6 items/kg in soils after annual amendment with 30 (field A) and 15 t/ha (field B) of sludge composts, which is significantly higher than that without compost application (field C, 5.0 items/kg). Correspondingly, MPs were found in earthworms with low quantities of 1.8 and 0.4 items/individual in fields A and B, respectively, while no MP was detected in field C. We speculate that sludge composts may act as a vehicle of MPs into soils and then enter soil biota and in turn influence the spread of MPs in the environment.

Pyridine-based ring systems are one of the most extensively used heterocycles in the field of drug design, primarily due to their profound effect on pharmacological activity, which has led to the discovery of numerous broad-spectrum therapeutic agents. In the US FDA database, there are 95 approved pharmaceuticals that stem from pyridine or dihydropyridine, including isoniazid and ethionamide (tuberculosis), delavirdine (HIV/AIDS), abiraterone acetate (prostate cancer), tacrine (Alzheimer's), ciclopirox (ringworm and athlete's foot), crizotinib (cancer), nifedipine (Raynaud's syndrome and premature birth), piroxicam (NSAID for arthritis), nilvadipine (hypertension), roflumilast (COPD), pyridostigmine (myasthenia gravis), and many more. Their remarkable therapeutic applications have encouraged researchers to prepare a larger number of biologically active compounds decorated with pyridine or dihydropyridine, expandeing the scope of finding a cure for other ailments. It is thus anticipated that myriad new pharmaceuticals containing the two heterocycles will be available in the forthcoming decade. This review examines the prospects of highly potent bioactive molecules to emphasize the advantages of using pyridine and dihydropyridine in drug design. We cover the most recent developments from 2010 to date, highlighting the ever-expanding role of both scaffolds in the field of medicinal chemistry and drug development.

Human serum albumin (HSA) is an abundant plasma protein, which attracts great interest in the pharmaceutical industry since it can bind a remarkable variety of drugs impacting their delivery and efficacy and ultimately altering the drug's pharmacokinetic and pharmacodynamic properties. Additionally, HSA is widely used in clinical settings as a drug delivery system due to its potential for improving targeting while decreasing the side effects of drugs. It is thus of great importance from the viewpoint of pharmaceutical sciences to clarify the structure, function, and properties of HSA-drug complexes. This review will succinctly outline the properties of binding site of drugs in IIA subdomain within the structure of HSA. We will also give an overview on the binding characterization of interactive association of drugs to human serum albumin that may potentially lead to significant clinical applications.

Chemical doping with heteroatoms can effectively modulate physicochemical and photochemical properties of carbon dots (CDs). However, the development of multi heteroatoms codoped carbon nanodots is still in its early stage. In this work, a facile hydrothermal synthesis strategy was applied to synthesize multi heteroatoms (nitrogen and phosphorus) codoped carbon nanodots (N,P-CDs) using glucose as carbon source, and ammonia, phosphoric acid as dopant, respectively. Compared with CDs, the multi heteroatoms doped CDs resulted in dramatic improvement in the electronic characteristics and surface chemical activities. Therefore, the N,P-CDs prepared as described above exhibited a strong blue emission and a sensitive response to Fe(3+). The N,P-CDs based fluorescent sensor was then applied to sensitively determine Fe(3+) with a detection limit of 1.8 nM. Notably, the prepared N,P-CDs possessed negligible cytotoxicity, excellent biocompatibility, and high photostability. It was also applied for label-free detection of Fe(3+) in complex biological samples and the fluorescence imaging of intracellular Fe(3+), which indicated its potential applications in clinical diagnosis and other biologically related study.

We demonstrate a facile and rapid in situ partial oxidation synthetic strategy for the fabrication of a mixed valence state Ce-MOF (MVCM) which exhibits intrinsic oxidase-like activity. Furthermore, on the basis of the excellent catalytic activity of the MCVM, a colorimetric approach for the high-throughput detection of biothiols in serum samples was established.

Abstract Hard carbon (HC) has become the most promising anode material for sodium‐ion batteries (SIBs), but its plateau capacity at ≈0.1 V (Na + /Na) is still much lower than that of graphite (372 mAh g −1 ) in lithium‐ion batteries (LIBs). Herein, a CO 2 ‐etching strategy is applied to generate abundant closed pores in starch‐derived hard carbon that effectively enhances Na + plateau storage. During CO 2 etching, open pores are first formed on the carbon matrix, which are in situ reorganized to closed pores through high‐temperature carbonization. This CO 2 ‐assisted pore‐regulation strategy increases the diameter and the capacity of closed pores in HC, and simultaneously maintains the microsphere morphology (10–30 µm in diameter). The optimal HC anode exhibits a Na‐storage capacity of 487.6 mAh g −1 with a high initial Coulomb efficiency of 90.56%. A record‐high plateau capacity of 351 mAh g −1 is achieved, owing to the abundant closed micropores generated by CO 2 ‐etching. Comprehensive in situ and ex situ tests unravel that the high Na + storage performance originates from the pore‐filling mechanism in the closed micropores.

In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (JGSC) model. In JGSC, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution. Furthermore, we extend JGSC to a robust JGSC (RJGSC) via replacing the least square loss function with a robust loss function, for achieving the same goals and also avoiding the impact of outliers. Finally, experimental results on real data sets showed that both JGSC and RJGSC outperformed the state-of-the-art algorithms in terms of k -nearest neighbor classification performance.

In image analysis, the images are often represented by multiple visual features (also known as multiview features), that aim to better interpret them for achieving remarkable performance of the learning. Since the processes of feature extraction on each view are separated, the multiple visual features of images may include overlap, noise, and redundancy. Thus, learning with all the derived views of the data could decrease the effectiveness. To address this, this paper simultaneously conducts a hierarchical feature selection and a multiview multilabel (MVML) learning for multiview image classification, via embedding a proposed a new block-row regularizer into the MVML framework. The block-row regularizer concatenating a Frobenius norm (F-norm) regularizer and an l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm regularizer is designed to conduct a hierarchical feature selection, in which the F-norm regularizer is used to conduct a high-level feature selection for selecting the informative views (i.e., discarding the uninformative views) and the 12,1-norm regularizer is then used to conduct a low-level feature selection on the informative views. The rationale of the use of a block-row regularizer is to avoid the issue of the over-fitting (via the block-row regularizer), to remove redundant views and to preserve the natural group structures of data (via the F-norm regularizer), and to remove noisy features (the 12,1-norm regularizer), respectively. We further devise a computationally efficient algorithm to optimize the derived objective function and also theoretically prove the convergence of the proposed optimization method. Finally, the results on real image datasets show that the proposed method outperforms two baseline algorithms and three state-of-the-art algorithms in terms of classification performance.

We report on a study of the process ${e}^{+}{e}^{\ensuremath{-}}\ensuremath{\rightarrow}{\ensuremath{\pi}}^{\ifmmode\pm\else\textpm\fi{}}{(D{\overline{D}}^{*})}^{\ensuremath{\mp}}$ at $\sqrt{s}=4.26\text{ }\text{ }\mathrm{GeV}$ using a $525\text{ }\mathrm{pb}{}^{\ensuremath{-}1}$ data sample collected with the BESIII detector at the BEPCII storage ring. A distinct charged structure is observed in the ${(D{\overline{D}}^{*})}^{\ensuremath{\mp}}$ invariant mass distribution. When fitted to a mass-dependent-width Breit-Wigner line shape, the pole mass and width are determined to be ${M}_{\mathrm{pole}}=\phantom{\rule{0ex}{0ex}}\mathbf{(}3883.9\ifmmode\pm\else\textpm\fi{}1.5\phantom{\rule{0.333em}{0ex}}(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}4.2\phantom{\rule{0.333em}{0ex}}(\mathrm{syst})\mathbf{)}\text{ }\text{ }\mathrm{MeV}/{c}^{2}$ and ${\mathrm{\ensuremath{\Gamma}}}_{\mathrm{pole}}=\mathbf{(}24.8\ifmmode\pm\else\textpm\fi{}3.3\phantom{\rule{0.333em}{0ex}}(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}11.0\phantom{\rule{0.333em}{0ex}}(\mathrm{syst})\mathbf{)}\text{ }\text{ }\mathrm{MeV}$. The mass and width of the structure, which we refer to as ${Z}_{c}(3885)$, are $2\ensuremath{\sigma}$ and $1\ensuremath{\sigma}$, respectively, below those of the ${Z}_{c}(3900)\ensuremath{\rightarrow}{\ensuremath{\pi}}^{\ifmmode\pm\else\textpm\fi{}}J/\ensuremath{\psi}$ peak observed by BESIII and Belle in ${\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}J/\ensuremath{\psi}$ final states produced at the same center-of-mass energy. The angular distribution of the $\ensuremath{\pi}{Z}_{c}(3885)$ system favors a ${J}^{P}={1}^{+}$ quantum number assignment for the structure and disfavors ${1}^{\ensuremath{-}}$ or ${0}^{\ensuremath{-}}$. The Born cross section times the $D{\overline{D}}^{*}$ branching fraction of the ${Z}_{c}(3885)$ is measured to be ${\ensuremath{\sigma}\mathbf{(}{e}^{+}{e}^{\ensuremath{-}}\ensuremath{\rightarrow}\phantom{\rule{0ex}{0ex}}{\ensuremath{\pi}}^{\ifmmode\pm\else\textpm\fi{}}{Z}_{c}(3885)}^{\ensuremath{\mp}}\mathbf{)}\ifmmode\times\else\texttimes\fi{}{\mathcal{B}\mathbf{(}{Z}_{c}(3885)}^{\ensuremath{\mp}}\ensuremath{\rightarrow}{(D{\overline{D}}^{*})}^{\ensuremath{\mp}}\mathbf{)}=\mathbf{(}83.5\ifmmode\pm\else\textpm\fi{}6.6\phantom{\rule{0.333em}{0ex}}(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}22.0\phantom{\rule{0.333em}{0ex}}(\mathrm{syst})\mathbf{)}\text{ }\text{ }\text{ }\mathrm{pb}$. Assuming the ${Z}_{c}(3885)\ensuremath{\rightarrow}D{\overline{D}}^{*}$ signal reported here and the ${Z}_{c}(3900)\ensuremath{\rightarrow}\ensuremath{\pi}J/\ensuremath{\psi}$ signal are from the same source, the partial width ratio $(\mathrm{\ensuremath{\Gamma}}({Z}_{c}(3885)\ensuremath{\rightarrow}D{\overline{D}}^{*})/\mathrm{\ensuremath{\Gamma}}({Z}_{c}(3900)\ensuremath{\rightarrow}\ensuremath{\pi}J/\ensuremath{\psi}))=6.2\ifmmode\pm\else\textpm\fi{}1.1\phantom{\rule{0.333em}{0ex}}(\mathrm{stat})\phantom{\rule{0.333em}{0ex}}\ifmmode\pm\else\textpm\fi{}2.7\phantom{\rule{0.333em}{0ex}}(\mathrm{syst})$ is determined.

We study the process e(+)e(-) -&gt; (D* (D) over bar*)(+/-)pi(-/+) at a center-of-mass energy of 4.26 GeV using a 827 pb(-1) data sample obtained with the BESIII detector at the Beijing Electron Positron Collider. Based on a partial reconstruction technique, the Born cross section is measured to be (137 +/- 9 +/- 15) pb. We observe a structure near the (D* (D) over bar*)(+/-) threshold in the pi(-/+) recoil mass spectrum, which we denote as the Z(c)(+/-) (4025). The measured mass and width of the structure are (4026.3 +/- 2.6 +/- 3.7) MeV/c(2) and (24.8 +/- 5.6 +/- 7.7) MeV, respectively. Its production ratio sigma(e(+)e(-) -&gt; Z(c)(+/-)(4025)pi(-/+)-&gt; (D* (D) over bar*)(+/-)pi(-/+)/sigma(e(+)e(-) -&gt; (D* (D) over bar*)(+/-)pi(-/+) is determined to be 0.65 +/- 0.09 +/- 0.06. The first uncertainties are statistical and the second are systematic.

Visible-infrared person re-identification (Re-ID) aims to match the pedestrian images of the same identity from different modalities. Existing works mainly focus on alleviating the modality discrepancy by aligning the distributions of features from different modalities. However, nuanced but discriminative information, such as glasses, shoes, and the length of clothes, has not been fully explored, especially in the infrared modality. Without discovering nuances, it is challenging to match pedestrians across modalities using modality alignment solely, which inevitably reduces feature distinctiveness. In this paper, we propose a joint Modality and Pattern Alignment Network (MPANet) to discover cross-modality nuances in different patterns for visible-infrared person Re-ID, which introduces a modality alleviation module and a pattern alignment module to jointly extract discriminative features. Specifically, we first propose a modality alleviation module to dislodge the modality information from the extracted feature maps. Then, We devise a pattern alignment module, which generates multiple pattern maps for the diverse patterns of a person, to discover nuances. Finally, we introduce a mutual mean learning fashion to alleviate the modality discrepancy and propose a center cluster loss to guide both identity learning and nuances discovering. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the superiority of MPANet over state-of-the-arts.

Most existing cross-modal hashing methods suffer from the scalability issue in the training phase. In this paper, we propose a novel cross-modal hashing approach with a linear time complexity to the training data size, to enable scalable indexing for multimedia search across multiple modals. Taking both the intra-similarity in each modal and the inter-similarity across different modals into consideration, the proposed approach aims at effectively learning hash functions from large-scale training datasets. More specifically, for each modal, we first partition the training data into $k$ clusters and then represent each training data point with its distances to $k$ centroids of the clusters. Interestingly, such a k-dimensional data representation can reduce the time complexity of the training phase from traditional O(n2) or higher to O(n), where $n$ is the training data size, leading to practical learning on large-scale datasets. We further prove that this new representation preserves the intra-similarity in each modal. To preserve the inter-similarity among data points across different modals, we transform the derived data representations into a common binary subspace in which binary codes from all the modals are "consistent" and comparable. nThe transformation simultaneously outputs the hash functions for all modals, which are used to convert unseen data into binary codes. Given a query of one modal, it is first mapped into the binary codes using the modal's hash functions, followed by matching the database binary codes of any other modals. Experimental results on two benchmark datasets confirm the scalability and the effectiveness of the proposed approach in comparison with the state of the art.

Two different types of coordination sheets made up of CoII ions, 3-hydroxypicolinate, and 4,4′-bipyridine interpenetrate to form a unique molecular-based magnetic material (see picture). This material displays a low-field metamagnetic transition and spin-canted behavior.