We study the magnetic moments and transition magnetic moments of $P_c$ and $P_{cs}$ states in the molecular picture. We first revisit the magnetic moments of $P_c$ states as the $S$ wave molecular states without coupled channel effects. The coupled c
hannel effects and the $D$ wave contributions are then investigated carefully. The coupled channel effects contribute to the change of $0.1sim 0.4$ nuclear magneton $mu_N$ for most cases while the $D$ wave only induces the variation of less than $0.03 ~mu_N$. In addition, we obtain the transition magnetic moments between different $P_c$ states and the related electromagnetic decay widths of $P_cto P_cgamma$. The magnetic moments of $P_{cs}$ are much different for the assumption of spin being 1/2 and 3/2. The study of electromagnetic properties will help us disclose further the structure of these unconventional states.
The interface stresses at of the solid-melt interface are, in general, anisotropic. The anisotropy in the interfacial stress can be evaluated using molecular dynamics (MD) and phase field crystal (PFC) models. In this paper, we report our results on
the evaluation of the anisotropy in interface stress in a BCC solid with its melt. Specifically, we study Fe using both MD and PFC models. We show that while both MD and PFC can be used for the evaluation, and the PFC and the amplitude equations based on PFC give quantitatively consistent results, the MD and PFC results are qualitatively the same but do not match quantitatively. We also find that even though the interfacial free energy is only weakly anisotropic in BCC interfacial stress anisotropy is strong. This strong anisotropy has implications for the equilibrium shapes, growth morphologies and other properties at nano-scale in these materials.
Due to its low storage cost and fast query speed, hashing has been widely used in large-scale image retrieval tasks. Hash bucket search returns data points within a given Hamming radius to each query, which can enable search at a constant or sub-line
ar time cost. However, existing hashing methods cannot achieve satisfactory retrieval performance for hash bucket search in complex scenarios, since they learn only one hash code for each image. More specifically, by using one hash code to represent one image, existing methods might fail to put similar image pairs to the buckets with a small Hamming distance to the query when the semantic information of images is complex. As a result, a large number of hash buckets need to be visited for retrieving similar images, based on the learned codes. This will deteriorate the efficiency of hash bucket search. In this paper, we propose a novel hashing framework, called multiple code hashing (MCH), to improve the performance of hash bucket search. The main idea of MCH is to learn multiple hash codes for each image, with each code representing a different region of the image. Furthermore, we propose a deep reinforcement learning algorithm to learn the parameters in MCH. To the best of our knowledge, this is the first work that proposes to learn multiple hash codes for each image in image retrieval. Experiments demonstrate that MCH can achieve a significant improvement in hash bucket search, compared with existing methods that learn only one hash code for each image.
Traditional person re-identification (ReID) methods typically represent person images as real-valued features, which makes ReID inefficient when the gallery set is extremely large. Recently, some hashing methods have been proposed to make ReID more e
fficient. However, these hashing methods will deteriorate the accuracy in general, and the efficiency of them is still not high enough. In this paper, we propose a novel hashing method, called deep multi-index hashing (DMIH), to improve both efficiency and accuracy for ReID. DMIH seamlessly integrates multi-index hashing and multi-branch based networks into the same framework. Furthermore, a novel block-wise multi-index hashing table construction approach and a search-aware multi-index (SAMI) loss are proposed in DMIH to improve the search efficiency. Experiments on three widely used datasets show that DMIH can outperform other state-of-the-art baselines, including both hashing methods and real-valued methods, in terms of both efficiency and accuracy.
Due to its low storage cost and fast query speed, hashing has been widely used for large-scale approximate nearest neighbor (ANN) search. Bucket search, also called hash lookup, can achieve fast query speed with a sub-linear time cost based on the in
verted index table constructed from hash codes. Many metrics have been adopted to evaluate hashing algorithms. However, all existing metrics are improper to evaluate the hash codes for bucket search. On one hand, all existing metrics ignore the retrieval time cost which is an important factor reflecting the performance of search. On the other hand, some of them, such as mean average precision (MAP), suffer from the uncertainty problem as the ranked list is based on integer-valued Hamming distance, and are insensitive to Hamming radius as these metrics only depend on relative Hamming distance. Other metrics, such as precision at Hamming radius R, fail to evaluate global performance as these metrics only depend on one specific Hamming radius. In this paper, we first point out the problems of existing metrics which have been ignored by the hashing community, and then propose a novel evaluation metric called radius aware mean average precision (RAMAP) to evaluate hash codes for bucket search. Furthermore, two coding strategies are also proposed to qualitatively show the problems of existing metrics. Experiments demonstrate that our proposed RAMAP can provide more proper evaluation than existing metrics.
We study pattern formation of skin cancers by means of numerical simulation of a binary system consisting of cancer and healthy cells. We extend the conventional Model H for macrophase separations by considering a logistic growth of cancer cells and
also a mechanical friction between dermis and epidermis. Importantly, our model exhibits a microphase separation due to the proliferation of cancer cells. By numerically solving the time evolution equations of the cancer composition and its velocity, we show that the phase separation kinetics strongly depends on the cell proliferation rate as well as on the strength of hydrodynamic interactions. A steady state diagram of cancer patterns is established in terms of these two dynamical parameters and some of the patterns correspond to clinically observed cancer patterns. Furthermore, we examine in detail the time evolution of the average composition of cancer cells and the characteristic length of the microstructures. Our results demonstrate that different sequence of cancer patterns can be obtained by changing the proliferation rate and/or hydrodynamic interactions.
Distributed sparse learning with a cluster of multiple machines has attracted much attention in machine learning, especially for large-scale applications with high-dimensional data. One popular way to implement sparse learning is to use $L_1$ regular
ization. In this paper, we propose a novel method, called proximal mbox{SCOPE}~(mbox{pSCOPE}), for distributed sparse learning with $L_1$ regularization. pSCOPE is based on a underline{c}ooperative underline{a}utonomous underline{l}ocal underline{l}earning~(mbox{CALL}) framework. In the mbox{CALL} framework of mbox{pSCOPE}, we find that the data partition affects the convergence of the learning procedure, and subsequently we define a metric to measure the goodness of a data partition. Based on the defined metric, we theoretically prove that pSCOPE is convergent with a linear convergence rate if the data partition is good enough. We also prove that better data partition implies faster convergence rate. Furthermore, pSCOPE is also communication efficient. Experimental results on real data sets show that pSCOPE can outperform other state-of-the-art distributed methods for sparse learning.
We report the fabrication of ionic liquid (IL) gated field-effect transistors (FETs) consisting of bilayer and few-layer MoS2. Our transport measurements indicate that the electron mobility about 60 cm2V-1s-1 at 250 K in ionic liquid gated devices ex
ceeds significantly that of comparable back-gated devices. IL-FETs display a mobility increase from about 100 cm2V-1s-1 at 180 K to about 220 cm2V-1s-1 at 77 K in good agreement with the true channel mobility determined from four-terminal measurements, ambipolar behavior with a high ON/OFF ratio >107 (104) for electrons (holes), and a near ideal sub-threshold swing of about 50 mV/dec at 250 K. We attribute the observed performance enhancement, specifically the increased carrier mobility that is limited by phonons, to the reduction of the Schottky barrier at the source and drain electrode by band bending caused by the ultrathin ionic-liquid dielectric layer.
We report electrical characterization of monolayer molybdenum disulfide (MoS2) devices using a thin layer of polymer electrolyte consisting of poly(ethylene oxide) (PEO) and lithium perchlorate (LiClO4) as both a contact-barrier reducer and channel m
obility booster. We find that bare MoS2 devices (without polymer electrolyte) fabricated on Si/SiO2 have low channel mobility and large contact resistance, both of which severely limit the field-effect mobility of the devices. A thin layer of PEO/ LiClO4 deposited on top of the devices not only substantially reduces the contact resistance but also boost the channel mobility, leading up to three-orders-of-magnitude enhancement of the field-effect mobility of the device. When the polymer electrolyte is used as a gate medium, the MoS2 field-effect transistors exhibit excellent device characteristics such as a near ideal subthreshold swing and an on/off ratio of 106 as a result of the strong gate-channel coupling.
A simple one-stage solution-based method was developed to produce graphene nanoribbons by sonicating graphite powder in organic solutions with polymer surfactant. The graphene nanoribbons were deposited on silicon substrate, and characterized by Rama
n spectroscopy and atomic force microscopy. Single-layer and few-layer graphene nanoribbons with a width ranging from sub-10 nm to tens of nm and length ranging from hundreds of nm to 1 {mu}m were routinely observed. Electrical transport properties of individual graphene nanoribbons were measured in both the back-gate and polymer-electrolyte top-gate configurations. The mobility of the graphene nanoribbons was found to be over an order of magnitude higher when measured in the latter than in the former configuration (without the polymer electrolyte), which can be attributed to the screening of the charged impurities by the counter-ions in the polymer electrolyte. This finding suggests that the charge transport in these solution-produced graphene nanoribbons is largely limited by charged impurity scattering.
