arXiv subjects

Xinran Wang

Publications and source records attributed to Xinran Wang.

At least 55 records · Page 3Linked to original sources

arXiv2023

New infinite families of near MDS codes holding $t$-designs

In ``Infinite families of near MDS codes holding $t$-designs, IEEE Trans. Inform. Theory, 2020, 66(9), pp. 5419-5428'', Ding and Tang made a breakthrough in constructing the first two infinite families of NMDS codes holding $2$-designs or $3$-designs. Up to now, there are only a few known infinite families of NMDS codes holding $t$-designs in the literature. The objective of this paper is to construct new infinite families of NMDS codes holding $t$-designs. We determine the weight enumerators of the NMDS codes and prove that the NMDS codes hold $2$-designs or $3$-designs. Compared with known $t$-designs from NMDS codes, ours have different parameters. Besides, several infinite families of optimal locally recoverable codes are also derived via the NMDS codes.

cs.IT↗

arXiv2023

Tailored plasmons in pentacene/graphene heterostructures with interlayer electron transfer

Van der Waals (vdW) heterostructures, which are produced by the precise assemblies of varieties of two-dimensional (2D) materials, have demonstrated many novel properties and functionalities. Here we report a nano-plasmonic study of vdW heterostructures that were produced by depositing ordered molecular layers of pentacene on top of graphene. We find through nano-infrared (IR) imaging that surface plasmons formed due to the collective oscillations of Dirac fermions in graphene are highly sensitive to the adjacent pentacene layers. In particular, the plasmon wavelength declines systematically but nonlinearly with increasing pentacene thickness. Further analysis and density functional theory (DFT) calculations indicate that the observed peculiar thickness dependence is mainly due to the tunneling-type electron transfer from pentacene to graphene. Our work unveils a new method for tailoring graphene plasmons and deepens our understanding of the intriguing nano-optical phenomena due to interlayer couplings in novel vdW heterostructures.

cond-mat.mes-hall↗

arXiv2022

Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders

Recommender Systems (RSs) have become increasingly important in many application domains, such as digital marketing. Conventional RSs often need to collect users' data, centralize them on the server-side, and form a global model to generate reliable recommendations. However, they suffer from two critical limitations: the personalization problem that the RSs trained traditionally may not be customized for individual users, and the privacy problem that directly sharing user data is not encouraged. We propose Personalized Federated Recommender Systems (PersonalFR), which introduces a personalized autoencoder-based recommendation model with Federated Learning (FL) to address these challenges. PersonalFR guarantees that each user can learn a personal model from the local dataset and other participating users' data without sharing local data, data embeddings, or models. PersonalFR consists of three main components, including AutoEncoder-based RSs (ARSs) that learn the user-item interactions, Partially Federated Learning (PFL) that updates the encoder locally and aggregates the decoder on the server-side, and Partial Compression (PC) that only computes and transmits active model parameters. Extensive experiments on two real-world datasets demonstrate that PersonalFR can achieve private and personalized performance comparable to that trained by centralizing all users' data. Moreover, PersonalFR requires significantly less computation and communication overhead than standard FL baselines.

cs.IR↗

arXiv2022

Constructions of cyclic codes and extended primitive cyclic codes with their applications

Linear codes with a few weights have many nice applications including combinatorial design, distributed storage system, secret sharing schemes and so on. In this paper, we construct two families of linear codes with a few weights based on special polynomials over finite fields. The first family of linear codes are extended primitive cyclic codes which are affine-invariant. The second family of linear codes are reducible cyclic codes. The parameters of these codes and their duals are determined. As the first application, we prove that these two families of linear codes hold $t$-designs, where $t=2,3$. As the second application, the minimum localities of the codes are also determined and optimal locally recoverable codes are derived.

cs.IT↗

arXiv2022

Symmetry breaking and anomalous conductivity in a double moiré superlattice

A double moiré superlattice can be realized by stacking three layers of atomically thin two-dimensional materials with designer interlayer twisting or lattice mismatches. In this novel structure, atomic reconstruction of constituent layers could introduce significant modifications to the lattice symmetry and electronic structure at small twist angles. Here, we employ conductive atomic force microscopy (cAFM) to investigate symmetry breaking and local electrical properties in twisted trilayer graphene. We observe clear double moiré superlattices with two distinct moire periods all over the sample. At neighboring domains of the large moiré, the current exhibit either two- or six-fold rotational symmetry, indicating delicate symmetry breaking beyond the rigid model. Moreover, an anomalous current appears at the 'A-A' stacking site of the larger moiré, contradictory to previous observations on twisted bilayer graphene. Both behaviors can be understood by atomic reconstruction, and we also show that the cAFM signal of twisted graphene samples is dominated by the tip-graphene contact resistance that maps the local work function of twisted graphene and the metallic tip qualitatively. Our results unveil cAFM is an effective probe for visualizing atomic reconstruction and symmetry breaking in novel moiré superlattices, which could provide new insights for exploring and manipulating more exotic quantum states based on twisted van der Waals heterostructures.

cond-mat.mes-hall↗

arXiv2022

A Data-Efficient Model-Based Learning Framework for the Closed-Loop Control of Continuum Robots

Traditional dynamic models of continuum robots are in general computationally expensive and not suitable for real-time control. Recent approaches using learning-based methods to approximate the dynamic model of continuum robots for control have been promising, although real data hungry -- which may cause potential damage to robots and be time consuming -- and getting poorer performance when trained with simulation data only. This paper presents a model-based learning framework for continuum robot closed-loop control that, by combining simulation and real data, shows to require only 100 real data to outperform a real-data-only controller trained using up to 10000 points. The introduced data-efficient framework with three control policies has utilized a Gaussian process regression (GPR) and a recurrent neural network (RNN). Control policy A uses a GPR model and a RNN trained in simulation to optimize control outputs for simulated targets; control policy B retrains the RNN in policy A with data generated from the GPR model to adapt to real robot physics; control policy C utilizes policy A and B to form a hybrid policy. Using a continuum robot with soft spines, we show that our approach provides an efficient framework to bridge the sim-to-real gap in model-based learning for continuum robots.

cs.RO↗

arXiv2021

Graphene nanoribbons for quantum electronics

Graphene nanoribbons (GNRs) are a family of one-dimensional (1D) materials carved from graphene lattice. GNRs possess high mobility and current carrying capability, sizable bandgap, and versatile electronic properties tailored by the orientations and open edge structures. These unique properties make GNRs promising candidates for prospective electronics applications including nano-sized field-effect transistors (FETs), spintronic devices, and quantum information processing. To fully exploit the potential of GNRs, fundamental understanding of structure-property relationship, precise control of atomic structures and scalable production are the main challenges. In the last several years, significant progress has been made toward atomically precise bottom-up synthesis of GNRs and heterojunctions that provide an ideal platform for functional molecular devices, as well as successful production of semiconducting GNR arrays on insulating substrates potentially useful for large-scale digital circuits. With further development, GNRs can be envisioned as a competitive candidate material in future quantum information sciences (QIS). In this Perspective, we review recent progress in GNR research and identify key challenges and new directions likely to develop in the near future.

cond-mat.mes-hall↗

arXiv2021

Controlling relaxation dynamics of excitonic states in monolayer transition metal dichalcogenides WS2 through interface engineering

Transition metal dichalcogenides (TMDs) are known to support complex excitonic states. Revealing the differences in relaxation dynamics among different excitonic species and elucidating the transition dynamics between them may provide important guidelines for designing TMD-based excitonic devices. Combining photoluminescence (PL) and reflectance contrast measurements with ultrafast pump-probe spectroscopy under cryogenic temperatures, we herein study the relaxation dynamics of neutral and charged excitons in a back-gate-controlled monolayer device. Pump-probe results reveal quite different relaxation dynamics of excitonic states under different interfacial conditions: while neutral excitons experience much longer lifetime than trions in monolayer WS2, the opposite is true in the WS2/h-BN heterostructure. It is found that the insertion of h-BN layer between the TMD monolayer and the substrate has a great influence on the lifetimes of different excitonic states. The h-BN flakes can not only screen the effects of impurities and defects at the interface, but also help establish a non-radiative transition from neutral excitons to trions to be the dominant relaxation pathway, under cryogenic temperature. Our findings highlight the important role interface may play in governing the transient properties of carriers in 2D semiconductors, and may also have implications for designing light-emitting and photo-detecting devices based on TMDs.

cond-mat.mes-hall↗

arXiv2021

Mimicing the Kane-Mele type spin orbit interaction by spin-flexual phonon coupling in graphene devices

On the efforts of enhancing the spin orbit interaction (SOI) of graphene for seeking the dissipationless quantum spin Hall devices, unique Kane-Mele type SOI and high mobility samples are desired. However, common external decoration often introduces extrinsic Rashba-type SOI and simultaneous impurity scattering. Here we show, by the EDTA-Dy molecule decorating, the Kane-Mele type SOI is mimicked with even improved carrier mobility. It is evidenced by the suppressed weak localization at equal carrier densities and simultaneous Elliot-Yafet spin relaxation. The extracted spin scattering time is monotonically dependent on the carrier elastic scattering time, where the Elliot-Yafet plot gives the interaction strength of 3.3 meV. Improved quantum Hall plateaus can be even seen after the external operation. This is attributed to the spin-flexural phonon coupling induced by the enhanced graphene ripples, as revealed by the in-plane magnetotransport measurement.

cond-mat.mes-hall↗

arXiv2020

Assisted Learning: A Framework for Multi-Organization Learning

In an increasing number of AI scenarios, collaborations among different organizations or agents (e.g., human and robots, mobile units) are often essential to accomplish an organization-specific mission. However, to avoid leaking useful and possibly proprietary information, organizations typically enforce stringent security constraints on sharing modeling algorithms and data, which significantly limits collaborations. In this work, we introduce the Assisted Learning framework for organizations to assist each other in supervised learning tasks without revealing any organization's algorithm, data, or even task. An organization seeks assistance by broadcasting task-specific but nonsensitive statistics and incorporating others' feedback in one or more iterations to eventually improve its predictive performance. Theoretical and experimental studies, including real-world medical benchmarks, show that Assisted Learning can often achieve near-oracle learning performance as if data and training processes were centralized.

cs.LG↗

arXiv2020

Preparation of the AlTiNiCuCox system high-entropy alloys and structural analysis

This study aimed to explore and develop a new material with high cost-effectiveness, excellent strength, light weight, high hardness, great wear resistance, corrosion resistance, and favorable oxidation resistance. Structural analysis suggested that, with the change in Co addition amount, the surface morphology and structure of the alloy system changed. XRD analysis indicated that, the alloy system was the FCC+BCC mixed structure. In addition, metallographical demonstrated that, with the increase in Co content, the dendritic crystal transformed from big block to dendritic structure, then to snowflake, gradually to petal-like, and finally to petal shape. SEM-EDS analysis revealed that, Cu element was enriched in interdendritic site, while Ti, Ni, Al and Co elements were enriched in dendrite. Besides, TEM and TEM-EDS analysis indicated that, there was nano-size precipitate of small particles in the Cu-enriched block region, along with dislocation; further, there was twin structure inside the dendrite, as well as the second phase with different morphology, and the second phase showed coherency with the matrix. The above analysis suggested that, the intercrystalline structure was the Cu-enriched phase of FCC structure; the internal matrix of grain was the NiTi and TiCo phases of BCC structure; and the second phases inside the grain were the AlCu2Ti,AlNi2Ti,AlCo2Ti and CuNi phases of FCC structure. Taken together, the AlTiNiCuCox system novel alloys have changed phase structures and phase types of the alloy system.

physics.app-ph↗

arXiv2020

Information Laundering for Model Privacy

In this work, we propose information laundering, a novel framework for enhancing model privacy. Unlike data privacy that concerns the protection of raw data information, model privacy aims to protect an already-learned model that is to be deployed for public use. The private model can be obtained from general learning methods, and its deployment means that it will return a deterministic or random response for a given input query. An information-laundered model consists of probabilistic components that deliberately maneuver the intended input and output for queries to the model, so the model's adversarial acquisition is less likely. Under the proposed framework, we develop an information-theoretic principle to quantify the fundamental tradeoffs between model utility and privacy leakage and derive the optimal design.

cs.CR↗

arXiv2020

Imitation Privacy

In recent years, there have been many cloud-based machine learning services, where well-trained models are provided to users on a pay-per-query scheme through a prediction API. The emergence of these services motivates this work, where we will develop a general notion of model privacy named imitation privacy. We show the broad applicability of imitation privacy in classical query-response MLaaS scenarios and new multi-organizational learning scenarios. We also exemplify the fundamental difference between imitation privacy and the usual data-level privacy.

cs.CR↗

arXiv2019

Integrating high-quality dielectrics with one-nanometer equivalent oxide thickness on two-dimensional electronic devices

Two-dimensional (2D) semiconductors are widely recognized as attractive channel materials for low-power electronics. However, an unresolved challenge is the integration of high-quality, ultrathin high-\k{appa} dielectrics that fully meet the roadmap requirements for low-power applications. With a dangling-bond free surface, the deposition of dielectrics by atomic layer deposition (ALD) on 2D materials is usually characterized with non-uniform nucleation and island formation, producing a highly porous dielectric layer with serious leakage particularly at the small equivalent oxide thickness (EOT) limit. Here, we report the robust ALD of highly uniform high-\k{appa} dielectric on 2D semiconductors by using ~0.3 nm-thick exclusively monolayer molecular crystal as seeding layer. Ultrathin dielectrics down to 1 nm EOT is realized on graphene, MoS2 and WSe2, with considerably reduced roughness, density of interface states, leakage current and improved breakdown field compared to prior methods. Taking advantage of the reduced EOT, we demonstrate graphene RF transistors operating at 60 GHz, as well as MoS2 and WSe2 complementary metal-oxide-semiconductor (CMOS) transistors with Vdd =0.8 V and ideal subthreshold swing (SS) of 60 mV/dec, 20 nm-channel-length MoS2 transistors with on/off ratio over 10^7. These studies highlight that our dielectric integration method is generally applicable for different 2D materials, and compatible with top-down fabrication process on large-area chemical vapor deposited films.

physics.app-ph↗

arXiv2019

Enhanced Interfacial Dzyaloshinskii-Moriya Interaction in annealed Pt/Co/MgO structures

The interfacial Dzyaloshinskii-Moriya interaction (iDMI) is attracting great interests for spintronics. An iDMI constant larger than 3 mJ/m^2 is expected to minimize the size of skyrmions and to optimize the DW dynamics. In this study, we experimentally demonstrate an enhanced iDMI in Pt/Co/X/MgO ultra-thin film structures with perpendicular magnetization. The iDMI constants were measured using a field-driven creep regime domain expansion method. The enhancement of iDMI with an atomically thin insertion of Ta and Mg is comprehensively understood with the help of ab-initio calculations. Thermal annealing has been used to crystallize the MgO thin layer for improving tunneling magneto-resistance (TMR), but interestingly it also provides a further increase of the iDMI constant. An increase of the iDMI constant up to 3.3 mJ/m^2 is shown, which could be promising for the scaling down of skyrmion electronics.

cond-mat.mes-hall↗

arXiv2019

Band Structure Engineering of Interfacial Semiconductors Based on Atomically Thin Lead Iodide Crystals

To explore new constituents in two-dimensional materials and to combine their best in van der Waals heterostructures, are in great demand as being unique platform to discover new physical phenomena and to design novel functionalities in interface-based devices. Herein, PbI2 crystals as thin as few-layers are first synthesized, particularly through a facile low-temperature solution approach with the crystals of large size, regular shape, different thicknesses and high-yields. As a prototypical demonstration of flexible band engineering of PbI2-based interfacial semiconductors, these PbI2 crystals are subsequently assembled with several transition metal dichalcogenide monolayers. The photoluminescence of MoS2 is strongly enhanced in MoS2/PbI2 stacks, while a dramatic photoluminescence quenching of WS2 and WSe2 is revealed in WS2/PbI2 and WSe2/PbI2 stacks. This is attributed to the effective heterojunction formation between PbI2 and these monolayers, but type I band alignment in MoS2/PbI2 stacks where fast-transferred charge carriers accumulate in MoS2 with high emission efficiency, and type II in WS2/PbI2 and WSe2/PbI2 stacks with separated electrons and holes suitable for light harvesting. Our results demonstrate that MoS2, WS2, WSe2 monolayers with very similar electronic structures themselves, show completely distinct light-matter interactions when interfacing with PbI2, providing unprecedent capabilities to engineer the device performance of two-dimensional heterostructures.

cond-mat.mes-hall↗

arXiv2018

Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment

Pedestrians in videos have a wide range of appearances such as body poses, occlusions, and complex backgrounds, and there exists the proposal shift problem in pedestrian detection that causes the loss of body parts such as head and legs. To address it, we propose part-level convolutional neural networks (CNN) for pedestrian detection using saliency and boundary box alignment in this paper. The proposed network consists of two sub-networks: detection and alignment. We use saliency in the detection sub-network to remove false positives such as lamp posts and trees. We adopt bounding box alignment on detection proposals in the alignment sub-network to address the proposal shift problem. First, we combine FCN and CAM to extract deep features for pedestrian detection. Then, we perform part-level CNN to recall the lost body parts. Experimental results on various datasets demonstrate that the proposed method remarkably improves accuracy in pedestrian detection and outperforms existing state-of-the-arts in terms of log average miss rate at false position per image (FPPI).

cs.CV↗

arXiv2018

Topological Phase Transition-Induced Tri-Axial Vector Magnetoresistance in (Bi1-xInx)2Se3 Nanodevices

We report the study of a tri-axial vector magnetoresistance (MR) in nonmagnetic (Bi1-xInx)2Se3 nanodevices at the composition of x = 0.08. We show a dumbbell-shaped in-plane negative MR up to room temperature as well as a large out-of-plane positive MR. MR at three directions is about in a -3%: -1%: 225% ratio at 2 K. Through both the thickness and composition-dependent magnetotransport measurements, we show that the in-plane negative MR is due to the topological phase transition enhanced intersurface coupling near the topological critical point. Our devices suggest the great potential for room-temperature spintronic applications, for example, vector magnetic sensors.

physics.app-ph↗