arXiv subjects

Jie Li

Publications and source records attributed to Jie Li.

At least 451 records · Page 25Linked to original sources

arXiv2022

Testing linearity in semi-functional partially linear regression models

This paper proposes a Kolmogorov-Smirnov type statistic and a Cramér-von Mises type statistic to test linearity in semi-functional partially linear regression models. Our test statistics are based on a residual marked empirical process indexed by a randomly projected functional covariate,which is able to circumvent the "curse of dimensionality" brought by the functional covariate. The asymptotic properties of the proposed test statistics under the null, the fixed alternative, and a sequence of local alternatives converging to the null at the $n^{1/2}$ rate are established. A straightforward wild bootstrap procedure is suggested to estimate the critical values that are required to carry out the tests in practical applications. Results from an extensive simulation study show that our tests perform reasonably well in finite samples.Finally, we apply our tests to the Tecator and AEMET datasets to check whether the assumption of linearity is supported by these datasets.

math.ST↗

arXiv2022

Optomechanical quantum teleportation

Quantum teleportation, the faithful transfer of an unknown input state onto a remote quantum system, is a key component in long distance quantum communication protocols and distributed quantum computing. At the same time, high frequency nano-optomechanical systems hold great promise as nodes in a future quantum network, operating on-chip at low-loss optical telecom wavelengths with long mechanical lifetimes. Recent demonstrations include entanglement between two resonators, a quantum memory and microwave to optics transduction. Despite these successes, quantum teleportation of an optical input state onto a long-lived optomechanical memory is an outstanding challenge. Here we demonstrate quantum teleportation of a polarization-encoded optical input state onto the joint state of a pair of nanomechanical resonators. Our protocol also allows for the first time to store and retrieve an arbitrary qubit state onto a dual-rail encoded optomechanical quantum memory. This work demonstrates the full functionality of a single quantum repeater node, and presents a key milestone towards applications of optomechanical systems as quantum network nodes.

quant-ph↗

arXiv2022

Stationary optomagnonic entanglement and magnon-to-optics quantum state transfer via opto-magnomechanics

We show how to prepare a steady-state entangled state between magnons and optical photons in an opto-magnomechanical configuration, where a mechanical vibration mode couples to a magnon mode in a ferrimagnet by the dispersive magnetostrictive interaction, and to an optical cavity by the radiation pressure. We find that, by appropriately driving the magnon mode and the cavity to simultaneously activate the magnomechanical Stokes and the optomechanical anti-Stokes scattering, a stationary optomagnonic entangled state can be created. We further show that, by activating the magnomechanical state-swap interaction and subsequently sending a weak red-detuned optical pulse to drive the cavity, the magnonic state can be read out in the cavity output field of the pulse via the mechanical transduction. The demonstrated entanglement and state-readout protocols in such a novel opto-magnomechanical configuration allow us to optically control, prepare, and read out quantum states of collective spin excitations in solids, and provide promising opportunities for the study of quantum magnonics, macroscopic quantum states, and magnonic quantum information processing.

quant-ph↗

arXiv2022

Efficient Calculation of NMR Shielding Constants Using Composite Method Approximations and Locally Dense Basis Sets

This paper presents a systematic study of applying composite method approximations with locally dense basis sets (LDBS) to efficiently calculate NMR shielding constants in small and medium-sized molecules. The pcSseg-n series of basis sets are shown to have similar accuracy to the pcS-n series when n $\geq1$ and can slightly reduce compute costs. We identify two different LDBS partition schemes that perform very effectively for density functional calculations. We select a large subset of the recent NS372 database containing 290 H, C, N, and O shielding values evaluated by reference methods on 106 molecules to carefully assess methods of the high, medium, and low compute costs to make practical recommendations. Our assessment covers conventional electronic structure methods (DFT and wavefunction) with global basis calculations, as well as their use in one of the satisfactory LDBS approaches, and a range of composite approaches, also with and without LDBS. Altogether 99 methods are evaluated. On this basis, we recommend different methods to reach three different levels of accuracy and time requirements across the four nuclei considered.

physics.chem-ph↗

arXiv2022

PMDS Array Codes With Small Sub-packetization, Small Repair Bandwidth/Rebuilding Access

Partial maximum distance separable (PMDS) codes are a kind of erasure codes where the nodes are divided into multiple groups with each forming an MDS code with a smaller code length, thus they allow repairing a failed node with only a few helper nodes and can correct all erasure patterns that are information-theoretically correctable. However, the repair of a failed node of PMDS codes still requires a large amount of communication if the group size is large. Recently, PMDS array codes with each local code being an MSR code were introduced to reduce the repair bandwidth further. However, they require extensive rebuilding access and unavoidably a significant sub packetization level. In this paper, we first propose two constructions of PMDS array codes with two global parities that have smaller sub-packetization levels and much smaller finite fields than the existing one. One construction can support an arbitrary number of local parities and has $(1+ε)$-optimal repair bandwidth (i.e., $(1+ε)$ times the optimal repair bandwidth), while the other one is limited to two local parities but has significantly smaller rebuilding access and its sub packetization level is only $2$. In addition, we present a construction of PMDS array code with three global parities, which has a smaller sub-packetization level as well as $(1+ε)$-optimal repair bandwidth, the required finite field is significantly smaller than existing ones.

cs.IT↗

arXiv2022

Spin-related electronic pathway through single molecule on Au(111)

Spin properties of organic molecules have attracted great interest for their potential applications in spintronic devices and quantum computing. Fe-tetraphenyl porphyrin (FeTPP) is of particular interest for its robust magnetic properties on metallic substrates. FeTPP is prepared in vacuum via on-surface synthesis. Molecular structure and spin-related transport properties are characterized by low-temperature scanning tunneling microscope and spectroscopy at 0.5 K. Density functional theory calculations are performed to understand molecular adsorption and spin distribution on Au(111). The molecular structure of FeTPP is distorted upon adsorption on the substrate. Spin excitations of FeTPP are observed on the Fe atom and high pyrrole groups in differential conductance spectra. The calculated spin density distribution indicates that the electron spin of FeTPP is mainly distributed on the Fe atom. The atomic transmission calculation indicates that electrons transport to substrate is mediated through Fe atom, when the tip is above the high pyrrole group.

cond-mat.mes-hall↗

arXiv2022

Depth Is All You Need for Monocular 3D Detection

A key contributor to recent progress in 3D detection from single images is monocular depth estimation. Existing methods focus on how to leverage depth explicitly, by generating pseudo-pointclouds or providing attention cues for image features. More recent works leverage depth prediction as a pretraining task and fine-tune the depth representation while training it for 3D detection. However, the adaptation is insufficient and is limited in scale by manual labels. In this work, we propose to further align depth representation with the target domain in unsupervised fashions. Our methods leverage commonly available LiDAR or RGB videos during training time to fine-tune the depth representation, which leads to improved 3D detectors. Especially when using RGB videos, we show that our two-stage training by first generating pseudo-depth labels is critical because of the inconsistency in loss distribution between the two tasks. With either type of reference data, our multi-task learning approach improves over the state of the art on both KITTI and NuScenes, while matching the test-time complexity of its single task sub-network.

cs.CV↗

arXiv2022

Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?

Building 3D perception systems for autonomous vehicles that do not rely on high-density LiDAR is a critical research problem because of the expense of LiDAR systems compared to cameras and other sensors. Recent research has developed a variety of camera-only methods, where features are differentiably "lifted" from the multi-camera images onto the 2D ground plane, yielding a "bird's eye view" (BEV) feature representation of the 3D space around the vehicle. This line of work has produced a variety of novel "lifting" methods, but we observe that other details in the training setups have shifted at the same time, making it unclear what really matters in top-performing methods. We also observe that using cameras alone is not a real-world constraint, considering that additional sensors like radar have been integrated into real vehicles for years already. In this paper, we first of all attempt to elucidate the high-impact factors in the design and training protocol of BEV perception models. We find that batch size and input resolution greatly affect performance, while lifting strategies have a more modest effect -- even a simple parameter-free lifter works well. Second, we demonstrate that radar data can provide a substantial boost to performance, helping to close the gap between camera-only and LiDAR-enabled systems. We analyze the radar usage details that lead to good performance, and invite the community to re-consider this commonly-neglected part of the sensor platform.

cs.CV↗

arXiv2022

Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack

Previous studies have verified that the functionality of black-box models can be stolen with full probability outputs. However, under the more practical hard-label setting, we observe that existing methods suffer from catastrophic performance degradation. We argue this is due to the lack of rich information in the probability prediction and the overfitting caused by hard labels. To this end, we propose a novel hard-label model stealing method termed \emph{black-box dissector}, which consists of two erasing-based modules. One is a CAM-driven erasing strategy that is designed to increase the information capacity hidden in hard labels from the victim model. The other is a random-erasing-based self-knowledge distillation module that utilizes soft labels from the substitute model to mitigate overfitting. Extensive experiments on four widely-used datasets consistently demonstrate that our method outperforms state-of-the-art methods, with an improvement of at most $8.27\%$. We also validate the effectiveness and practical potential of our method on real-world APIs and defense methods. Furthermore, our method promotes other downstream tasks, \emph{i.e.}, transfer adversarial attacks.

cs.CV↗

arXiv2022

Incorporation of Human Knowledge into Data Embeddings to Improve Pattern Significance and Interpretability

Embedding is a common technique for analyzing multi-dimensional data. However, the embedding projection cannot always form significant and interpretable visual structures that foreshadow underlying data patterns. We propose an approach that incorporates human knowledge into data embeddings to improve pattern significance and interpretability. The core idea is (1) externalizing tacit human knowledge as explicit sample labels and (2) adding a classification loss in the embedding network to encode samples' classes. The approach pulls samples of the same class with similar data features closer in the projection, leading to more compact (significant) and class-consistent (interpretable) visual structures. We give an embedding network with a customized classification loss to implement the idea and integrate the network into a visualization system to form a workflow that supports flexible class creation and pattern exploration. Patterns found on open datasets in case studies, subjects' performance in a user study, and quantitative experiment results illustrate the general usability and effectiveness of the approach.

cs.HC↗

arXiv2022

Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase

Situating at the core of Artificial Intelligence (AI), Machine Learning (ML), and more specifically, Deep Learning (DL) have embraced great success in the past two decades. However, unseen class label prediction is far less explored due to missing classes being invisible in training ML or DL models. In this work, we propose a fuzzy inference system to cope with such a challenge by adopting TSK+ fuzzy inference engine in conjunction with the Curvature-based Feature Selection (CFS) method. The practical feasibility of our system has been evaluated by predicting the positioning labels of networking devices within the realm of the Internet of Things (IoT). Competitive prediction performance confirms the efficiency and efficacy of our system, especially when a large number of continuous class labels are unseen during the model training stage.

cs.LG↗

arXiv2022

Parameter-Efficient Conformers via Sharing Sparsely-Gated Experts for End-to-End Speech Recognition

While transformers and their variant conformers show promising performance in speech recognition, the parameterized property leads to much memory cost during training and inference. Some works use cross-layer weight-sharing to reduce the parameters of the model. However, the inevitable loss of capacity harms the model performance. To address this issue, this paper proposes a parameter-efficient conformer via sharing sparsely-gated experts. Specifically, we use sparsely-gated mixture-of-experts (MoE) to extend the capacity of a conformer block without increasing computation. Then, the parameters of the grouped conformer blocks are shared so that the number of parameters is reduced. Next, to ensure the shared blocks with the flexibility of adapting representations at different levels, we design the MoE routers and normalization individually. Moreover, we use knowledge distillation to further improve the performance. Experimental results show that the proposed model achieves competitive performance with 1/3 of the parameters of the encoder, compared with the full-parameter model.

eess.AS↗

arXiv2022

On Collective Robustness of Bagging Against Data Poisoning

Bootstrap aggregating (bagging) is an effective ensemble protocol, which is believed can enhance robustness by its majority voting mechanism. Recent works further prove the sample-wise robustness certificates for certain forms of bagging (e.g. partition aggregation). Beyond these particular forms, in this paper, \emph{we propose the first collective certification for general bagging to compute the tight robustness against the global poisoning attack}. Specifically, we compute the maximum number of simultaneously changed predictions via solving a binary integer linear programming (BILP) problem. Then we analyze the robustness of vanilla bagging and give the upper bound of the tolerable poison budget. Based on this analysis, \emph{we propose hash bagging} to improve the robustness of vanilla bagging almost for free. This is achieved by modifying the random subsampling in vanilla bagging to a hash-based deterministic subsampling, as a way of controlling the influence scope for each poisoning sample universally. Our extensive experiments show the notable advantage in terms of applicability and robustness.

cs.CR↗

arXiv2022

Mechanical Bistability in Kerr-modified Cavity Magnomechanics

Bistable mechanical vibration is observed in a cavity magnomechanical system, which consists of a microwave cavity mode, a magnon mode, and a mechanical vibration mode of a ferrimagnetic yttrium-iron-garnet (YIG) sphere. The bistability manifests itself in both the mechanical frequency and linewidth under a strong microwave drive field, which simultaneously activates three different kinds of nonlinearities, namely, magnetostriction, magnon self-Kerr, and magnon-phonon cross-Kerr nonlinearities. The magnon-phonon cross-Kerr nonlinearity is first predicted and measured in magnomechanics. The system enters a regime where Kerr-type nonlinearities strongly modify the conventional cavity magnomechanics that possesses only a radiation-pressure-like magnomechanical coupling. Three different kinds of nonlinearities are identified and distinguished in the experiment. Our work demonstrates a new mechanism for achieving mechanical bistability by combining magnetostriction and Kerr-type nonlinearities, and indicates that such Kerr-modified cavity magnomechanics provides a unique platform for studying many distinct nonlinearities in a single experiment.

quant-ph↗

arXiv2022

FedIPR: Ownership Verification for Federated Deep Neural Network Models

Federated learning models are collaboratively developed upon valuable training data owned by multiple parties. During the development and deployment of federated models, they are exposed to risks including illegal copying, re-distribution, misuse and/or free-riding. To address these risks, the ownership verification of federated learning models is a prerequisite that protects federated learning model intellectual property rights (IPR) i.e., FedIPR. We propose a novel federated deep neural network (FedDNN) ownership verification scheme that allows private watermarks to be embedded and verified to claim legitimate IPR of FedDNN models. In the proposed scheme, each client independently verifies the existence of the model watermarks and claims respective ownership of the federated model without disclosing neither private training data nor private watermark information. The effectiveness of embedded watermarks is theoretically justified by the rigorous analysis of conditions under which watermarks can be privately embedded and detected by multiple clients. Moreover, extensive experimental results on computer vision and natural language processing tasks demonstrate that varying bit-length watermarks can be embedded and reliably detected without compromising original model performances. Our watermarking scheme is also resilient to various federated training settings and robust against removal attacks.

cs.LG↗

arXiv2022

Distinguishing two-component anomalous Hall effect from topological Hall effect

In transport, the topological Hall effect (THE) presents itself as non-monotonic features (or humps and dips) in the Hall signal and is widely interpreted as a sign of chiral spin textures, like magnetic skyrmions. However, when anomalous Hall effect (AHE) is also present, the co-existence of two AHEs could give rise to similar artifacts, making it difficult to distinguish between genuine THE with AHE and two-component AHE. Here we confirm genuine THE with AHE by means of transport and magneto-optical Kerr effect (MOKE) microscopy, in which magnetic skyrmions are directly observed, and find that genuine THE occurs in the transition region of the AHE. In sharp contrast, the artifact "THE", or two-component AHE occurs well beyond the saturation of the "AHE component" (under the false assumption of THE+AHE). Furthermore, we distinguish artifact "THE" from genuine THE by three methods: 1. Minor loops, 2. Temperature dependence, 3. Gate dependence. Minor loops of genuine THE with AHE are always within the full loop, while minor loops of the artifact "THE" may reveal a single loop that cannot fit into the "AHE component". Besides, the temperature or gate dependence of the artifact "THE" may also be accompanied by a polarity change of the "AHE component", as the non-monotonic features vanish, while the temperature dependence of genuine THE with AHE reveals no such change. Our work may help future researchers to exercise cautions and use these methods to examine carefully in order to ascertain genuine THE.

cond-mat.mtrl-sci↗

arXiv2022

Cr$_3$X$_4$ (X=Se, Te) monolayers as new platform to realize robust spin filter, spin diode and spin valve

Two-dimensional ferromagnetic (FM) half-metals are promising candidates for advanced spintronic devices with small-size and high-capacity. Motivated by recent report on controlling synthesis of FM Cr$_3$Te$_4$ nanosheet, herein, to explore the potential application in spintronics, we designed spintronic devices based on Cr$_3$X$_4$ (X=Se, Te) monolayers and investigated their spin transport properties. We found that Cr$_3$Te$_4$ monolayer based device shows spin filtering and dual spin diode effect when applying bias voltage, while Cr$_3$S$_4$ monolayer is an excellent platform to realize a spin valve. The different transport properties are primarily ascribed to the semiconducting spin channel, which is close to and away from the Fermi level in Cr$_3$Te$_4$ and Cr$_3$Se$_4$ monolayers, respectively. Interestingly, the current in monolayer Cr$_3$Se$_4$ based device also displays a negative differential resistance effect (NDRE) and a high magnetoresistance ratio (up to 2*10$^3$). Moreover, we found thermally induced spin filtering effect and NDRE in Cr$_3$Se$_4$ junction when applying temperature gradient instead of bias voltage. These theoretical findings highlight the potential of Cr$_3$X$_4$ (X=Se, Te) monolayers in spintronic applications and put forward realistic materials to realize nanosale spintronic device.

cond-mat.mtrl-sci↗

arXiv2022

Seeking Subjectivity in Visual Emotion Distribution Learning

Visual Emotion Analysis (VEA), which aims to predict people's emotions towards different visual stimuli, has become an attractive research topic recently. Rather than a single label classification task, it is more rational to regard VEA as a Label Distribution Learning (LDL) problem by voting from different individuals. Existing methods often predict visual emotion distribution in a unified network, neglecting the inherent subjectivity in its crowd voting process. In psychology, the \textit{Object-Appraisal-Emotion} model has demonstrated that each individual's emotion is affected by his/her subjective appraisal, which is further formed by the affective memory. Inspired by this, we propose a novel \textit{Subjectivity Appraise-and-Match Network (SAMNet)} to investigate the subjectivity in visual emotion distribution. To depict the diversity in crowd voting process, we first propose the \textit{Subjectivity Appraising} with multiple branches, where each branch simulates the emotion evocation process of a specific individual. Specifically, we construct the affective memory with an attention-based mechanism to preserve each individual's unique emotional experience. A subjectivity loss is further proposed to guarantee the divergence between different individuals. Moreover, we propose the \textit{Subjectivity Matching} with a matching loss, aiming at assigning unordered emotion labels to ordered individual predictions in a one-to-one correspondence with the Hungarian algorithm. Extensive experiments and comparisons are conducted on public visual emotion distribution datasets, and the results demonstrate that the proposed SAMNet consistently outperforms the state-of-the-art methods. Ablation study verifies the effectiveness of our method and visualization proves its interpretability.

cs.CV↗