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Lei Fu

Publications and source records attributed to Lei Fu.

At least 37 records · Page 2Linked to original sources
arXiv2024

Electrically tunable moiré magnetism in twisted double bilayers of chromium triiodide

Moiré superlattices in van der Waals structures can be used to control the electronic properties of the material and lead to emergent correlated and topological phenomena. Its first demonstration in van der Waals magnets exhibited noncollinear states and domain structures with, however, limited manipulation. Here we report electrically tunable moiré magnetism in twisted double bilayers - that is, a bilayer plus a bilayer with a twist angle between them - of layered antiferromagnet CrI3. Using magneto-optical Kerr effect microscopy, we observe the coexistence of antiferromagnetic and ferromagnetic order with nonzero net magnetization - a hallmark of moiré magnetism. Such magnetic state extends over a wide range of twist angles (with transitions at around 0° and above 20°) and exhibits a nonmonotonic temperature dependence. We further demonstrate voltage-assisted magnetic switching. The observed nontrivial magnetic states and unprecedented control by twist angle, temperature and electrical gating are supported by the simulated phase diagram of the moiré magnetism.

cond-mat.mtrl-sci↗
arXiv2024

Differentiation of Multi-objective Data-driven Decision Pipeline

Real-world scenarios frequently involve multi-objective data-driven optimization problems, characterized by unknown problem coefficients and multiple conflicting objectives. Traditional two-stage methods independently apply a machine learning model to estimate problem coefficients, followed by invoking a solver to tackle the predicted optimization problem. The independent use of optimization solvers and prediction models may lead to suboptimal performance due to mismatches between their objectives. Recent efforts have focused on end-to-end training of predictive models that use decision loss derived from the downstream optimization problem. However, these methods have primarily focused on single-objective optimization problems, thus limiting their applicability. We aim to propose a multi-objective decision-focused approach to address this gap. In order to better align with the inherent properties of multi-objective optimization problems, we propose a set of novel loss functions. These loss functions are designed to capture the discrepancies between predicted and true decision problems, considering solution space, objective space, and decision quality, named landscape loss, Pareto set loss, and decision loss, respectively. Our experimental results demonstrate that our proposed method significantly outperforms traditional two-stage methods and most current decision-focused methods.

cs.LG↗
arXiv2024

Patent Value Characterization -- An Empirical Analysis of Elevator Industry Patents

The global patent application count has steadily increased, achieving eight consecutive years of growth.The global patent industry has shown a general trend of expansion. This is attributed to the increasing innovation activities, particularly in the fields of technology, healthcare, and biotechnology. Some emerging market countries, such as China and India, have experienced significant growth in the patent domain, becoming important participants in global patent activities.

cs.DL↗
arXiv2023

Client Selection in Federated Learning: Principles, Challenges, and Opportunities

As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) has received tremendous attention from both industry and academia. In a typical FL scenario, clients exhibit significant heterogeneity in terms of data distribution and hardware configurations. Thus, randomly sampling clients in each training round may not fully exploit the local updates from heterogeneous clients, resulting in lower model accuracy, slower convergence rate, degraded fairness, etc. To tackle the FL client heterogeneity problem, various client selection algorithms have been developed, showing promising performance improvement. In this paper, we systematically present recent advances in the emerging field of FL client selection and its challenges and research opportunities. We hope to facilitate practitioners in choosing the most suitable client selection mechanisms for their applications, as well as inspire researchers and newcomers to better understand this exciting research topic.

cs.LG↗
arXiv2023

Benchmarking UWB-Based Infrastructure-Free Positioning and Multi-Robot Relative Localization: Dataset and Characterization

Ultra-wideband (UWB) positioning has emerged as a low-cost and dependable localization solution for multiple use cases, from mobile robots to asset tracking within the Industrial IoT. The technology is mature and the scientific literature contains multiple datasets and methods for localization based on fixed UWB nodes. At the same time, research in UWB-based relative localization and infrastructure-free localization is gaining traction, further domains. tools and datasets in this domain are scarce. Therefore, we introduce in this paper a novel dataset for benchmarking infrastructure-free relative localization targeting the domain of multi-robot systems. Compared to previous datasets, we analyze the performance of different relative localization approaches for a much wider variety of scenarios with varying numbers of fixed and mobile nodes. A motion capture system provides ground truth data, are multi-modal and include inertial or odometry measurements for benchmarking sensor fusion methods. Additionally, the dataset contains measurements of ranging accuracy based on the relative orientation of antennas and a comprehensive set of measurements for ranging between a single pair of nodes. Our experimental analysis shows that high accuracy can be localization, but the variability of the ranging error is significant across different settings and setups.

cs.RO↗
arXiv2023

Event-driven Fabric Blockchain -- ROS 2 Interface: Towards Secure and Auditable Teleoperation of Mobile Robots

The integration of blockchain technology in robotic systems has been met by the community with a combination of hype and skepticism. The current literature shows that there is indeed potential for more secure and trustable distributed robotic systems. However, it is still unclear in what aspects of robotics beyond high-level decision making can blockchain technology be indeed usable. This paper explores the limits of a permissioned blockchain framework, Hyperledger Fabric, for teleoperation. Remote operation of mobile robots can benefit from the auditability and security properties of a blockchain. We study the potential benefits and the main limitations of such an approach. We introduce a new design and implementation for a event-driven Fabric-ROS 2 bridge that is able to maintain lower latencies at higher network loads than previous solutions. We also show this opens the door to more realistic use cases and applications. Our experiments with small aerial robots show latencies in the hundreds of milliseconds and simultaneous control of both a single and multi-robot system. We analyze the main trade-offs and limitations for real-world near real-time remote teleoperation.

cs.RO↗
arXiv2023

Is Alice Really in Wonderland? UWB-Based Proof of Location for UAVs with Hyperledger Fabric Blockchain

Remote identification of Unmanned Aerial Vehicles (UAVs) is becoming increasingly important since more UAVs are being widely used for different needs in urban areas. For example, in the US and in the EU, identification and position broadcasting is already a requirement for the use of drones. However, the current solutions do not validate the position of the UAV but its identity, while trusting the given position. Therefore, a more advanced solution enabling the proof of location is needed to avoid spoofing. We propose the combination of a permissioned blockchain managed by public authorities together with UWB-based communication to approach this. Specifically, we leverage the identity management tools from Hyperledger Fabric, an open-source permissioned blockchain framework, and ultra-wideband (UWB) ranging, leading to situated communication (i.e., simultaneous communication and localization). This approach allows us to prove both the UAV identity and also the location it broadcasts through interaction with ground infrastructure in known locations. Our initial experiments show that the proposed approach is viable and UWB transceivers can be used for UAVs to validate both their identity and position with ground infrastructure deployed in known locations.

cs.RO↗
arXiv2023

Physics Symbolic Learner for Discovering Ground-Motion Models Via NGA-West2 Database

Ground-motion model (GMM) is the basis of many earthquake engineering studies. In this study, a novel physics-informed symbolic learner (PISL) method based on the Nest Generation Attenuation-West2 database is proposed to automatically discover mathematical equation operators as symbols. The sequential threshold ridge regression algorithm is utilized to distill a concise and interpretable explicit characterization of complex systems of ground motions. In addition to the basic variables retrieved from previous GMMs, the current PISL incorporates two a priori physical conditions, namely, distance and amplitude saturation. GMMs developed using the PISL, an empirical regression method (ERM), and an artificial neural network (ANN) are compared in terms of residuals and extrapolation based on obtained data of peak ground acceleration and velocity. The results show that the inter- and intra-event standard deviations of the three methods are similar. The functional form of the PISL is more concise than that of the ERM and ANN. The extrapolation capability of the PISL is more accurate than that of the ANN. The PISL-GMM used in this study provide a new paradigm of regression that considers both physical and data-driven machine learning and can be used to identify the implied physical relationships and prediction equations of ground motion variables in different regions.

cs.LG↗
arXiv2022

Federated Learning Hyper-Parameter Tuning from a System Perspective

Federated learning (FL) is a distributed model training paradigm that preserves clients' data privacy. It has gained tremendous attention from both academia and industry. FL hyper-parameters (e.g., the number of selected clients and the number of training passes) significantly affect the training overhead in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters imposes a heavy burden on FL practitioners because applications have different training preferences. In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements in FL training. FedTune iteratively adjusts FL hyper-parameters during FL training and can be easily integrated into existing FL systems. Through extensive evaluations of FedTune for diverse applications and FL aggregation algorithms, we show that FedTune is lightweight and effective, achieving 8.48%-26.75% system overhead reduction compared to using fixed FL hyper-parameters. This paper assists FL practitioners in designing high-performance FL training solutions. The source code of FedTune is available at https://github.com/DataSysTech/FedTune.

cs.LG↗
arXiv2022

The $p$-adic Gelfand-Kapranov-Zelevinsky hypergeometric complex

To a torus action on a complex vector space, Gelfand, Kapranov and Zelevinsky introduce a system of differential equations, which are now called the GKZ hypergeometric system. Its solutions are GKZ hypergeometric functions. We study the $p$-adic counterpart of the GKZ hypergeometric system. The $p$-adic GKZ hypergeometric complex is a twisted relative de Rham complex of over-convergent differential forms with logarithmic poles. It is an over-holonomic object in the derived category of arithmetic $\mathcal D$-modules with Frobenius structures. Traces of Frobenius on fibers at Techmüller points of the GKZ hypergeometric complex define the hypergeometric function over the finite field introduced by Gelfand and Graev. Over the non-degenerate locus, the GKZ hypergeometric complex defines an over-convergent $F$-isocrystal. It is the crystalline companion of the $\ell$-adic GKZ hypergeometric sheaf that we constructed before. Our method is a combination of Dwork's theory and the theory of arithmetic $\mathcal D$-modules of Berthelot.

math.AG↗
arXiv2022

Gravitational wave signals in the deci-Hz range from neutrinos during the proto-neutron star cooling phase

We investigate the gravitational waves (GWs) at low frequencies produced by neutrinos that are emitted anisotropically from the proto-neutron star (PNS) during its cooling phase that lasts about a minute. We first give a formulation based on the spherical-harmonic expansion of the neutrino luminosity to obtain the gravitational waveform as well as the characteristic strain. In the absence of multi-dimensional simulations of PNS cooling, from which we can extract reliable data on the neutrino luminosities as a function of solid angle, we construct them by hand. In the first model, the time evolution is approximated by piece-wise exponential functions (PEFs); in the second model we employ the time evolution obtained in a 1D cooling simulation for all harmonic components for simplicity; In both cases, we consider not only axisymmetric components but also non-axisymmetric ones; for the latter, in particular, we consider as the third model axisymetric neutrino emissions that are misaligned with the rotation axis and, as a result, rotate with the PNS. We find from the first model that the decay times in PEF at late phases can be inferred from the positions of bumps and dips in the characteristic strain of the GW in the case of a slow cooling, whereas it may be obtained by identifying the positions of slope change in the case of rapid cooling, which may be induced by convections in PNS. We also find from the first model that the rotation of PNS can be reflected in GW waveforms. Finally, we compared the characteristic strains with sensitivity curves GW detectors, finding that above phenomena are expected to be detected by DECIGO if the PNS is located at 10kpc.

astro-ph.HE↗
arXiv2021

$\ell$-adic Tautological Systems

Tautological systems was introduced in Lian-Yau as the system of differential equations satisfied by period integrals of hyperplane sections of some complex projective homogenous varieties. We introduce the $\ell$-adic tautological systems for the case where the ground field is of characteristic $p$.

math.AG↗
arXiv2020

On Katz's $(A,B)$-exponential sums

We deduce Katz's theorems for $(A,B)$-exponential sums over finite fields using $\ell$-adic cohomology and a theorem of Denef-Loeser, removing the hypothesis that $A+B$ is relatively prime to the characteristic $p$. In some degenerate cases, the Betti number estimate is improved using toric decomposition and Adolphson-Sperber's bound for the degree of $L$-functions. Applying the facial decomposition theorem in \cite{W1}, we prove that the universal family of $(A,B)$-polynomials is generically ordinary for its $L$-function when $p$ is in certain arithmetic progression.

math.AG↗
arXiv2020

Unirational Differential Curves and Differential Rational Parametrizations

In this paper, we study unirational differential curves and the corresponding differential rational parametrizations. We first investigate basic properties of proper differential rational parametrizations for unirational differential curves. Then we show that the implicitization problem of proper linear differential rational parametric equations can be solved by means of differential resultants. Furthermore, for linear differential curves, we give an algorithm to determine whether an implicitly given linear differential curve is unirational and, in the affirmative case, to compute a proper differential rational parametrization for the differential curve.

math.AG↗
arXiv2019

Deformations and Rigidity of $\ell$-adic Sheaves

Let $X$ be a smooth connected projective algebraic curve over an algebraically closed field, and let $S$ be a finite nonempty closed subset in $X$. We study deformations of $\overline{\mathbb F}_\ell$-sheaves. The universal deformation space is a formal scheme. Its generic fiber has a rigid analytic space structure. By studying this rigid analytic space, we prove a conjecture of Katz which says that if a lisse $\overline{\mathbb Q}_\ell$-sheaf $\mathcal F$ on $X-S$ is irreducible and rigid, then we have $\mathrm{dim}\, H^1(X,j_\ast\mathcal End(\mathcal F))=2g$, where $j:X-S\to X$ is the open immersion, and $g$ is the genus of $X$.

math.AG↗
arXiv2017

l-adic realization of some aspect of Landau-Ginzburg B-models

The Landau-Ginzburg B-model for a germ of a holomorphic function with an isolated critical point is constructed by K. Saito and finished by M. Saito. Douai and Sabbah construct the Landau-Ginzburg B-models for some Laurent polynomials. The construction relies on analytic procedures, and one can not expect it can be done by purely algebraic method. In this note, we work out the l-adic realization of the algebraic part of the construction.

math.AG↗
arXiv2016

Micro-reflectance and transmittance spectroscopy: a versatile and powerful tool to characterize 2D materials

Optical spectroscopy techniques such as differential reflectance and transmittance have proven to be very powerful techniques to study 2D materials. However, a thorough description of the experimental setups needed to carry out these measurements is lacking in the literature. We describe a versatile optical microscope setup to carry out differential reflectance and transmittance spectroscopy in 2D materials with a lateral resolution of ~1 micron in the visible and near-infrared part of the spectrum. We demonstrate the potential of the presented setup to determine the number of layers of 2D materials and to characterize their fundamental optical properties such as excitonic resonances. We illustrate its performance by studying mechanically exfoliated and chemical vapor-deposited transition metal dichalcogenide samples.

cond-mat.mes-hall↗