arXiv subjects

Lei Shi

Publications and source records attributed to Lei Shi.

At least 235 records · Page 13Linked to original sources

arXiv2023

Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate

Decentralized optimization is an emerging paradigm in distributed learning in which agents achieve network-wide solutions by peer-to-peer communication without the central server. Since communication tends to be slower than computation, when each agent communicates with only a few neighboring agents per iteration, they can complete iterations faster than with more agents or a central server. However, the total number of iterations to reach a network-wide solution is affected by the speed at which the agents' information is ``mixed'' by communication. We found that popular communication topologies either have large maximum degrees (such as stars and complete graphs) or are ineffective at mixing information (such as rings and grids). To address this problem, we propose a new family of topologies, EquiTopo, which has an (almost) constant degree and a network-size-independent consensus rate that is used to measure the mixing efficiency. In the proposed family, EquiStatic has a degree of $Θ(\ln(n))$, where $n$ is the network size, and a series of time-dependent one-peer topologies, EquiDyn, has a constant degree of 1. We generate EquiDyn through a certain random sampling procedure. Both of them achieve an $n$-independent consensus rate. We apply them to decentralized SGD and decentralized gradient tracking and obtain faster communication and better convergence, theoretically and empirically. Our code is implemented through BlueFog and available at \url{https://github.com/kexinjinnn/EquiTopo}

math.OC↗

arXiv2023

Microwave heating as a universal method to transform confined molecules into armchair graphene nanoribbons

Armchair graphene nanoribbons (AGNRs) with sub-nanometer width are potential materials for fabrication of novel nanodevices thanks to their moderate direct band gaps. AGNRs are usually synthesized by polymerizing precursor molecules on substrate surface. However, it is time-consuming and not suitable for large-scale production. AGNRs can also be grown by transforming precursor molecules inside single-walled carbon nanotubes via furnace annealing, but the obtained AGNRs are normally twisted. In this work, microwave heating is applied for transforming precursor molecules into AGNRs. The fast heating process allows synthesizing the AGNRs in seconds. Several different molecules were successfully transformed into AGNRs, suggesting that it is a universal method. More importantly, as demonstrated by Raman spectroscopy, aberration-corrected high-resolution transmission electron microscopy and theoretical calculations, less twisted AGNRs are synthesized by the microwave heating than the furnace annealing. Our results reveal a route for rapid production of AGNRs in large scale, which would benefit future applications in novel AGNRs-based semiconductor devices.

cond-mat.mtrl-sci↗

arXiv2023

Simulation of environmental impacts on the synthesis of carbyne with more than 6000 atoms for emerging continuously tunable energy barriers in CNT-based transistors

Transistors made up of carbon nanotubes CNT have demonstrated excellent current-voltage characteristics which outperform some high-grade silicon-based transistors. A continuously tunable energy barrier across semiconductor interfaces is desired to make the CNT-based transistors more robust. Despite the direct band gap of carbyne inside a CNT can be widely tuned by strain, the size of carbyne cannot be controlled easily. The production of a monoatomic chain with more than 6000 carbon atoms is an enormous technological challenge. To predict the optimal chain length of a carbyne in different molecular environments, we have developed a Monte Carlo model in which a finite-length carbyne with a size of 4000-15000 atoms is encapsulated by a CNT at finite temperatures. Our simulation shows that the stability of the carbyne@nanotube is strongly influenced by the nature and porosity of the CNT, the external pressure, the temperature and the chain length. We have observed an initiation of chain-breaking process in a compressed carbyne@nanotube. Our work provides much needed input for optimising the carbyne length to produce carbon chains much longer than 6000 atoms at ~300K. Design rules are proposed for synthesizing ~1% strained carbyne@(6,5)CNT as a component in CNT-based transistors to tune the energy barriers continuously.

cond-mat.mtrl-sci↗

arXiv2022

Imaging with an ultra-thin reciprocal lens

Imaging is of great importance in everyday life and various fields of science and technology. Conventional imaging is achieved by bending light rays originating from an object with a lens. Such ray bending requires space-variant structures, inevitably introducing a geometric center to the lens. To overcome the limitations arising from the conventional imaging mechanism, we consider imaging elements that employ a different mechanism, which we call reciprocal lenses. This type of imaging element relies on ray shifting, enabled by momentum-space-variant phase modulations in periodic structures. As such, it has the distinct advantage of not requiring alignment with a geometric center. Moreover, upright real images can be produced directly with a single reciprocal lens as the directions of rays are not changed. We realized an ultra-thin reciprocal lens based on a photonic crystal slab. We characterized the ray shifting behavior of the reciprocal lens and demonstrated imaging. Our work gives an alternative mechanism for imaging, and provides a new way to modulate electromagnetic waves.

physics.optics↗

arXiv2022

A Decentralized Framework for Kernel PCA with Projection Consensus Constraints

This paper studies kernel PCA in a decentralized setting, where data are distributively observed with full features in local nodes and a fusion center is prohibited. Compared with linear PCA, the use of kernel brings challenges to the design of decentralized consensus optimization: the local projection directions are data-dependent. As a result, the consensus constraint in distributed linear PCA is no longer valid. To overcome this problem, we propose a projection consensus constraint and obtain an effective decentralized consensus framework, where local solutions are expected to be the projection of the global solution on the column space of local dataset. We also derive a fully non-parametric, fast and convergent algorithm based on alternative direction method of multiplier, of which each iteration is analytic and communication-effcient. Experiments on a truly parallel architecture are conducted on real-world data, showing that the proposed decentralized algorithm is effective to utilize information of other nodes and takes great advantages in running time over the central kernel PCA.

cs.DC↗

arXiv2022

Simple bots breed social punishment in humans

Costly punishment has been suggested as a key mechanism for stabilizing cooperation in one-shot games. However, recent studies have revealed that the effectiveness of costly punishment can be diminished by second-order free riders (i.e., cooperators who never punish defectors) and antisocial punishers (i.e., defectors who punish cooperators). In a two-stage prisoner's dilemma game, players not only need to choose between cooperation and defection in the first stage, but also need to decide whether to punish their opponent in the second stage. Here, we extend the theory of punishment in one-shot games by introducing simple bots, who consistently choose prosocial punishment and do not change their actions over time. We find that this simple extension of the game allows prosocial punishment to dominate in well-mixed and networked populations, and that the minimum fraction of bots required for the dominance of prosocial punishment monotonically increases with increasing dilemma strength. Furthermore, if humans possess a learning bias toward a "copy the majority" rule or if bots are present at higher degree nodes in scale-free networks, the fully dominance of prosocial punishment is still possible at a high dilemma strength. These results indicate that introducing bots can be a significant factor in establishing prosocial punishment. We therefore, provide a novel explanation for the evolution of prosocial punishment, and note that the contrasting results that emerge from the introduction of different types of bots also imply that the design of the bots matters.

physics.soc-ph↗

arXiv2022

Robust and Efficient Network Reconstruction in Complex System via Adaptive Signal Lasso

Network reconstruction is important to the understanding and control of collective dynamics in complex systems. Most real networks exhibit sparsely connected properties, and the connection parameter is a signal (0 or 1). Well-known shrinkage methods such as lasso or compressed sensing (CS) to recover structures of complex networks cannot suitably reveal such a property; therefore, the signal lasso method was proposed recently to solve the network reconstruction problem and was found to outperform lasso and CS methods. However, signal lasso suffers the problem that the estimated coefficients that fall between 0 and 1 cannot be successfully selected to the correct class. We propose a new method, adaptive signal lasso, to estimate the signal parameter and uncover the topology of complex networks with a small number of observations. The proposed method has three advantages: (1) It can effectively uncover the network topology with high accuracy and is capable of completely shrinking the signal parameter to either 0 or 1, which eliminates the unclassified portion in network reconstruction; (2) The method performs well in scenarios of both sparse and dense signals and is robust to noise contamination; (3) The method only needs to select one tuning parameter versus two in signal lasso, which greatly reduces the computational cost and is easy to apply. The theoretical properties of this method are studied, and numerical simulations from linear regression, evolutionary games, and Kuramoto models are explored. The method is illustrated with real-world examples from a human behavioral experiment and a world trade web.

physics.soc-ph↗

arXiv2022

Nitrogen-tailored quasiparticle energy gaps of polyynes

Polyyne, a sp1-hybridized linear allotrope of carbon, has a tunable quasiparticle energy gap, which depends on the terminated chemical ending groups as well as the chain length. Previously, nitrogen doping was utilized to tailor the properties of different kinds of allotrope of carbon. However, how the nitrogen doping tailors the properties of the polyyne remains unexplored. Here, we applied the GW method to study the quasiparticle energy gaps of the N-doped polyynes with different lengths. When a C atom is substituted by a N atom in a polyyne, the quasiparticle energy gap varies with the substituted position in the polyyne. The modification is particularly pronounced when the second-nearest-neighboring carbon atom of a hydrogen atom is substituted. In addition, the nitrogen doping makes the Fermi level closer to the lowest unoccupied molecular orbital, resulting in a n-type semiconductor. Our results suggest another route to tailor the electronic properties of polyyne in addition to the length of polyyne and the terminated chemical ending groups.

cond-mat.mtrl-sci↗

arXiv2022

Ultra-clean isotope engineered double-walled carbon nanotubes as tailored hosts to trace the growth of carbyne

Increasing attention is currently given to carbyne, the sp1 hybridized one-dimensional carbon allotrope, because of its predicted outstanding mechanical, optical, and electrical properties. Although recently substantial progress has been reported on confined carbyne synthesized inside double-walled carbon nanotubes (DWCNTs), its formation mechanism and precursors for growth remain elusive. Here, we show a rational design of isotope engineered ultra-clean DWCNTs as tailored hosts to trace the growth of carbyne, which allows to identify the precursor and unravel the formation mechanism of carbyne during high-vacuum annealing at high-temperatures. Using this approach, ultra-clean DWCNTs with 80.4% 13C-enriched inner walls and outer tubes of naturally abundant served to unambiguously prove that only the carbonaceous materials inside the DWCNTs can act as precursors. The exchange of C atoms between inner and outer tubes happens without any growth of carbyne. After applying a secondary oxidation step, it is possible to produce the carbonaceous precursors from the partially oxidized DWCNTs. In this manner, not only carbyne with a record of ~28.8% 13C enrichment is grown, but concomitant healing, reorganization and regrowth of the DWCNTs occurs. This work enables to identify the precursor and trace the growth mechanism of confined carbyne with engineered properties. This is a crucial step, towards accessing the full application potential of confined carbyne hybrids by tailoring not only the isotopic fillers, but also the inner and outer tubes of the DWCNT hosts.

cond-mat.mtrl-sci↗

arXiv2022

Generalization Properties of hyper-RKHS and its Applications

This paper generalizes regularized regression problems in a hyper-reproducing kernel Hilbert space (hyper-RKHS), illustrates its utility for kernel learning and out-of-sample extensions, and proves asymptotic convergence results for the introduced regression models in an approximation theory view. Algorithmically, we consider two regularized regression models with bivariate forms in this space, including kernel ridge regression (KRR) and support vector regression (SVR) endowed with hyper-RKHS, and further combine divide-and-conquer with Nyström approximation for scalability in large sample cases. This framework is general: the underlying kernel is learned from a broad class, and can be positive definite or not, which adapts to various requirements in kernel learning. Theoretically, we study the convergence behavior of regularized regression algorithms in hyper-RKHS and derive the learning rates, which goes beyond the classical analysis on RKHS due to the non-trivial independence of pairwise samples and the characterisation of hyper-RKHS. Experimentally, results on several benchmarks suggest that the employed framework is able to learn a general kernel function form an arbitrary similarity matrix, and thus achieves a satisfactory performance on classification tasks.

cs.LG↗

arXiv2022

Exploiting potentialities for space-based quantum communication network: downlink quantum key distribution modelling and scheduling analysis

With the goal of a space-based quantum network is to have satellites distribute keys between any nodes on the ground, we consider an evolved quantum network from a near-term form, in which a space-based relay, satellite executes a sequence of satellite-based quantum key distribution (SatQKD) missions, allowing any two ground nodes to have a shared secure key. Accordingly, we develop a comprehensive framework for the dynamic simulation of SatQKD and consider scheduling QKD downlink in future space-based quantum communication network. The embedding of precise orbital model including beam diffractions and atmosphere effects makes our framework more realistic. Incorporated with the local meteorological data to channel loss model, the cloud cover contribution to transmission disturbance is quantified to realize a quasi-experimental scenario. We provide a trade-off consideration between the duration available for communications and its corresponding average link loss during one orbit, which could be used to support decisions involving the locations of ground stations and the selection of the orbital parameters for a quantum satellite. Our work also shows that satellite downlink schedule could allow for the possibility to consider strategies for different SatQKD missions such as extending connection for distant ground nodes, prioritized delivery, or promoting keys utilization, which can be used as a guideline to support decisions for future satellite application.

quant-ph↗

arXiv2022

Capacity dependent analysis for functional online learning algorithms

This article provides convergence analysis of online stochastic gradient descent algorithms for functional linear models. Adopting the characterizations of the slope function regularity, the kernel space capacity, and the capacity of the sampling process covariance operator, significant improvement on the convergence rates is achieved. Both prediction problems and estimation problems are studied, where we show that capacity assumption can alleviate the saturation of the convergence rate as the regularity of the target function increases. We show that with properly selected kernel, capacity assumptions can fully compensate for the regularity assumptions for prediction problems (but not for estimation problems). This demonstrates the significant difference between the prediction problems and the estimation problems in functional data analysis.

stat.ML↗

arXiv2022

INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations

XAI with natural language processing aims to produce human-readable explanations as evidence for AI decision-making, which addresses explainability and transparency. However, from an HCI perspective, the current approaches only focus on delivering a single explanation, which fails to account for the diversity of human thoughts and experiences in language. This paper thus addresses this gap, by proposing a generative XAI framework, INTERACTION (explaIn aNd predicT thEn queRy with contextuAl CondiTional varIational autO-eNcoder). Our novel framework presents explanation in two steps: (step one) Explanation and Label Prediction; and (step two) Diverse Evidence Generation. We conduct intensive experiments with the Transformer architecture on a benchmark dataset, e-SNLI. Our method achieves competitive or better performance against state-of-the-art baseline models on explanation generation (up to 4.7% gain in BLEU) and prediction (up to 4.4% gain in accuracy) in step one; it can also generate multiple diverse explanations in step two.

cs.CL↗

arXiv2022

Solving parametric partial differential equations with deep rectified quadratic unit neural networks

Implementing deep neural networks for learning the solution maps of parametric partial differential equations (PDEs) turns out to be more efficient than using many conventional numerical methods. However, limited theoretical analyses have been conducted on this approach. In this study, we investigate the expressive power of deep rectified quadratic unit (ReQU) neural networks for approximating the solution maps of parametric PDEs. The proposed approach is motivated by the recent important work of G. Kutyniok, P. Petersen, M. Raslan and R. Schneider (Gitta Kutyniok, Philipp Petersen, Mones Raslan, and Reinhold Schneider. A theoretical analysis of deep neural networks and parametric pdes. Constructive Approximation, pages 1-53, 2021), which uses deep rectified linear unit (ReLU) neural networks for solving parametric PDEs. In contrast to the previously established complexity-bound $\mathcal{O}\left(d^3\log_{2}^{q}(1/ ε) \right)$ for ReLU neural networks, we derive an upper bound $\mathcal{O}\left(d^3\log_{2}^{q}\log_{2}(1/ ε) \right)$ on the size of the deep ReQU neural network required to achieve accuracy $ε>0$, where $d$ is the dimension of reduced basis representing the solutions. Our method takes full advantage of the inherent low-dimensionality of the solution manifolds and better approximation performance of deep ReQU neural networks. Numerical experiments are performed to verify our theoretical result.

math.NA↗

arXiv2022

Coefficient-based Regularized Distribution Regression

In this paper, we consider the coefficient-based regularized distribution regression which aims to regress from probability measures to real-valued responses over a reproducing kernel Hilbert space (RKHS), where the regularization is put on the coefficients and kernels are assumed to be indefinite. The algorithm involves two stages of sampling, the first stage sample consists of distributions and the second stage sample is obtained from these distributions. Asymptotic behaviors of the algorithm in different regularity ranges of the regression function are comprehensively studied and learning rates are derived via integral operator techniques. We get the optimal rates under some mild conditions, which matches the one-stage sampled minimax optimal rate. Compared with the kernel methods for distribution regression in the literature, the algorithm under consideration does not require the kernel to be symmetric and positive semi-definite and hence provides a simple paradigm for designing indefinite kernel methods, which enriches the theme of the distribution regression. To the best of our knowledge, this is the first result for distribution regression with indefinite kernels, and our algorithm can improve the saturation effect.

stat.ML↗

arXiv2022

LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexia

Prior work has explored the writing challenges experienced by people with dyslexia, and the potential for new spelling, grammar, and word retrieval technologies to address these challenges. However, the capabilities for natural language generation demonstrated by the latest class of large language models (LLMs) highlight an opportunity to explore new forms of human-AI writing support tools. In this paper, we introduce LaMPost, a prototype email-writing interface that explores the potential for LLMs to power writing support tools that address the varied needs of people with dyslexia. LaMPost draws from our understanding of these needs and introduces novel AI-powered features for email-writing, including: outlining main ideas, generating a subject line, suggesting changes, rewriting a selection. We evaluated LaMPost with 19 adults with dyslexia, identifying many promising routes for further exploration (including the popularity of the "rewrite" and "subject line" features), but also finding that the current generation of LLMs may not surpass the accuracy and quality thresholds required to meet the needs of writers with dyslexia. Surprisingly, we found that participants' awareness of the AI had no effect on their perception of the system, nor on their feelings of autonomy, expression, and self-efficacy when writing emails. Our findings yield further insight into the benefits and drawbacks of using LLMs as writing support for adults with dyslexia and provide a foundation to build upon in future research.

cs.HC↗

arXiv2022

Piecewise dominant sequences and the cocenter of the cyclotomic quiver Hecke algebras

We study the cocenter of the cyclotomic quiver Hecke algebra $R^Λ_α$ associated to an {\it arbitrary} symmetrizable Cartan matrix $A=(a_{ij})_{i,j}\in I$, $Λ\in P^+$ and $α\in Q_n^+$. We introduce a notion called "piecewise dominant sequence" and use it to construct some explicit homogeneous elements which span the maximal degree component of the cocenter of $R^Λ_α$. We show that the minimal degree components of the cocenter of $R^Λ_α$ is spanned by the image of some KLR idempotent $e(ν)$, where each $ν\in I^α$ is piecewise dominant. As an application, we show that the weight space $L(Λ)_{Λ-α}$ of the irreducible highest weight module $L(Λ)$ over $\mathfrak{g}(A)$ is nonzero (equivalently, $R^Λ_α\neq 0$) if and only if there exists a piecewise dominant sequence $ν\in I^α$.

math.RT↗

arXiv2022

Finite size mediated radiative coupling of lasing plasmonic bound state in continuum

Radiative properties of lasing plasmonic bound state in continuum are analyzed. The topological charge of the lasing signal is analyzed in the far field as well as in the source plane of the finite sized plasmonic lattice. The physical mechanism enabling the coupling of the BIC to radiation continuum is identified. We show that while the BICs have their origin in multipolar resonances, their far-field radiation properties are governed by the position dependent dipole moment distribution induced by the symmetry breaking in a finite plasmonic lattice. Remarkably, this dipole-moment enabled coupling to radiation continuum maintains the essential topological features of the infinite lattice BICs.

physics.optics↗