arXiv subjects

Lu Li

Publications and source records attributed to Lu Li.

At least 145 records · Page 8Linked to original sources

arXiv2021

The In-Flight Realtime Trigger and Localization Software of GECAM

Realtime trigger and localization of bursts are the key functions of GECAM, which is an all-sky gamma-ray monitor launched in Dec 10, 2020. We developed a multifunctional trigger and localization software operating on the CPU of the GECAM electronic box (EBOX). This onboard software has the following features: high trigger efficiency for real celestial bursts with a suppression of false triggers caused by charged particle bursts and background fluctuation, dedicated localization algorithm optimized for short and long bursts respetively, short time latency of the trigger information which is downlinked throught the BeiDou satellite navigation System (BDS). This paper presents the detailed design and deveopment of this trigger and localization software system of GECAM, including the main functions, general design, workflow and algorithms, as well as the verification and demonstration of this software, including the on-ground trigger tests with simulated gamma-ray bursts made by a dedicated X-ray tube and the in-flight performance to real gamma-ray bursts and magnetar bursts.

astro-ph.IM↗

arXiv2021

Minimal Cycle Representatives in Persistent Homology using Linear Programming: an Empirical Study with User's Guide

Cycle representatives of persistent homology classes can be used to provide descriptions of topological features in data. However, the non-uniqueness of these representatives creates ambiguity and can lead to many different interpretations of the same set of classes. One approach to solving this problem is to optimize the choice of representative against some measure that is meaningful in the context of the data. In this work, we provide a study of the effectiveness and computational cost of several $\ell_1$-minimization optimization procedures for constructing homological cycle bases for persistent homology with rational coefficients in dimension one, including uniform-weighted and length-weighted edge-loss algorithms as well as uniform-weighted and area-weighted triangle-loss algorithms. We conduct these optimizations via standard linear programming methods, applying general-purpose solvers to optimize over column bases of simplicial boundary matrices. Our key findings are: (i) optimization is effective in reducing the size of cycle representatives, (ii) the computational cost of optimizing a basis of cycle representatives exceeds the cost of computing such a basis in most data sets we consider, (iii) the choice of linear solvers matters a lot to the computation time of optimizing cycles, (iv) the computation time of solving an integer program is not significantly longer than the computation time of solving a linear program for most of the cycle representatives, using the Gurobi linear solver, (v) strikingly, whether requiring integer solutions or not, we almost always obtain a solution with the same cost and almost all solutions found have entries in {-1, 0, 1} and therefore, are also solutions to a restricted $\ell_0$ optimization problem, and (vi) we obtain qualitatively different results for generators in Erdős-Rényi random clique complexes.

math.AT↗

arXiv2021

Towards a Multispectral RGB-IR-UV-D Vision System -- Seeing the Invisible in 3D

In this paper, we present the development of a sensing system with the capability to compute multispectral point clouds in real-time. The proposed multi-eye sensor system effectively registers information from the visible, (long-wave) infrared, and ultraviolet spectrum to its depth sensing frame, thus enabling to measure a wider range of surface features that are otherwise hidden to the naked eye. For that, we designed a new cross-calibration apparatus that produces consistent features which can be sensed by each of the cameras, therefore, acting as a multispectral "chessboard". The performance of the sensor is evaluated with two different cases of studies, where we show that the proposed system can detect "hidden" features of a 3D environment.

cs.RO↗

arXiv2021

Hybrid Reasoning Network for Video-based Commonsense Captioning

The task of video-based commonsense captioning aims to generate event-wise captions and meanwhile provide multiple commonsense descriptions (e.g., attribute, effect and intention) about the underlying event in the video. Prior works explore the commonsense captions by using separate networks for different commonsense types, which is time-consuming and lacks mining the interaction of different commonsense. In this paper, we propose a Hybrid Reasoning Network (HybridNet) to endow the neural networks with the capability of semantic-level reasoning and word-level reasoning. Firstly, we develop multi-commonsense learning for semantic-level reasoning by jointly training different commonsense types in a unified network, which encourages the interaction between the clues of multiple commonsense descriptions, event-wise captions and videos. Then, there are two steps to achieve the word-level reasoning: (1) a memory module records the history predicted sequence from the previous generation processes; (2) a memory-routed multi-head attention (MMHA) module updates the word-level attention maps by incorporating the history information from the memory module into the transformer decoder for word-level reasoning. Moreover, the multimodal features are used to make full use of diverse knowledge for commonsense reasoning. Experiments and abundant analysis on the large-scale Video-to-Commonsense benchmark show that our HybridNet achieves state-of-the-art performance compared with other methods.

cs.CV↗

arXiv2021

Topological surface conduction in Kondo insulator YbB$_{12}$

Kondo insulators have recently aroused great interest because they are promising materials that host a topological insulator state caused by the strong electron interactions. Moreover, recent observations of the quantum oscillations in the insulating state of Kondo insulators have come as a great surprise. Here, to investigate the surface electronic state of a prototype Kondo insulator YbB$_{12}$, we measured transport properties of single crystals and microstructures. In all samples, the temperature dependence of the electrical resistivity is insulating at high temperatures and the resistivity exhibits a plateau at low temperatures. The magnitude of the plateau value decreases with reducing sample thickness, which is quantitatively consistent with the surface electronic conduction in the bulk insulating YbB$_{12}$. Moreover, the magnetoresistance of the microstructures exhibits a weak-antilocalization effect at low field. These results are consistent with the presence of topologically protected surface state, suggesting that YbB$_{12}$ is a candidate material of the topological Kondo insulator. The high field resistivity measurements up to $μ_0H$ = 50 T of the microstructures provide supporting evidence that the quantum oscillations of the resistivity in YbB$_{12}$ occurs in the insulating bulk.

cond-mat.str-el↗

arXiv2021

The x-index: A new citation-distance-based index to measure academic influence

An important issue in the field of academic measurement is how to evaluate academic influence scientifically and comprehensively, which can help government and research organizations better allocate academic resources and recruit researchers. It is generally accepted that using weighted citations to measure academic influence is more reasonable than treating all citations equally. Given the limitations of the existing c-index, the first index in bibliometric literature that measures output based on the quantity and quality of received citations, we propose the x-index, which assigns weight to each citation according to its distance. By defining collaboration distance and citation distance, we first analyze the properties of the collaboration network and citation distance, then perform theoretical and empirical analyses on c-index to reveal its shortcomings, finally, we suggest the x-index and conduct experiments and analysis on the x-index. Experimental results demonstrate that compared with the c-index, h-index, and g-index, the x-index shows a stronger discriminatory power.

cs.DL↗

arXiv2021

Robust Gaussian Process Regression Based on Iterative Trimming

The Gaussian process (GP) regression can be severely biased when the data are contaminated by outliers. This paper presents a new robust GP regression algorithm that iteratively trims the most extreme data points. While the new algorithm retains the attractive properties of the standard GP as a nonparametric and flexible regression method, it can greatly improve the model accuracy for contaminated data even in the presence of extreme or abundant outliers. It is also easier to implement compared with previous robust GP variants that rely on approximate inference. Applied to a wide range of experiments with different contamination levels, the proposed method significantly outperforms the standard GP and the popular robust GP variant with the Student-t likelihood in most test cases. In addition, as a practical example in the astrophysical study, we show that this method can precisely determine the main-sequence ridge line in the color-magnitude diagram of star clusters.

cs.LG↗

arXiv2021

A new symmetric linearly implicit exponential integrator preserving polynomial invariants or Lyapunov functions for conservative or dissipative systems

We present a new linearly implicit exponential integrator that preserves the polynomial first integrals or Lyapunov functions for the conservative and dissipative stiff equations, respectively. The method is tested by both oscillated ordinary differential equations and partial differential equations, e.g., an averaged system in wind-induced oscillation, the Fermi-Pasta-Ulam systems, and the polynomial pendulum oscillators. The numerical simulations confirm the conservative properties of the proposed method and demonstrate its good behavior in superior running speed when compared with fully implicit schemes for long-time simulations.

math.NA↗

arXiv2021

Quantum matter in ultrahigh magnetic fields

In writing this report we had two goals in mind. The first is to provide a survey of a subset of discoveries from recent experiments performed on quantum matter in high magnetic fields, and to anticipate the scientific opportunities to be realized in even higher fields. Hopefully, the survey will convey a sense of the excitement and pace of high-magnetic-field research in the quantum-matter community to a broader audience (undergraduates, especially). The second goal is to discuss the comparative merits of two options: a pulsed-field facility for attaining a magnetic field of 150 Tesla (of duration 1-10 msec) or a DC field facility that attains 60 Tesla. A workshop involving leading scientists involved with quantum phenomena in high magnetic fields was held at NSF, Alexandria Sep. 21,22 (2017) to address these issues.

cond-mat.str-el↗

arXiv2021

Rademacher complexity of noisy quantum circuits

Noise in quantum systems is a major obstacle to implementing many quantum algorithms on large quantum circuits. In this work, we study the effects of noise on the Rademacher complexity of quantum circuits, which is a measure of statistical complexity that quantifies the richness of classes of functions generated by these circuits. We consider noise models that are represented by convex combinations of unitary channels and provide both upper and lower bounds for the Rademacher complexities of quantum circuits characterized by these noise models. In particular, we find a lower bound for the Rademacher complexity of noisy quantum circuits that depends on the Rademacher complexity of the corresponding noiseless quantum circuit as well as the free robustness of the circuit. Our results show that the Rademacher complexity of quantum circuits decreases with the increase in noise.

quant-ph↗

arXiv2021

Phase-Field Modeling of Selective Laser Brazing of Diamond Grits

Diamond grit is widely used in cutting, grinding, and polishing tools for its superior mechanical properties and performance in machining hard materials. Selective laser brazing (SLB) of diamond grits is a new additive manufacturing technique that has great potential to fabricate the next generation of high-performance diamond tools. However, fundamental understanding and quantitative analysis for the design and tuning of the SLB process and the resulting bonding efficiency are not yet established as the process is complicated by heating, fusion, wetting, solidification, grit migration, bonding, reaction, and the interplay between these effects. We present a thermodynamically consistent phase-field theoretical model for the prediction of melting and wetting of SLB on diamond grits using a powder-based additive manufacturing technique. The melting dynamics is driven by laser heating in a chamber filled with argon gas and is coupled with the motion of multiple three-phase contact lines. The relevant wetting dynamics, interfacial morphology, and temperature distribution are computationally resolved in a simplified 2D configuration.

physics.flu-dyn↗

arXiv2021

Unusual high-field metal in a Kondo insulator

Within condensed-matter systems, strong electronic interactions often lead to exotic quantum phases. A recent manifestation of this is the unexpected observation of magnetic quantum oscillations and metallic thermal transport, both properties of systems with Fermi surfaces of itinerant quasiparticles, in the Kondo insulators SmB6 and YbB$_{12}$. To understand these phenomena, it is informative to study their evolution as the energy gap of the Kondo-Insulator state is closed by a large magnetic field. We show here that both the quantum-oscillation frequency and the cyclotron mass display a strong field dependence in the resulting high-field metallic state in $_{12}$. By tracking the Fermi-surface area, we conclude that the same quasiparticle band gives rise to the quantum oscillations in both insulating and metallic states. These data are understood most simply using a two-fluid picture where unusual quasiparticles, contributing little or nothing to charge transport, coexist with conventional fermions. In the metallic state this leads to a heavy-fermion bad metal with negligible magnetoresistance, relatively high resistivity and a very large Kadowaki-Woods ratio, underlining the exotic nature of the fermion ensemble inhabiting $_{12}$.

cond-mat.str-el↗

arXiv2021

Effects of quantum resources on the statistical complexity of quantum circuits

We investigate how the addition of quantum resources changes the statistical complexity of quantum circuits by utilizing the framework of quantum resource theories. Measures of statistical complexity that we consider include the Rademacher complexity and the Gaussian complexity, which are well-known measures in computational learning theory that quantify the richness of classes of real-valued functions. We derive bounds for the statistical complexities of quantum circuits that have limited access to certain resources and apply our results to two special cases: (1) stabilizer circuits that are supplemented with a limited number of T gates and (2) instantaneous quantum polynomial-time Clifford circuits that are supplemented with a limited number of CCZ gates. We show that the increase in the statistical complexity of a quantum circuit when an additional quantum channel is added to it is upper bounded by the free robustness of the added channel. Finally, we derive bounds for the generalization error associated with learning from training data arising from quantum circuits.

quant-ph↗

arXiv2021

Phase-Field Modeling of Wetting and Balling Dynamics in Powder Bed Fusion Process

In a powder bed fusion additive manufacturing (AM) process, the balling effect has a significant impact on the surface quality of the printing parts. Surface wetting helps the bonding between powder and substrate and the inter-particle fusion, whereas the balling effect forms large spheroidal beads around the laser beam and causes voids, discontinuities, and poor surface roughness during the printing process. To better understand the transient dynamics, a theoretical model with a simplified 2D configuration is developed to investigate the underlying fluid flow and heat transfer, phase transition, and interfacial instability along with the laser heating. We demonstrate that the degree of wetting and fast solidification counter-balance the balling effect, and the Rayleigh-Plateau flow instability plays an important role for cases with relatively low substrate wettability and high scanning rate.

physics.flu-dyn↗

arXiv2021

On the statistical complexity of quantum circuits

In theoretical machine learning, the statistical complexity is a notion that measures the richness of a hypothesis space. In this work, we apply a particular measure of statistical complexity, namely the Rademacher complexity, to the quantum circuit model in quantum computation and study how the statistical complexity depends on various quantum circuit parameters. In particular, we investigate the dependence of the statistical complexity on the resources, depth, width, and the number of input and output registers of a quantum circuit. To study how the statistical complexity scales with resources in the circuit, we introduce a resource measure of magic based on the $(p,q)$ group norm, which quantifies the amount of magic in the quantum channels associated with the circuit. These dependencies are investigated in the following two settings: (i) where the entire quantum circuit is treated as a single quantum channel, and (ii) where each layer of the quantum circuit is treated as a separate quantum channel. The bounds we obtain can be used to constrain the capacity of quantum neural networks in terms of their depths and widths as well as the resources in the network.

quant-ph↗

arXiv2020

CLUE: A Chinese Language Understanding Evaluation Benchmark

The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP). The problem, however, is that most such benchmarks are limited to English, which has made it difficult to replicate many of the successes in English NLU for other languages. To help remedy this issue, we introduce the first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark. CLUE is an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text. To establish results on these tasks, we report scores using an exhaustive set of current state-of-the-art pre-trained Chinese models (9 in total). We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on Chinese NLU. Our benchmark is released at https://www.CLUEbenchmarks.com

cs.CL↗

arXiv2020

OCNLI: Original Chinese Natural Language Inference

Despite the tremendous recent progress on natural language inference (NLI), driven largely by large-scale investment in new datasets (e.g., SNLI, MNLI) and advances in modeling, most progress has been limited to English due to a lack of reliable datasets for most of the world's languages. In this paper, we present the first large-scale NLI dataset (consisting of ~56,000 annotated sentence pairs) for Chinese called the Original Chinese Natural Language Inference dataset (OCNLI). Unlike recent attempts at extending NLI to other languages, our dataset does not rely on any automatic translation or non-expert annotation. Instead, we elicit annotations from native speakers specializing in linguistics. We follow closely the annotation protocol used for MNLI, but create new strategies for eliciting diverse hypotheses. We establish several baseline results on our dataset using state-of-the-art pre-trained models for Chinese, and find even the best performing models to be far outpaced by human performance (~12% absolute performance gap), making it a challenging new resource that we hope will help to accelerate progress in Chinese NLU. To the best of our knowledge, this is the first human-elicited MNLI-style corpus for a non-English language.

cs.CL↗

arXiv2020

Outcome regression-based estimation of conditional average treatment effect

The research is about a systematic investigation on the following issues. First, we construct different outcome regression-based estimators for conditional average treatment effect under, respectively, true (oracle), parametric, nonparametric and semiparametric dimension reduction structure. Second, according to the corresponding asymptotic variance functions, we answer the following questions when supposing the models are correctly specified: what is the asymptotic efficiency ranking about the four estimators in general? how is the efficiency related to the affiliation of the given covariates in the set of arguments of the regression functions? what do the roles of bandwidth and kernel function selections play for the estimation efficiency; and in which scenarios should the estimator under semiparametric dimension reduction regression structure be used in practice? As a by-product, the results show that any outcome regression-based estimation should be asymptotically more efficient than any inverse probability weighting-based estimation. All these results give a relatively complete picture of the outcome regression-based estimation such that the theoretical conclusions could provide guidance for practical use when more than one estimations can be applied to the same problem. Several simulation studies are conducted to examine the performances of these estimators in finite sample cases and a real dataset is analyzed for illustration.

math.ST↗