arXiv subjects

Yu Luo

Publications and source records attributed to Yu Luo.

At least 145 records · Page 8Linked to original sources

arXiv2023

Physics-data-driven intelligent optimization for large-scale meta-devices

Meta-devices have gained significant attention and have been widely utilized in optical systems for focusing and imaging, owing to their lightweight, high-integration, and exceptional-flexibility capabilities. However, based on the assumption of local phase approximation, traditional design method neglect the local lattice coupling effect between adjacent meta-atoms, thus harming the practical performance of meta-devices. Using physics-driven or data-driven optimization algorithms can effectively solve the aforementioned problems. Nevertheless, both of the methods either involve considerable time costs or require a substantial amount of data sets. Here, we propose a physics-data-driven approach based "intelligent optimizer" that enables us to adaptively modify the sizes of the studied meta-atom according to the sizes of its surrounding ones. Such a scheme allows to mitigate the undesired local lattice coupling effect, and the proposed network model works well on thousands of datasets with a validation loss of 3*10-3. Experimental results show that the 1-mm-diameter metalens designed with the "intelligent optimizer" possesses a relative focusing efficiency of 93.4% (as compared to ideal focusing) and a Strehl ratio of 0.94. In contrast to the previous inverse design method, our method significantly boosts designing efficiency with five orders of magnitude reduction in time. Our design approach may sets a new paradigm for devising large-scale meta-devices.

physics.optics↗

arXiv2023

Blockchain-based Decentralized Co-governance: Innovations and Solutions for Sustainable Crowdfunding

This thesis provides an in-depth exploration of the Decentralized Co-governance Crowdfunding (DCC) Ecosystem, a novel solution addressing prevailing challenges in conventional crowdfunding methods faced by MSMEs and innovative projects. Among the problems it seeks to mitigate are high transaction costs, lack of transparency, fraud, and inefficient resource allocation. Leveraging a comprehensive review of the existing literature on crowdfunding economic activities and blockchain's impact on organizational governance, we propose a transformative socio-economic model based on digital tokens and decentralized co-governance. This ecosystem is marked by a tripartite community structure - the Labor, Capital, and Governance communities - each contributing uniquely to the ecosystem's operation. Our research unfolds the evolution of the DCC ecosystem through distinct phases, offering a novel understanding of socioeconomic dynamics in a decentralized digital world. It also delves into the intricate governance mechanism of the ecosystem, ensuring integrity, fairness, and a balanced distribution of value and wealth.

cs.CY↗

arXiv2023

Perception and Semantic Aware Regularization for Sequential Confidence Calibration

Deep sequence recognition (DSR) models receive increasing attention due to their superior application to various applications. Most DSR models use merely the target sequences as supervision without considering other related sequences, leading to over-confidence in their predictions. The DSR models trained with label smoothing regularize labels by equally and independently smoothing each token, reallocating a small value to other tokens for mitigating overconfidence. However, they do not consider tokens/sequences correlations that may provide more effective information to regularize training and thus lead to sub-optimal performance. In this work, we find tokens/sequences with high perception and semantic correlations with the target ones contain more correlated and effective information and thus facilitate more effective regularization. To this end, we propose a Perception and Semantic aware Sequence Regularization framework, which explore perceptively and semantically correlated tokens/sequences as regularization. Specifically, we introduce a semantic context-free recognition and a language model to acquire similar sequences with high perceptive similarities and semantic correlation, respectively. Moreover, over-confidence degree varies across samples according to their difficulties. Thus, we further design an adaptive calibration intensity module to compute a difficulty score for each samples to obtain finer-grained regularization. Extensive experiments on canonical sequence recognition tasks, including scene text and speech recognition, demonstrate that our method sets novel state-of-the-art results. Code is available at https://github.com/husterpzh/PSSR.

cs.CV↗

arXiv2023

A set of moment tensor potentials for zirconium with increasing complexity

Machine learning force fields (MLFFs) are an increasingly popular choice for atomistic simulations due to their high fidelity and improvable nature. Here, we propose a hybrid small-cell approach that combines attributes of both offline and active learning to systematically expand a quantum mechanical (QM) database while constructing MLFFs with increasing model complexity. Our MLFFs employ the moment tensor potential formalism. During this process, we quantitatively assessed structural properties, elastic properties, dimer potential energies, melting temperatures, phase stability, point defect formation energies, point defect migration energies, free surface energies, and generalized stacking fault (GSF) energies of Zr as predicted by our MLFFs. Unsurprisingly, model complexity has a positive correlation with prediction accuracy. We also find that the MLFFs wee able to predict the properties of out-of-sample configurations without directly including these specific configurations in the training dataset. Additionally, we generated 100 MLFFs of high complexity (1513 parameters each) that reached different local optima during training. Their predictions cluster around the benchmark DFT values, but subtle physical features such as the location of local minima on the GSFE surface are washed out by statistical noise.

physics.comp-ph↗

arXiv2023

Local Group Dwarf Galaxy Detection Limit in the CSST survey

We predict the dwarf galaxy detection limits for the upcoming Chinese Space Station Telescope (CSST) survey that will cover 17,500 deg$^{2}$ of the sky with a wide field of view of 1.1 deg$^2$. The point-source depth reaches 26.3 mag in the $g$ band and 25.9 mag in the $i$ band. Constructing mock survey data based on the designed photometric bands, we estimate the recovery rate of artificial dwarf galaxies from mock point-source photometric catalogues. The detection of these artificial dwarf galaxies is strongly dependent on their distance, magnitude and size, in agreement with searches in current surveys. We expect CSST to enable the detection of dwarf galaxies with $M_V = -3.0$ and $μ_{250} = 32.0$ mag/arcsec$^2$ (surface-brightness limit for a system of half-light radius $r_{\rm h}$ = 250 pc at 400 kpc, and $M_V = -4.9$ and $μ_{250} = 30.5$ mag/arcsec$^2$ around the Andromeda galaxy. Beyond the Local Group, the CSST survey will achieve $M_V = -5.8$, and $μ_{250}$ = 29.7 mag/arcsec$^2$ in the distance range of 1--2 Mpc, opening up an exciting discovery space for faint field dwarf galaxies. With its optical bands, wide survey footprint, and space resolution, CSST will undoubtedly expand our knowledge of low-mass dwarf galaxies to an unprecedented volume.

astro-ph.GA↗

arXiv2023

Context-Aware Selective Label Smoothing for Calibrating Sequence Recognition Model

Despite the success of deep neural network (DNN) on sequential data (i.e., scene text and speech) recognition, it suffers from the over-confidence problem mainly due to overfitting in training with the cross-entropy loss, which may make the decision-making less reliable. Confidence calibration has been recently proposed as one effective solution to this problem. Nevertheless, the majority of existing confidence calibration methods aims at non-sequential data, which is limited if directly applied to sequential data since the intrinsic contextual dependency in sequences or the class-specific statistical prior is seldom exploited. To the end, we propose a Context-Aware Selective Label Smoothing (CASLS) method for calibrating sequential data. The proposed CASLS fully leverages the contextual dependency in sequences to construct confusion matrices of contextual prediction statistics over different classes. Class-specific error rates are then used to adjust the weights of smoothing strength in order to achieve adaptive calibration. Experimental results on sequence recognition tasks, including scene text recognition and speech recognition, demonstrate that our method can achieve the state-of-the-art performance.

cs.AI↗

arXiv2022

Exceptional Laurent biorthogonal polynomials through spectral transformations of generalized eigenvalue problems

A formulation is given for the spectral transformation of the generalized eigenvalue problem through the decomposition of the second-order differential operators. This allows us to construct some Laurent biorthogonal polynomial systems with gaps in the degree of the polynomial sequence. These correspond to an exceptional-type extension of the orthogonal polynomials, as an extension of the Laurent biorthogonal polynomials. Specifically, we construct the exceptional extension of the Hendriksen-van Rossum polynomials, which are biorthogonal analogs of the classical orthogonal polynomials. Similar to the cases of exceptional extensions of classical orthogonal polynomials, both of state-deletion and state-addition occur.

math.CA↗

arXiv2022

Can we constrain galaxy geometry parameters using spatially integrated SED fitting?

Sophisticated spectral energy distribution (SED) models describe dust attenuation and emission using geometry parameters. This treatment is natural since dust effects are driven by the underlying star-dust geometry in galaxies. An example is the Starduster SED model, which divides a galaxy into a stellar disk, a stellar bulge, and a dust disk. This work utilises the Starduster SED model to study the efficacy of inferring geometry parameters using spatially integrated SED fitting. Our method fits the SED model to mock photometry produced by combining a semi-analytic model with the same SED model. Our fitting results imply that the disk radius can be constrained, while the inclination angle, dust disk to stellar disk radius ratio, bulge radius and intrinsic bulge to total luminosity ratio are unconstrained, even though 21 filters from UV to FIR are used. We also study the impact of S/N, finding that the increase of S/N (up to 80) brings limited improvements to the results. We provide a detailed discussion to explain these findings, and point out the implications for models with more general geometry.

astro-ph.GA↗

arXiv2022

Effects of galaxy intrinsic alignment on weak lensing peak statistics

The galaxy intrinsic alignment (IA) is a dominant source of systematics in weak lensing (WL) studies. In this paper, by employing large simulations with semi-analytical galaxy formation, we investigate the IA effects on WL peak statistics. Different simulated source galaxy samples of different redshift distributions are constructed, where both WL shear and IA signals are included. Convergence reconstruction and peak statistics are then performed for these samples. Our results show that the IA effects on peak abundances mainly consist of two aspects. One is the additional contribution from IA to the shape noise. The other is from the satellite IA that can affect the peak signals from their host clusters significantly. The latter depends on the level of inclusion in a shear sample of the satellite galaxies of the clusters that contribute to WL peaks, and thus is sensitive to the redshift distribution of source galaxies. We pay particular attention to satellite IA and adjust it artificially in the simulations to analyze the dependence of the satellite IA impacts on its strength. This information can potentially be incorporated into the modeling of WL peak abundances, especially for high peaks physically originated from massive clusters of galaxies, and thus to mitigate the IA systematics on the cosmological constraints derived from WL peaks.

astro-ph.CO↗

arXiv2022

Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing

Fine-grained entity typing (FET) aims to deduce specific semantic types of the entity mentions in text. Modern methods for FET mainly focus on learning what a certain type looks like. And few works directly model the type differences, that is, let models know the extent that one type is different from others. To alleviate this problem, we propose a type-enriched hierarchical contrastive strategy for FET. Our method can directly model the differences between hierarchical types and improve the ability to distinguish multi-grained similar types. On the one hand, we embed type into entity contexts to make type information directly perceptible. On the other hand, we design a constrained contrastive strategy on the hierarchical structure to directly model the type differences, which can simultaneously perceive the distinguishability between types at different granularity. Experimental results on three benchmarks, BBN, OntoNotes, and FIGER show that our method achieves significant performance on FET by effectively modeling type differences.

cs.CL↗

arXiv2022

ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal Understanding

Visual question answering is an important task in both natural language and vision understanding. However, in most of the public visual question answering datasets such as VQA, CLEVR, the questions are human generated that specific to the given image, such as `What color are her eyes?'. The human generated crowdsourcing questions are relatively simple and sometimes have the bias toward certain entities or attributes. In this paper, we introduce a new question answering dataset based on image-ChiQA. It contains the real-world queries issued by internet users, combined with several related open-domain images. The system should determine whether the image could answer the question or not. Different from previous VQA datasets, the questions are real-world image-independent queries that are more various and unbiased. Compared with previous image-retrieval or image-caption datasets, the ChiQA not only measures the relatedness but also measures the answerability, which demands more fine-grained vision and language reasoning. ChiQA contains more than 40K questions and more than 200K question-images pairs. A three-level 2/1/0 label is assigned to each pair indicating perfect answer, partially answer and irrelevant. Data analysis shows ChiQA requires a deep understanding of both language and vision, including grounding, comparisons, and reading. We evaluate several state-of-the-art visual-language models such as ALBEF, demonstrating that there is still a large room for improvements on ChiQA.

cs.CL↗

arXiv2022

An Extended Halo-based Group/Cluster finder: application to the DESI legacy imaging surveys DR8

We extend the halo-based group finder developed by \citet[][]{Yang2005a} to use data {\it simultaneously} with either photometric or spectroscopic redshifts. A mock galaxy redshift survey constructed from a high-resolution N-body simulation is used to evaluate the performance of this extended group finder. For galaxies with magnitude ${\rm z\le 21}$ and redshift $0<z\le 1.0$ in the DESI legacy imaging surveys (the Legacy Surveys), our group finder successfully identifies more than 60\% of the members in about $90\%$ of halos with mass $\ga 10^{12.5}\msunh$. Detected groups with mass $\ga 10^{12.0}\msunh$ have a purity (the fraction of true groups) greater than 90\%. The halo mass assigned to each group has an uncertainty of about 0.2 dex at the high mass end $\ga 10^{13.5}\msunh$ and 0.40 dex at the low mass end. Groups with more than 10 members have a redshift accuracy of $\sim 0.008$. We apply this group finder to the Legacy Surveys DR8 and find 5.2 Million groups with at least 3 members. About 387,000 of these groups have at least 10 members. The resulting catalog containing 3D coordinates, richness, halo masses, and total group luminosities, is made publicly available.

astro-ph.GA↗

arXiv2022

Quenching of Massive Disk Galaxies in the IllustrisTNG Simulation

A rare population of massive disk galaxies have been found to invade the red sequence dominated by early-type galaxies. These red/quenched massive disk galaxies have recently gained great interest into their formation and origins. The usually proposed quenching mechanisms, such as bar quenching and environment quenching, seem not suitable for those bulge-less quenched disks in low-density environment. In this paper, we use the IllustrisTNG-300 simulation to investigate the formation of massive quenched central disk galaxies. It is found that these galaxies contain less gas and harbor giant supermassive black holes(SMBHs) (above $ 10^{8}M_{\odot}$) than their star forming counterparts. By tracing their formation history, we found that quenched disk galaxies formed early and preserved disk morphology for cosmological time scales. They have experienced less than one major merger on average and it is mainly mini-mergers (mass ratio $<$1/10) that contribute to the growth of their SMBHs. In the Illustris-TNG simulation the black hole feedback mode switches from thermal to kinetic feedback when the black hole mass is more massive than $\sim 10^{8}M_{\odot}$, which is more efficient to eject gas outside of the galaxy and to suppress further cooling of hot gaseous halo. We conclude that kinetic AGN feedback in massive red/quenched disk galaxy is the dominant quenching mechanism.

astro-ph.GA↗

arXiv2022

Spatio-Temporal Federated Learning for Massive Wireless Edge Networks

This paper presents a novel approach to conduct highly efficient federated learning (FL) over a massive wireless edge network, where an edge server and numerous mobile devices (clients) jointly learn a global model without transporting the huge amount of data collected by the mobile devices to the edge server. The proposed FL approach is referred to as spatio-temporal FL (STFL), which jointly exploits the spatial and temporal correlations between the learning updates from different mobile devices scheduled to join STFL in various training epochs. The STFL model not only represents the realistic intermittent learning behavior from the edge server to the mobile devices due to data delivery outage, but also features a mechanism of compensating loss learning updates in order to mitigate the impacts of intermittent learning. An analytical framework of STFL is proposed and employed to study the learning capability of STFL via its convergence performance. In particular, we have assessed the impact of data delivery outage, intermittent learning mitigation, and statistical heterogeneity of datasets on the convergence performance of STFL. The results provide crucial insights into the design and analysis of STFL-based wireless networks.

cs.LG↗

arXiv2021

Projective robustness for quantum channels and measurements and their operational significance

Recently, the projective robustness of quantum states has been introduced in [arXiv:2109.04481(2021)]. It shows that the projective robustness is a useful resource monotone and can comprehensively characterize capabilities and limitations of probabilistic protocols manipulating quantum resources deterministically. In this paper, we will extend the projective robustness to any convex resource theories of quantum channels and measurements. First, We introduce the projective robustness of quantum channels and prove that it satisfies some good properties, especially sub- or supermultiplicativity under any free quantum process. Moreover, we use the projective robustness of channels to give lower bounds on the errors and overheads in any channel resource distillation. Meanwhile, we show that the projective robustness of channels quantifies the maximal advantage that a given channel outperforms all free channels in simultaneous discrimination and exclusion of a fixed state ensemble. Second, we define the projective robustness of quantum measurements and prove that it exactly quantifies the maximal advantage that a given measurement provides over all free measurements in simultaneous discrimination and exclusion of two fixed state ensembles. Finally, within a specific channel resource setting based on measurement incompatibility, we show that the projective robustness of quantum channels coincides with the projective robustness of measurement incompatibility.

quant-ph↗

arXiv2021

The Formation of M101-alike Galaxies in the Cold Dark Matter Model

The population of satellite galaxies in a host galaxy is a combination of the cumulative accretion of subhaloes and their associated star formation efficiencies, therefore, the luminosity distribution of satellites provides valuable information of both dark matter properties and star formation physics. Recently, the luminosity function of satellites in nearby Milky Way-mass galaxies has been well measured to satellites as faint as Leo I with $M_{V} \sim -8$. In addition to the finding of the diversity in the satellite luminosity functions, it has been noticed that there is a big gap among the magnitude of satellites in some host galaxies, such as M101, where the gap is around 5 in magnitude, noticeably larger than the prediction from the halo abundance matching method. The reason of this gap is still unknown. In this paper, we use a semi-analytical model of galaxy formation, combined with high-resolution N-body simulation, to investigate the probability and origin of such big gap in M101-alike galaxies. We found that, although M101 analogues are very rare with probability of \sim 0.1%-0.2% in the local universe, their formation is a natural outcome of the CDM model. The gap in magnitude is mainly due to the mass of the accreted subhaloes, not from the stochastic star formation in them. We also found that the gap is correlated with the total satellite mass and host halo mass. By tracing the formation history of M101 type galaxies, we find that they likely formed after $z \sim 1$ due to the newly accreted bright satellites. The gap is not in a stable state, and it will disappear in ~7 Gyr due to mergers of bright satellites with the central galaxy.

astro-ph.GA↗

arXiv2021

Free-electron radiation engineering via structured environments

Free-electron radiation results from the interaction between swift electrons and the local electromagnetic environment. Recent advances in material technologies provide powerful tools to control light emission from free electrons and may facilitate many intriguing applications of free-electron radiation in particle detections, lasers, quantum information processing, etc. Here, we provide a brief overview on the recent theoretical developments and experimental observations of spontaneous free-electron radiation in various structured environments, including two-dimensional materials, metasurfaces, metamaterials, and photonic crystals. We also report on research progresses on the stimulated free-electron radiation that results from the interaction between free electrons and photonic quasi-particles induced by the external field. Moreover, we provide an outlook of potential research directions for this vigorous realm of free-electron radiation.

physics.optics↗

arXiv2021

A Brewster route to Cherenkov detectors

The Cherenkov effect enables a valuable tool, known as the Cherenkov detector, to identify high-energy particles via the measurement of the Cherenkov cone. However, the sensitivity and momentum coverage of such detectors are intrinsically limited by the refractive index of the host material. Especially, identifying particles with energy above multiple gigaelectronvolts requires host materials with a near-unity refractive index, which are often limited to large and bulky gas chambers. Overcoming this fundamental material limit is important for future particle detectors yet remains a long-standing scientific challenge. Here, we propose a different paradigm for Cherenkov detectors that utilizes the broadband angular filter made from stacks of variable one-dimensional photonic crystals. Owing to the Brewster effect, the angular filter is transparent only to Cherenkov photons from a precise incident angle, and particle identification is achieved by mapping each Cherenkov angle to the peak-intensity position of transmitted photons in the detection plane. This unique property of the angular filter is exceptionally beneficial to Cherenkov detection as it enables the realization of a non-dispersive pseudo refractive index over the entire visible spectrum. Moreover, such a pseudo refractive index can be flexibly tuned to arbitrary values, including those close to unity. Our angular-selective Brewster paradigm offers a feasible solution to implement compact and highly sensitive Cherenkov detectors especially in beam lines and it can cover a wide momentum range using readily available dielectric materials.

physics.optics↗