arXiv subjects

Nan Li

Publications and source records attributed to Nan Li.

At least 217 records · Page 12Linked to original sources

arXiv2023

Far-field model of two-color laser driven terahertz radiation including field element interference and plasma response

The two-color laser field scheme for broad-band terahertz (THz) radiation from air has been intensively and broadly investigated due to the simplicity in technology and relative high yield efficiency. Experiments showed that the detected THz angular distribution is usually conical with a dip in the laser propagation axis which is unfavorable for its application, and the formation of an oscillating tail in the THz waveform has not yet been well understood. Here, we develop an electric field element interference model in which each local current source has a phase velocity determined by the laser pulse and a Gaussian temporal profile, rather than an ideal point source, as well as the current oscillation caused by the plasma dynamics is considered. By introducing the temporal interference, effects of the phase velocity and the Gaussian profile of the current can correct the THz angular distribution. Therefore, by adjusting the laser pulse duration and the group velocity, the THz angular distribution can be controlled and the dip could be even eliminated. Besides, the plasma dynamics can result in the formation of an oscillating tail in the THz waveform and sunken spikes in the corresponding spectrum. This is similar to the experimental results which are usually explained by the water vapor absorption. In turn, the spikes in the spectrum could provide a way to measure the plasma filament density.

physics.plasm-ph↗

arXiv2023

A Unified Safety Protection and Extension Governor

In this paper, we propose a supervisory control scheme that unifies the abilities of safety protection and safety extension. It produces a control that is able to keep the system safe indefinitely when such a control exists. When such a control does not exist due to abnormal system states, it optimizes the control to maximize the time before any safety violation, which translates into more time to seek recovery and/or mitigate any harm. We describe the scheme and develop an approach that integrates the two capabilities into a single constrained optimization problem with only continuous variables. For linear systems with convex constraints, the problem reduces to a convex quadratic program and is easy to solve. We illustrate the proposed safety supervisor with an automotive example.

eess.SY↗

arXiv2023

Scheduling Dosage of Proton Pump Inhibitors Using Constrained Optimization With Gastric Acid Secretion Model

Dosage schedule of the Proton Pump Inhibitors (PPIs) is critical for gastric acid disorder treatment. In this paper, we develop a constrained optimization based approach for scheduling the PPIs dosage. In particular, we exploit a mathematical prediction model describing the gastric acid secretion, and use it within the optimization algorithm to predict the acid level. The dosage of the PPIs which is used to enforce acid level constraints is computed by solving a constrained optimization problem. Simulation results show that the proposed approach can successfully suppress the gastric acid level with less PPIs intake compared with the conventional fixed PPIs dosage regimen, which may reduce the long-term side effects of the PPIs.

eess.SY↗

arXiv2023

Understanding thermal induced escape mechanism of optically levitated sphere in vacuum

The escape phenomenon, mainly caused by thermal effects, is known as an obstacle to the further practical application of optical levitation system in vacuum. Irregular photophoresis induced by thermal effects can act as an amplifier of Brownian motion. Studies on this topic provide interpretation for particle escaping phenomenon during the pressure decreasing process, as well as valuable insights into the micro- and nanoscale thermal effects in optical trap in vacuum. In this paper, we derive and test a dynamic model for the motion of an optically levitated particle in a non-equilibrium state and demonstrate the escaping mechanism of heated particles. The result of theoretical investigations is consistent with experimental escape at 0.1mbar. This work reveals and provides a theoretical basis for the stable operation of laser levitated oscillator in high vacuum and pave the way for the practicability of ultra-sensitive sensing devices.

physics.optics↗

arXiv2023

DCNetBench: Scaleable Data Center Network Benchmarking

Data center networking is the central infrastructure of the modern information society. However, benchmarking them is very challenging as the real-world network traffic is difficult to model, and Internet service giants treat the network traffic as confidential. Several industries have published a few publicly available network traces. However, these traces are collected from specific data center environments, e.g., applications, network topology, protocols, and hardware devices, and thus cannot be scaled to different users, underlying technologies, and varying benchmarking requirements. This article argues we should scale different data center applications and environments in designing, implementing, and evaluating data center networking benchmarking. We build DCNetBench, the first application-driven data center network benchmarking that can scale to different users, underlying technologies, and varying benchmarking requirements. The methodology is as follows. We built an emulated system that can simulate networking with different configurations. Then we run applications on the emulated systems to capture the realistic network traffic patterns; we analyze and classify these patterns to model and replay those traces. Finally, we provide an automatic benchmarking framework to support this pipeline. The evaluations on DCNetBench show its scaleability, effectiveness, and diversity for data center network benchmarking.

cs.NI↗

arXiv2023

Primordial black holes and scalar-induced gravitational waves from the perturbations on the inflaton potential in peak theory

A perturbation on the background inflaton potential can lead inflation into the ultraslow-roll stage and can thus remarkably enhance the power spectrum ${\cal P}_{\cal R}(k)$ of the primordial curvature perturbation on small scales. Such an enhanced ${\cal P}_{\cal R}(k)$ will result in primordial black holes (PBHs), contributing a significant fraction of dark matter, and will simultaneously generate sizable scalar-induced gravitational waves (SIGWs) as a secondorder effect. In this work, we calculate the PBH abundances $f_{\rm PBH}(M)$ and SIGW spectra $Ω_{\rm GW}(f)$ in peak theory. We obtain the PBHs with desirable abundances in one or two typical mass windows at $10^{-17}\, M_\odot$, $10^{-13}\, M_\odot$, and $30\, M_\odot$, respectively. At the same time, the relevant SIGWs are expected to be observed by the next-generation gravitational wave detectors, without spoiling the current constraint. Especially, the SIGW associated with the PBH of $30\, M_\odot$ can also interpret the potential isotropic stochastic gravitational wave background from the NANOGrav 12.5-year dataset.

astro-ph.CO↗

arXiv2023

Attention-based Feature Compression for CNN Inference Offloading in Edge Computing

This paper studies the computational offloading of CNN inference in device-edge co-inference systems. Inspired by the emerging paradigm semantic communication, we propose a novel autoencoder-based CNN architecture (AECNN), for effective feature extraction at end-device. We design a feature compression module based on the channel attention method in CNN, to compress the intermediate data by selecting the most important features. To further reduce communication overhead, we can use entropy encoding to remove the statistical redundancy in the compressed data. At the receiver, we design a lightweight decoder to reconstruct the intermediate data through learning from the received compressed data to improve accuracy. To fasten the convergence, we use a step-by-step approach to train the neural networks obtained based on ResNet-50 architecture. Experimental results show that AECNN can compress the intermediate data by more than 256x with only about 4% accuracy loss, which outperforms the state-of-the-art work, BottleNet++. Compared to offloading inference task directly to edge server, AECNN can complete inference task earlier, in particular, under poor wireless channel condition, which highlights the effectiveness of AECNN in guaranteeing higher accuracy within time constraint.

cs.CV↗

arXiv2023

Discovering strongly lensed quasar candidates with catalogue-based methods from DESI Legacy Surveys

The Hubble tension, revealed by a $\sim 5σ$ discrepancy between measurements of the Hubble-Lemaitre constant from early- and local-Universe observations, is one of the most significant problems in modern cosmology. In order to better understand the origin of this mismatch, independent techniques to measure $H_0$, such as strong lensing time delays, are required. Notably, the sample size of such systems is key to minimising statistical uncertainties and cosmic variance, which can be improved by exploring the datasets of large-scale sky surveys like DESI (Dark Energy Spectroscopic Instrument). We identify possible strong lensing time-delay systems within DESI by selecting candidate multiply imaged lensed quasars from a catalogue of 24,440,816 candidate QSOs contained in the 9th data release of the DESI Legacy Imaging Surveys (DESI-LS). Using a friend-of-friends-like algorithm on spatial co-ordinates, our method generates an initial list of compact quasar groups. This list is subsequently filtered using a measure of the similarity of colours of a group's members and the likelihood that they are quasars. A visual inspection finally selects candidate strong lensing systems based on the spatial configuration of the group members. We identify 620 new candidate multiply imaged lensed quasars (101 Grade-A, 214 Grade-B, 305 Grade-C). This number excludes 53 known spectroscopically confirmed systems and existing candidate systems identified in other similar catalogues. When available, these new candidates will be further checked by combining the spectroscopic and photometric data from DESI. The catalogues and images of the candidates in this work are available online (https://github.com/EigenHermit/lensed_qso_cand_catalogue_He-22/).

astro-ph.CO↗

arXiv2022

Kähler Finsler manifolds with curvatures bounded from below

We obtain a partial parallelism of the complex structure on Kähler Finsler manifolds. As applications, we prove Synge-Tsukamoto theorem and Bonnet-Myers theorem for positively curved Kähler Finsler manifolds. Moreover, we generalize a comparison theorem due to Ni-Zheng by introducing the notion of orthogonal Ricci curvature to Kähler Finsler geometry.

math.DG↗

arXiv2022

Counting compatible indexing systems for $C_{p^n}$

We count the number of compatible pairs of indexing systems for the cyclic group $C_{p^n}$. Building on work of Balchin--Barnes--Roitzheim, we show that this sequence of natural numbers is another family of Fuss--Catalan numbers. We count this two different ways: showing how the conditions of compatibility give natural recursive formulas for the number of admissible sets and using an enumeration of ways to extend indexing systems by conceptually simpler pieces.

math.AT↗

arXiv2022

Enhanced cosmic-ray antihelium production from dark matter annihilation through light mediators

Cosmic-ray (CR) antihelium is an important probe for the indirect search of dark matter (DM) annihilation in the Galaxy. However, due to stringent constraints from the measurements of CR antiprotons and $γ$-rays, the flux of CR antihelium from the conventional DM direct annihilation into Standard Model final states is expected to be far below the sensitivity of the current experiments. We show that the production of antihelium can be significantly enhanced if the DM particles annihilate through light mediator particles with a mass $m_ϕ\approx 8$ GeV close to the antihelium production threshold. After taking into account the constraints from the AMS-02 antiproton data and the Fermi-LAT $γ$-ray data on the spheroidal dwarf galaxies, we find that in this scenario the CR antihelium flux can be enhanced by three orders of magnitude, which makes it within the sensitivity of the ongoing AMS-02 experiment.

hep-ph↗

arXiv2022

Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process

As the complexity of modern manufacturing technologies increases, traditional trial-and-error design, which requires iterative and expensive simulations, becomes unreliable and time-consuming. This difficulty is especially significant for the design of hot-stamped safety-critical components, such as ultra-high-strength-steel (UHSS) B-pillars. To reduce design costs and ensure manufacturability, scalar-based Artificial-Intelligence-empowered surrogate modelling (SAISM) has been investigated and implemented, which can allow real-time manufacturability-constrained structural design optimisation. However, SAISM suffers from low accuracy and generalisability, and usually requires a high volume of training samples. To solve this problem, an image-based Artificial-intelligence-empowered surrogate modelling (IAISM) approach is developed in this research, in combination with an auto-decoder-based blank shape generator. The IAISM, which is based on a Mask-Res-SE-U-Net architecture, is trained to predict the full thinning field of the as-formed component given an arbitrary blank shape. Excellent prediction performance of IAISM is achieved with only 256 training samples, which indicates the small-data learning nature of engineering AI tasks using structured data representations. The trained auto-decoder, trained Mask-Res-SE-U-Net, and Adam optimiser are integrated to conduct blank optimisation by modifying the latent vector. The optimiser can rapidly find blank shapes that satisfy manufacturability criteria. As a high-accuracy and generalisable surrogate modelling and optimisation tool, the proposed pipeline is promising to be integrated into a full-chain digital twin to conduct real-time, multi-objective design optimisation.

cs.CV↗

arXiv2022

Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing

For time-critical IoT applications using deep learning, inference acceleration through distributed computing is a promising approach to meet a stringent deadline. In this paper, we implement a working prototype of a new distributed inference acceleration method HALP using three raspberry Pi 4. HALP accelerates inference by designing a seamless collaboration among edge devices (EDs) in Edge Computing. We maximize the parallelization between communication and computation among the collaborative EDs by optimizing the task partitioning ratio based on the segment-based partitioning. Experimental results show that the distributed inference HALP achieves 1.7x inference acceleration for VGG-16. Then, we combine distributed inference with conventional neural network model compression by setting up different shrinking hyperparameters for MobileNet-V1. In this way, we can further accelerate inference but at the cost of inference accuracy loss. To strike a balance between latency and accuracy, we propose dynamic model selection to select a model which provides the highest accuracy within the latency constraint. It is shown that the model selection with distributed inference HALP can significantly improve service reliability compared to the conventional stand-alone computation.

cs.CV↗

arXiv2022

Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications

This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We systematically analyze the effect of image DCT coefficients on inference accuracy and propose the channel-agnostic effectiveness encoding for offloading by transmitting the most meaningful task data first. This scheme can well utilize all available communication resource and strike a balance between transmission latency and inference accuracy. Then, we design an effectiveness decoding by implementing a novel image augmentation process for convolutional neural network (CNN) training, through which an original CNN model is transformed into a Robust CNN model. We use the proposed training method to generate Robust MobileNet-v2 and Robust ResNet-50. The proposed Edge Intelligence framework consists of the proposed effectiveness encoding and effectiveness decoding. The experimental results show that the effectiveness decoding using the Robust CNN models perform consistently better under various image distortions caused by channel errors or limited communication resource. The proposed Edge Intelligence framework using semantic communication significantly outperforms the conventional approach under latency and data rate constraints, in particular, under ultra stringent deadlines and low data rate.

cs.CV↗

arXiv2022

The integral closure of a primary ideal is not always primary

In 1936, Krull asked if the integral closure of a primary ideal is still primary. Fifty years later, Huneke partially answered this question by giving a primary polynomial ideal whose integral closure is not primary in a regular local ring of characteristic $p=2$. We provide counterexamples to Krull's question regarding polynomial rings with any characteristics. We also find that the Jacobian ideal $J$ of the polynomial $f = x^6 + y^6 + x^4 z t + z^3$ given by Briançon and Speder in 1975 is a counterexample to Krull's question. Let $V_1$ be the hypersurface defined by $f = 0$ and $V_2$ be its singular locus. Briançon and Speder proved that Whitney equisingularity does not imply Zariski equisingularity by showing that the pair $(V_1 \setminus V_2,\ V_2)$ satisfies Whitney's conditions around the origin but fails Zariski's equisingular conditions. We discover that the pair $(V_1 \setminus V_2,\ V_2)$ fails Whitney's conditions at the variety of the embedded prime of the integral closure $\bar{J}$, which means that $V_1$ is not Whitney regular along $V_2$. Moreover, we also show that Whitney stratification of this hypersurface is different from the stratification of isosingular sets given by Hauenstein and Wampler, which is related to Thom-Boardman singularity.

math.AC↗

arXiv2022

Unsupervised Galaxy Morphological Visual Representation with Deep Contrastive Learning

Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented performance on galaxy morphological classification. Such networks mostly follow the supervised learning paradigm which requires sufficient labelled data for training. However, it is an expensive and complicated process of labeling for million galaxies, particularly for the forthcoming survey projects. In this paper, we present an approach based on contrastive learning with aim for learning galaxy morphological visual representation using only unlabeled data. Considering the properties of low semantic information and contour dominated of galaxy image, the feature extraction layer of the proposed method incorporates vision transformers and convolutional network to provide rich semantic representation via the fusion of the multi-hierarchy features. We train and test our method on 3 classifications of datasets from Galaxy Zoo 2 and SDSS-DR17, and 4 classifications from Galaxy Zoo DECaLS. The testing accuracy achieves 94.7%, 96.5% and 89.9% respectively. The experiment of cross validation demonstrates our model possesses transfer and generalization ability when applied to the new datasets. The code that reveals our proposed method and pretrained models are publicly available and can be easily adapted to new surveys.

astro-ph.GA↗

arXiv2022

Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers

Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently. The next-generation large scale sky imaging surveys are expected to discover thousands of cluster-scale strong lenses, which would lead to unprecedented opportunities for applying cluster-scale strong lenses to solve astrophysical and cosmological problems. However, the large dataset challenges astronomers to identify and extract strong lensing signals, particularly strongly lensed arcs, because of their complexity and variety. Hence, we propose a framework to detect cluster-scale strongly lensed arcs, which contains a transformer-based detection algorithm and an image simulation algorithm. We embed prior information of strongly lensed arcs at cluster-scale into the training data through simulation and then train the detection algorithm with simulated images. We use the trained transformer to detect strongly lensed arcs from simulated and real data. Results show that our approach could achieve 99.63 % accuracy rate, 90.32 % recall rate, 85.37 % precision rate and 0.23 % false positive rate in detection of strongly lensed arcs from simulated images and could detect almost all strongly lensed arcs in real observation images. Besides, with an interpretation method, we have shown that our method could identify important information embedded in simulated data. Next step, to test the reliability and usability of our approach, we will apply it to available observations (e.g., DESI Legacy Imaging Surveys) and simulated data of upcoming large-scale sky surveys, such as the Euclid and the CSST.

astro-ph.IM↗

arXiv2022

Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing

This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and Edge servers' available capacity, we use early-exit mechanism to terminate the computation earlier to meet the deadline of inference tasks. We design a reward function to trade off the communication, computation and inference accuracy, and formulate the offloading problem of CNN inference as a maximization problem with the goal of maximizing the average inference accuracy and throughput in long term. To solve the maximization problem, we propose a graph reinforcement learning-based early-exit mechanism (GRLE), which outperforms the state-of-the-art work, deep reinforcement learning-based online offloading (DROO) and its enhanced method, DROO with early-exit mechanism (DROOE), under different dynamic scenarios. The experimental results show that GRLE achieves the average accuracy up to 3.41x over graph reinforcement learning (GRL) and 1.45x over DROOE, which shows the advantages of GRLE for offloading decision-making in dynamic MEC.

cs.LG↗