arXiv subjects

Jie Lin

Publications and source records attributed to Jie Lin.

At least 109 records · Page 6Linked to original sources

arXiv2022

OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization

As Deep Neural Networks (DNNs) usually are overparameterized and have millions of weight parameters, it is challenging to deploy these large DNN models on resource-constrained hardware platforms, e.g., smartphones. Numerous network compression methods such as pruning and quantization are proposed to reduce the model size significantly, of which the key is to find suitable compression allocation (e.g., pruning sparsity and quantization codebook) of each layer. Existing solutions obtain the compression allocation in an iterative/manual fashion while finetuning the compressed model, thus suffering from the efficiency issue. Different from the prior art, we propose a novel One-shot Pruning-Quantization (OPQ) in this paper, which analytically solves the compression allocation with pre-trained weight parameters only. During finetuning, the compression module is fixed and only weight parameters are updated. To our knowledge, OPQ is the first work that reveals pre-trained model is sufficient for solving pruning and quantization simultaneously, without any complex iterative/manual optimization at the finetuning stage. Furthermore, we propose a unified channel-wise quantization method that enforces all channels of each layer to share a common codebook, which leads to low bit-rate allocation without introducing extra overhead brought by traditional channel-wise quantization. Comprehensive experiments on ImageNet with AlexNet/MobileNet-V1/ResNet-50 show that our method improves accuracy and training efficiency while obtains significantly higher compression rates compared to the state-of-the-art.

cs.CV↗

arXiv2021

Anisotropic thermal characterisation of large-format lithium-ion pouch cells

Temperature strongly impacts battery performance, safety and durability, but modelling heat transfer requires accurately measured thermal properties. Herein we propose new approaches to characterise the heat capacity and anisotropic thermal-conductivity components for lithium-ion pouch cells. Heat capacity was estimated by applying Newton's law of cooling to an insulated container within which the cell was submerged in warmed dielectric fluid. Thermal conductivity was quantified by heating one side of the cell and measuring the opposing temperature distribution with infra-red thermography, then inverse modelling with the anisotropic heat equation. Experiments were performed on commercial 20 Ah lithium iron phosphate (LFP) pouch cells. At 100% state-of-charge (SOC), the heat capacity of a 489 g, 224 mL pouch cell was 541 J/K. The through-plane and in-plane thermal conductivities were respectively 0.52 and 26.6 W/(mK). Capturing anisotropies in conductivity is important for accurate thermal simulations. State-of-charge dependence was also probed by testing at 50% SOC: the heat capacity dropped by 6% and thermal conductivity did not significantly change.

physics.chem-ph↗

arXiv2021

A catalogue of 323 cataclysmic variables from LAMOST DR6

In this work, we present a catalog of cataclysmic variables (CVs) identified from the Sixth Data Release (DR6) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). To single out the CV spectra, we introduce a novel machine-learning algorithm called UMAP to screen out a total of 169,509 H$α$-emission spectra, and obtain a classification accuracy of the algorithm of over 99.6$\%$ from the cross-validation set. We then apply the template matching program PyHammer v2.0 to the LAMOST spectra to obtain the optimal spectral type with metallicity, which helps us identify the chromospherically active stars and potential binary stars from the 169,509 spectra. After visually inspecting all the spectra, we identify 323 CV candidates from the LAMOST database, among them 52 objects are new. We further discuss the new CV candidates in subtypes based on their spectral features, including five DN subtype during outbursts, five NL subtype and four magnetic CVs (three AM Her type and one IP type). We also find two CVs that have been previously identified by photometry, and confirm their previous classification by the LAMOST spectra.

astro-ph.SR↗

arXiv2021

Machine Learning on Cloud with Blockchain: A Secure, Verifiable and Fair Approach to Outsource the Linear Regression for Data Analysis

Linear Regression (LR) is a classical machine learning algorithm which has many applications in the cyber physical social systems (CPSS) to shape and simplify the way we live, work and communicate. This paper focuses on the data analysis for CPSS when the Linear Regression is applied. The training process of LR is time-consuming since it involves complex matrix operations, especially when it gets a large scale training dataset In the CPSS. Thus, how to enable devices to efficiently perform the training process of the Linear Regression is of significant importance. To address this issue, in this paper, we present a secure, verifiable and fair approach to outsource LR to an untrustworthy cloud-server. In the proposed scheme, computation inputs/outputs are obscured so that the privacy of sensitive information is protected against cloud-server. Meanwhile, computation result from cloud-server is verifiable. Also, fairness is guaranteed by the blockchain, which ensures that the cloud gets paid only if he correctly performed the outsourced workload. Based on the presented approach, we exploited the fair, secure outsourcing system on the Ethereum blockchain. We analysed our presented scheme on theoretical and experimental, all of which indicate that the presented scheme is valid, secure and efficient.

cs.CR↗

arXiv2021

Multiscale coupling of surface temperature with solid diffusion in large lithium-ion pouch cells

Untangling the relationship between reactions, mass transfer, and temperature within lithium-ion batteries enables control approaches that mitigate thermal hot spots and slow degradation. Here, we develop an efficient physics-based pouch-cell model to simulate lock-in thermography experiments, which synchronously record the applied current, cell voltage, and surface-temperature distribution. Prior modelling efforts have been confounded by experimental temperature profiles whose characteristics suggest anisotropic heat conduction. Accounting for a multiscale coupling between heat flow and solid-state diffusion rationalizes this surface-temperature nonuniformity. We extend an earlier streamlined model based on the popular Doyle--Fuller--Newman theory, augmented by a local heat balance. The reduced-order model is exploited to parametrize and simulate commercial 20 Ah lithium iron phosphate (LFP) cells at currents up to 80 A. This work highlights how microscopic intercalation processes produce distinctive macroscopic heat signatures in large-format cells, as well as how heat signatures can be exploited to fingerprint material properties.

physics.chem-ph↗

arXiv2021

Minute-cadence Observations of the LAMOST Fields with the TMTS: I. Methodology of Detecting Short-period Variables and Results from the first-year Survey

Tsinghua University-Ma Huateng Telescopes for Survey (TMTS), located at Xinglong Station of NAOC, has a field of view upto 18 deg^2. The TMTS has started to monitor the LAMOST sky areas since 2020, with the uninterrupted observations lasting for about 6 hours on average for each sky area and a cadence of about 1 minute. Here we introduce the data analysis and preliminary scientific results for the first-year observations, which covered 188 LAMOST plates ( about 1970 deg^2). These observations have generated over 4.9 million uninterrupted light curves, with at least 100 epochs for each of them. These light curves correspond to 4.26 million Gaia-DR2 sources, among which 285 thousand sources are found to have multi-epoch spectra from the LAMOST. By analysing these light curves with the Lomb-Scargle periodograms, we identify more than 3700 periodic variable star candidates with periods below 7.5 hours, primarily consisting of eclipsing binaries and Delta Scuti stars. Those short-period binaries will provide important constraints on theories of binary evolution and possible sources for space gravitational wave experiments in the future. Moreover, we also identified 42 flare stars by searching rapidly-evolving signals in the light curves. The densely-sampled light curves from the TMTS allow us to better quantify the shapes and durations for these flares.

astro-ph.IM↗

arXiv2021

Aging Maxwell fluids

Many experiments show that protein condensates formed by liquid-liquid phase separation exhibit aging rheological properties. Quantitatively, recent experiments by Jawerth et al. (Science 370, 1317, 2020) show that protein condensates behave as aging Maxwell fluids with an increasing relaxation time as the condensates age. Despite the universality of this aging phenomenon, a theoretical understanding of this aging behavior is lacking. In this work, we propose a mesoscopic model of protein condensates in which a phase transition from aging phase to non-aging phase occurs as the control parameter changes, such as temperature. The model predicts that protein condensates behave as viscoelastic Maxwell fluids at all ages, with the macroscopic viscosity increasing over time. The model also predicts that protein condensates are non-Newtonian fluids under a constant shear rate with the shear stress increasing over time. Our model successfully explains multiple existing experimental observations and also makes general predictions that are experimentally testable.

cond-mat.soft↗

arXiv2021

PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery

Non-maximum Suppression (NMS) is an essential postprocessing step in modern convolutional neural networks for object detection. Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottleneck in convolutional object detection pipelines. MaxpoolNMS is introduced as a parallelizable alternative to GreedyNMS, which in turn enables faster speed than GreedyNMS at comparable accuracy. However, MaxpoolNMS is only capable of replacing the GreedyNMS at the first stage of two-stage detectors like Faster-RCNN. There is a significant drop in accuracy when applying MaxpoolNMS at the final detection stage, due to the fact that MaxpoolNMS fails to approximate GreedyNMS precisely in terms of bounding box selection. In this paper, we propose a general, parallelizable and configurable approach PSRR-MaxpoolNMS, to completely replace GreedyNMS at all stages in all detectors. By introducing a simple Relationship Recovery module and a Pyramid Shifted MaxpoolNMS module, our PSRR-MaxpoolNMS is able to approximate GreedyNMS more precisely than MaxpoolNMS. Comprehensive experiments show that our approach outperforms MaxpoolNMS by a large margin, and it is proven faster than GreedyNMS with comparable accuracy. For the first time, PSRR-MaxpoolNMS provides a fully parallelizable solution for customized hardware design, which can be reused for accelerating NMS everywhere.

cs.CV↗

arXiv2021

Dimension Reduction in Quantum Key Distribution for Continuous- and Discrete-Variable Protocols

We develop a method to connect the infinite-dimensional description of optical continuous-variable quantum key distribution (QKD) protocols to a finite-dimensional formulation. The secure key rates of the optical QKD protocols can then be evaluated using recently-developed reliable numerical methods for key rate calculations. We apply this method to obtain asymptotic key rates for discrete-modulated continuous-variable QKD protocols, which are of practical significance due to their experimental simplicity and potential for large-scale deployment in quantum-secured networks. Importantly, our security proof does not require the photon-number cutoff assumption relied upon in previous works. We also demonstrate that our method can provide practical advantages over the flag-state squasher when applied to discrete-variable protocols.

quant-ph↗

arXiv2021

Optical and Ultraviolet Monitoring of the Black Hole X-ray Binary MAXI J1820+070/ASASSN-18ey for 18 Months

MAXI J1820+070 is a low-mass black hole X-ray binary system with high luminosity in both optical and X-ray bands during the outburst periods. We present extensive photometry in X-ray, ultraviolet, and optical bands, as well as densely-cadenced optical spectra, covering the phase from the beginning of optical outburst to $\sim$550 days. During the rebrightening process, the optical emission preceded the X-ray by 20.80 $\pm$ 2.85 days. The spectra are characterized by blue continua and emission features of Balmer series, He I, He II lines and broad Bowen blend. The pseudo equivalent width (pEW) of emission lines are found to show anticorrelations with the X-ray flux measured at comparable phases, which is due to the increased suppression by the optical continuum. At around the X-ray peak, the full width at half maximums (FWHMs) of H$_β$ and He II $λ$4686 tend to stabilize at 19.4 Angstrom and 21.8 Angstrom, which corresponds to the line forming region at a radius of 1.7 and 1.3 R_sun within the disk. We further analyzed the absolute fluxes of the lines and found that the fluxes of H$_β$ and He II $λ$4686 show positive correlations with the X-ray flux, favoring that the irradiation model is responsible for the optical emission. However, the fact that X-ray emission experiences a dramatic flux drop at t$\sim$200 days after the outburst, while the optical flux only shows little variations suggests that additional energy such as viscous energy may contribute to the optical radiation in addition to the X-ray irradiation.

astro-ph.HE↗

arXiv2021

Special values of $L$-functions and the refined Gan-Gross-Prasad conjecture

We prove explicit rationality-results for Asai- $L$-functions, $L^S(s,Π',{\rm As}^\pm)$, and Rankin-Selberg $L$-functions, $L^S(s,Π\timesΠ')$, over arbitrary CM-fields $F$, relating critical values to explicit powers of $(2πi)$. Besides determining the contribution of archimedean zeta-integrals to our formulas as concrete powers of $(2πi)$, it is one of the advantages of our approach, that it applies to very general non-cuspidal isobaric automorphic representations $Π'$ of ${\rm GL}_n(\mathbb A_F)$. As an application, this enables us to establish a certain algebraic version of the Gan--Gross--Prasad conjecture, as refined by N.\ Harris, for totally definite unitary groups. As another application we obtain a generalization of a result of Harder--Raghuram on quotients of consecutive critical values, proved by them for totally real fields, and achieved here for arbitrary CM-fields $F$ and pairs $(Π,Π')$ of relative rank one.

math.NT↗

arXiv2021

Deligne's conjecture for automorphic motives over CM-fields

The present paper is devoted to the relations between Deligne's conjecture on critical values of motivic $L$-functions and the multiplicative relations between periods of arithmetically normalized automorphic forms on unitary groups. In the first place, we combine the Ichino--Ikeda--Neal-Harris (IINH) formula -- which is now a theorem -- with an analysis of cup products of coherent cohomological automorphic forms on Shimura varieties to establish relations between certain automorphic periods and critical values of Rankin-Selberg and Asai $L$-functions of ${\rm GL}(n)\times{\rm GL}(m)$ over CM fields. By reinterpreting these critical values in terms of automorphic periods of holomorphic automorphic forms on unitary groups, we show that the automorphic periods of holomorphic forms can be factored as products of coherent cohomological forms, compatibly with a motivic factorization predicted by the Tate conjecture. All of these results are conditional on a conjecture on non-vanishing of twists of automorphic $L$-functions of ${\rm GL}(n)$ by anticyclotomic characters of finite order, and are stated under a certain regularity condition.

math.NT↗

arXiv2021

Numerical Calculations of Finite Key Rate for General Quantum Key Distribution Protocols

Finite key analysis of quantum key distribution (QKD) is an important tool for any QKD implementation. While much work has been done on the framework of finite key analysis, the application to individual protocols often relies on the the specific protocol being simple or highly symmetric as well as represented in small finite-dimensional Hilbert spaces. In this work, we extend our pre-existing reliable, efficient, tight, and generic numerical method for calculating the asymptotic key rate of device-dependent QKD protocols in finite-dimensional Hilbert spaces to the finite key regime using the security analysis framework of Renner. We explain how this extension preserves the reliability, efficiency, and tightness of the asymptotic method. We then explore examples which illustrate both the generality of our method as well as the importance of parameter estimation and data processing within the framework.

quant-ph↗

arXiv2021

Security proof of practical quantum key distribution with detection-efficiency mismatch

Quantum key distribution (QKD) protocols with threshold detectors are driving high-performance QKD demonstrations. The corresponding security proofs usually assume that all physical detectors have the same detection efficiency. However, the efficiencies of the detectors used in practice might show a mismatch depending on the manufacturing and setup of these detectors. A mismatch can also be induced as the different spatial-temporal modes of an incoming signal might couple differently to a detector. Here we develop a method that allows to provide security proofs without the usual assumption. Our method can take the detection-efficiency mismatch into account without having to restrict the attack strategy of the adversary. Especially, we do not rely on any photon-number cut-off of incoming signals such that our security proof is directly applicable to practical situations. We illustrate our method for a receiver that is designed for polarization encoding and is sensitive to a number of spatial-temporal modes. In our detector model, the absence of quantum interference between any pair of spatial-temporal modes is assumed. For a QKD protocol with this detector model, we can perform a security proof with characterized efficiency mismatch and without photon-number cut-off assumption. Our method also shows that in the absence of efficiency mismatch in our detector model, the key rate increases if the loss due to detection inefficiency is assumed to be outside of the adversary's control, as compared to the view where for a security proof this loss is attributed to the action of the adversary.

quant-ph↗

arXiv2021

A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition

Despite the vast literature on Human Activity Recognition (HAR) with wearable inertial sensor data, it is perhaps surprising that there are few studies investigating semisupervised learning for HAR, particularly in a challenging scenario with class imbalance problem. In this work, we present a new benchmark, called A*HAR, towards semisupervised learning for class-imbalanced HAR. We evaluate state-of-the-art semi-supervised learning method on A*HAR, by combining Mean Teacher and Convolutional Neural Network. Interestingly, we find that Mean Teacher boosts the overall performance when training the classifier with fewer labelled samples and a large amount of unlabeled samples, but the classifier falls short in handling unbalanced activities. These findings lead to an interesting open problem, i.e., development of semi-supervised HAR algorithms that are class-imbalance aware without any prior knowledge on the class distribution for unlabeled samples. The dataset and benchmark evaluation are released at https://github.com/I2RDL2/ASTAR-HAR for future research.

cs.LG↗

arXiv2021

Privacy-Preserving Cloud-Aided Broad Learning System

With the rapid development of artificial intelligence and the advent of the 5G era, deep learning has received extensive attention from researchers. Broad Learning System (BLS) is a new deep learning model proposed recently, which shows its effectiveness in many fields, such as image recognition and fault detection. However, the training process still requires vast computations, and therefore cannot be accomplished by some resource-constrained devices. To solve this problem, the resource-constrained device can outsource the BLS algorithm to cloud servers. Nevertheless, some security challenges also follow with the use of cloud computing, including the privacy of the data and the correctness of returned results. In this paper, we propose a secure, efficient, and verifiable outsourcing algorithm for BLS. This algorithm not only improves the efficiency of the algorithm on the client but also ensures that the clients sensitive information is not leaked to the cloud server. In addition, in our algorithm, the client can verify the correctness of returned results with a probability of almost 1. Finally, we analyze the security and efficiency of our algorithm in theory and prove our algorithms feasibility through experiments.

cs.CR↗

arXiv2021

Blockchain Aided Privacy-Preserving Outsourcing Algorithms of Bilinear Pairings for Internet of Things Devices

Bilinear pairing is a fundamental operation that is widely used in cryptographic algorithms (e.g., identity-based cryptographic algorithms) to secure IoT applications. Nonetheless, the time complexity of bilinear pairing is $O(n^3)$, making it a very time-consuming operation, especially for resource-constrained IoT devices. Secure outsourcing of bilinear pairing has been studied in recent years to enable computationally weak devices to securely outsource the bilinear pairing to untrustworthy cloud servers. However, the state-of-art algorithms often require to pre-compute and store some values, which results in storage burden for devices. In the Internet of Things, devices are generally with very limited storage capacity. Thus, the existing algorithms do not fit the IoT well. In this paper, we propose a secure outsourcing algorithm of bilinear pairings, which does not require pre-computations. In the proposed algorithm, the outsourcer side's efficiency is significantly improved compared with executing the original bilinear pairing operation. At the same time, the privacy of the input and output is ensured. Also, we apply the Ethereum blockchain in our outsourcing algorithm to enable fair payments, which ensures that the cloud server gets paid only when he correctly accomplished the outsourced work. The theoretical analysis and experimental results show that the proposed algorithm is efficient and secure.

cs.CR↗

arXiv2020

The Tsinghua University-Ma Huateng Telescopes for Survey: Overview and Performance of the System

Over the past decade, time-domain astronomy in optical bands has developed rapidly with the operations of some wide-field survey facilities. However, most of these surveys are conducted with only a single band, and simultaneous color information is usually unavailable for the objects monitored during the survey. Here we present introductions to the system of Tsinghua University-Ma Huateng Telescopes for Survey (TMTS), which consists of an array of four optical telescopes installed on a single equatorial mount. Such a system is designed to get multiband photometry simultaneously for stars and transients discovered during the survey. The optics of each telescope is a modified Hamilton-Newtonian system, covering the wavelengths from 400 to 900 nm, with a field of view (FoV) of about 4.5 deg2 and a plate scale of 1.86"/pixel when combining with a 4K*4K QHY4040 CMOS detector. The TMTS system can have a FoV of about 9 deg2 when monitoring the sky with two bands (i.e., SDSS g and r filters) at the same time, and a maximum FoV of ~18 deg2 when four telescopes monitor different sky areas in monochromatic filter mode. For an exposure time of 60 s, the average 3σ detection limit of the TMTS system can reach at ~19.4 mag in Luminous filter and at ~18.7 mag in SDSS r filter. The preliminary discovery obtained during the first few months' survey is briefly discussed. As this telescope array is located at the Xinglong Observatory of NAOC, it can have an excellent synergy with the spectroscopic survey by the LAMOST (with a FoV of about 20 deg2) at the same site, which will benefit the studies of stellar and binary physics besides the transient sciences.

astro-ph.IM↗