arXiv subjects

Jie Lin

Publications and source records attributed to Jie Lin.

At least 91 records · Page 5Linked to original sources

arXiv2023

Minute-cadence Observations of the LAMOST Fields with the TMTS: III. Statistic Study of the Flare Stars from the First Two Years

Tsinghua University-Ma Huateng Telescopes for Survey (TMTS) aims to detect fast-evolving transients in the Universe, which has led to the discovery of thousands of short-period variables and eclipsing binaries since 2020. In this paper, we present the observed properties of 125 flare stars identified by the TMTS within the first two years, with an attempt to constrain their eruption physics. As expected, most of these flares were recorded in late-type red stars with $G_{\rm BP}-G_{\rm RP}$ > 2.0 mag, however, the flares associated with bluer stars tend to be on average more energetic and have broader profiles. The peak flux (F_peak) of the flare is found to depend strongly on the equivalent duration (ED) of the energy release, i.e., $F_{\rm peak} \propto {\rm ED}^{0.72\pm0.04}$, which is consistent with results derived from the Kepler and Evryscope samples. This relation is likely related to the magnetic loop emission, while -- for the more popular non-thermal electron heating model -- a specific time evolution may be required to generate this relation. We notice that flares produced by hotter stars have a flatter $F_{\rm peak} \propto {\rm ED}$ relation compared to that from cooler stars. This is related to the statistical discrepancy in light-curve shape of flare events with different colors. In spectra from LAMOST, we find that flare stars have apparently stronger H alpha emission than inactive stars, especially at the low temperature end, suggesting that chromospheric activity plays an important role in producing flares. On the other hand, the subclass having frequent flares are found to show H alpha emission of similar strength in their spectra to that recorded with only a single flare but similar effective temperature, implying that the chromospheric activity may not be the only trigger for eruptions.

astro-ph.SR↗

arXiv2023

Properties and Asteroseismological analysis of a new ZZ ceti discovered by TMTS

Tsinghua university-Ma Huateng Telescope for Survey (TMTS) aims to discover rapidly evolving transients by monitoring the northern sky. The TMTS catalog is cross-matched with the white dwarf (WD) catalog of Gaia EDR3, and light curves of more than a thousand WD candidates are obtained so far. Among them, the WD TMTS J23450729+5813146 (hereafter J2345) is one interesting common source. Based on the light curves from the TMTS and follow-up photometric observations, periods of 967.113 s, 973.734 s, 881.525 s, 843.458 s, 806.916 s and 678.273 s are identified. In addition, the TESS observations suggest a 3.39 h period but this can be attributed to the rotation of a comoving M dwarf located within 3". The spectroscopic observation indicates that this WD is DA type with Teff = 11778+/-617K,log g = 8.38+/-0.31,mass=0.84+/-0.20Msun and age=0.704+/-0.377 Gyrs. Asteroseismological analysis reveals a global best-fit solution of Teff =12110+/-10K and mass=0.760+/-0.005Msun,consistent with the spectral fitting results, and Oxygen and Carbon abundances in the core center are 0.73 and 0.27, respectively. The distance derived from the intrinsic luminosity given by asteroseismology is 93 parsec, which is in agreement with the distance of 98 parsec from Gaia DR3. Additionally, kinematic study shows that this WD is likely a thick disk star. The mass of its zero-age main-sequence mass is estimated to be 3.08 Msun and has a main-sequence plus cooling age of roughly 900 Myrs.

astro-ph.SR↗

arXiv2023

Gradient Aligned Attacks via a Few Queries

Black-box query attacks, which rely only on the output of the victim model, have proven to be effective in attacking deep learning models. However, existing black-box query attacks show low performance in a novel scenario where only a few queries are allowed. To address this issue, we propose gradient aligned attacks (GAA), which use the gradient aligned losses (GAL) we designed on the surrogate model to estimate the accurate gradient to improve the attack performance on the victim model. Specifically, we propose a gradient aligned mechanism to ensure that the derivatives of the loss function with respect to the logit vector have the same weight coefficients between the surrogate and victim models. Using this mechanism, we transform the cross-entropy (CE) loss and margin loss into gradient aligned forms, i.e. the gradient aligned CE or margin losses. These losses not only improve the attack performance of our gradient aligned attacks in the novel scenario but also increase the query efficiency of existing black-box query attacks. Through theoretical and empirical analysis on the ImageNet database, we demonstrate that our gradient aligned mechanism is effective, and that our gradient aligned attacks can improve the attack performance in the novel scenario by 16.1\% and 31.3\% on the $l_2$ and $l_{\infty}$ norms of the box constraint, respectively, compared to four latest transferable prior-based query attacks. Additionally, the gradient aligned losses also significantly reduce the number of queries required in these transferable prior-based query attacks by a maximum factor of 2.9 times. Overall, our proposed gradient aligned attacks and losses show significant improvements in the attack performance and query efficiency of black-box query attacks, particularly in scenarios where only a few queries are allowed.

cs.CV↗

arXiv2023

Active random force promotes diffusion in bacterial cytoplasm

Experiments have found that diffusion in metabolically active cells is much faster than in dormant cells, especially for large particles. However, the mechanism of this size-dependent diffusion enhancement in living cells is still unclear. In this work, we approximate the net effect of metabolic processes as a white-noise active force and simulate a model system of bacterial cytoplasm with a highly polydisperse particle size distribution. We find that diffusion enhancement in active cells relative to dormant cells can be more substantial for large particles. Our simulations agree quantitatively with the experimental data of Escherichia coli, suggesting an autocorrelation function of the active force proportional to the cube of particle radius. We demonstrate that such a white-noise active force is equivalent to an active force of about 0.57 pN with random orientation. Our work unveils an emergent simplicity of random processes inside living cells.

physics.bio-ph↗

arXiv2023

Minute-Cadence Observations of the LAMOST Fields with the TMTS: II. Catalogues of Short-Period Variable Stars from the First Two-Year Surveys

Over the past few years, wide-field time-domain surveys like ZTF and OGLE have led to discoveries of various types of interesting short-period stellar variables, such as ultracompact eclipsing binary white dwarfs, rapidly rotating magnetised white dwarfs (WDs), transitional cataclysmic variables between hydrogen-rich and helium accretion, and blue large-amplitude pulsators (BLAPs), which greatly enrich our understandings of stellar physics under some extreme conditions. In this paper, we report the first-two-year discoveries of short-period variables (i.e., P<2 hr) by the Tsinghua University-Ma Huateng Telescopes for Survey (TMTS). TMTS is a multi-tube telescope system with a field of view up to 18 deg^2, which started to monitor the LAMOST sky areas since 2020 and generated uninterrupted minute-cadence light curves for about ten million sources within 2 years. Adopting the Lomb-Scargle periodogram with period-dependent thresholds for the maximum powers, we identify over 1 100 sources that exhibit a variation period shorter than 2 hr. Compiling the light curves with the Gaia magnitudes and colours, LAMOST spectral parameters, VSX classifications, and archived observations from other prevailing time-domain survey missions, we identified 1 076 as delta Scuti stars, which allows us study their populations and physical properties in the short-period regime. The other 31 sources include BLAPs, subdwarf B variables (sdBVs), pulsating WDs, ultracompact/short-period eclipsing/ellipsoidal binaries, cataclysmic variables below the period gap, etc., which are highly interesting and worthy of follow-up investigations.

astro-ph.SR↗

arXiv2023

FACM: Intermediate Layer Still Retain Effective Features against Adversarial Examples

In strong adversarial attacks against deep neural networks (DNN), the generated adversarial example will mislead the DNN-implemented classifier by destroying the output features of the last layer. To enhance the robustness of the classifier, in our paper, a \textbf{F}eature \textbf{A}nalysis and \textbf{C}onditional \textbf{M}atching prediction distribution (FACM) model is proposed to utilize the features of intermediate layers to correct the classification. Specifically, we first prove that the intermediate layers of the classifier can still retain effective features for the original category, which is defined as the correction property in our paper. According to this, we propose the FACM model consisting of \textbf{F}eature \textbf{A}nalysis (FA) correction module, \textbf{C}onditional \textbf{M}atching \textbf{P}rediction \textbf{D}istribution (CMPD) correction module and decision module. The FA correction module is the fully connected layers constructed with the output of the intermediate layers as the input to correct the classification of the classifier. The CMPD correction module is a conditional auto-encoder, which can not only use the output of intermediate layers as the condition to accelerate convergence but also mitigate the negative effect of adversarial example training with the Kullback-Leibler loss to match prediction distribution. Through the empirically verified diversity property, the correction modules can be implemented synergistically to reduce the adversarial subspace. Hence, the decision module is proposed to integrate the correction modules to enhance the DNN classifier's robustness. Specially, our model can be achieved by fine-tuning and can be combined with other model-specific defenses.

cs.CV↗

arXiv2023

Fuzziness-tuned: Improving the Transferability of Adversarial Examples

With the development of adversarial attacks, adversairal examples have been widely used to enhance the robustness of the training models on deep neural networks. Although considerable efforts of adversarial attacks on improving the transferability of adversarial examples have been developed, the attack success rate of the transfer-based attacks on the surrogate model is much higher than that on victim model under the low attack strength (e.g., the attack strength $ε=8/255$). In this paper, we first systematically investigated this issue and found that the enormous difference of attack success rates between the surrogate model and victim model is caused by the existence of a special area (known as fuzzy domain in our paper), in which the adversarial examples in the area are classified wrongly by the surrogate model while correctly by the victim model. Then, to eliminate such enormous difference of attack success rates for improving the transferability of generated adversarial examples, a fuzziness-tuned method consisting of confidence scaling mechanism and temperature scaling mechanism is proposed to ensure the generated adversarial examples can effectively skip out of the fuzzy domain. The confidence scaling mechanism and the temperature scaling mechanism can collaboratively tune the fuzziness of the generated adversarial examples through adjusting the gradient descent weight of fuzziness and stabilizing the update direction, respectively. Specifically, the proposed fuzziness-tuned method can be effectively integrated with existing adversarial attacks to further improve the transferability of adverarial examples without changing the time complexity. Extensive experiments demonstrated that fuzziness-tuned method can effectively enhance the transferability of adversarial examples in the latest transfer-based attacks.

cs.LG↗

arXiv2023

Evolution of the post merger remnants from the coalescence of oxygen-neon and carbon-oxygen white dwarf pairs

Although multidimensional simulations have investigated the processes of double WD mergers, post-merger evolution only focused on the carbon-oxygen (CO) WD or helium (He) WD merger remnants. In this work, we investigate for the first time the evolution of the remnants stemmed from the merger of oxygen-neon (ONe) WDs with CO WDs. Our simulation results indicate that the merger remnants can evolve to hydrogen- and helium-deficient giants with maximum radius of about 300Rsun. Our models show evidence that merger remnants more massive than 1.95Msun can ignite Ne before significant mass-loss ensues, and they thus would become electron-capture supernovae (ECSNe). However, remnants with initial masses less than 1.90Msun will experience further core contraction and longer evolutionary time before reaching at the conditions for Ne-burning. Therefore their fates are more dependent on mass-loss rates due to stellar winds, and thus more uncertain. Relatively high mass-loss rates would cause such remnants to end their lives as ONe WDs. Our evolutionary models can naturally explain the observational properties of the double WD merger remnant IRAS 00500+6713 (J005311). As previously suggested in the literature, we propose and justify that J005311 may be the remnant from the coalescence of an ONe WD and an CO WD. We deduce that the final outcome of J005311 would be a massive ONe WD rather than a supernova explosion. Our investigations may be able to provide possible constraints on the wind mass-loss properties of the giants which have CO-dominant envelopes.

astro-ph.SR↗

arXiv2023

An X-Ray-dim "Isolated'' Neutron Star in a Binary?

We report the discovery of a dark companion to 2MASS J15274848+3536572 with an orbital period of 6.14 hr. Combining the radial velocity from LAMOST observations and modelling of the multiband light curve, one obtains a mass function of $\simeq 0.131~\rm M_{\odot}$, an inclination of $45.20^\circ{}^{+0.13^{\circ}}_{-0.20^{\circ}}$, and a mass ratio of $0.631^{+0.014}_{-0.003}$, which demonstrate the binary nature of the dark companion with mass of $0.98 \pm 0.03\rm M_{\odot}$ and a main-sequence K9-M0 star of $0.62 \pm 0.01~\rm M_{\odot}$. LAMOST optical spectra at a range of orbital phases reveal extra-peaked Halpha emission that suggests the presence of an accretion disk. The dark companion does not seem to be a white dwarf because of the lack of any observed dwarf nova outbursts in the long-term data archive, although a magnetic white dwarf cannot be excluded. Alternatively, we propose a scenario wherein the dark companion is a neutron star, but we have not detected radio pulsations or a single pulse from the system with the FAST (Five-hundred-meter Aperture Spherical radio Telescope), which hints at a radio-quiet compact object. If the dark companion is identified as a neutron star, it will be the nearest ( ~ 118 pc) and lightest neutron star. Furthermore, a kinematic analysis of the system's orbit in the galaxy may suggest its supernova event is associated with the radionuclide $^{60} \rm Fe$ signal observed from the deep-sea crusts. This radio-quiet and X-ray-dim nearby neutron star may resemble an XDINS (X-ray-dim isolated neutron star), but in a binary.

astro-ph.HE↗

arXiv2023

On Representation Knowledge Distillation for Graph Neural Networks

Knowledge distillation is a learning paradigm for boosting resource-efficient graph neural networks (GNNs) using more expressive yet cumbersome teacher models. Past work on distillation for GNNs proposed the Local Structure Preserving loss (LSP), which matches local structural relationships defined over edges across the student and teacher's node embeddings. This paper studies whether preserving the global topology of how the teacher embeds graph data can be a more effective distillation objective for GNNs, as real-world graphs often contain latent interactions and noisy edges. We propose Graph Contrastive Representation Distillation (G-CRD), which uses contrastive learning to implicitly preserve global topology by aligning the student node embeddings to those of the teacher in a shared representation space. Additionally, we introduce an expanded set of benchmarks on large-scale real-world datasets where the performance gap between teacher and student GNNs is non-negligible. Experiments across 4 datasets and 14 heterogeneous GNN architectures show that G-CRD consistently boosts the performance and robustness of lightweight GNNs, outperforming LSP (and a global structure preserving variant of LSP) as well as baselines from 2D computer vision. An analysis of the representational similarity among teacher and student embedding spaces reveals that G-CRD balances preserving local and global relationships, while structure preserving approaches are best at preserving one or the other. Our code is available at https://github.com/chaitjo/efficient-gnns

cs.LG↗

arXiv2023

Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis

3D point cloud analysis has drawn a lot of research attention due to its wide applications. However, collecting massive labelled 3D point cloud data is both time-consuming and labor-intensive. This calls for data-efficient learning methods. In this work we propose PointDisc, a point discriminative learning method to leverage self-supervisions for data-efficient 3D point cloud classification and segmentation. PointDisc imposes a novel point discrimination loss on the middle and global level features produced by the backbone network. This point discrimination loss enforces learned features to be consistent with points belonging to the corresponding local shape region and inconsistent with randomly sampled noisy points. We conduct extensive experiments on 3D object classification, 3D semantic and part segmentation, showing the benefits of PointDisc for data-efficient learning. Detailed analysis demonstrate that PointDisc learns unsupervised features that well capture local and global geometry.

cs.CV↗

arXiv2022

An 18.9-minute Blue Large-Amplitude Pulsator Crossing the 'Hertzsprung Gap' of Hot Subdwarfs

Blue large-amplitude pulsators (BLAPs) represent a new and rare class of hot pulsating stars with unusually large amplitudes and short periods. Up to now, only 24 confirmed BLAPs have been identified from more than one billion monitored stars, including a group with pulsation period longer than $\sim 20$ min (classical BLAPs, hereafter) and the other group with pulsation period below $\sim 8$ min. The evolutionary path that could give rise to such kinds of stellar configurations is unclear. Here we report on a comprehensive study of the peculiar BLAP discovered by the Tsinghua University - Ma Huateng Telescopes for Survey (TMTS), TMTS J035143.63+584504.2 (TMTS-BLAP-1). This new BLAP has an 18.9 min pulsation period and is similar to the BLAPs with a low surface gravity and an extended helium-enriched envelope, suggesting that it is a low-gravity BLAP at the shortest-period end. In particular, the long-term monitoring data reveal that this pulsating star has an unusually large rate of period change, P_dot/P=2.2e-6/yr. Such a significant and positive value challenges its origins from both helium-core pre-white-dwarfs and core helium-burning subdwarfs, but is consistent with that derived from shell helium-burning subdwarfs. The particular pulsation period and unusual rate of period change indicate that TMTS-BLAP-1 is at a short-lived (~10^6 yr) phase of shell-helium ignition before the stable shell-helium burning; in other words, TMTS-BLAP-1 is going through a "Hertzsprung gap" of hot subdwarfs.

astro-ph.SR↗

arXiv2022

The radio signal of PSR B0950+08 is detected over the whole pulse phase

Pulsars are the "lighthouses" in the universe. Periodic pulses with the duty-cycle ~10% are detected when the radio beam of the rotating pulsar sweeps across the telescope. In this report, the 160 min-data of a nearby pulsar, PSR B0950+08, observed with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) is analysed. Thanks to the extremely high sensitivity of FAST, it is found that the radiation of PSR B0950+08 could be detected over the entire pulse period. To investigate the radiative characteristics of the pulsar' "bridge emission", a function, $Θ$(n), is defined to reveal the weak radiation there. It is suggested that the narrow peaks of both the main and the inter pulses could be radiated at low altitude, while other weak emission (e.g., the "bridges") from upper magnetosphere though its radiative mechanism is still a matter of debate. The measured mean pulse behaviors are consistent with previous results in the phase of strong emission of this pulsar, and both the frequency-independent separation between the inter-pulse and main pulse and the narrow pulse width may support a double-pole model. In order to understand the magnetospheric geometry of this pulsar, further polarization calibrated observation with FAST and a proper determination of the baseline emission, especially during the weak emission phase, are surely required.

astro-ph.HE↗

arXiv2022

Long-tailed Recognition by Learning from Latent Categories

In this work, we address the challenging task of long-tailed image recognition. Previous long-tailed recognition methods commonly focus on the data augmentation or re-balancing strategy of the tail classes to give more attention to tail classes during the model training. However, due to the limited training images for tail classes, the diversity of tail class images is still restricted, which results in poor feature representations. In this work, we hypothesize that common latent features among the head and tail classes can be used to give better feature representation. Motivated by this, we introduce a Latent Categories based long-tail Recognition (LCReg) method. Specifically, we propose to learn a set of class-agnostic latent features shared among the head and tail classes. Then, we implicitly enrich the training sample diversity via applying semantic data augmentation to the latent features. Extensive experiments on five long-tailed image recognition datasets demonstrate that our proposed LCReg is able to significantly outperform previous methods and achieve state-of-the-art results.

cs.CV↗

arXiv2022

Robust Interior Point Method for Quantum Key Distribution Rate Computation

Security proof methods for quantum key distribution, QKD, that are based on the numerical key rate calculation problem, are powerful in principle. However, the practicality of the methods are limited by computational resources and the efficiency and accuracy of the underlying algorithms for convex optimization. We derive a stable reformulation of the convex nonlinear semidefinite programming, SDP, model for the key rate calculation problems. We use this to develop an efficient, accurate algorithm. The stable reformulation is based on novel forms of facial reduction, FR, for both the linear constraints and nonlinear quantum relative entropy objective function. This allows for a Gauss-Newton type interior-point approach that avoids the need for perturbations to obtain strict feasibility, a technique currently used in the literature. The result is high accuracy solutions with theoretically proven lower bounds for the original QKD from the FR stable reformulation. This provides novel contributions for FR for general SDP. We report on empirical results that dramatically improve on speed and accuracy, as well as solving previously intractable problems.

quant-ph↗

arXiv2022

Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis

In this paper, we present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges. The MuSe 2022 focuses on humor detection, emotional reactions and multimodal emotional stress utilizing different modalities and data sets. In our work, different kinds of multimodal features are extracted, including acoustic, visual, text and biological features. These features are fused by TEMMA and GRU with self-attention mechanism frameworks. In this paper, 1) several new audio features, facial expression features and paragraph-level text embeddings are extracted for accuracy improvement. 2) we substantially improve the accuracy and reliability of multimodal sentiment prediction by mining and blending the multimodal features. 3) effective data augmentation strategies are applied in model training to alleviate the problem of sample imbalance and prevent the model from learning biased subject characters. For the MuSe-Humor sub-challenge, our model obtains the AUC score of 0.8932. For the MuSe-Reaction sub-challenge, the Pearson's Correlations Coefficient of our approach on the test set is 0.3879, which outperforms all other participants. For the MuSe-Stress sub-challenge, our approach outperforms the baseline in both arousal and valence on the test dataset, reaching a final combined result of 0.5151.

cs.CV↗

arXiv2022

Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction

Deep neural networks are easily attacked by imperceptible perturbation. Presently, adversarial training (AT) is the most effective method to enhance the robustness of the model against adversarial examples. However, because adversarial training solved a min-max value problem, in comparison with natural training, the robustness and generalization are contradictory, i.e., the robustness improvement of the model will decrease the generalization of the model. To address this issue, in this paper, a new concept, namely confidence threshold (CT), is introduced and the reducing of the confidence threshold, known as confidence threshold reduction (CTR), is proven to improve both the generalization and robustness of the model. Specifically, to reduce the CT for natural training (i.e., for natural training with CTR), we propose a mask-guided divergence loss function (MDL) consisting of a cross-entropy loss term and an orthogonal term. The empirical and theoretical analysis demonstrates that the MDL loss improves the robustness and generalization of the model simultaneously for natural training. However, the model robustness improvement of natural training with CTR is not comparable to that of adversarial training. Therefore, for adversarial training, we propose a standard deviation loss function (STD), which minimizes the difference in the probabilities of the wrong categories, to reduce the CT by being integrated into the loss function of adversarial training. The empirical and theoretical analysis demonstrates that the STD based loss function can further improve the robustness of the adversarially trained model on basis of guaranteeing the changeless or slight improvement of the natural accuracy.

cs.LG↗

arXiv2022

FFConv: Fast Factorized Convolutional Neural Network Inference on Encrypted Data

Homomorphic Encryption (HE), allowing computations on encrypted data (ciphertext) without decrypting it first, enables secure but prohibitively slow Convolutional Neural Network (CNN) inference for privacy-preserving applications in clouds. To reduce the inference latency, one approach is to pack multiple messages into a single ciphertext in order to reduce the number of ciphertexts and support massive parallelism of Homomorphic Multiply-Accumulate (HMA) operations between ciphertexts. Despite the faster HECNN inference, the mainstream packing schemes Dense Packing (DensePack) and Convolution Packing (ConvPack) introduce expensive rotation overhead, which prolongs the inference latency of HECNN for deeper and wider CNN architectures. In this paper, we propose a low-rank factorization method named FFConv dedicated to efficient ciphertext packing for reducing both the rotation overhead and HMA operations. FFConv approximates a d x d convolution layer with low-rank factorized convolutions, in which a d x d low-rank convolution with fewer channels is followed by a 1 x 1 convolution to restore the channels. The d x d low-rank convolution with DensePack leads to significantly reduced rotation operations, while the rotation overhead of 1 x 1 convolution with ConvPack is close to zero. To our knowledge, FFConv is the first work that is capable of reducing the rotation overhead incurred by DensePack and ConvPack simultaneously, without introducing additional special blocks into the HECNN inference pipeline. Compared to prior art LoLa and Falcon, our method reduces the inference latency by up to 88% and 21%, respectively, with comparable accuracy on MNIST and CIFAR-10.

cs.CR↗