arXiv subjects

Tong Liu

Publications and source records attributed to Tong Liu.

At least 163 records · Page 9Linked to original sources

arXiv2024

Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector

This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples. While transformer-based open-set detectors, such as DE-ViT, show promise in traditional few-shot object detection, their generalization to CD-FSOD remains unclear: 1) can such open-set detection methods easily generalize to CD-FSOD? 2) If not, how can models be enhanced when facing huge domain gaps? To answer the first question, we employ measures including style, inter-class variance (ICV), and indefinable boundaries (IB) to understand the domain gap. Based on these measures, we establish a new benchmark named CD-FSOD to evaluate object detection methods, revealing that most of the current approaches fail to generalize across domains. Technically, we observe that the performance decline is associated with our proposed measures: style, ICV, and IB. Consequently, we propose several novel modules to address these issues. First, the learnable instance features align initial fixed instances with target categories, enhancing feature distinctiveness. Second, the instance reweighting module assigns higher importance to high-quality instances with slight IB. Third, the domain prompter encourages features resilient to different styles by synthesizing imaginary domains without altering semantic contents. These techniques collectively contribute to the development of the Cross-Domain Vision Transformer for CD-FSOD (CD-ViTO), significantly improving upon the base DE-ViT. Experimental results validate the efficacy of our model.

cs.CV↗

arXiv2024

Laboratorial radiative shocks with multiple parameters and first quantifying verifications to core-collapse supernovae

We present experiments to reproduce the characteristics of core-collapse supernovae with different stellar masses and initial explosion energies in the laboratory. In the experiments, shocks are driven in 1.2 atm and 1.9 atm xenon gas by laser with energy from 1600J to 2800J on the SGIII prototype laser facility. The average shock velocities and shocked densities are obtained from experiments. Experimental results reveal that higher laser energy and lower Xe gas density led to higher shock velocity, and lower Xe gas initial density has a higher compression. Modeling of the experiments using the 2D radiation hydrodynamic codes Icefire shows excellent agreement with the experimental results and gives the temperature. These results will contribute to time-domain astrophysical systems, such as gravitational supernovae, where a strong radiative shock propagates outward from the center of the star after the core collapses.

astro-ph.HE↗

arXiv2024

The discovery of three pulsars in the globular cluster M15 with the FAST

We present the discovery of three pulsars in the Globular Cluster (GC) M15 (NGC 7078) by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). PSR J2129+1210J (M15J) is a millisecond pulsar with a spin period of 11.84 ms and a dispersion measure of 66.68 pc cm-3. Both PSR J2129+1210K and L (M15K and L) are long-period pulsars with spin periods of 1928 ms and 3961 ms, respectively. M15L is the GC pulsar with the longest spin period known. The timing solutions of M15A to M15H are updated. As predicted by Ridolfi et al.(2018), the flux density of M15C keeps decreasing and the latest detection in our dataset was on December 20th, 2022. We have also detected M15I's signal for the first time since its discovery. Current timing suggests that it is an isolated pulsar.

astro-ph.HE↗

arXiv2024

Nucleosynthesis contribution of neutrino-dominated accretion flows to the solar neighborhood

The elemental abundances of stars reflect the complex enrichment history of the galaxy. To explore and explain the metal enrichment history of the cosmic environment near our solar system, we study the evolution of $^{56} \mathrm{Fe}$ abundance over time and [Mg/Fe] versus [Fe/H] evolution in the solar neighborhood. Core-collapse supernovae make the dominant contribution in the early stages, while Type Ia supernovae (SNe Ia) have a delayed and dominant impact in the later stages. In this work, we consider the nucleosynthesis contribution of neutrino-dominated accretion flows (NDAFs) formed at the end of the lives of massive stars. The results show that the [Fe/H] gradually increases over time and eventually reaches $\rm [Fe/H]=0$ and above, reproducing the chemical enrichment process in the solar neighborhood. Before the onset of SNe Ia, the ratio of $^{56} \mathrm{Fe}$ mass to the total gas mass increases by a factor of at most $\sim 1.44$ when NDAFs are taken into account. We find that by including NDAF in our models, the agreement with the observed metallicity distribution of metal-poor stars in the solar neighborhood ($\rm < 1~kpc$) is improved, while not significantly altering the location of the metallicity peak. This inclusion can also reproduce the observed evolutionary change of [Mg/Fe] at [Fe/H] $\sim -1.22$, bringing the ratio to match the solar abundance. Our results provide an extensive understanding of metallicity evolution in the solar environments by highlighting the nucleosynthesis contribution of NDAF outflows in the solar neighborhood.

astro-ph.HE↗

arXiv2024

Heterogeneous Graph Sequence Neural Networks for Dynamic Traffic Assignment

Traffic assignment and traffic flow prediction provide critical insights for urban planning, traffic management, and the development of intelligent transportation systems. An efficient model for calculating traffic flows over the entire transportation network could provide a more detailed and realistic understanding of traffic dynamics. However, existing traffic prediction approaches, such as those utilizing graph neural networks, are typically limited to locations where sensors are deployed and cannot predict traffic flows beyond sensor locations. To alleviate this limitation, inspired by fundamental relationship that exists between link flows and the origin-destination (OD) travel demands, we proposed the Heterogeneous Spatio-Temporal Graph Sequence Network (HSTGSN). HSTGSN exploits dependency between origin and destination nodes, even when it is long-range, and learns implicit vehicle route choices under different origin-destination demands. This model is based on a heterogeneous graph which consists of road links, OD links (virtual links connecting origins and destinations) and a spatio-temporal graph encoder-decoder that captures the spatio-temporal relationship between OD demands and flow distribution. We will show how the graph encoder-decoder is able to recover the incomplete information in the OD demand, by using node embedding from the graph decoder to predict the temporal changes in flow distribution. Using extensive experimental studies on real-world networks with complete/incomplete OD demands, we demonstrate that our method can not only capture the implicit spatio-temporal relationship between link traffic flows and OD demands but also achieve accurate prediction performance and generalization capability.

cs.LG↗

arXiv2024

Stochastic gravitational wave background from cosmological neutrino-dominated accretion flows

We investigate the stochastic gravitational wave background (SGWB) from neutrino-dominated accretion flows (NDAFs) based on the results of our fallback core-collapse supernova (CCSN) simulations. We find that the predicted SGWB is mainly determined by the typical CCSN initial explosion energy and progenitor metallicity. For the optimistic cases in which the typical initial explosion energy is low, the SGWB from NDAFs without disk outflows might be detected by next-generation space-based interferometers such as Decihertz Interferometer Gravitational wave Observatory (DECIGO) and Big Bang Observer (BBO). In the low-frequency regime $\sim10^{-3}-10^{-1}$ Hz, this background is comparable to that expected from standard inflationary models. Therefore, the SGWB from NDAFs may become a foreground for searches of the SGWB generated in the inflationary epoch. Combining the diffuse NDAF neutrino background and SGWB from NDAFs, one may constrain the properties of the CCSNe and NDAFs.

astro-ph.HE↗

arXiv2024

The CR Volume for Black Holes and the Corresponding Entropy Variation: A Review

This paper reviews the work done on black hole interior volume, entropy, and evaporation. An insight into the basics for understanding the interior volume is presented. A general analogy to investigate the interior volume of a black hole, the associated quantum mode's entropy, and the evolution relation between the interior and exterior entropy is explained. Using this analogy, we predicted the future of information stored in a BH, its radiation, and evaporation. The results are noted in tables (\ref{tab:1}) and (\ref{tab:2}). To apply this analogy in BH space-time, we investigated the interior volume, entropy, and evaluation relation for different types of BHs. Finally, we also investigated the nature of BH radiation and the probability of particle emission during the evaporation process.

gr-qc↗

arXiv2024

Phase induced localization transition

Localization phenomenon is an important research field in condensed matter physics. However, due to the complexity and subtlety of disordered syestems, new localization phenomena always emerge unexpectedly. For example, it is generally believed that the phase of the hopping term does not affect the localization properties of the system, so the calculation of the phase is often ignored in the study of localization. Here, we introduce a quasiperiodic model and demonstrate that the phase change of the hopping term can significantly alter the localization properties of the system through detailed numerical simulations such as the inverse participation ratio and multifractal analysis. This phase-induced localization transition provides valuable information for the study of localization physics.

cond-mat.dis-nn↗

arXiv2024

Over the Edge of Chaos? Excess Complexity as a Roadblock to Artificial General Intelligence

In this study, we explored the progression trajectories of artificial intelligence (AI) systems through the lens of complexity theory. We challenged the conventional linear and exponential projections of AI advancement toward Artificial General Intelligence (AGI) underpinned by transformer-based architectures, and posited the existence of critical points, akin to phase transitions in complex systems, where AI performance might plateau or regress into instability upon exceeding a critical complexity threshold. We employed agent-based modelling (ABM) to simulate hypothetical scenarios of AI systems' evolution under specific assumptions, using benchmark performance as a proxy for capability and complexity. Our simulations demonstrated how increasing the complexity of the AI system could exceed an upper criticality threshold, leading to unpredictable performance behaviours. Additionally, we developed a practical methodology for detecting these critical thresholds using simulation data and stochastic gradient descent to fine-tune detection thresholds. This research offers a novel perspective on AI advancement that has a particular relevance to Large Language Models (LLMs), emphasising the need for a tempered approach to extrapolating AI's growth potential and underscoring the importance of developing more robust and comprehensive AI performance benchmarks.

cs.AI↗

arXiv2024

Temperature-scaling surprisal estimates improve fit to human reading times -- but does it do so for the "right reasons"?

A wide body of evidence shows that human language processing difficulty is predicted by the information-theoretic measure surprisal, a word's negative log probability in context. However, it is still unclear how to best estimate these probabilities needed for predicting human processing difficulty -- while a long-standing belief held that models with lower perplexity would provide more accurate estimates of word predictability, and therefore lead to better reading time predictions, recent work has shown that for very large models, psycholinguistic predictive power decreases. One reason could be that language models might be more confident of their predictions than humans, because they have had exposure to several magnitudes more data. In this paper, we test what effect temperature-scaling of large language model (LLM) predictions has on surprisal estimates and their predictive power of reading times of English texts. Firstly, we show that calibration of large language models typically improves with model size, i.e. poorer calibration cannot account for poorer fit to reading times. Secondly, we find that temperature-scaling probabilities lead to a systematically better fit to reading times (up to 89% improvement in delta log likelihood), across several reading time corpora. Finally, we show that this improvement in fit is chiefly driven by words that are composed of multiple subword tokens.

cs.CL↗

arXiv2024

ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation

In industrial recommendation systems, there are several mini-apps designed to meet the diverse interests and needs of users. The sample space of them is merely a small subset of the entire space, making it challenging to train an efficient model. In recent years, there have been many excellent studies related to cross-domain recommendation aimed at mitigating the problem of data sparsity. However, few of them have simultaneously considered the adaptability of both sample and representation continual transfer setting to the target task. To overcome the above issue, we propose a Entire space Continual and Adaptive Transfer learning framework called ECAT which includes two core components: First, as for sample transfer, we propose a two-stage method that realizes a coarse-to-fine process. Specifically, we perform an initial selection through a graph-guided method, followed by a fine-grained selection using domain adaptation method. Second, we propose an adaptive knowledge distillation method for continually transferring the representations from a model that is well-trained on the entire space dataset. ECAT enables full utilization of the entire space samples and representations under the supervision of the target task, while avoiding negative migration. Comprehensive experiments on real-world industrial datasets from Taobao show that ECAT advances state-of-the-art performance on offline metrics, and brings +13.6% CVR and +8.6% orders for Baiyibutie, a famous mini-app of Taobao.

cs.IR↗

arXiv2024

Timing and Scintillation Studies of Pulsars in Globular Cluster M3 (NGC 5272) with FAST

We present the phase-connected timing solutions of all the five pulsars in globular cluster (GC) M3 (NGC 5272), namely PSRs M3A to F (PSRs J1342+2822A to F), with the exception of PSR M3C, from FAST archival data. In these timing solutions, those of PSRs M3E, and F are obtained for the first time. We find that PSRs M3E and F have low mass companions, and are in circular orbits with periods of 7.1 and 3.0 days, respectively. For PSR M3C, we have not detected it in all the 41 observations. We found no X-ray counterparts for these pulsars in archival Chandra images in the band of 0.2-20 keV. We noticed that the pulsars in M3 seem to be native. From the Auto-Correlation Function (ACF) analysis of the M3A's and M3B's dynamic spectra, the scintillation timescale ranges from $7.0\pm0.3$ min to $60.0\pm0.6$ min, and the scintillation bandwidth ranges from $4.6\pm0.2$ MHz to $57.1\pm1.1$ MHz. The measured scintillation bandwidths from the dynamic spectra indicate strong scintillation, and the scattering medium is anisotropic. From the secondary spectra, we captured a scintillation arc only for PSR M3B with a curvature of $649\pm23 {\rm m}^{-1} {\rm mHz}^{-2}$.

astro-ph.HE↗

arXiv2024

HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy

Full-parameter fine-tuning has become the go-to choice for adapting language models (LMs) to downstream tasks due to its excellent performance. As LMs grow in size, fine-tuning the full parameters of LMs requires a prohibitively large amount of GPU memory. Existing approaches utilize zeroth-order optimizer to conserve GPU memory, which can potentially compromise the performance of LMs as non-zero order optimizers tend to converge more readily on most downstream tasks. In this paper, we propose a novel optimizer-independent end-to-end hierarchical fine-tuning strategy, HiFT, which only updates a subset of parameters at each training step. HiFT can significantly reduce the amount of gradients and optimizer state parameters residing in GPU memory at the same time, thereby reducing GPU memory usage. Our results demonstrate that: (1) HiFT achieves comparable performance to parameter-efficient fine-tuning and standard full parameter fine-tuning. (2) HiFT supports various optimizers including AdamW, AdaGrad, SGD, etc. (3) HiFT can save more than 60\% GPU memory compared with standard full-parameter fine-tuning for 7B model. (4) HiFT enables full-parameter fine-tuning of a 7B model on single 48G A6000 with a precision of 32 using the AdamW optimizer, without using any memory saving techniques.

cs.LG↗

arXiv2024

High-efficiency generation of vectorial holograms with metasurfaces

Holography plays a crucial role in optics applications, but it traditionally requires complex setup and bulky devices, being unfavourable for optics integration. While metasurface-based holograms are ultra-compact and easy to realize, holographic images generated are mostly restricted to scalar ones, with a few recent attempts on vectorial holograms suffering from complex meta-structures and low efficiencies. Here, we propose and experimentally demonstrate an efficient meta-platform to generate vectorial holograms with arbitrarily designed wave fronts and polarization distributions based on ultra-compact metaatoms. Combining GS algorithm and the wave-decomposition technique, we establish a generic strategy to retrieve the optical property, i.e., the distributions of reflection phase and polarization-conversion capability of the metasurface to generate a target vectorial holographic image. We next design a series of high-efficiency and deep-subwavelength metaatoms exhibiting arbitrarily designed reflection phases and polarization-conversion capabilities, and experimentally characterize their optical properties. Based on these metaatoms, we finally realize a series of meta-holograms that can generate pre-designed vectorial holographic images upon external illuminations, and experimentally characterize their working performances. Our work provides a high-efficiency and ultra-thin platform to generate vectorial holographic images, which can find many applications in onchip photonics.

physics.optics↗

arXiv2024

Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstruction

In recent years, large language models (LLMs) have demonstrated notable success across various tasks, but the trustworthiness of LLMs is still an open problem. One specific threat is the potential to generate toxic or harmful responses. Attackers can craft adversarial prompts that induce harmful responses from LLMs. In this work, we pioneer a theoretical foundation in LLMs security by identifying bias vulnerabilities within the safety fine-tuning and design a black-box jailbreak method named DRA (Disguise and Reconstruction Attack), which conceals harmful instructions through disguise and prompts the model to reconstruct the original harmful instruction within its completion. We evaluate DRA across various open-source and closed-source models, showcasing state-of-the-art jailbreak success rates and attack efficiency. Notably, DRA boasts a 91.1% attack success rate on OpenAI GPT-4 chatbot.

cs.CR↗

arXiv2024

Detection of Acetone as a Gas Biomarker for Diabetes Based on Gas Sensor Technology

With the continuous development and improvement of medical services, there is a growing demand for improving diabetes diagnosis. Exhaled breath analysis, characterized by its speed, convenience, and non-invasive nature, is leading the trend in diagnostic development. Studies have shown that the acetone levels in the breath of diabetes patients are higher than normal, making acetone a basis for diabetes breath analysis. This provides a more readily accepted method for early diabetes prevention and monitoring. Addressing issues such as the invasive nature, disease transmission risks, and complexity of diabetes testing, this study aims to design a diabetes gas biomarker acetone detection system centered around a sensor array using gas sensors and pattern recognition algorithms. The research covers sensor selection, sensor preparation, circuit design, data acquisition and processing, and detection model establishment to accurately identify acetone. Titanium dioxide was chosen as the nano gas-sensitive material to prepare the acetone gas sensor, with data collection conducted using STM32. Filtering was applied to process the raw sensor data, followed by feature extraction using principal component analysis. A recognition model based on support vector machine algorithm was used for qualitative identification of gas samples, while a recognition model based on backpropagation neural network was employed for quantitative detection of gas sample concentrations. Experimental results demonstrated recognition accuracies of 96% and 97.5% for acetone-ethanol and acetone-methanol mixed gases, and 90% for ternary acetone, ethanol, and methanol mixed gases.

eess.SP↗

arXiv2024

FAST Discovery of Eight Isolated Millisecond Pulsars in NGC 6517

We present the discovery of 8 isolated millisecond pulsars in Globular Cluster (GC) NGC 6517 using the Five-Hundred-meter Aperture Spherical radio Telescope (FAST). The spin periods of those pulsars (namely PSR J1801-0857K to R, or, NGC 6517K to R) are all shorter than 10 ms. With these discoveries, NGC 6517 is currently the GC with the most known pulsars in the FAST sky. The largest difference in dispersion measure of the pulsars in NGC 6517 is 11.2 cm$^{-3}$ pc, the second among all GCs. The fraction of isolated pulsars in this GC (16 of 17, 94$\%$) is consistent with previous studies indicating an overabundance of isolated pulsars in the densest GCs, especially in those undergoing cluster core collapse. Considering the FAST GC pulsar discoveries, we modeled the GC pulsar population using the empirical Bayesian method described by Turk and Lorimer with the recent counts. Using this approach, we find that the expected number of potential pulsars in GCs seems to be correlated with the central escape velocity, hence, the GCs Liller 1, NGC 6441, M54 (NGC 6715), and $ω$-Cen (NGC 5139) are expected to host the largest numbers of pulsars.

astro-ph.HE↗

arXiv2024

GRB afterglows with energy injections in AGN accretion disks

Active galactic nucleus (AGN) disks are widely considered potential hosts for various high-energy transients, including gamma-ray bursts (GRBs). The reactivation of GRB central engines can provide additional energy to shocks formed during the interaction of the initially ejected GRB jets with the circumburst material, commonly referred to as energy injections. In this paper, we study GRBs occurring in AGN disks within the context of energy injections. We adopt the standard external forward shock (EFS) model and consider both short- and long-duration GRB scenarios. Light curves for two types of radiation, namely the radiation from the heated disk material (RHDM) and GRB afterglows, are computed. We find that the energy injection facilitates the EFS to break out from the photosphere of the low-density AGN disk at relativistic velocity. Moreover, the energy injection almost does not affect the RHDM but significantly enhances the peak flux of the GRB afterglows.

astro-ph.HE↗