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As a manner to augment pre-trained large language models (LLM), knowledge injection is critical to develop vertical domain large models and has been widely studied. Although most current approaches, including parameter-efficient fine-tuning (PEFT) and block expansion methods, uniformly apply knowledge across all LLM layers, it raises the question: are all layers equally crucial for knowledge injection? We begin by evaluating the importance of each layer in finding the optimal layer range for knowledge injection. Intuitively, the more important layers should play a more critical role in knowledge injection and deserve a denser injection. We observe performance dips in question-answering benchmarks after the removal or expansion of the shallow layers, and the degradation shrinks as the layer gets deeper, indicating that the shallow layers hold the key to knowledge injection. This insight leads us to propose the S strategy, a post-pretraining strategy of selectively enhancing shallow layers while pruning the less effective deep ones. Based on this strategy, we introduce Llama Slayer-8B and Llama Slayer-8B-Instruct. We experimented on the corpus of code $\&$ math and demonstrated the effectiveness of our strategy. Further experiments across different LLM, Mistral-7B, and a legal corpus confirmed the general applicability of the approach, underscoring its wide-ranging efficacy. Our code is available at: \https://github.com/txchen-USTC/Llama-Slayer

Recent progress in generative AI technology has made audio deepfakes remarkably more realistic. While current research on anti-spoofing systems primarily focuses on assessing whether a given audio sample is fake or genuine, there has been limited attention on discerning the specific techniques to create the audio deepfakes. Algorithms commonly used in audio deepfake generation, like text-to-speech (TTS) and voice conversion (VC), undergo distinct stages including input processing, acoustic modeling, and waveform generation. In this work, we introduce a system designed to classify various spoofing attributes, capturing the distinctive features of individual modules throughout the entire generation pipeline. We evaluate our system on two datasets: the ASVspoof 2019 Logical Access and the Multi-Language Audio Anti-Spoofing Dataset (MLAAD). Results from both experiments demonstrate the robustness of the system to identify the different spoofing attributes of deepfake generation systems., Comment: Accepted by INTERSPEECH 2024

Relativistic exact two-component coupled-cluster calculations of molecular sensitivity factors for nuclear Schiff moments (NSMs) are reported. We focus on molecules containing heavy nuclei, especially octupole-deformed nuclei. Analytic relativistic coupled-cluster gradient techniques are used and serve as useful tools for identifying candidate molecules that sensitively probe for physics beyond the Standard Model in the hadronic sector. Notably, these tools enable straightforward ``black-box'' calculations. Two competing chemical mechanisms that contribute to the NSM are analyzed, illuminating the physics of ligand effects on NSM sensitivity factors.

State space models (SSMs) with selection mechanisms and hardware-aware architectures, namely Mamba, have recently demonstrated significant promise in long-sequence modeling. Since the self-attention mechanism in transformers has quadratic complexity with image size and increasing computational demands, the researchers are now exploring how to adapt Mamba for computer vision tasks. This paper is the first comprehensive survey aiming to provide an in-depth analysis of Mamba models in the field of computer vision. It begins by exploring the foundational concepts contributing to Mamba's success, including the state space model framework, selection mechanisms, and hardware-aware design. Next, we review these vision mamba models by categorizing them into foundational ones and enhancing them with techniques such as convolution, recurrence, and attention to improve their sophistication. We further delve into the widespread applications of Mamba in vision tasks, which include their use as a backbone in various levels of vision processing. This encompasses general visual tasks, Medical visual tasks (e.g., 2D / 3D segmentation, classification, and image registration, etc.), and Remote Sensing visual tasks. We specially introduce general visual tasks from two levels: High/Mid-level vision (e.g., Object detection, Segmentation, Video classification, etc.) and Low-level vision (e.g., Image super-resolution, Image restoration, Visual generation, etc.). We hope this endeavor will spark additional interest within the community to address current challenges and further apply Mamba models in computer vision.

Recently, infrared small target detection (ISTD) has made significant progress, thanks to the development of basic models. Specifically, the models combining CNNs with transformers can successfully extract both local and global features. However, the disadvantage of the transformer is also inherited, i.e., the quadratic computational complexity to sequence length. Inspired by the recent basic model with linear complexity for long-distance modeling, Mamba, we explore the potential of this state space model for ISTD task in terms of effectiveness and efficiency in the paper. However, directly applying Mamba achieves suboptimal performances due to the insufficient harnessing of local features, which are imperative for detecting small targets. Instead, we tailor a nested structure, Mamba-in-Mamba (MiM-ISTD), for efficient ISTD. It consists of Outer and Inner Mamba blocks to adeptly capture both global and local features. Specifically, we treat the local patches as "visual sentences" and use the Outer Mamba to explore the global information. We then decompose each visual sentence into sub-patches as "visual words" and use the Inner Mamba to further explore the local information among words in the visual sentence with negligible computational costs. By aggregating the visual word and visual sentence features, our MiM-ISTD can effectively explore both global and local information. Experiments on NUAA-SIRST and IRSTD-1k show the superior accuracy and efficiency of our method. Specifically, MiM-ISTD is $8 \times$ faster than the SOTA method and reduces GPU memory usage by 62.2$\%$ when testing on $2048 \times 2048$ images, overcoming the computation and memory constraints on high-resolution infrared images., Comment: The first Mamba-based model for infrared small target detection

In pediatric cardiology, the accurate and immediate assessment of cardiac function through echocardiography is crucial since it can determine whether urgent intervention is required in many emergencies. However, echocardiography is characterized by ambiguity and heavy background noise interference, causing more difficulty in accurate segmentation. Present methods lack efficiency and are prone to mistakenly segmenting some background noise areas, such as the left ventricular area, due to noise disturbance. To address these issues, we introduce P-Mamba, which integrates the Mixture of Experts (MoE) concept for efficient pediatric echocardiographic left ventricular segmentation. Specifically, we utilize the recently proposed ViM layers from the vision mamba to enhance our model's computational and memory efficiency while modeling global dependencies.In the DWT-based Perona-Malik Diffusion (PMD) Block, we devise a PMD Block for noise suppression while preserving the left ventricle's local shape cues. Consequently, our proposed P-Mamba innovatively combines the PMD's noise suppression and local feature extraction capabilities with Mamba's efficient design for global dependency modeling. We conducted segmentation experiments on two pediatric ultrasound datasets and a general ultrasound dataset, namely Echonet-dynamic, and achieved state-of-the-art (SOTA) results. Leveraging the strengths of the P-Mamba block, our model demonstrates superior accuracy and efficiency compared to established models, including vision transformers with quadratic and linear computational complexity.

How to effectively interact audio with vision has garnered considerable interest within the multi-modality research field. Recently, a novel audio-visual segmentation (AVS) task has been proposed, aiming to segment the sounding objects in video frames under the guidance of audio cues. However, most existing AVS methods are hindered by a modality imbalance where the visual features tend to dominate those of the audio modality, due to a unidirectional and insufficient integration of audio cues. This imbalance skews the feature representation towards the visual aspect, impeding the learning of joint audio-visual representations and potentially causing segmentation inaccuracies. To address this issue, we propose AVSAC. Our approach features a Bidirectional Audio-Visual Decoder (BAVD) with integrated bidirectional bridges, enhancing audio cues and fostering continuous interplay between audio and visual modalities. This bidirectional interaction narrows the modality imbalance, facilitating more effective learning of integrated audio-visual representations. Additionally, we present a strategy for audio-visual frame-wise synchrony as fine-grained guidance of BAVD. This strategy enhances the share of auditory components in visual features, contributing to a more balanced audio-visual representation learning. Extensive experiments show that our method attains new benchmarks in AVS performance.

Infrared small target detection (ISTD) is critical to national security and has been extensively applied in military areas. ISTD aims to segment small target pixels from background. Most ISTD networks focus on designing feature extraction blocks or feature fusion modules, but rarely describe the ISTD process from the feature map evolution perspective. In the ISTD process, the network attention gradually shifts towards target areas. We abstract this process as the directional movement of feature map pixels to target areas through convolution, pooling and interactions with surrounding pixels, which can be analogous to the movement of thermal particles constrained by surrounding variables and particles. In light of this analogy, we propose Thermal Conduction-Inspired Transformer (TCI-Former) based on the theoretical principles of thermal conduction. According to thermal conduction differential equation in heat dynamics, we derive the pixel movement differential equation (PMDE) in the image domain and further develop two modules: Thermal Conduction-Inspired Attention (TCIA) and Thermal Conduction Boundary Module (TCBM). TCIA incorporates finite difference method with PMDE to reach a numerical approximation so that target body features can be extracted. To further remove errors in boundary areas, TCBM is designed and supervised by boundary masks to refine target body features with fine boundary details. Experiments on IRSTD-1k and NUAA-SIRST demonstrate the superiority of our method.

Using a semi-analytical model of the evolution of the Milky Way, we show how secular evolution can create distinct overdensities in the phase space of various properties (e.g. age vs metallicity or abundance ratios vs age) corresponding to the thin and thick discs. In particular, we show how key properties of the Solar vicinity can be obtained by secular evolution, with no need for external or special events, like galaxy mergers or paucity in star formation. This concerns the long established double-branch behaviour of [alpha/Fe] vs metallicity and the recently found non-monotonic evolution of the stellar abundance gradient, evaluated at the birth radii of stars. We extend the discussion to other abundance ratios and we suggest a classification scheme, based on the nature of the corresponding yields (primary vs secondary or odd elements) and on the lifetimes of their sources (short-lived vs long-lived ones). The latter property is critical in determining the single- or double- branch behavior of an elementary abundance ratio in the Solar neighborhood. We underline the high diagnostic potential of this finding, which can help to separate clearly elements with sources evolving on different timescales and help determining the site of e.g. the r-process(es). We define the "abundance distance" between the thin and thick disc sequences as an important element for such a separation. We also show how the inside-out evolution of the Milky Way disc leads rather to a single-branch behavior in other disc regions., Comment: 20 pages, 16 figures, typos corrected, matches published version in MNRAS

Purpose: The Low-Energy X-ray telescope (LE) is a main instrument of the Insight-HXMT mission and consists of 96 Swept Charge Devices (SCD) covering the 1-10 keV energy band. The energy gain and resolution are continuously calibrated by analysing Cassiopeia A (Cas A) and blank sky data, while the effective areas are also calibrated with the observations of the Crab Nebula. In this paper, we present the evolution of the in-orbit performances of LE in the first 5 years since launch. Methods: The Insight-HXMT Data Analysis Software package (HXMTDAS) is utilized to extract the spectra of Cas A, blank sky, and Crab Nebula using different Good Time Interval (GTI) selections. We fit a model with a power-law continuum and several Gaussian lines to different ranges of Cas A and blank sky spectra to get peak energies of their lines through xspec. After updating the energy gain calibration in CALibration DataBase (CALDB), we rerun the Cas A data to obtain the energy resolution. An empirical function is used to modify the simulated effective areas so that the background-subtracted spectrum of the Crab Nebula can best match the standard model of the Crab Nebula. Results: The energy gain, resolution, and effective areas are calibrated every month. The corresponding calibration results are duly updated in CALDB, which can be downloaded and used for the analysis of Insight-HXMT data. Simultaneous observations with NuSTAR and NICER can also be used to verify our derived results. Conclusion: LE is a well calibrated X-ray telescope working in 1-10 keV band. The uncertainty of LE gain is less than 20 eV in 2-9 keV band and the uncertainty of LE resolution is less than 15eV. The systematic errors of LE, compared to the model of the Crab Nebula, are lower than 1.5% in 1-10 keV., Comment: 20 pages, 10 figures, submitted

As a space X-ray imaging mission dedicated to time-domain astrophysics, the Einstein Probe (EP) carries two kinds of scientific payloads, the wide-field X-ray telescope (WXT) and the follow-up X-ray telescope (FXT). FXT utilizes Wolter-I type mirrors and the pn-CCD detectors. In this work, we investigate the in-orbit background of FXT based on Geant4 simulation. The impact of various space components present in the EP orbital environment are considered, such as the cosmic photon background, cosmic ray primary and secondary particles (e.g. protons, electrons and positrons), albedo gamma rays, and the low-energy protons near the geomagnetic equator. The obtained instrumental background at 0.5-10 keV, which is mainly induced by cosmic ray protons and cosmic photon background, corresponds to a level of $\sim$3.1$\times$10$^{-2}$ counts s$^{-1}$ keV$^{-1}$ in the imaging area of the focal plane detector (FPD), i.e. 3.7$\times$10$^{-3}$ counts s$^{-1}$ keV$^{-1}$ cm$^{-2}$ after normalization. Compared with the instrumental background, the field of view (FOV) background, which is induced by cosmic photons reflected by the optical mirror, dominates below 2 keV. Based on the simulated background level within the focal spot (a 30$^{\prime\prime}$-radius circle), the sensitivity of FXT is calculated, which could theoretically achieve several $\mu$crab (in the order of 10$^{-14}$ erg cm$^{-2}$ s$^{-1}$) in 0.5-2 keV and several tens of $\mu$crab (in the order of 10$^{-13}$ erg cm$^{-2}$ s$^{-1}$) in 2-10 keV for a pointed observation with an exposure of 25 minutes. This sensitivity becomes worse by a factor of $\sim2$ if additional 10% systematic uncertainty of the background subtraction is included., Comment: 38 pages, 19 figures, accepted for publication in Astroparticle Physics

With the fast development of mobile edge computing (MEC), there is an increasing demand for running complex applications on the edge. These complex applications can be represented as workflows where task dependencies are explicitly specified. To achieve better Quality of Service (QoS), for instance, faster response time and lower energy consumption, computation offloading is widely used in the MEC environment. However, many existing computation offloading strategies only focus on independent computation tasks but overlook the task dependencies. Meanwhile, most of these strategies are based on search algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) which are often time-consuming and hence not suitable for many delay-sensitive complex applications in MEC. Therefore, a highly efficient graph-based strategy was proposed in our recent work but it can only deal with simple workflow applications with linear (namely sequential) structure. For solving these problems, a novel graph-based strategy is proposed for workflow applications in MEC. Specifically, this strategy can deal with complex workflow applications with nonlinear (viz. parallel, selective and iterative) structures. Meanwhile, the offloading decision plan with the lowest energy consumption of the end-device under the deadline constraint can be found by using the graph-based partition technique. We have comprehensively evaluated our strategy using both a real-world case study on a MEC based UAV (Unmanned Aerial Vehicle) delivery system and extensive simulation experiments on the FogWorkflowSim platform for MEC based workflow applications. The evaluation results successfully demonstrate the effectiveness of our proposed strategy and its overall better performance than other representative strategies., Comment: 14 pages, 16 figures

A black hole X-ray binary produces hard X-ray radiation from its corona and disk when the accreting matter heats up. During an outburst, the disk and corona co-evolves with each other. However, such an evolution is still unclear in both its geometry and dynamics. Here we report the unusual decrease of the reflection fraction in MAXI J1820+070, which is the ratio of the coronal intensity illuminating the disk to the coronal intensity reaching the observer, as the corona is observed to contrast during the decay phase. We postulate a jet-like corona model, in which the corona can be understood as a standing shock where the material flowing through. In this dynamical scenario, the decrease of the reflection fraction is a signature of the corona's bulk velocity. Our findings suggest that as the corona is observed to get closer to the black hole, the coronal material might be outflowing faster., Comment: 11 pages, 5 figures; published in Nature Communications; An artist's impression of Figure 5 is updated here, and the animation is available at http://youbei.work/research/

Nowadays, as data becomes increasingly complex and distributed, data analyses often involve several related datasets that are stored on different servers and probably owned by different stakeholders. While there is an emerging need to provide these stakeholders with a full picture of their data under a global context, conventional visual analytical methods, such as dimensionality reduction, could expose data privacy when multi-party datasets are fused into a single site to build point-level relationships. In this paper, we reformulate the conventional t-SNE method from the single-site mode into a secure distributed infrastructure. We present a secure multi-party scheme for joint t-SNE computation, which can minimize the risk of data leakage. Aggregated visualization can be optionally employed to hide disclosure of point-level relationships. We build a prototype system based on our method, SMAP, to support the organization, computation, and exploration of secure joint embedding. We demonstrate the effectiveness of our approach with three case studies, one of which is based on the deployment of our system in real-world applications., Comment: 12 pages, 10 figures. Conditionally accepted by VAST 2020

We constructed a comprehensive model atom for NeI -- NeII using the most up-to-date atomic data available and evaluated the non-local thermodynamic equilibrium (NLTE) line formation for NeI and NeII in classical 1D models representing the atmospheres of B-type stars. We find that the large NLTE strengthening of the NeI lines corresponding to the 2p$^5$3p $-$ 2p$^5$3s transitions array occurs due to extremely small photoionization cross-sections of 2p$^5$3s levels that leads to strong overpopulation of these levels relative to their LTE populations. The deviations from LTE for the most NeII lines are small and do not exceed 0.11~dex in the absolute value. We analysed 20 lines of NeI and 13 lines of NeII for twenty-four B-type stars in the temperature range of 10\,400 $\le$ \Teff\ $\le$ 33\,400~K. For five stars, the NLTE leads to consistent abundances of NeI and NeII, while the difference in LTE abundance can reach up to 0.50~dex. The using of the experimental oscillator strengths recently measured by Piracha et al. (2015) leads to smaller line-by-line scatter for the most investigated stars. The averaged neon abundance in twenty-four B-type stars in solar neighborhood is 8.02$\pm$0.05. This value may provide indirect constraints on solar photospheric neon abundance., Comment: 20 pages, 9 figures, accepted to Astrophysical Journal

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results., Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech. Astron. arXiv admin note: text overlap with arXiv:1910.04434

The low energy (LE) X-ray telescope is one of the three main instruments of the Insight-Hard X-ray Modulation Telescope (Insight-HXMT). It is equipped with Swept Charge Device (SCD) sensor arrays with a total geometrical area of 384 cm2 and an energy band from 0.7 keV to 13 keV. In order to evaluate the particle induced X-ray background and the cosmic X-ray background simultaneously, LE adopts collimators to define four types of Field Of Views (FOVs). LE is constituted of three detector boxes (LEDs) and an electric control box (LEB) and achieves a good energy resolution of 140 eV at 5.9 keV, an excellent time resolution of 0.98 ms, as well as an extremely low pileup (<1% at 18000 cts/s). Detailed performance tests and calibration on the ground have been performed, including energy-channel relation, energy response, detection efficiency and time response., Comment: submitted to Science China Physics, Mechanics & Astronomy

The recently discovered neutron star transient Swift J0243.6+6124 has been monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT). Based on the obtained data, we investigate the broadband spectrum of the source throughout the outburst. We estimate the broadband flux of the source and search for possible cyclotron line in the broadband spectrum. No evidence of line-like features is, however, found up to $\rm 150~keV$. In the absence of any cyclotron line in its energy spectrum, we estimate the magnetic field of the source based on the observed spin evolution of the neutron star by applying two accretion torque models. In both cases, we get consistent results with $B\rm \sim 10^{13}~G$, $D\rm \sim 6~kpc$ and peak luminosity of $\rm >10^{39}~erg~s^{-1}$ which makes the source the first Galactic ultraluminous X-ray source hosting a neutron star., Comment: published

In this paper we present the enhanced X-ray Timing and Polarimetry mission - eXTP. eXTP is a space science mission designed to study fundamental physics under extreme conditions of density, gravity and magnetism. The mission aims at determining the equation of state of matter at supra-nuclear density, measuring effects of QED, and understanding the dynamics of matter in strong-field gravity. In addition to investigating fundamental physics, eXTP will be a very powerful observatory for astrophysics that will provide observations of unprecedented quality on a variety of galactic and extragalactic objects. In particular, its wide field monitoring capabilities will be highly instrumental to detect the electro-magnetic counterparts of gravitational wave sources. The paper provides a detailed description of: (1) the technological and technical aspects, and the expected performance of the instruments of the scientific payload; (2) the elements and functions of the mission, from the spacecraft to the ground segment., Comment: Accepted for publication on Sci. China Phys. Mech. Astron. (2019)