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We investigate the phase transitions in a one-dimensional system with colored noise. Previous studies indicated that the phase diagram of this system included extended and disorder-induced localized phases. However, by studying the properties of wave functions, we find that this phase diagram can be further refined, revealing the existence of a bound phase for the large potential amplitude $W$ and noise control parameter $\alpha$. In the bound phase, the wave function cannot extend throughout the entire chain, tails decay faster than exponentially and its distribution expands as the system size increases. By adjusting the potential amplitude to induce a transition from the extended phase to the bound phase, we find that bound states coexist with extended states in the spectrum. In contrast, when the system transitions from the Anderson localized phase to the bound phase, we do not observe the obvious coexistence of Anderson localized and bound states. Finally, by performing the time evolution, we find that the dynamic transition point of the bound phase is inconsistent with the static one for large $\alpha$., Comment: 10 pages, 12 figures, To be published in Physical Review B

The critical states of disordered systems are intriguing subjects within the realm of condensed matter physics and complex systems. These states manifest in materials where disorder plays a significant role, and are distinguished by their multifractal structure and self-similarity. However, accurately characterizing critical states continues to pose a significant challenge. In this study, we argue that critical states exhibit a certain invariance in both position and momentum spaces, leading to their delocalization in both domains. More specifically, it is expected that typical physical quantities characterizing critical states, such as the inverse participation ratio and information entropy, should exhibit invariance in both position space and momentum space. Subsequent numerical simulations validate the correctness of this invariance, thereby establishing a robust foundation for future experimental validation of critical states., Comment: Comments are welcome

Defect engineering in two-dimensional (2D) materials is essential for advancing applications such as gas sensing, single-atom catalysis, and guided nanoparticle self-assembly, enabling the creation of materials with tailored functionalities. This study investigates ion implantation effects on highly ordered pyrolytic graphite (HOPG) surfaces, using scanning tunneling microscopy (STM) and density functional theory (DFT) simulations to identify distinct defect structures. High-energy heavy ions cause inelastic scattering, increasing surface damage, while gold atoms deposited onto defect sites preferentially form atomic clusters. Through focused ion beam techniques, spatially distributed defects were engineered, guiding the self-assembly of nanoparticles. This research highlights the precision of ion irradiation for modifying HOPG surfaces, with significant implications for catalysis, nanotechnology, and the development of functional materials with controlled nanoscale properties., Comment: 16pages, 6figures

The detection of suspended single particles (SSPs) in water is a crucial element for monitoring water ecosystems. Although there are a variety of sensory methods, it is still an unrealized goal to combine multi-wavelength polarized light scattering and fluorescence excitation-emission matrix(EEM) to characterize and classify SSP in water,which can deeply characterize and classify SSPs with different properties. In this paper, we propose an efficient optical method that can simultaneously obtain the SSP's Stokes parameters of four wavelengths and the fluorescence excitation-emission matrix (EEM) within 125 ns at 120 degree scattering angle in water. We use Mie scattering simulation to validate our method and obtain the optimal parameters for the experiments. We demonstrate the high performance of our method in terms of sensitivity and accuracy through experiments involving microalgae, microplastics and sediments, as well as control experiments under different illumination conditions and classification experiments of harmful microalgae by using a multi-layer perceptron (MLP) to classify underwater suspended particles and harmful microalgae based on our method, and show that our method achieves better results than using single-wavelength light. Our method paves the way for the detection of SSPs and polarization research in marine science.

Classification of quantum phases is one of the most important areas of research in condensed matter physics. In this work, we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised learning. Firstly, we choose two advanced unsupervised learning algorithms, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS), to explore the distinct phases of Aubry-Andr\'{e}-Harper model and quasiperiodic p-wave model. The unsupervised learning results match well with traditional numerical diagonalization. Finally, we compare the similarity of different algorithms and find that the highest similarity between the results of unsupervised learning algorithms and those of traditional algorithms has exceeded 98\%. Our work sheds light on applications of unsupervised learning for phase classification.

The brightest ever gamma-ray burst (GRB) 221009A displays a significant emission line component around 10 MeV. As the GRB central engine is neutron-rich, we propose that the emission line could be originally due to the 2.223 MeV gamma-rays following neutron capture with protons. The measured line profile can be adequately fitted with a neutron capture model that involves thermal broadening and a bulk Doppler shift. The spectral modeling reveals a Doppler factor varying from 5.1 to 2.1 for the neutron-rich component, along with a temperature increase from 300 keV to about 900 keV, during the time interval of 280--360 s since the trigger, with about $10^{-2}$ $M_\odot$ deuteriums produced in the process. We argue that neutron capture can take place in the outer shell of a structure jet. Disk winds could be another possible site., Comment: 5 pages, 2 figures, 1 table, Submitted to ApJ Letters on July 9, 2024, referees' reports not received so far

Supply chain networks are critical to the operational efficiency of industries, yet their increasing complexity presents significant challenges in mapping relationships and identifying the roles of various entities. Traditional methods for constructing supply chain networks rely heavily on structured datasets and manual data collection, limiting their scope and efficiency. In contrast, recent advancements in Natural Language Processing (NLP) and large language models (LLMs) offer new opportunities for discovering and analyzing supply chain networks using unstructured text data. This paper proposes a novel approach that leverages LLMs to extract and process raw textual information from publicly available sources to construct a comprehensive supply chain graph. We focus on the civil engineering sector as a case study, demonstrating how LLMs can uncover hidden relationships among companies, projects, and other entities. Additionally, we fine-tune an LLM to classify entities within the supply chain graph, providing detailed insights into their roles and relationships. The results show that domain-specific fine-tuning improves classification accuracy, highlighting the potential of LLMs for industry-specific supply chain analysis. Our contributions include the development of a supply chain graph for the civil engineering sector, as well as a fine-tuned LLM model that enhances entity classification and understanding of supply chain networks., Comment: 11 pages, 4 figures

Upcycling pre-trained dense language models into sparse mixture-of-experts (MoE) models is an efficient approach to increase the model capacity of already trained models. However, optimal techniques for upcycling at scale remain unclear. In this work, we conduct an extensive study of upcycling methods and hyperparameters for billion-parameter scale language models. We propose a novel "virtual group" initialization scheme and weight scaling approach to enable upcycling into fine-grained MoE architectures. Through ablations, we find that upcycling outperforms continued dense model training. In addition, we show that softmax-then-topK expert routing improves over topK-then-softmax approach and higher granularity MoEs can help improve accuracy. Finally, we upcycled Nemotron-4 15B on 1T tokens and compared it to a continuously trained version of the same model on the same 1T tokens: the continuous trained model achieved 65.3% MMLU, whereas the upcycled model achieved 67.6%. Our results offer insights and best practices to effectively leverage upcycling for building MoE language models.

A stellar-mass black hole (BH) surrounded by a neutrino-dominated accretion flow (NDAF) is generally considered to be the central engine of gamma-ray bursts (GRBs). Neutrinos escaping from the disk will annihilate out of the disk to produce the fireball that could power GRBs with blackbody (BB) components. The initial GRB jet power and fireball launch radius are related to the annihilation luminosity and annihilation height of the NDAFs, respectively. In this paper, we collect 7 GRBs with known redshifts and identified BB components to test whether the NDAF model works. We find that, in most cases, the values of the accretion rates and the central BH properties are all in the reasonable range, suggesting that these BB components indeed originate from the neutrino annihilation process., Comment: 8 pages, 1 table, 1 figure, accepted for publication in ApJ

Parking challenges escalate significantly during large events such as concerts and sports games, yet few studies address dynamic parking lot assignments in these occasions. This paper introduces a smart navigation system designed to optimize parking assignments efficiently during major events, employing a mixed search algorithm that considers diverse drivers characteristics. We validated our system through simulations conducted in Berkeley, CA during the "Big Game" showcasing the advantages of our novel parking assignment approach., Comment: arXiv admin note: text overlap with arXiv:2406.05135

Diffusion models have recently achieved remarkable advancements in terms of image quality and fidelity to textual prompts. Concurrently, the safety of such generative models has become an area of growing concern. This work introduces a novel type of jailbreak, which triggers T2I models to generate the image with visual text, where the image and the text, although considered to be safe in isolation, combine to form unsafe content. To systematically explore this phenomenon, we propose a dataset to evaluate the current diffusion-based text-to-image (T2I) models under such jailbreak. We benchmark nine representative T2I models, including two close-source commercial models. Experimental results reveal a concerning tendency to produce unsafe content: all tested models suffer from such type of jailbreak, with rates of unsafe generation ranging from 8\% to 74\%. In real-world scenarios, various filters such as keyword blocklists, customized prompt filters, and NSFW image filters, are commonly employed to mitigate these risks. We evaluate the effectiveness of such filters against our jailbreak and found that, while current classifiers may be effective for single modality detection, they fail to work against our jailbreak. Our work provides a foundation for further development towards more secure and reliable T2I models.

The realization of fault-tolerant quantum computing requires the execution of quantum error-correction (QEC) schemes, to mitigate the fragile nature of qubits. In this context, to ensure the success of QEC, a protocol capable of implementing both qubit reset and leakage reduction is highly desirable. We demonstrate such a protocol in an architecture consisting of fixed-frequency transmon qubits pair-wise coupled via tunable couplers -- an architecture that is compatible with the surface code. We use tunable couplers to transfer any undesired qubit excitation to the readout resonator of the qubit, from which this excitation decays into the feedline. In total, the combination of qubit reset, leakage reduction, and coupler reset takes only 83ns to complete. Our reset scheme is fast, unconditional, and achieves fidelities well above 99%, thus enabling fixed-frequency qubit architectures as future implementations of fault-tolerant quantum computers. Our protocol also provides a means to both reduce QEC cycle runtime and improve algorithmic fidelity on quantum computers.

Heterogeneous materials, crucial in various engineering applications, exhibit complex multiscale behavior, which challenges the effectiveness of traditional computational methods. In this work, we introduce the Micromechanics Transformer ({\em Micrometer}), an artificial intelligence (AI) framework for predicting the mechanical response of heterogeneous materials, bridging the gap between advanced data-driven methods and complex solid mechanics problems. Trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, Micrometer can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of microstructures, material properties under any loading conditions and We demonstrate the accuracy and computational efficiency of Micrometer through applications in computational homogenization and multiscale modeling, where Micrometer achieves 1\% error in predicting macroscale stress fields while reducing computational time by up to two orders of magnitude compared to conventional numerical solvers. We further showcase the adaptability of the proposed model through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in mechanical analysis of solid materials. Our work represents a significant step towards AI-driven innovation in computational solid mechanics, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications., Comment: 36 pages, 12 figures, 9 tables

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., Comment: 7 pages, 2 figures, 1 supplement (8 pages, 3 figures, 2 tables), accepted for publication in Science Bulletin

The phenomenon of synchronization, where entities exhibit stable oscillations with aligned frequencies and phases, has been detected in diverse areas of natural science. It plays a crucial role in achieving frequency locking in multiple applications such as microwave communication and signal processing. The study of synchronization in quantum systems has gained significant interest, particularly in developing robust methods for synchronizing distant objects. Here, we demonstrate that synchronization between the boundary sites of one-dimensional generalized Aubry-Andr\'e-Harper models can be induced through applying dissipation on the central sites. Two types of synchronization, stemming from the topological edge states, are characterized by the off-diagonal or diagonal correlations between the boundary sites. We analyze the relaxation rate to realize the synchronization and its acceleration with bulk dissipation. Remarkably, the synchronous oscillations maintain steady amplitude and frequency in the thermodynamic limit. Moreover, we show that the synchronization is robust against the perturbations in the Hamiltonian and initial states, highlighting its potential for practical implementation in quantum networks.

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., Comment: 10 pages, 4 figures, accepted for publication in ApJ

Let $K$ be a unramified $p$-adic field with the absolute Galois group $G_K$ and $T$ a crystalline $\mathbb Z_p$-representation of $G_K$. We study the graded pieces of integral filtration on $D_{\rm dR}(T)$ given by Nyggard filtration of the attached Breuil-Kisin module of $T$. We show that the $i$-graded piece has nontrivial $p$-torsion only if $ i = r_j +m p$ for a Hodge-Tate weight $ r_j$ of $T$ and $m$ a positive integer., Comment: 10 pages

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., Comment: 9 pages, 5 figures

Fermionic Gaussian states are eigenstates of quadratic Hamiltonians and widely used in quantum many-body problems. We propose a highly efficient algorithm that converts fermionic Gaussian states to matrix product states. It can be formulated for finite-size systems without translation invariance, but becomes particularly appealing when applied to infinite systems with translation invariance. If the ground states of a topologically ordered system on infinite cylinders are expressed as matrix product states, then the fixed points of the transfer matrix can be harnessed to filter out the anyon eigenbasis, also known as minimally entangled states. This allows for efficient computation of universal properties such as entanglement spectrum and modular matrices. The potential of our method is demonstrated by numerical calculations in two chiral spin liquids that have the same topological orders as the bosonic Laughlin and Moore-Read states, respectively. The anyon eigenbasis for the first one has been worked out before and serves as a useful benchmark. The anyon eigenbasis of the second one is, however, not transparent and its successful construction provides a nontrivial corroboration of our method., Comment: 13 pages, 7 figures

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., Comment: 10 pages, 1 table, 5 figures, accepted for publication in ApJ. arXiv admin note: text overlap with arXiv:2403.16856

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., Comment: 19 pages, 7 figures, Accepted in Journal of New Astronomy Reviews

Electrocardiogram (ECG) has emerged as a widely accepted diagnostic instrument for cardiovascular diseases (CVD). The standard clinical 12-lead ECG configuration causes considerable inconvenience and discomfort, while wearable devices offers a more practical alternative. To reduce information gap between 12-lead ECG and single-lead ECG, this study proposes a multi-channel masked autoencoder (MCMA) for reconstructing 12-Lead ECG from arbitrary single-lead ECG, and a comprehensive evaluation benchmark, ECGGenEval, encompass the signal-level, feature-level, and diagnostic-level evaluations. MCMA can achieve the state-of-the-art performance. In the signal-level evaluation, the mean square errors of 0.0317 and 0.1034, Pearson correlation coefficients of 0.7885 and 0.7420. In the feature-level evaluation, the average standard deviation of the mean heart rate across the generated 12-lead ECG is 1.0481, the coefficient of variation is 1.58%, and the range is 3.2874. In the diagnostic-level evaluation, the average F1-score with two generated 12-lead ECG from different single-lead ECG are 0.8233 and 0.8410., Comment: It is a revised version.The open-source code is publicly available at https://github.com/CHENJIAR3/MCMA

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.

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.

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.

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 $649\pm23 {\rm m}^{-1} {\rm mHz}^{-2}$., Comment: 14 pages, 4 figures, accepted for publication in The Astrophysical Journal

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.

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., Comment: 9 pages, 14 figures

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 $\omega$-Cen (NGC 5139) are expected to host the largest numbers of pulsars., Comment: 21 pages, 2 figures, accepted for publication in The Astrophysical Journal Letters

Reinforcement learning (RL) has proven highly effective in addressing complex decision-making and control tasks. However, in most traditional RL algorithms, the policy is typically parameterized as a diagonal Gaussian distribution with learned mean and variance, which constrains their capability to acquire complex policies. In response to this problem, we propose an online RL algorithm termed diffusion actor-critic with entropy regulator (DACER). This algorithm conceptualizes the reverse process of the diffusion model as a novel policy function and leverages the capability of the diffusion model to fit multimodal distributions, thereby enhancing the representational capacity of the policy. Since the distribution of the diffusion policy lacks an analytical expression, its entropy cannot be determined analytically. To mitigate this, we propose a method to estimate the entropy of the diffusion policy utilizing Gaussian mixture model. Building on the estimated entropy, we can learn a parameter $\alpha$ that modulates the degree of exploration and exploitation. Parameter $\alpha$ will be employed to adaptively regulate the variance of the added noise, which is applied to the action output by the diffusion model. Experimental trials on MuJoCo benchmarks and a multimodal task demonstrate that the DACER algorithm achieves state-of-the-art (SOTA) performance in most MuJoCo control tasks while exhibiting a stronger representational capacity of the diffusion policy., Comment: NeurIPS2024 Accepted

Since flight delay hurts passengers, airlines, and airports, its prediction becomes crucial for the decision-making of all stakeholders in the aviation industry and thus has been attempted by various previous research. However, previous delay predictions are often categorical and at a highly aggregated level. To improve that, this study proposes to apply the novel Temporal Fusion Transformer model and predict numerical airport arrival delays at quarter hour level for U.S. top 30 airports. Inputs to our model include airport demand and capacity forecasts, historic airport operation efficiency information, airport wind and visibility conditions, as well as enroute weather and traffic conditions. The results show that our model achieves satisfactory performance measured by small prediction errors on the test set. In addition, the interpretability analysis of the model outputs identifies the important input factors for delay prediction.

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., Comment: 12 pages, 6 figures, 2 tables, accepted for publication in ApJ

Our goal is to study $p$-adic local systems on a rigid-analytic variety with semistable formal model. We prove that such a local system is semistable if and only if so are its restrictions to the points corresponding to the irreducible components of the special fiber. For this, the main body of the paper concerns analytic prismatic $F$-crystals on the absolute logarithmic prismatic site of a semistable $p$-adic log formal scheme. Analyzing Breuil-Kisin log prisms, we obtain a prismatic purity theorem and deduce the above purity theorem for semistable local systems.

Artificial neuronal devices are the basic building blocks for neuromorphic computing systems, which have been motivated by realistic brain emulation. Aiming for these applications, various device concepts have been proposed to mimic the neuronal dynamics and functions. While till now, the artificial neuron devices with high efficiency, high stability and low power consumption are still far from practical application. Due to the special insulator-metal phase transition, Vanadium Dioxide (VO2) has been considered as an idea candidate for neuronal device fabrication. However, its intrinsic insulating state requires the VO2 neuronal device to be driven under large bias voltage, resulting in high power consumption and low frequency. Thus in the current study, we have addressed this challenge by preparing oxygen vacancies modulated VO2 film(VO2-x) and fabricating the VO2-x neuronal devices for Spiking Neural Networks (SNNs) construction. Results indicate the neuron devices can be operated under lower voltage with improved processing speed. The proposed VO2-x based back-propagation SNNs (BP-SNNs) system, trained with the MNIST dataset, demonstrates excellent accuracy in image recognition. Our study not only demonstrates the VO2-x based neurons and SNN system for practical application, but also offers an effective way to optimize the future neuromorphic computing systems by defect engineering strategy., Comment: 18 pages,4 figures

Stellar black holes (sBHs) are widely believed to exist in the accretion disks of active galactic nuclei (AGNs). Previous studies often focus on the transient emission produced by embedded sBHs. Here, we explore the possible observational consequences of an AGN accretion disk that contains a population of accreting sBHs. Embedded accreting sBHs change the effective temperature distribution of the AGN accretion disk by heating gas in the outer regions. Two possible observational consequences are presented. First, the spectral energy distribution has a turnover feature at $\sim 4700\ \textrm{\AA}$ when the supermassive black hole (SMBH) mass is $\sim 10^8\ M_{\odot}$, which can help explain the observed shallow spectral shape at wavelengths $>5000\ \textrm{\AA}$ for the Sloan Digital Sky Survey quasar composite spectrum. Second, the half-light radius of a given relatively long wavelength is significantly larger than for an AGN disk without sBHs, which can be tested by microlensing observations. With appropriate sBH distributions, the model can be reconciled with quasar microlensing disk sizes. We propose that the half-light radius-wavelength relation can be utilized to investigate the distributions of embedded sBHs in AGN accretion disks., Comment: 11 pages, 5 figures, Accepted to ApJL

Neutrino-dominated accretion flows (NDAFs) are one of the important MeV neutrino sources and significantly contribute to the cosmic diffuse neutrino background. In this paper, we investigate the spectrum of diffuse NDAF neutrino background (DNNB) by fully considering the effects of the progenitor properties and initial explosion energies based on core-collapse supernova (CCSN) simulations, and estimate the detectable event rate by Super-Kamiokande detector. We find that the predicted background neutrino flux 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 diffuse flux of DNNB is comparable to the diffuse supernova neutrino background, which might be detected by the upcoming larger neutrino detectors such as Hyper-Kamiokande, JUNO, and DUNE. Moreover, the strong outflows from NDAFs could dramatically decrease their contribution to the neutrino background., Comment: 12 pages, 7 figures, 1 table, accepted for publication in ApJ

The atmosphere of Triton was probed directly by observing a ground-based stellar occultation on 6 October 2022. This rare event yielded 23 positive light curves collected from 13 separate observation stations contributing to our campaign. The significance of this event lies in its potential to directly validate the modest pressure fluctuation on Triton, a phenomenon not definitively verified by previous observations, including only five stellar occultations, and the Voyager 2 radio occultation in 1989. Using an approach consistent with a comparable study, we precisely determined a surface pressure of $14.07_{-0.13}^{+0.21}~\mathrm{\mu bar}$ in 2022. This new pressure rules out any significant monotonic variation in pressure between 2017 and 2022 through direct observations, as it is in alignment with the 2017 value. Additionally, both the pressures in 2017 and 2022 align with the 1989 value. This provides further support for the conclusion drawn from the previous volatile transport model simulation, which is consistent with the observed alignment between the pressures in 1989 and 2017; that is to say, the pressure fluctuation is modest. Moreover, this conclusion suggests the existence of a northern polar cap extended down to at least $45^\circ$N$-60^\circ$N and the presence of nitrogen between $30^\circ$S and $0^\circ$., Comment: Astronomy & Astrophysics, in press. 9 pages, 2 figures, 3 tables

The concept of guiding center is potent in astrophysics, space plasmas, fusion researches, and arc plasmas to solve the multi-scale dynamics of magnetized plasmas. In this letter, we rigorously prove that the guiding center dynamics can generally be described as a constrained canonical Hamiltonian system with two constraints in six dimensional phase space, and that the solution flow of the guiding center lies on a canonical symplectic sub-manifold. The guiding center can thus be modeled as a pseudo-particle with an intrinsic magnetic moment, which properly replaces the charged particle dynamics on time scales larger than the gyro-period. The complete dynamical behaviors, such as the velocity and force, of the guiding center pseudo-particle can be clearly deduced from the model. Furthermore, a series of related theories, such as symplectic numerical methods, the canonical gyro-kinetic theory, and canonical particle-in-cell algorithms can be systematically developed based on the canonical guiding center system. The canonical guiding center theory also provides an enlightenment for the origin of the intrinsic magnetic moment., Comment: 14pages, 4 figures