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Xuan Zhang

Publications and source records attributed to Xuan Zhang.

At least 145 records · Page 8Linked to original sources
arXiv2023

In-orbit background simulation of a type-B CATCH satellite

The Chasing All Transients Constellation Hunters (CATCH) space mission plans to launch three types of micro-satellites (A, B, and C). The type-B CATCH satellites are dedicated to locating transients and detecting their time-dependent energy spectra. A type-B satellite is equipped with lightweight Wolter-I X-ray optics and an array of position-sensitive multi-pixel Silicon Drift Detectors. To optimize the scientific payloads for operating properly in orbit and performing the observations with high sensitivities, this work performs an in-orbit background simulation of a type-B CATCH satellite using the Geant4 toolkit. It shows that the persistent background is dominated by the cosmic X-ray diffuse background and the cosmic-ray protons. The dynamic background is also estimated considering trapped charged particles in the radiation belts and low-energy charged particles near the geomagnetic equator, which is dominated by the incident electrons outside the aperture. The simulated persistent background within the focal spot is used to estimate the observation sensitivity, i.e. 4.22$\times$10$^{-13}$ erg cm$^{-2}$ s$^{-1}$ with an exposure of 10$^{4}$ s and a Crab-like source spectrum, which can be utilized further to optimize the shielding design. The simulated in-orbit background also suggests that the magnetic diverter just underneath the optics may be unnecessary in this kind of micro-satellites, because the dynamic background induced by charged particles outside the aperture is around 3 orders of magnitude larger than that inside the aperture.

astro-ph.IM↗
arXiv2023

The Stellar Abundances and Galactic Evolution Survey (SAGES) -- -- I. General Description and the First Data Release (DR1)

The Stellar Abundances and Galactic Evolution Survey (SAGES) of the northern sky is a specifically-designed multi-band photometric survey aiming to provide reliable stellar parameters with accuracy comparable to those from low-resolution optical spectra. It was carried out with the 2.3-m Bok telescope of Steward Observatory and three other telescopes. The observations in the $u_s$ and $v_s$ passband produced over 36,092 frames of images in total, covering a sky area of $\sim9960$ degree$^2$. The median survey completeness of all observing fields for the two bands are of $u_{\rm s}=20.4$ mag and $v_s=20.3$ mag, respectively, while the limiting magnitudes with signal-to-noise ratio (S/N) of 100 are $u_s\sim17$ mag and $v_s\sim18$ mag, correspondingly. We combined our catalog with the data release 1 (DR1) of the first of Panoramic Survey Telescope And Rapid Response System (Pan-STARRS1, PS1) catalog, and obtained a total of 48,553,987 sources which have at least one photometric measurement in each of the SAGES $u_s$ and $v_s$ and PS1 $grizy$ passbands, which is the DR1 of SAGES and it will be released in our paper. We compare our $gri$ point-source photometry with those of PS1 and found an RMS scatter of $\sim2$% in difference of PS1 and SAGES for the same band. We estimated an internal photometric precision of SAGES to be on the order of $\sim1$%. Astrometric precision is better than $0^{\prime\prime}.2$ based on comparison with the DR1 of Gaia mission. In this paper, we also describe the final end-user database, and provide some science applications.

astro-ph.GA↗
arXiv2023

The mHz quasi-regular modulations of 4U 1630--47 during its 1998 outburst

We present the results of a detailed timing and spectral analysis of the quasi-regular modulation (QRM) phenomenon in the black hole X-ray binary 4U 1630--47 during its 1998 outburst observed by Rossi X-ray Timing Explore (RXTE). We find that the $\sim$ 50-110 mHz QRM is flux dependent, and the QRM is detected with simultaneous low frequency quasi-periodic oscillations (LFQPOs). According to the behavior of the power density spectrum, we divide the observations into four groups. In the first group, namely behavior A, LFQPOs are detected, but no mHz QRM. The second group, namely behavior B, a QRM with frequency above $\sim$ 88 mHz is detected and the $\sim$ 5 Hz and $\sim$ 7 Hz LFQPOs are almost overlapping. In the third group, namely behavior C, the QRM frequency below $\sim$ 88 mHz is detected and the LFQPOs are significantly separated. In the forth group, namely behavior D, neither QRM nor LFQPOs are detected. We study the energy-dependence of the fractional rms, centroid frequency, and phase-lag of QRM and LFQPOs for behavior B and C. We then study the evolution of QRM and find that the frequency of QRM increases with hardness, while its rms decreases with hardness. We also analyze the spectra of each observation, and find that the QRM rms of behavior B has a positive correlation with $\rm F_{\rm powerlaw}$ / $\rm F_{\rm total}$. Finally, we give our understanding for this mHz QRM phenomena.

astro-ph.HE↗
arXiv2023

PDNPulse: Sensing PCB Anomaly with the Intrinsic Power Delivery Network

The ubiquitous presence of printed circuit boards (PCBs) in modern electronic systems and embedded devices makes their integrity a top security concern. To take advantage of the economies of scale, today's PCB design and manufacturing are often performed by suppliers around the globe, exposing them to many security vulnerabilities along the segmented PCB supply chain. Moreover, the increasing complexity of the PCB designs also leaves ample room for numerous sneaky board-level attacks to be implemented throughout each stage of a PCB's lifetime, threatening many electronic devices. In this paper, we propose PDNPulse, a power delivery network (PDN) based PCB anomaly detection framework that can identify a wide spectrum of board-level malicious modifications. PDNPulse leverages the fact that the PDN's characteristics are inevitably affected by modifications to the PCB, no matter how minuscule. By detecting changes to the PDN impedance profile and using the Frechet distance-based anomaly detection algorithms, PDNPulse can robustly and successfully discern malicious modifications across the system. Using PDNPulse, we conduct extensive experiments on seven commercial-off-the-shelf PCBs, covering different design scales, different threat models, and seven different anomaly types. The results confirm that PDNPulse creates an effective security asymmetry between attack and defense.

cs.CR↗
arXiv2023

Discovering Opportunities in New York City's Discovery Program: Disadvantaged Students in Highly Competitive Markets

Discovery program (DISC) is a policy used by the New York City Department of Education (NYC DOE) to increase the number of admissions of students from low socio-economic background to specialized high schools. This policy has been instrumental in increasing the number of disadvantaged students attending these schools. However, assuming that students care more about the school they are assigned to rather than the type of seat they occupy (\emph{school-over-seat hypothesis}), our empirical analysis using data from 12 recent academic years shows that DISC creates about 950 in-group blocking pairs each year amongst disadvantaged students, impacting about 650 disadvantaged students every year. Moreover, we find that this program does not respect improvements, thus unintentionally creating an incentive to under-perform. These experimental results are confirmed by our theoretical analysis. In order to alleviate the concerns caused by DISC, we explore two alternative policies: the minority reserve (MR) and the joint-seat allocation (JSA) mechanisms. As our main theoretical contribution, we introduce a feature of markets, that we term high competitiveness, and we show that under this condition, JSA dominates MR for all disadvantaged students. We give sufficient conditions under which high competitiveness is verified. Data from NYC DOE satisfies the high competitiveness condition, and for this dataset our empirical results corroborate our theoretical predictions, showing the superiority of JSA. Given that JSA can be implemented by a simple modification of the classical deferred acceptance algorithm with responsive preference lists, we believe that, when the school-over-seat hypothesis holds, the discovery program can be changed for the better by implementing the JSA mechanism, leading in particular to aligned incentives for the top-performing disadvantaged students.

cs.GT↗
arXiv2023

Effect of solute atoms segregation on Al grain boundary properties by First-principles study

First-principles calculations were carried out to study the segregation behavior of Mg, and Cu and their effect on the energy and mechanical properties of different Al grain boundaries (GBs). Four symmetrical tilt GBs were selected for study, namely Σ5[001](210) GB, Σ5[001](310) GB, Σ9[110](221) GB, and Σ11[110](332) GB. The results show that both Mg and Cu have a segregation tendency at the GBs, and the segregation tendency of Cu is stronger than Mg. Mg is prone to form substitutional segregation at the GBs, but Cu is more likely to segregate at the interstitial sites. The segregation of Mg and Cu can reduce GB energy, and the GB energy continues to decrease with the increase of the segregation concentration. First-principles calculation tensile test shows that the segregation of Mg has a negative effect on the strength of GBs, and the GB strength decreases with the increase of the Mg concentration, while the GB strength was gradually enhanced with the increase of the Cu concentration. The strength of Σ5(210) GB and Σ9(221) GB are more sensitive to the segregation of solute atoms than the other two GBs. By calculating the charge density and the density of states of the pristine and the segregated GBs, it was found that the segregation of Mg caused charge depletion and structure expansion at the GBs, while the segregation of Cu increases the charge density of GBs and form new bonds with the surrounding Al atoms. The results provide useful information for improving the mechanical properties of materials by using the concept of GB segregation engineering.

cond-mat.mtrl-sci↗
arXiv2023

First-principles study of solute atoms segregation in Al Σ5(210) metastable grain boundaries

Grain boundary (GB) segregation of solute atoms plays an important role in the microstructure and macroscopic mechanical properties of materials. The study of GB segregation of solute atoms using computational simulation has become one of the hot spots in recent years. However, most studies mainly focus on ground-state GB structures with the lowest energy, and the impact of GB metastability with higher energy on solute segregation remains poorly understood. In this work, the first-principles method based on the density functional theory was adopted to investigate the effect of solute atoms Mg and Cu segregation on ground-state Σ5(210) GB (GB-I) and metastable GBs(GB-II, GB-III) in Al. GB energy, segregation energy, and theoretical tensile strength of Mg and Cu segregation at three GBs were calculated. The results show that both Mg and Cu have a large driving force to segregate to Al GBs, which reduces the GB energy and improves improve GB stability. The segregation of Mg and Cu on GB-III induces the transformation of the GB structural unit and the GB structural phase transformations. For the above three GBs, Cu segregation increases the theoretical tensile strength of GBs to varying degrees. The segregation of Mg would reduce the resistance of GB-I and GB-II, but enhances the strength of GB-III. The effect of solute atoms segregation on the mechanical properties of GBs was investigated by charge density distribution and density of states.

cond-mat.mtrl-sci↗
arXiv2023

BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model

With the continuous development and change exhibited by large language model (LLM) technology, represented by generative pretrained transformers (GPTs), many classic scenarios in various fields have re-emerged with new opportunities. This paper takes ChatGPT as the modeling object, incorporates LLM technology into the typical book resource understanding and recommendation scenario for the first time, and puts it into practice. By building a ChatGPT-like book recommendation system (BookGPT) framework based on ChatGPT, this paper attempts to apply ChatGPT to recommendation modeling for three typical tasks, book rating recommendation, user rating recommendation, and book summary recommendation, and explores the feasibility of LLM technology in book recommendation scenarios. At the same time, based on different evaluation schemes for book recommendation tasks and the existing classic recommendation models, this paper discusses the advantages and disadvantages of the BookGPT in book recommendation scenarios and analyzes the opportunities and improvement directions for subsequent LLMs in these scenarios.

cs.IR↗
arXiv2023

A Score-Based Model for Learning Neural Wavefunctions

Quantum Monte Carlo coupled with neural network wavefunctions has shown success in computing ground states of quantum many-body systems. Existing optimization approaches compute the energy by sampling local energy from an explicit probability distribution given by the wavefunction. In this work, we provide a new optimization framework for obtaining properties of quantum many-body ground states using score-based neural networks. Our new framework does not require explicit probability distribution and performs the sampling via Langevin dynamics. Our method is based on the key observation that the local energy is directly related to scores, defined as the gradient of the logarithmic wavefunction. Inspired by the score matching and diffusion Monte Carlo methods, we derive a weighted score matching objective to guide our score-based models to converge correctly to ground states. We first evaluate our approach with experiments on quantum harmonic traps, and results show that it can accurately learn ground states of atomic systems. By implicitly modeling high-dimensional data distributions, our work paves the way toward a more efficient representation of quantum systems.

physics.comp-ph↗
arXiv2023

Site-testing at the Muztagh-ata Site.V. Nighttime Cloud Amount during the Last Five Years

The clarity of nights is the major factor that should be carefully considered for optical/infrared astronomical observatories in site-testing campaigns. Cloud coverage is directly related to the amount of time available for scientific observations at observatories. In this article, we report on the results of detailed night-time cloud statistics and continuous observing me derived from ground-based all-sky cameras at the Muztagh-ata site from 2017 to 2021. Results obtained from acquisition data show that the proportion of the annual observing me at the Muztagh-ata site is 65%, and the best period with the least cloud coverage and longer continuous observing time is from September to February. We made a comparison of the monthly mean observing nights obtained from our all-sky cameras and CLARA dataset, results show that the discrepancy between them may depend on the cloud top heights. On average, this site can provide 175 clear nights and 169 nights with at least 4 hours of continuous observing time per year.

astro-ph.IM↗
arXiv2023

3D Molecular Geometry Analysis with 2D Graphs

Ground-state 3D geometries of molecules are essential for many molecular analysis tasks. Modern quantum mechanical methods can compute accurate 3D geometries but are computationally prohibitive. Currently, an efficient alternative to computing ground-state 3D molecular geometries from 2D graphs is lacking. Here, we propose a novel deep learning framework to predict 3D geometries from molecular graphs. To this end, we develop an equilibrium message passing neural network (EMPNN) to better capture ground-state geometries from molecular graphs. To provide a testbed for 3D molecular geometry analysis, we develop a benchmark that includes a large-scale molecular geometry dataset, data splits, and evaluation protocols. Experimental results show that EMPNN can efficiently predict more accurate ground-state 3D geometries than RDKit and other deep learning methods. Results also show that the proposed framework outperforms self-supervised learning methods on property prediction tasks.

physics.chem-ph↗
arXiv2023

CamJ: Enabling System-Level Energy Modeling and Architectural Exploration for In-Sensor Visual Computing

CMOS Image Sensors (CIS) are fundamental to emerging visual computing applications. While conventional CIS are purely imaging devices for capturing images, increasingly CIS integrate processing capabilities such as Deep Neural Network (DNN). Computational CIS expand the architecture design space, but to date no comprehensive energy model exists. This paper proposes CamJ, a detailed energy modeling framework that provides a component-level energy breakdown for computational CIS and is validated against nine recent CIS chips. We use CamJ to demonstrate three use-cases that explore architectural trade-offs including computing in vs. off CIS, 2D vs. 3D-stacked CIS design, and analog vs. digital processing inside CIS. The code of CamJ is available at: https://github.com/horizon-research/CamJ

cs.AR↗
arXiv2023

DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection

Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S-T) framework has proved to be effective in solving this challenge. However, previous works based on S-T only empirically applied constraints on normal data and fused multi-level information. In this study, we propose an improved model called DeSTSeg, which integrates a pre-trained teacher network, a denoising student encoder-decoder, and a segmentation network into one framework. First, to strengthen the constraints on anomalous data, we introduce a denoising procedure that allows the student network to learn more robust representations. From synthetically corrupted normal images, we train the student network to match the teacher network feature of the same images without corruption. Second, to fuse the multi-level S-T features adaptively, we train a segmentation network with rich supervision from synthetic anomaly masks, achieving a substantial performance improvement. Experiments on the industrial inspection benchmark dataset demonstrate that our method achieves state-of-the-art performance, 98.6% on image-level AUC, 75.8% on pixel-level average precision, and 76.4% on instance-level average precision.

cs.CV↗
arXiv2023

Fair Notification Optimization: An Auction Approach

Notifications are important for the user experience in mobile apps and can influence their engagement. However, too many notifications can be disruptive for users. A typical mobile app usually has several types of notification, managed by distinct teams with objectives that are possibly conflicting with each other, or even with the overall platform objective. Therefore, there is a need for careful curation of notifications sent to users of these different types. In this work, we study a novel centralized approach for notification optimization, where we view the opportunities to send user notifications as items and types of notifications as buyers in an auction market. Furthermore, the auction setup is unique, and the platform has the ability to subsidize the bids from the notification types. Using tools from fair division, we study the application of competitive equilibrium for addressing this problem. We show that an Eisenberg-Gale-style convex program allows us to find an allocation that is fair to all notification types in hindsight. Using the dual of the formulation, we present an online algorithm that allocates notifications via first-price auctions using a pacing-multiplier approach. Secondly, we introduce an approach based on second-price auctions and pacing, which has the benefit of working well with existing advertising systems built for second-price auctions. Through an A/B test in production, we show that the second price-based auction system improves over a decentralized notification optimization system, leading to its launch in production for some Instagram notifications. Further, through simulations on Instagram notification data and a subsequent production A/B test, we compare the outcomes of first-price and second-price auctions and show that the former has more stable pacing multipliers.

cs.GT↗
arXiv2023

RTGPU: Real-Time GPU Scheduling of Hard Deadline Parallel Tasks with Fine-Grain Utilization

Many emerging cyber-physical systems, such as autonomous vehicles and robots, rely heavily on artificial intelligence and machine learning algorithms to perform important system operations. Since these highly parallel applications are computationally intensive, they need to be accelerated by graphics processing units (GPUs) to meet stringent timing constraints. However, despite the wide adoption of GPUs, efficiently scheduling multiple GPU applications while providing rigorous real-time guarantees remains a challenge. In this paper, we propose RTGPU, which can schedule the execution of multiple GPU applications in real-time to meet hard deadlines. Each GPU application can have multiple CPU execution and memory copy segments, as well as GPU kernels. We start with a model to explicitly account for the CPU and memory copy segments of these applications. We then consider the GPU architecture in the development of a precise timing model for the GPU kernels and leverage a technique known as persistent threads to implement fine-grained kernel scheduling with improved performance through interleaved execution. Next, we propose a general method for scheduling parallel GPU applications in real time. Finally, to schedule multiple parallel GPU applications, we propose a practical real-time scheduling algorithm based on federated scheduling and grid search (for GPU kernel segments) with uniprocessor fixed priority scheduling (for multiple CPU and memory copy segments). Our approach provides superior schedulability compared with previous work, and gives real-time guarantees to meet hard deadlines for multiple GPU applications according to comprehensive validation and evaluation on a real NVIDIA GTX1080Ti GPU system.

cs.DC↗
arXiv2023

Non-Hermitian Physics-Inspired Voltage-Controlled Oscillators with Resistive Tuning

This paper presents a non-Hermitian physics-inspired voltage-controlled oscillator (VCO) topology, which is termed parity-time-symmetric topology. The VCO consists of two coupled inductor-capacitor (LC) cores with a balanced gain and loss profile. Due to the interplay between the gain/loss and their coupling, an extra degree of freedom is enabled via resistive tuning, which can enhance the frequency tuning range (FTR) beyond the bounds of conventional capacitive or inductive tuning. A silicon prototype is implemented in a standard 130 nm bulk CMOS process with a core area of 0.15mm^2. Experimental results show that it achieves a 3.1x FTR improvement and 30% phase noise reduction of the baseline VCO with the same amount of capacitive tuning ability.

cs.ET↗
arXiv2023

Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identification

Neural network-based models have found wide use in automatic long-term electrocardiogram (ECG) analysis. However, such black box models are inadequate for analysing physiological signals where credibility and interpretability are crucial. Indeed, how to make ECG analysis transparent is still an open problem. In this study, we develop a Tsetlin machine (TM) based architecture for premature ventricular contraction (PVC) identification by analysing long-term ECG signals. The architecture is transparent by describing patterns directly with logical AND rules. To validate the accuracy of our approach, we compare the TM performance with those of convolutional neural networks (CNNs). Our numerical results demonstrate that TM provides comparable performance with CNNs on the MIT-BIH database. To validate interpretability, we provide explanatory diagrams that show how TM makes the PVC identification from confirming and invalidating patterns. We argue that these are compatible with medical knowledge so that they can be readily understood and verified by a medical doctor. Accordingly, we believe this study paves the way for machine learning (ML) for ECG analysis in clinical practice.

eess.SP↗
arXiv2023

Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size

Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools tend to produce clauses with many literals (long clauses). As such, they become less interpretable. Further, longer clauses increase the switching activity of the clause logic in hardware, consuming more power. This paper introduces a novel variant of TM learning - Clause Size Constrained TMs (CSC-TMs) - where one can set a soft constraint on the clause size. As soon as a clause includes more literals than the constraint allows, it starts expelling literals. Accordingly, oversized clauses only appear transiently. To evaluate CSC-TM, we conduct classification, clustering, and regression experiments on tabular data, natural language text, images, and board games. Our results show that CSC-TM maintains accuracy with up to 80 times fewer literals. Indeed, the accuracy increases with shorter clauses for TREC, IMDb, and BBC Sports. After the accuracy peaks, it drops gracefully as the clause size approaches a single literal. We finally analyze CSC-TM power consumption and derive new convergence properties.

cs.LG↗