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Researcher profile

Nan Zhang

Nan Zhang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Methodologyastro-ph.COComputationeess.SPgr-qchep-thMachine Learningastro-ph.EPastro-ph.GAcond-mat.mes-hallcond-mat.mtrl-scicond-mat.str-elDistributed, Parallel, and Cluster Computingeess.AShep-phNetworking and Internet Architecturephysics.app-phphysics.opticsSound

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Published work

19 published item(s)

preprint2026arXiv

A census of star-formation and gas mass tracers in two lensed $z \sim 4$ dusty star-forming galaxies

We present new and archival Atacama Large Millimeter/submillimeter Array (ALMA) observations of two strongly lensed dusty star-forming galaxies (DSFGs) selected from the South Pole Telescope survey, SPT0418-47 $(z = 4.225)$ and SPT2147-50 $(z = 3.760)$. We study the [C II], CO(7-6), [C I](2-1), and, in SPT0418-47, $p$-H$_2$O emission, which along with the underlying continuum (rest-frame 160 $μ$m and 380 $μ$m) are routinely used as tracers of gas mass and/or star-formation rate (SFR). We perform a pixel-by-pixel analysis of both sources in the image plane to study the resolved Kennicutt-Schmidt relation, finding generally good agreement between the slopes of the SFR versus gas mass surface density using the different tracers. Using lens modeling methods, we find that the dust emission is more compact than the line emission in both sources, with CO(7-6) and [C I](2-1) similar in extent and [C II] the most extended, reminiscent of recent findings of extended [C II] spatial distributions in galaxies at similar cosmic epochs. We develop the [C I](2-1) / CO(7-6) flux density ratio as an observable proxy for gas depletion timescale ($τ_{\rm dep}$), which can be applied to large samples of DSFGs, in lieu of more detailed inferences of this timescale which require analysis of observations at multiple wavelengths. Furthermore, the extended [C II] emission in both sources, compared to the total continuum and line emission, suggests that [C II], used in recent years as a molecular gas mass and SFR tracer in high-$z$ galaxies, may not always be a suitable tracer of these physical quantities.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Why Do Reasoning Models Lose Coverage? The Role of Data and Forks in the Road

Recent progress in large language models has led to the emergence of reasoning models, which have shown strong performance on complex tasks through specialized fine-tuning procedures. While these methods reliably improve pass@1 accuracy, prior works have observed that they show a coverage shrinkage behavior, where pass@k degrades relative to the base model. In this paper, we investigate the reasoning shrinkage arise under SFT-based post-training. We hypothesize that this behavior is driven by properties of the fine-tuning data, specifically related to decision points or "forks in the road" scenarios where model faces indecipherable patterns with multiple valid reasoning paths. To test this hypothesis, we design controlled case studies that simulate such decision-point settings, spanning indecipherable nodes in graph branching, and reasoning modes. By tracking post-training dynamics in these settings, we find that the shrinkage phenomenon is tightly correlated with the prevalence of decision-point scenarios in the training data. We also demonstrate that this shrinkage behavior can be partially mitigated through targeted data synthesis design of decision-points, and a more systematic diversity-encouraging decoding mechanism. Our findings identify data-centric factors as a key driver of shrinkage in reasoning models and highlight diversity-aware designs as an effective lever for controlling it.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Dynamic Service Scheduling and Resource Management in Energy-Harvesting Multi-access Edge Computing

Multi-access Edge Computing (MEC) delivers low-latency services by hosting applications near end-users. To promote sustainability, these systems are increasingly integrated with renewable Energy Harvesting (EH) technologies, enabling operation where grid electricity is unavailable. However, balancing the intermittent nature of harvested energy with dynamic user demand presents a significant resource allocation challenge. This work proposes an online strategy for an MEC system powered exclusively by EH to address this trade-off. Our strategy dynamically schedules computational tasks with dependencies and governs energy consumption through real-time decisions on server frequency scaling and service module migration. Experiments using real-world datasets demonstrate our algorithm's effectiveness in efficiently utilizing harvested energy while maintaining low service latency.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

For Intelligent and Higher Spectrum Efficiency: A Variable Packing Ratio Transmission System Based on Faster-than-Nyquist and Deep Learning

With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable. Faster than Nyquist (FTN) signaling, proposed in the 1970s, is a promising paradigm for improving spectrum utilization. This paper proposes intelligent variable-packing-ratio (VPR)-based transmissions for high spectrum efficiency (SE) and security, respectively. Aided by deep learning (DL)-based estimation, the proposed scheme for high SE can achieve a higher capacity with negligible modification to existing communication paradigms (e.g., spectrum allocation or frame structure). Also, for VPR-based secure transmission, a dynamic generation scheme is proposed to produce randomly distributed positions to switch the packing ratio, which can effectively avoid detections and attacks. In addition, we propose a simplified DL-based packing ratio estimation for both of these two scenarios so that the receiver can estimate the packing ratio without any in-band or out-band control messages. Simulation results show that the proposed simplified estimation achieves nearly the same accuracy and convergence speed as the original multi-branch fully-connected structure with a complexity reduction of 20 folds. Finally, we derive the closed-form SE of the proposed VPR transmission under different channels. The numerical results validate the correctness of the derivation and demonstrate the SE gains of the VPR scheme beyond conventional Nyquist transmission.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Joint Estimation Approach to Sparse Additive Ordinary Differential Equations

Ordinary differential equations (ODEs) are widely used to characterize the dynamics of complex systems in real applications. In this article, we propose a novel joint estimation approach for generalized sparse additive ODEs where observations are allowed to be non-Gaussian. The new method is unified with existing collocation methods by considering the likelihood, ODE fidelity and sparse regularization simultaneously. We design a block coordinate descent algorithm for optimizing the non-convex and non-differentiable objective function. The global convergence of the algorithm is established. The simulation study and two applications demonstrate the superior performance of the proposed method in estimation and improved performance of identifying the sparse structure.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A spherical harmonics method for processing anisotropic X-ray atomic pair distribution functions

A general spherical harmonics method is described for extracting anisotropic pair distribution functions (PDF) in this work. In the structural study of functional crystallized materials, the investigation of the local structures under the application of external stimuli, such as electric field and stress, is in urgent need. A well-established technique for local structure studies is PDF analysis, but the extraction of the X-ray PDF data is usually based on angular integrations of isotropic X-ray structure functions, which is no longer valid for the anisotropic responses of the materials under orientation-dependent stimuli. Therefore, we have developed an advanced spherical harmonics method to transform two-dimensional X-ray diffraction patterns into anisotropic PDF data, based on three-dimensional diffraction geometry and Fourier transform. The electrical-field-induced local structural change in the PbZr0.54Ti0.46O3 ceramics is then presented to demonstrate the method's effectiveness.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Density Regression with Conditional Support Points

Density regression characterizes the conditional density of the response variable given the covariates, and provides much more information than the commonly used conditional mean or quantile regression. However, it is often computationally prohibitive in applications with massive data sets, especially when there are multiple covariates. In this paper, we develop a new data reduction approach for the density regression problem using conditional support points. After obtaining the representative data, we exploit the penalized likelihood method as the downstream estimation strategy. Based on the connections among the continuous ranked probability score, the energy distance, the $L_2$ discrepancy and the symmetrized Kullback-Leibler distance, we investigate the distributional convergence of the representative points and establish the rate of convergence of the density regression estimator. The usefulness of the methodology is illustrated by modeling the conditional distribution of power output given multivariate environmental factors using a large scale wind turbine data set. Supplementary materials for this article are available online.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DT-SV: A Transformer-based Time-domain Approach for Speaker Verification

Speaker verification (SV) aims to determine whether the speaker's identity of a test utterance is the same as the reference speech. In the past few years, extracting speaker embeddings using deep neural networks for SV systems has gone mainstream. Recently, different attention mechanisms and Transformer networks have been explored widely in SV fields. However, utilizing the original Transformer in SV directly may have frame-level information waste on output features, which could lead to restrictions on capacity and discrimination of speaker embeddings. Therefore, we propose an approach to derive utterance-level speaker embeddings via a Transformer architecture that uses a novel loss function named diffluence loss to integrate the feature information of different Transformer layers. Therein, the diffluence loss aims to aggregate frame-level features into an utterance-level representation, and it could be integrated into the Transformer expediently. Besides, we also introduce a learnable mel-fbank energy feature extractor named time-domain feature extractor that computes the mel-fbank features more precisely and efficiently than the standard mel-fbank extractor. Combining Diffluence loss and Time-domain feature extractor, we propose a novel Transformer-based time-domain SV model (DT-SV) with faster training speed and higher accuracy. Experiments indicate that our proposed model can achieve better performance in comparison with other models.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dynamical Modeling for non-Gaussian Data with High-dimensional Sparse Ordinary Differential Equations

Ordinary differential equations (ODE) have been widely used for modeling dynamical complex systems. For high-dimensional ODE models where the number of differential equations is large, it remains challenging to estimate the ODE parameters and to identify the sparse structure of the ODE models. Most existing methods exploit the least-square based approach and are only applicable to Gaussian observations. However, as discrete data are ubiquitous in applications, it is of practical importance to develop dynamic modeling for non-Gaussian observations. New methods and algorithms are developed for both parameter estimation and sparse structure identification in high-dimensional linear ODE systems. First, the high-dimensional generalized profiling method is proposed as a likelihood-based approach with ODE fidelity and sparsity-inducing regularization, along with efficient computation based on parameter cascading. Second, two versions of the two-step collocation methods are extended to the non-Gaussian set-up by incorporating the iteratively reweighted least squares technique. Simulations show that the profiling procedure has excellent performance in latent process and derivative fitting and ODE parameter estimation, while the two-step collocation approach excels in identifying the sparse structure of the ODE system. The usefulness of the proposed methods is also demonstrated by analyzing three real datasets from Google trends, stock market sectors, and yeast cell cycle studies.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Generating a dynamical M2 brane from super-gravitons in a pp-wave background

We present a detail study of dynamically generating a M2 brane from super-gravitons (or D0 branes) in a pp-wave background possessing maximal spacetime SUSY. We have three kinds of dynamical solutions depending on the excess energy which appears as an order parameter signalling a critical phenomenon about the solutions. As the excess energy is below a critical value, we have two branches of the solution, one can have its size zero while the other cannot for each given excess energy. However there can be an instanton tunnelling between the two. Once the excess energy is above the critical value, we have a single solution whose dynamical behavior is basically independent of the background chosen and whose size can be zero at some instant. A by product of this study is that the size of particles or extended objects can grow once there is a non-zero excess energy even without the presence of a background flux, therefore lending support to the spacetime uncertainty principle.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Hall anomaly, Quantum Oscillations and Possible Lifshitz Transitions in Kondo Insulator YbB$_{12}$: Evidence for Unconventional Charge Transport

In correlated electronic systems, strong interactions and the interplay between different degrees of freedom may give rise to anomalous charge transport properties, which can be tuned by external parameters like temperature and magnetic field. Recently, magnetic quantum oscillations and metallic low-temperature thermal conductivity have been observed in the Kondo insulator YbB$_{12}$, whose resistivity is a few orders of magnitude higher than those of conventional metals. As yet, these unusual observations are not fully understood. Here we present a detailed investigation of the behavior of YbB$_{12}$ under intense magnetic fields using both transport and torque magnetometry measurements. A low-field Hall anomaly, reminiscent of the Hall response associated with &#34;strange-metal&#34; physics, develops at $T < 1.5$ K. At two characteristic magnetic fields ($μ_0H_1= 19.6$ T and $μ_0H_2 \sim 31$ T), signatures appear in the Hall coefficient, magnetic torque, and magnetoresistance. We suggest that they are likely to be field-induced Lifshitz transitions. Moreover, above 35 T, the background resistivity displays an unusual, nonmetallic $T^α$-behavior, with $α$ being field-dependent and varying between -1.5 and -2. By normalizing the Shubnikov-de Haas oscillation amplitude to this $T^α$-dependence, the calculated cyclotron mass becomes more consistent with that deduced from de Haas-van Alphen oscillations. Our results support a novel two-fluid scenario in YbB$_{12}$: a Fermi-liquid-like fluid of charge-neutral quasiparticles coexists with charge carriers that remain in a nonmetallic state. The former experience successive Lifshitz transitions and develop Landau quantization in applied magnetic fields, whilst scattering between both fluids allows the Shubnikov-de Haas effect to be observed in the electrical transport.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Optimal Subsampling for Large Sample Ridge Regression

Subsampling is a popular approach to alleviating the computational burden for analyzing massive datasets. Recent efforts have been devoted to various statistical models without explicit regularization. In this paper, we develop an efficient subsampling procedure for the large sample linear ridge regression. In contrast to the ordinary least square estimator, the introduction of the ridge penalty leads to a subtle trade-off between bias and variance. We first investigate the asymptotic properties of the subsampling estimator and then propose to minimize the asymptotic-mean-squared-error criterion for optimality. The resulting subsampling probability involves both ridge leverage score and L2 norm of the predictor. To further reduce the computational cost for calculating the ridge leverage scores, we propose the algorithm with efficient approximation. We show by synthetic and real datasets that the algorithm is both statistically accurate and computationally efficient compared with existing subsampling based methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Prospects for detecting axion-like particles via the decay $Z\rightarrow af\bar{f}$ at future $Z$ factories

We investigate the prospects for detecting axion-like particles (ALPs, dubbed as &#34;a&#34;) via the decay $Z\rightarrow a f\bar{f}$ at future $Z$ factories. Considering the decay channels $a\rightarrow μ^+ μ^-$ and $a\rightarrow b \bar{b}$ , four types of signals $μ^+ μ^- /E$, $b b /E$, $e^+ e^- μ^+ μ^-$ and $e^+ e^- b b$ are explored. We demonstrate that these channels are promising for detecting ALPs at $Z$ factories and obtain the sensitivity bounds on the couplings $g_{aZZ}$ and $g_{aγZ}$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A 6G White Paper on Connectivity for Remote Areas

In many places all over the world rural and remote areas lack proper connectivity that has led to increasing digital divide. These areas might have low population density, low incomes, etc., making them less attractive places to invest and operate connectivity networks. 6G could be the first mobile radio generation truly aiming to close the digital divide. However, in order to do so, special requirements and challenges have to be considered since the beginning of the design process. The aim of this white paper is to discuss requirements and challenges and point out related, identified research topics that have to be solved in 6G. This white paper first provides a generic discussion, shows some facts and discusses targets set in international bodies related to rural and remote connectivity and digital divide. Then the paper digs into technical details, i.e., into a solutions space. Each technical section ends with a discussion and then highlights identified 6G challenges and research ideas as a list.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Complementary lateral-spin-orbit building blocks for programmable logic and in-memory computing

Current-driven switching of nonvolatile spintronic materials and devices based on spin-orbit torques offer fast data processing speed, low power consumption, and unlimited endurance for future information processing applications. Analogous to conventional CMOS technology, it is important to develop a pair of complementary spin-orbit devices with differentiated magnetization switching senses as elementary building blocks for realizing sophisticated logic functionalities. Various attempts using external magnetic field or complicated stack/circuit designs have been proposed, however, plainer and more feasible approaches are still strongly desired. Here we show that a pair of two locally laser annealed perpendicular Pt/Co/Pt devices with opposite laser track configurations and thereby inverse field-free lateral spin-orbit torques (LSOTs) induced switching senses can be adopted as such complementary spin-orbit building blocks. By electrically programming the initial magnetization states (spin down/up) of each sample, four Boolean logic gates of AND, OR, NAND and NOR, as well as a spin-orbit half adder containing an XOR gate, were obtained. Moreover, various initialization-free, working current intensity-programmable stateful logic operations, including the material implication (IMP) gate, were also demonstrated by regarding the magnetization state as a logic input. Our complementary LSOT building blocks provide an applicable way towards future efficient spin logics and in-memory computing architectures.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Constraints on the generalized natural inflation after Planck 2018

Based on the dynamics of single scalar field slow-roll inflation and the theory of reheating, we investigate the generalized natural inflationary (GNI) model. Concretely, we give constraints on the scalar spectral index $n_{s}$ and tensor-to scalar ratio $r$ for $Λ$CDM $+r$ model according to the latest data from Plack 2018 TT,TE,EE+lowE+lensing (P18) and BICEP2/Keck 2015 season (BK15), i.e., $n_{s}=0.9659\pm0.0044$ at $68\%$ confidence level (CL) and $r<0.0623$ at $95\%$CL. We find that the GNI model is favored by P18 plus BK15 in the ranges of $\log_{10}(f/M_{p})=0.62^{+0.17}_{-0.18}$ and $m=0.35^{+0.13}_{-0.23}$ at $68\%$CL. In addition, the corresponding predictions of the general and two-phase reheating are respectively discussed. It follows that the parameter $m$ has the significant effect on the model behaviors.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Graded nanocomposite metamaterials for a double-sided radiative cooling architecture with a record breaking cooling power density

As an emerging electricity-free cooling technology, radiative cooling employs outer space as the heat sink. With this, a sky-facing thermal emitter is usually required. Due to the black-body radiation limit at ambient temperature, the maximum cooling power density for a single-faced radiative cooling device is ~156.9 W/m2. Here we report a double-sided radiative cooling architecture using graded nanocomposite metamaterials (GNM) designed for a vertically aligned thermal emitter. This GNM structure possesses an optical absorption of over 90% throughout the solar spectrum, and exceeds 90% reflection in the mid-infrared spectral region. With this configuration, both sides of a planar thermal emitter can be used to perform radiative cooling and a record cooling power density beyond 280 W/m2 was realized in a single thin-film thermal emitter. Under the standard pressure, we realized a temperature reduction of 14 degree Celsius below the ambient temperature in the laboratory environment, and over 12 degree Celsius in the outdoor test.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On the Divergence of First Order Resonance Widths at Low Eccentricities

Orbital resonances play an important role in the dynamics of planetary systems. Classical theoretical analyses found in textbooks report that libration widths of first order mean motion resonances diverge for nearly circular orbits. Here we examine the nature of this divergence with a non-perturbative analysis of a few first order resonances interior to a Jupiter-mass planet. We show that a first order resonance has two branches, the pericentric and the apocentric resonance zone. As the eccentricity approaches zero, the centers of these zones diverge away from the nominal resonance location but their widths shrink. We also report a novel finding of &#34;bridges&#34; between adjacent first order resonances: at low eccentricities, the apocentric libration zone of a first order resonance smoothly connects with the pericentric libration zone of the neighboring first order resonance. These bridges may facilitate resonant migration across large radial distances in planetary systems, entirely in the low eccentricity regime.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Primordial Gravitational Waves Spectrum in the Coupled-Scalar-Tachyon Bounce Universe

We derive in detail the equations of motion for the tensorial modes of primordial metric perturbations in the Coupled-Scalar-Tachyon Bounce Universe. We solve for the gravitational wave equations in the pre-bounce contraction and the post-bounce expansion epochs. To match the solutions of the tensor perturbations, we idealise the bounce process yet retaining the essential physical properties of the bounce universe. We put forward two matching conditions: one ensures the continuity of the gravitational wave functions and the other respects the symmetric nature of the bounce dynamics. The matching conditions connect the two independent modes of gravitational waves solutions before and after the bounce. We further analyse the scale dependence and time dependence of the gravitational waves spectra in the bounce universe and compare them with the primordial spectrum in the single field inflation scenario. We discuss the implications to early universe physics and present model independent observational signatures extracted from the bounce universe.

0 citations0 reviewsNo code linkedOpen access