arXiv subjects

Tong Su

Publications and source records attributed to Tong Su.

At least 19 recordsLinked to original sources

arXiv2026

Ultralow p-type contact resistance for ultra-nanoscaled 2D-materials transistors

High contact resistance is one of the main bottlenecks for practical two-dimensional (2D) materials transistors, especially for p-type transistors and future 2D ultra-nanoscaled (sub-10 nm) FETs (PMOS + CMOS). We develop self-consistent contact resistance models for metal-2D semiconductor-metal devices to capture the essential interface physics for both vertical and edge configurations. Our calculations have been verified with various recent experiments of p-type and n-type contacts. For a given set of materials, the model determines the scaling of contact resistance over a wide range of device parameters including channel length (100s nm down to sub-10 nm), doping and mobility of the 2D materials, contact length of the electrodes, and applied voltages. These results identify the key factors in order to reduce the contact resistance for p-type 2D semiconductor WSe$_2$ towards the sub-10 nm channel length scale that are readily to be realized by future experiments. It is found that the effect of source-limited current saturation is the key challenge for down scaling 2D FET to sub-10 nm channel length. Two topological semi-metals as potential electrodes are proposed for 2D p-type semiconducting WSe$_2$ with our predicted contact resistance $R_c<$ 100 $\Omega \; {\rm \mu m}$ approaching the quantum limit. Our model is also verified with the computational expensive full quantum atomistic model that is currently limited to a few nm scale.

cond-mat.mes-hall↗

arXiv2026

Chips in the Flatland : 2D Semiconductors for Future Computing Electronic

As transistor scaling approaches its fundamental physical limits in the Angstrom era, two-dimensional (2D) semiconductors have emerged as the promising channel material candidates for future computing. While the device physics of 2D semiconductors have been rigorously explored, translating these nanodevices into fully functional integrated circuits remains a largely uncharted frontier. This review bridges the gap between material- and device-centric breakthroughs and circuit-level chip design in 2D semiconductors, a valley of death that has so far prevented translation of high-performance individual transistors into functional chips. We track the evolution of 2D semi-conductor field-effect transistors from basic Boolean logic families and standard cells to complex chip architectures, including recent milestones in RISC-V and monolithic CMOS microprocessors. Critically, we highlight the indispensable role of multiscale compact modeling, spanning semiclassical, quantum-hybrid and data-driven approaches, as the necessary link between device physics and the electronic design automation workflows for scalable chip development. By summarizing recent breakthroughs and identifying the bottlenecks in both fab and fabless trajectories of 2D semiconductors, this review shall provide insights that motivates the translation of proof-of-concept 2D transistors into fully functional computing chips, paving a way towards future Angstrom era computing technology empowered by 2D semiconductors.

physics.app-ph↗

arXiv2026

SecCodeBench-V2 Technical Report

We introduce SecCodeBench-V2, a publicly released benchmark for evaluating Large Language Model (LLM) copilots' capabilities of generating secure code. SecCodeBench-V2 comprises 98 generation and fix scenarios derived from Alibaba Group's industrial productions, where the underlying security issues span 22 common CWE (Common Weakness Enumeration) categories across five programming languages: Java, C, Python, Go, and JavaScript. SecCodeBench-V2 adopts a function-level task formulation: each scenario provides a complete project scaffold and requires the model to implement or patch a designated target function under fixed interfaces and dependencies. For each scenario, SecCodeBench-V2 provides executable proof-of-concept (PoC) test cases for both functional validation and security verification. All test cases are authored and double-reviewed by security experts, ensuring high fidelity, broad coverage, and reliable ground truth. Beyond the benchmark itself, we build a unified evaluation pipeline that assesses models primarily via dynamic execution. For most scenarios, we compile and run model-generated artifacts in isolated environments and execute PoC test cases to validate both functional correctness and security properties. For scenarios where security issues cannot be adjudicated with deterministic test cases, we additionally employ an LLM-as-a-judge oracle. To summarize performance across heterogeneous scenarios and difficulty levels, we design a Pass@K-based scoring protocol with principled aggregation over scenarios and severity, enabling holistic and comparable evaluation across models. Overall, SecCodeBench-V2 provides a rigorous and reproducible foundation for assessing the security posture of AI coding assistants, with results and artifacts released at https://alibaba.github.io/sec-code-bench. The benchmark is publicly available at https://github.com/alibaba/sec-code-bench.

cs.CR↗

arXiv2026

Revised AGN Spectral Model Reveals a More Significant Role in Cosmic Reionization

Reionization marks one of the most important phase transitions in the history of the Universe, during which neutral baryonic matter was transformed into ionized plasma. While star-forming galaxies are widely regarded as the primary drivers of this process, the extent to which active galactic nuclei (AGNs) contribute remains a subject of ongoing investigation. In this study, we integrate a physically motivated AGN spectral energy distribution (SED) model with state-of-the-art observations to reassess the contribution of AGNs to cosmic reionization. Our findings indicate that adopting a more sophisticated AGN SED model could substantially increase the predicted ionizing photon output by a factor of 3$\sim$4, elevating AGNs to a more significant role ($\approx$20\%) in maintaining reionization than previously estimated. The inclusion of abundant faint AGNs further amplifies this contribution by a factor of a few. These conclusions remain robust across a wide range of accretion rates and ionizing photon escape fractions. Collectively, our results suggest that AGNs may have played a more prominent and previously underestimated role in the reionization of the Universe.

astro-ph.GA↗

arXiv2025

Back to basics: Little Red Dots as galaxies and dust-obscured AGNs in a synthetic NIRCam sky simulated with L-GalaxiesBH

The enigmatic Little Red Dots (LRDs) discovered by the James Webb Space Telescope (JWST) exhibit properties challenging their interpretation as common galaxies or Active Galactic Nuclei (AGN). Understanding their nature is key to placing them within our picture of early galaxy and massive black hole (MBH) evolution. To this aim, we build a realistic comparison between LRD observations with photometric properties of galaxies and AGN simulated by the L-GalaxiesBH model in a NIRCam mock sky. We model stellar continua and emission lines, the MBH emission from accretion disk, infrared radiation from dusty torus, and lines from narrow and broad line regions, accounting for dust attenuation and obscuration. Using realistic photometric cuts, we select a population of LRDs including both AGN and galaxies. The LRD fraction peaks at 40% ($\sim10^{-4}\rm Mpc^{-3}$) at $z\sim4$. Our LRDs are central galaxies spanning $M_*=10^8-10^{10.5}\rm M_\odot$. A population of galaxies with $M_*<10^9\rm M_\odot$ appear as LRDs due to older stellar populations. At higher masses, LRDs dominate the halo and stellar mass functions ($M_{\rm vir} > 10^{11.5}\rm M_\odot$, $M_* > 10^{9.5}\rm M_\odot$), and the interplay between AGN and galaxy emission drives the LRD selection. AGN dominate rest-frame UV-optical emission, while dust obscuration is secondary. LRDs host lighter MBHs ($\sim 10^{6.5}\rm M_\odot$) than non-LRDs ($\sim 10^{7.5}\rm M_\odot$), with fainter emission unable to balance their hosts Balmer breaks. We find no evidence for dominant heavy-seed origin of MBHs. LRD Galaxies (97% hosting MBHs) and LRD AGNs are disk-dominated, with LRD AGNs showing larger bulges formed mainly via disk instabilities.

astro-ph.GA↗

arXiv2025

Public Communication with Externalities

This paper develops a model in which a sender strategically communicates with a group of receivers whose payoffs depend on the sender's information. It is shown that aggregate payoff externalities create an endogenous conflict of interests between the sender and the receivers, rendering full information revelation, in general infeasible. We demonstrate that an exogenous bias in the sender's preferences can improve public information provision and raise welfare. Two applications of the setup are discussed.

econ.TH↗

arXiv2025

Gaussian Processes in Power Systems: Techniques, Applications, and Future Works

The increasing integration of renewable energy sources (RESs) and distributed energy resources (DERs) has significantly heightened operational complexity and uncertainty in modern power systems. Concurrently, the widespread deployment of smart meters, phasor measurement units (PMUs) and other sensors has generated vast spatiotemporal data streams, enabling advanced data-driven analytics and decision-making in grid operations. In this context, Gaussian processes (GPs) have emerged as a powerful probabilistic framework, offering uncertainty quantification, non-parametric modeling, and predictive capabilities to enhance power system analysis and control. This paper presents a comprehensive review of GP techniques and their applications in power system operation and control. GP applications are reviewed across three key domains: GP-based modeling, risk assessment, and optimization and control. These areas serve as representative examples of how GP can be utilized in power systems. Furthermore, critical challenges in GP applications are discussed, and potential research directions are outlined to facilitate future power system operations.

eess.SY↗

arXiv2025

Modeling the Spectral Energy Distribution of Active Galactic Nuclei: Implications for Cosmological Simulations of Galaxy Formation

Modeling the spectral energy distribution (SED) of active galactic nuclei (AGN) plays a very important role in constraining modern cosmological simulations of galaxy formation. Here, we utilize an advanced supermassive black hole (SMBH) accretion disk model to compute the accretion flow structure and AGN SED across a wide range of black hole mass ($M_{\rm SMBH}$) and dimensionless accretion rates $\dot{m}(\equiv \dot{M}_{\rm acc}/\dot{M}_\mathrm{Edd})$, where $\dot{M}_{\rm acc}$ is the mass flow rate through the disk and $\dot{M}_\mathrm{Edd}$ is the Eddington mass accretion rate. We find that the radiative efficiency is mainly influenced by $\dot m$, while contributions of $M_{\rm SMBH}$ and $\dot{m}$ to the bolometric luminosity are comparably important. We have developed new scaling relationships that relate the bolometric luminosity of an AGN to its luminosities in the hard X-ray, soft X-ray, and optical bands. Our results align with existing literature at high luminosities but suggest lower luminosities in the hard and soft X-ray bands for AGNs with low bolometric luminosities than commonly reported values. Combining with the semi-analytical model of galaxy formation \textsc{L-Galaxies} and Millennium dark matter simulation for the distribution of ($M_{\rm SMBH}, \dot{m}$) at different redshift, we find the model predictions align well with observational data at redshifts below 1 but deviates for higher redshifts regarding AGN detection fraction and luminosity functions. This deviation may arise from improper treatment of SMBH growth at high redshifts in the model or bias from limited observational data. This AGN SED calculation can be readily applied in other cosmological simulations.

astro-ph.GA↗

arXiv2024

Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translation

Neural Machine Translation (NMT) systems built on multilingual sequence-to-sequence Language Models (msLMs) fail to deliver expected results when the amount of parallel data for a language, as well as the language's representation in the model are limited. This restricts the capabilities of domain-specific NMT systems for low-resource languages (LRLs). As a solution, parallel data from auxiliary domains can be used either to fine-tune or to further pre-train the msLM. We present an evaluation of the effectiveness of these two techniques in the context of domain-specific LRL-NMT. We also explore the impact of domain divergence on NMT model performance. We recommend several strategies for utilizing auxiliary parallel data in building domain-specific NMT models for LRLs.

cs.CL↗

arXiv2024

A Survey of Open-Source Power System Dynamic Simulators with Grid-Forming Inverter for Machine Learning Applications

The emergence of grid-forming (GFM) inverter technology and the increasing role of machine learning in power systems highlight the need for evaluating the latest dynamic simulators. Open-source simulators offer distinct advantages in this field, being both free and highly customizable, which makes them well-suited for scientific research and validation of the latest models and methods. This paper provides a comprehensive survey and comparison of the latest open-source simulators that support GFM, with a focus on their capabilities and performance in machine-learning applications.

eess.SY↗

arXiv2024

Neural Network Certification Informed Power System Transient Stability Preventive Control with Renewable Energy

Existing machine learning-based surrogate modeling methods for transient stability constrained-optimal power flow (TSC-OPF) lack certifications in the presence of unseen disturbances or uncertainties. This may lead to divergence of TSC-OPF or insecure control strategies. This paper proposes a neural network certification-informed power system transient stability preventive control method considering the impacts of various uncertainty resources, such as errors from measurements, fluctuations in renewable energy sources (RESs) and loads, etc. A deep belief network (DBN) is trained to estimate the transient stability, replacing the time-consuming time-domain simulation-based calculations. Then, DBN is embedded into the iterations of the primal-dual interior-point method to solve TSC-OPF. To guarantee the robustness of the solutions, the neural network verifier $\alpha, \beta$-CROWN to deal with uncertainties from RESs and loads is proposed. The yielded certification results allow us to further adjust the transient stability safety margin under the iterated TSC-OPF solution process, balancing system security and economics. Numerical results on a modified western South Carolina 500-bus system demonstrate that the proposed method can efficiently and quickly obtain the safety-verified preventive control strategy through RES curtailment and generator dispatch with only a slight increase in cost.

eess.SY↗

arXiv2024

Learning Dual Transformers for All-In-One Image Restoration from a Frequency Perspective

This work aims to tackle the all-in-one image restoration task, which seeks to handle multiple types of degradation with a single model. The primary challenge is to extract degradation representations from the input degraded images and use them to guide the model's adaptation to specific degradation types. Building on the insight that various degradations affect image content differently across frequency bands, we propose a new dual-transformer approach comprising two components: a frequency-aware Degradation estimation transformer (Dformer) and a degradation-adaptive Restoration transformer (Rformer). The Dformer captures the essential characteristics of various degradations by decomposing the input into different frequency components. By understanding how degradations affect these frequency components, the Dformer learns robust priors that effectively guide the restoration process. The Rformer then employs a degradation-adaptive self-attention module to selectively focus on the most affected frequency components, guided by the learned degradation representations. Extensive experimental results demonstrate that our approach outperforms existing methods in five representative restoration tasks, including denoising, deraining, dehazing, deblurring, and low-light enhancement. Additionally, our method offers benefits for handling, real-world degradations, spatially variant degradations, and unseen degradation levels.

cs.CV↗

arXiv2024

A Review of Safe Reinforcement Learning Methods for Modern Power Systems

Given the availability of more comprehensive measurement data in modern power systems, reinforcement learning (RL) has gained significant interest in operation and control. Conventional RL relies on trial-and-error interactions with the environment and reward feedback, which often leads to exploring unsafe operating regions and executing unsafe actions, especially when deployed in real-world power systems. To address these challenges, safe RL has been proposed to optimize operational objectives while ensuring safety constraints are met, keeping actions and states within safe regions throughout both training and deployment. Rather than relying solely on manually designed penalty terms for unsafe actions, as is common in conventional RL, safe RL methods reviewed here primarily leverage advanced and proactive mechanisms. These include techniques such as Lagrangian relaxation, safety layers, and theoretical guarantees like Lyapunov functions to rigorously enforce safety boundaries. This paper provides a comprehensive review of safe RL methods and their applications across various power system operations and control domains, including security control, real-time operation, operational planning, and emerging areas. It summarizes existing safe RL techniques, evaluates their performance, analyzes suitable deployment scenarios, and examines algorithm benchmarks and application environments. The paper also highlights real-world implementation cases and identifies critical challenges such as scalability in large-scale systems and robustness under uncertainty, providing potential solutions and outlining future directions to advance the reliable integration and deployment of safe RL in modern power systems.

eess.SY↗

arXiv2024

The nature of the accretion physics in quiescent black hole system LB-1

LB-1 is a binary system that has drawn great attention since its discovery in 2019. The nature of the two components of LB-1 is not very clear, which however is suggested very possibly to be a B-type star plus a black hole (BH). In this paper, we first calculate the wind mass-loss rate of the B-type star. We then calculate the mass capture rate by the BH, with which as the initial mass accretion rate, we calculate the truncation radius of the accretion disk and the corresponding emergent spectra of the accretion flow (comprising an inner advection-dominated accretion flow (ADAF) + an outer truncated accretion disk) within the framework of the disk evaporation model. It is found that the predicted truncation radius of the accretion disk with appropriate model parameters is consistent with observations inferred from the observed broad H$_\alpha$ emission line. The predicted X-ray luminosity is definitely below the estimated upper limits with the sensitivity of Chandra X-ray Observatory of the X-ray luminosity $\sim 2\times 10^{31}$ erg/s. Finally, we argue that if the disk evaporation model indeed reflects the intrinsic physics of the accretion flow, the value of the viscosity parameter $\alpha$ is constrained to be $\alpha \gtrsim 0.05$ (with BH mass being $68M_{\rm \odot}$), or $\alpha \gtrsim 0.003$ (with BH mass being $21M_{\rm \odot}$) to match the observed upper limit of the X-ray luminosity of LB-1.

astro-ph.HE↗

arXiv2024

Densely Distilling Cumulative Knowledge for Continual Learning

Continual learning, involving sequential training on diverse tasks, often faces catastrophic forgetting. While knowledge distillation-based approaches exhibit notable success in preventing forgetting, we pinpoint a limitation in their ability to distill the cumulative knowledge of all the previous tasks. To remedy this, we propose Dense Knowledge Distillation (DKD). DKD uses a task pool to track the model's capabilities. It partitions the output logits of the model into dense groups, each corresponding to a task in the task pool. It then distills all tasks' knowledge using all groups. However, using all the groups can be computationally expensive, we also suggest random group selection in each optimization step. Moreover, we propose an adaptive weighting scheme, which balances the learning of new classes and the retention of old classes, based on the count and similarity of the classes. Our DKD outperforms recent state-of-the-art baselines across diverse benchmarks and scenarios. Empirical analysis underscores DKD's ability to enhance model stability, promote flatter minima for improved generalization, and remains robust across various memory budgets and task orders. Moreover, it seamlessly integrates with other CL methods to boost performance and proves versatile in offline scenarios like model compression.

cs.LG↗

arXiv2024

Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation

Parameter-efficient fine-tuning (PEFT) methods are increasingly vital in adapting large-scale pre-trained language models for diverse tasks, offering a balance between adaptability and computational efficiency. They are important in Low-Resource Language (LRL) Neural Machine Translation (NMT) to enhance translation accuracy with minimal resources. However, their practical effectiveness varies significantly across different languages. We conducted comprehensive empirical experiments with varying LRL domains and sizes to evaluate the performance of 8 PEFT methods with in total of 15 architectures using the SacreBLEU score. We showed that 6 PEFT architectures outperform the baseline for both in-domain and out-domain tests and the Houlsby+Inversion adapter has the best performance overall, proving the effectiveness of PEFT methods.

cs.CL↗

arXiv2024

Simulating emission line galaxies for the next generation of large-scale structure surveys

We investigate emission line galaxies across cosmic time by combining the modified L-Galaxies semi-analytical galaxy formation model with the JiuTian cosmological simulation. We improve the tidal disruption model of satellite galaxies in L-Galaxies to address the time dependence problem. We utilise the public code CLOUDY to compute emission line ratios for a grid of HII region models. The emission line models assume the same initial mass function as that used to generate the spectral energy distribution of semi-analytical galaxies, ensuring a coherent treatment for modelling the full galaxy spectrum. By incorporating these emission line ratios with galaxy properties, we reproduce observed luminosity functions for H$\alpha$, H$\beta$, [OII], and [OIII] in the local Universe and at high redshifts. We also find good agreement between model predictions and observations for auto-correlation and cross-correlation functions of [OII]-selected galaxies, as well as their luminosity dependence. The bias of emission line galaxies depends on both luminosity and redshift. At lower redshifts, it remains constant with increasing luminosity up to around $\sim 10^{42.5}\rm \, erg\,s^{-1}$ and then rises steeply for higher luminosities. The transition luminosity increases with redshift and becomes insignificant above $z$=1.5. Generally, galaxy bias shows an increasing trend with redshift. However, for luminous galaxies, the bias is higher at low redshifts, as the strong luminosity dependence observed at low redshifts diminishes at higher redshifts. We provide a fitting formula for the bias of emission line galaxies as a function of luminosity and redshift, which can be utilised for large-scale structure studies with future galaxy surveys.

astro-ph.GA↗

arXiv2023

Gd-Based Solvated Shells for Defect Passivation of CsPbBr$_3$ Nanoplatelets Enabling Efficient Color-Saturated Blue Electroluminescence

Reduced-dimensional CsPbBr$_3$ nanoplatelets (NPLs) are promising candidates for color-saturated blue emitters, yet their electroluminescence performance is hampered by non-radiative recombination, which is associated with bromine vacancies. Here, we show that a post-synthetic treatment of CsPbBr$_3$ NPLs with GdBr$_3$-dimethylformamide (DMF) can effectively eliminate defects while preserving the color. According to a combined experimental and theoretical study, Gd$^{3+}$ ions are less reactive with NPLs as a result of compact interaction between them and DMF, and this stable Gd$^{3+}$-DMF solvation structure makes Brions more available and allows them to move more freely. Consequently, defects are rapidly passivated and photoluminescence quantum yield increases dramatically (from 35 to ~100%), while the surface ligand density and emission color remain unchanged. The result is a remarkable electroluminescence efficiency of 2.4% (at 464 nm), one of the highest in pure blue perovskite NPL light-emitting diodes. It is noteworthy that the conductive NPL film shows a high photoluminescence quantum yield of 80%, demonstrating NPLs' significant electroluminescence potential with further device structure design.

physics.app-ph↗