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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.

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. Recognizing that various degradations affect image content differently across frequency bands, we propose a new all-in-one image restoration approach from a frequency perspective, leveraging advanced vision transformers. Our method consists of two main components: a frequency-aware Degradation prior learning transformer (Dformer) and a degradation-adaptive Restoration transformer (Rformer). The Dformer captures the essential characteristics of various degradations by decomposing inputs 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 the existing methods on four representative restoration tasks, including denoising, deraining, dehazing and deblurring. Additionally, our method offers benefits for handling spatially variant degradations and unseen degradation levels., Comment: 8 pages

Due to the availability of more comprehensive measurement data in modern power systems, there has been significant interest in developing and applying reinforcement learning (RL) methods for operation and control. Conventional RL training is based on trial-and-error and reward feedback interaction with either a model-based simulated environment or a data-driven and model-free simulation environment. These methods often lead to the exploration of actions in unsafe regions of operation and, after training, the execution of unsafe actions when the RL policies are deployed in real power systems. A large body of literature has proposed safe RL strategies to prevent unsafe training policies. In power systems, safe RL represents a class of RL algorithms that can ensure or promote the safety of power system operations by executing safe actions while optimizing the objective function. While different papers handle the safety constraints differently, the overarching goal of safe RL methods is to determine how to train policies to satisfy safety constraints while maximizing rewards. This paper provides a comprehensive review of safe RL techniques and their applications in different power system operations and control, including optimal power generation dispatch, voltage control, stability control, electric vehicle (EV) charging control, buildings' energy management, electricity market, system restoration, and unit commitment and reserve scheduling. Additionally, the paper discusses benchmarks, challenges, and future directions for safe RL research in power systems.

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., Comment: 11 pages, 2 figures. Submitted to The Astrophysical Journal, comments are welcome

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., Comment: 12 pages; Continual Leanrning; Class-incremental Learning; Knowledge Distillation; Forgetting

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., Comment: Accepted to the Findings of NAACL 2024

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., Comment: 22 pages, 18 figures, 5 tables, including 3 Appendix; Accepted by MNRAS

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.

Motion forecasting is a key module in an autonomous driving system. Due to the heterogeneous nature of multi-sourced input, multimodality in agent behavior, and low latency required by onboard deployment, this task is notoriously challenging. To cope with these difficulties, this paper proposes a novel agent-centric model with anchor-informed proposals for efficient multimodal motion prediction. We design a modality-agnostic strategy to concisely encode the complex input in a unified manner. We generate diverse proposals, fused with anchors bearing goal-oriented scene context, to induce multimodal prediction that covers a wide range of future trajectories. Our network architecture is highly uniform and succinct, leading to an efficient model amenable for real-world driving deployment. Experiments reveal that our agent-centric network compares favorably with the state-of-the-art methods in prediction accuracy, while achieving scene-centric level inference latency., Comment: CVPR 2023 (Highlight)

Recent experiment has uncovered semimetal bismuth (Bi) as an excellent electrical contact to monolayer MoS$_2$ with ultralow contact resistance. The contact physics of the broader semimetal/monolayer-semiconductor family beyond Bi/MoS$_2$, however, remains largely unexplored thus far. Here we perform a comprehensive first-principle density functional theory investigation on the electrical contact properties between six archetypal two-dimensional (2D) transition metal dichalcogenide (TMDC) semiconductors, i.e. MoS$_2$, WS$_2$, MoSe$_2$, WSe$_2$, MoTe$_2$ and WTe$_2$, and two representative types of semimetals, Bi and antimony (Sb). As Bi and Sb work functions energetically aligns well with the TMDC conduction band edge, Ohmic or nearly-Ohmic $n$-type contacts are prevalent. The interlayer distance of semimetal/TMDC contacts are significantly larger than that of the metal/TMDC counterparts, which results in only weak metalization of TMDC upon contact formation. Intriguingly, such weak metalization generates semimetal-induced gap states (MIGS) that extends below the conduction band minimum, thus offering an effective mechanism to reduce or eliminate the $n$-type Schottky barrier height (SBH) while still preserving the electronic structures of 2D TMDC. A modified Schottky-Mott rule that takes into account SMIGS, interface dipole potential, and Fermi level shifting is proposed, which provides an improved agreement with the DFT-simulated SBH. We further show that the tunneling-specific resistivity of Sb/TMDC contacts are generally lower than the Bi counterparts, thus indicating a better charge injection efficiency can be achieved through Sb contacts. Our findings reveal the promising potential of Bi and Sb as excellent companion electrode materials for advancing 2D semiconductor device technology., Comment: 12 pages, 7 figures

Two-dimensional (2D) niobium oxydihalide NbOI$_2$ has been recently demonstrated as an excellent in-plane piezoelectric and nonlinear optical materials. Here we show that Janus niobium oxydihalide, NbO$XY$ (X, Y = Cl, Br, I and X$\neq$Y), is a multifunctional anisotropic semiconductor family with exceptional piezoelectric, electronic, photocatalytic and optical properties. NbO$XY$ are stable and mechancially flexible monolayers with band gap around the visible light regime of $\sim 1.9$ eV. The anisotropic carrier mobility of NbO$XY$ lies in the range of $10^3 \sim 10^4$ cm$^2$V$^{-1}$s$^{-1}$, which represents some of the highest among 2D semiconductors of bandgap $\gtrsim 2$ eV. Inversion symmetry breaking in Janus NbO$XY$ generates sizable out-of-plane $d_{31}$ piezoelectric response while still retaining a strong in-plane piezoelectricity. Remarkably, NbO$XY$ exhibits an additional out-of-plane piezoelectric response, $d_{32}$ as large as 0.55 pm/V. G$_0$W$_0$-BSE calculation further reveals the strong linear optical dichroism of NbO$XY$ in the visible-to-ultraviolet regime. The optical absorption peaks with $14\sim18$ \% in the deep UV regime ($5\sim6$ eV), outperforming the vast majority of other 2D materials. The high carrier mobility, strong optical absorption, sizable built-in electric field and band alignment compatible with overall water splitting further suggest the strengths of NbO$XY$ in energy conversion application. We further propose a directional stress sensing device to demonstrate how the out-of-plane piezoelectricity can be harnessed for functional device applications. Our findings unveil NbO$XY$ as an exceptional multifunctional 2D semiconductor for flexible electronics, optoelectronics, UV photonics, piezoelectric and sustainable energy applications., Comment: 16 Pages, 7 Figures, 3 Tables

Two-dimensional (2D) layered dielectrics offers a compelling route towards the design of next-generation ultimately compact nanoelectronics. Motivated by recent high-throughput computational prediction of LaO$X$ ($X$ = Br, Cl) as an exceptional 2D dielectrics that significantly outperforms HfO$_2$ even in the monolyaer limit, we investigate the interface properties between LaOX and the archetypal 2D semiconductors of monolayer transition metal dichacolgenides (TMDCs) $M$S$_2$ ($M$ = Mo, W) using first-principle density functional theory simulations. We show that LaO$X$ monolayers interacts weakly with $M$S$_2$ via van der Waals forces with negligible hybridization and interfacial charge transfer, thus conveniently preserving the electronic properties of 2D TMDCs upon contact formation. The conduction and valance band offsets of the interfaces exhibit a sizable value ranging from 0.7 to 1.4 eV, suggesting the capability of LaO$X$ as a gate dielectric materials. Based on Murphy-Good electron emission model, we demonstrate that LaOCl/MoS$_2$ is a versatile dielectric/semiconductor combinations that are compatible to both NMOS and PMOS applications with leakage current lower than $10^{-7}$ Acm$^{-2}$, while LaO$X$/WS$_2$ is generally compatible with PMOS application. The presence of an interfacial tunneling potential barrier at the van der Waals gap further provide an additional mechanism to suppress the leakage current. Our findings reveal the role LaO$X$ as an excellent dielectric companion to 2D TMDC and shall provide useful insights for leveraging the dielectric strength of LaO$X$ in the design of high-performance 2D nanodevices., Comment: 10 pages, 4 Figures, 3 Tables

To safely navigate in various complex traffic scenarios, autonomous driving systems are generally equipped with a motion forecasting module to provide vital information for the downstream planning module. For the real-world onboard applications, both accuracy and latency of a motion forecasting model are essential. In this report, we present an effective and efficient solution, which ranks the 3rd place in the Argoverse 2 Motion Forecasting Challenge 2022.

Two-dimensional (2D) materials van der Waals heterostructures (vdWHs) provides a revolutionary route towards high-performance solar energy conversion devices beyond the conventional silicon-based pn junction solar cells. Despite tremendous research progress accomplished in recent years, the searches of vdWHs with exceptional excitonic solar cell conversion efficiency and optical properties remain an open theoretical and experimental quest. Here we show that the vdWH family composed of MoSi$_2$N$_4$ and WSi$_2$N$_4$ monolayers provides a compelling material platform for developing high-performance ultrathin excitonic solar cells and photonics devices. Using first-principle calculations, we construct and classify 51 types of MoSi$_2$N$_4$ and WSi$_2$N$_4$-based [(Mo,W)Si$_2$N$_4$] vdWHs composed of various metallic, semimetallic, semiconducting, insulating and topological 2D materials. Intriguingly, MoSi$_2$N$_4$/(InSe, WSe$_2$) are identified as Type-II vdWHs with exceptional excitonic solar cell power conversion efficiency reaching well over 20%, which are competitive to state-of-art silicon solar cells. The (Mo,W)Si$_2$N$_4$ vdWH family exhibits strong optical absorption in both the visible and ultraviolet regimes. Exceedingly large peak ultraviolet absorptions over 40%, approaching the maximum absorption limit of a free-standing 2D material, can be achieved in (Mo,W)Si$_2$N$_4$/$\alpha_2$-(Mo,W)Ge$_2$P$_4$ vdWHs. Our findings unravel the enormous potential of (Mo,W)Si$_2$N$_4$ vdWHs in designing ultimately compact excitonic solar cell device technology., Comment: 30 pages, 7 figures

As a core technology of the autonomous driving system, pedestrian trajectory prediction can significantly enhance the function of active vehicle safety and reduce road traffic injuries. In traffic scenes, when encountering with oncoming people, pedestrians may make sudden turns or stop immediately, which often leads to complicated trajectories. To predict such unpredictable trajectories, we can gain insights into the interaction between pedestrians. In this paper, we present a novel generative method named Spatial Interaction Transformer (SIT), which learns the spatio-temporal correlation of pedestrian trajectories through attention mechanisms. Furthermore, we introduce the conditional variational autoencoder (CVAE) framework to model the future latent motion states of pedestrians. In particular, the experiments based on large-scale trafc dataset nuScenes [2] show that SIT has an outstanding performance than state-of-the-art (SOTA) methods. Experimental evaluation on the challenging ETH and UCY datasets conrms the robustness of our proposed model