arXiv subjects

Rui Zhu

Publications and source records attributed to Rui Zhu.

At least 109 records · Page 6Linked to original sources

arXiv2023

DSR-Diff: Depth Map Super-Resolution with Diffusion Model

Color-guided depth map super-resolution (CDSR) improve the spatial resolution of a low-quality depth map with the corresponding high-quality color map, benefiting various applications such as 3D reconstruction, virtual reality, and augmented reality. While conventional CDSR methods typically rely on convolutional neural networks or transformers, diffusion models (DMs) have demonstrated notable effectiveness in high-level vision tasks. In this work, we present a novel CDSR paradigm that utilizes a diffusion model within the latent space to generate guidance for depth map super-resolution. The proposed method comprises a guidance generation network (GGN), a depth map super-resolution network (DSRN), and a guidance recovery network (GRN). The GGN is specifically designed to generate the guidance while managing its compactness. Additionally, we integrate a simple but effective feature fusion module and a transformer-style feature extraction module into the DSRN, enabling it to leverage guided priors in the extraction, fusion, and reconstruction of multi-model images. Taking into account both accuracy and efficiency, our proposed method has shown superior performance in extensive experiments when compared to state-of-the-art methods. Our codes will be made available at https://github.com/shiyuan7/DSR-Diff.

cs.CV↗

arXiv2023

Nanoscale Cathodoluminescence Spectroscopy Probing the Nitride Quantum Wells in an Electron Microcope

To gain a deeper understanding of the luminescence of multiquantum wells and the factors affecting it on a microscopic level, cathodoluminescence combined with scanning transmission electron microscopy and spectroscopy was used to reveal the luminescence of In0.15Ga0.85N five-period multiquantum wells. The composition-wave-energy relationship was established in combination with energy-dispersive X-ray spectroscopy , and the bandgaps of In0.15Ga0.85N and GaN in multiple quantum wells were extracted by electron energy loss spectroscopy to understand the features of cathodoluminescence luminescence spectra. The luminescence differences between different periods of multiquantum wells and the effects on the luminescence of multiple quantum wells owing to defects such as composition fluctuation and dislocations were revealed. Our study establishing the direct correspondence between the atomic structure of InxGa1-xN multiquantum wells and photoelectric properties, provides useful information for nitride applications.

physics.app-ph↗

arXiv2023

Reconstructing masses for semi-invisibly decaying particles pair-produced at lepton colliders

We present a set of Lorentz invariant kinematic variables for reconstructing mass of semi-invisible decaying particles pair-produced at lepton colliders, $m_{\rm RC}^{\rm min}$, $m_{\rm RC}^{\rm max}$ and $m_{\rm LSP}^{\rm max}$, with analytical formulas. They give the minimal and maximum bounds of the decaying particle mass and upper bound of the invisible particle mass. In the search of new physics, these variables can greatly enhance the statistical significance of signal. For the process of smuon pair production at $\sqrt{s}=240~{\rm GeV}$ lepton collider of 5 ab$^{-1}$, the cross section detection limit is pushed by one order, and the expected exclusion and discovery limits are set above $\sqrt{s}\big/2$ and go into the off-shell region. Moreover, these variables can also be used to improve the precision of $W$-boson mass measurement in full leptonic decayed channel. At future lepton collider, the precision can reach to $2\sim 3$ MeV level.

hep-ph↗

arXiv2023

Business Process Text Sketch Automation Generation Using Large Language Model

Business Process Management (BPM) is gaining increasing attention as it has the potential to cut costs while boosting output and quality. Business process document generation is a crucial stage in BPM. However, due to a shortage of datasets, data-driven deep learning techniques struggle to deliver the expected results. We propose an approach to transform Conditional Process Trees (CPTs) into Business Process Text Sketches (BPTSs) using Large Language Models (LLMs). The traditional prompting approach (Few-shot In-Context Learning) tries to get the correct answer in one go, and it can find the pattern of transforming simple CPTs into BPTSs, but for close-domain and CPTs with complex hierarchy, the traditional prompts perform weakly and with low correctness. We suggest using this technique to break down a difficult CPT into a number of basic CPTs and then solve each one in turn, drawing inspiration from the divide-and-conquer strategy. We chose 100 process trees with depths ranging from 2 to 5 at random, as well as CPTs with many nodes, many degrees of selection, and cyclic nesting. Experiments show that our method can achieve a correct rate of 93.42%, which is 45.17% better than traditional prompting methods. Our proposed method provides a solution for business process document generation in the absence of datasets, and secondly, it becomes potentially possible to provide a large number of datasets for the process model extraction (PME) domain.

cs.CL↗

arXiv2023

Direct detection of cosmic ray-boosted puffy dark matter

For the light relativistic dark matter (DM) boosted by high energy cosmic ray, its scattering cross section with the nucleon is sensitively dependent on the momentum-transfer and such an dependence is caused by the mediator in the scattering. For puffy DM particle with a size, the momentum-transfer dependence can also arise from the DM radius effect. All these momentum-transfer dependences should be considered. In this note we study the direct detection limits on the cosmic ray-boosted puffy DM for a simplified model with a light mediator. For comparison, we first re-derive the direct detection limits on the cosmic ray-boosted point-like DM. We display the limits on various planes of parameters and find that the limits for the cosmic ray-boosted puffy DM are stronger than for the point-like DM.

hep-ph↗

arXiv2023

Factorized Inverse Path Tracing for Efficient and Accurate Material-Lighting Estimation

Inverse path tracing has recently been applied to joint material and lighting estimation, given geometry and multi-view HDR observations of an indoor scene. However, it has two major limitations: path tracing is expensive to compute, and ambiguities exist between reflection and emission. Our Factorized Inverse Path Tracing (FIPT) addresses these challenges by using a factored light transport formulation and finds emitters driven by rendering errors. Our algorithm enables accurate material and lighting optimization faster than previous work, and is more effective at resolving ambiguities. The exhaustive experiments on synthetic scenes show that our method (1) outperforms state-of-the-art indoor inverse rendering and relighting methods particularly in the presence of complex illumination effects; (2) speeds up inverse path tracing optimization to less than an hour. We further demonstrate robustness to noisy inputs through material and lighting estimates that allow plausible relighting in a real scene. The source code is available at: https://github.com/lwwu2/fipt

cs.CV↗

arXiv2023

Sommerfeld enhancement for puffy self-interacting dark matter

We examine the Sommerfeld enhancement effect for the puffy self-interacting dark matter. We find out two new parameters to classify the self-scattering cross section into the Born, the resonance and the classical regimes for the puffy dark matter. Then we observe that the resonance peaks for the puffy dark matter self-scattering and for the Sommerfeld enhancement effect have the same locations. Further, we find that for a large ratio between $R_χ$ (radius of a puffy dark matter particle) and $1/m_ϕ$ (force range), the Sommerfeld enhancement factor approaches to 1 (no enhancement). Finally, for the puffy SIDM scenario to solve the small-scale problems, the values of the Sommerfeld enhancement factor are displayed in the allowed parameter regions.

hep-ph↗

arXiv2023

Asymmetric Chiral Coupling in a Topological Resonator

Chiral light-matter interactions supported by topological edge modes at the interface of valley photonic crystals provide a robust method to implement the unidirectional spin transfer. The valley topological photonic crystals possess a pair of counterpropagating edge modes. The edge modes are robust against the sharp bend of $60^{\circ}$ and $120^{\circ}$, which can form a resonator with whispering gallery modes. Here, we demonstrate the asymmetric emission of chiral coupling from single quantum dots in a topological resonator by tuning the coupling between a quantum emitter and a resonator mode. Under a magnetic field in Faraday configuration, the exciton state from a single quantum dot splits into two exciton spin states with opposite circularly polarized emissions due to Zeeman effect. Two branches of the quantum dot emissions couple to a resonator mode in different degrees, resulting in an asymmetric chiral emission. Without the demanding of site-control of quantum emitters for chiral quantum optics, an extra degree of freedom to tune the chiral contrast with a topological resonator could be useful for the development of on-chip integrated photonic circuits.

cond-mat.mes-hall↗

arXiv2023

On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have yet seen an attempt to develop foundation models for geospatial artificial intelligence (GeoAI). In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI. We first investigate the potential of many existing FMs by testing their performances on seven tasks across multiple geospatial subdomains including Geospatial Semantics, Health Geography, Urban Geography, and Remote Sensing. Our results indicate that on several geospatial tasks that only involve text modality such as toponym recognition, location description recognition, and US state-level/county-level dementia time series forecasting, these task-agnostic LLMs can outperform task-specific fully-supervised models in a zero-shot or few-shot learning setting. However, on other geospatial tasks, especially tasks that involve multiple data modalities (e.g., POI-based urban function classification, street view image-based urban noise intensity classification, and remote sensing image scene classification), existing foundation models still underperform task-specific models. Based on these observations, we propose that one of the major challenges of developing a FM for GeoAI is to address the multimodality nature of geospatial tasks. After discussing the distinct challenges of each geospatial data modality, we suggest the possibility of a multimodal foundation model which can reason over various types of geospatial data through geospatial alignments. We conclude this paper by discussing the unique risks and challenges to develop such a model for GeoAI.

cs.AI↗

arXiv2023

A concise review on some Higgs-related new physics models in light of current experiments

Higgs boson may serve as a portal to new physics beyond the standard model (BSM) which is implied by theoretical naturalness or experimental anomalies. In this note we briefly survey some Higgs-related BSM physics models, including the low energy SUSY (focusing on the minimal SUSY model), the little Higgs models, the two-Higgs-doublet models and the simplest singlet extensions of the Higgs sector. For each illustrated BSM model, we emphatically elucidate its phenomenological power in explaining current measurements of the muon g-2, the W-boson mass and the dark matter. For the singlet extensions like the xSM and 2HDM+S, we discuss the induced cosmic phase transition and the dark matter relic density as well as the vacuum stability. Finally, we give an outlook.

hep-ph↗

arXiv2023

EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph for Smart Transportation System

Over the past decade, the electric vehicle industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV-centric knowledge graph (EVKG) as a comprehensive, cross-domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV-related knowledge, including EV adoption, electric vehicle supply equipment, and electricity transmission network, to support decision-making related to EV technology development, infrastructure planning, and policy-making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV-relevant ontology modules from existing well-known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision-making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV-related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV-related knowledge into a single, easily accessible resource, the EVKG supports decision-makers in making informed choices about EV technology development, infrastructure planning, and policy-making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape.

cs.AI↗

arXiv2023

Light higgsino scenario confronted with muon g-2

Light higgsinos below several hundred GeV are favored or required by the naturalness of low energy supersymmetry. If only higgsinos are light while other sparticles are sufficiently heavy, we have the so-called light higgsino scenario. Confronted with the muon $g-2$ data, this scenario is examined in this work. Since in this scenario the LSP (lightest sparticle) is higgsino-like, we need to also consider the dark matter constraints. Assuming a light higgsino mass parameter $μ$ in the range of 100-400 GeV while gaugino mass parameters above TeV, we explore the parameter space under the muon $g-2$ data and the dark matter constraints. We find that, to explain the muon $g-2$ anomaly at $2σ$, the winos and sleptons are respectively upper bounded by 3 TeV and 800 GeV. In this case, we find that the light higgsino-like dark matter can sizably scatter with nucleon and thus the allowed parameter space can be covered almost fully by the future LZ dark matter detection project. We also perform a Monte Carlo simulation to figure out the potential of HL-LHC to detect the light sleptons in this scenario. It turns out that compared with the current LHC limits, the HL-LHC can further cover a part of the parameter space.

hep-ph↗

arXiv2023

Controllable Spin-Resolved Photon Emission Enhanced by Slow-Light Mode in Photonic Crystal Waveguides on Chip

We report the slow-light enhanced spin-resolved in-plane emission from a single quantum dot (QD) in a photonic crystal waveguide (PCW). The slow light dispersions in PCWs are designed to match the emission wavelengths of single QDs. The resonance between two spin states emitted from a single QD and a slow light mode of a waveguide is investigated under a magnetic field with Faraday configuration. Two spin states of a single QD experience different degrees of enhancement as their emission wavelengths are shifted by combining diamagnetic and Zeeman effects with an optical excitation power control. A circular polarization degree up to 0.81 is achieved by changing the off-resonant excitation power. Strongly polarized photon emission enhanced by a slow light mode shows great potential to attain controllable spin-resolved photon sources for integrated optical quantum networks on chip.

cond-mat.mes-hall↗

arXiv2022

The Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Release Six to Nine

We report the fourth installment in the series of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey, which includes quasars observed between September, 2017 and June, 2021. There are in total 13,066 quasars reliably identified, of which 6,685 are newly discovered that are not reported in the SDSS DR14 quasar catalog or Million Quasars catalog. Because LAMOST does not provide accurate absolute flux calibration, we re-calibrate the spectra with the SDSS/Pan-STARRS1 multi-band photometric data. The emission line properties of H$α$, H$β$, Mg\,{\sc ii} and C\,{\sc iv}, and the continuum luminosities are measured by fitting the re-calibrated spectra. We also estimate the single-epoch virial black hole masses ($\rm M_{BH}$) using the derived emission line and continuum parameters. This is the first time that the emission line and continuum fluxes were estimated based on LAMOST re-calibrated quasar spectra. The catalog and spectra for these quasars are available online. After the nine-year LAMOST quasar survey, there are in total 56,175 identified quasars, of which 24,127 are newly discovered. The LAMOST quasar survey not only discovers a great number of new quasars, but also provides a database for investigating the spectral variability of the quasars observed by both LAMOST and SDSS, and finding rare quasars including changing-look quasars and broad absorption line quasars.

astro-ph.GA↗

arXiv2022

Topological phase transition in disordered elastic quantum spin Hall system

We investigate the effect of disorder on topologically nontrivial states in a two dimension (2D) mechanical system. We first propose a quantum spin Hall (QSH) insulator based on an out-of-plane spring-mass model and analytically study the interplay between the disorder and topology in both topologically trivial and nontrivial systems. We adopt the spin Bott index to characterize the topological property in disordered mechanical systems. By tracking the evolution of the spin Bott index with the increase of disorders, we quantitatively demonstrate the disorder induced transition from a topologically nontrivial QSH insulator to a trivial insulator. We then validate the topological phase transition through transient analysis in discrete lattices. Finally, we design a phononic crystal based on the discrete spring-mass model and numerically verify the topologically protected states along the boundary between the trivial insulator and disordered topological QSH insulator in a continuous system. This work puts a step forward in understanding the role of disorder in a 2D topological classical system.

physics.app-ph↗

arXiv2022

Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback

An important goal in artificial intelligence is to create agents that can both interact naturally with humans and learn from their feedback. Here we demonstrate how to use reinforcement learning from human feedback (RLHF) to improve upon simulated, embodied agents trained to a base level of competency with imitation learning. First, we collected data of humans interacting with agents in a simulated 3D world. We then asked annotators to record moments where they believed that agents either progressed toward or regressed from their human-instructed goal. Using this annotation data we leveraged a novel method - which we call "Inter-temporal Bradley-Terry" (IBT) modelling - to build a reward model that captures human judgments. Agents trained to optimise rewards delivered from IBT reward models improved with respect to all of our metrics, including subsequent human judgment during live interactions with agents. Altogether our results demonstrate how one can successfully leverage human judgments to improve agent behaviour, allowing us to use reinforcement learning in complex, embodied domains without programmatic reward functions. Videos of agent behaviour may be found at https://youtu.be/v_Z9F2_eKk4.

cs.LG↗

arXiv2022

A brief survey of low energy supersymmetry under current experiments

This is a brief overview on the low energy supersymmetry in light of current experiments including the LHC searches, the dark matter (DM) detections, the muon g-2 and the CDF II measurement of the W-boson mass. We focus on the minimal framework of supersymmetry, namely the minimal supersymmetric model (MSSM), and obtain the following conclusions: (i) The MSSM can survive all current experiments, albeit suffering from the little hierarchy problem due to the heavy stops pushed up by the LHC searches; (ii) The DM relic density can be readily achieved by the thermal freeze-out of the lightest neutralino and the null results of DM direct detections are typically driving the parameter space to the bino-like lightest neutralino region; (iii) The muon g-2 anomaly reported by FNAL and BNL can be explained at 2-sigma level, which indicates light sleptons and electroweakinos possibly accessible at the HL-LHC; (iv) The CDF II measurement of the W-boson mass can be marginally explained, but requires light stops near TeV which may soon be covered by the LHC searches.

hep-ph↗

arXiv2022

Understanding Impacts of Task Similarity on Backdoor Attack and Detection

With extensive studies on backdoor attack and detection, still fundamental questions are left unanswered regarding the limits in the adversary's capability to attack and the defender's capability to detect. We believe that answers to these questions can be found through an in-depth understanding of the relations between the primary task that a benign model is supposed to accomplish and the backdoor task that a backdoored model actually performs. For this purpose, we leverage similarity metrics in multi-task learning to formally define the backdoor distance (similarity) between the primary task and the backdoor task, and analyze existing stealthy backdoor attacks, revealing that most of them fail to effectively reduce the backdoor distance and even for those that do, still much room is left to further improve their stealthiness. So we further design a new method, called TSA attack, to automatically generate a backdoor model under a given distance constraint, and demonstrate that our new attack indeed outperforms existing attacks, making a step closer to understanding the attacker's limits. Most importantly, we provide both theoretic results and experimental evidence on various datasets for the positive correlation between the backdoor distance and backdoor detectability, demonstrating that indeed our task similarity analysis help us better understand backdoor risks and has the potential to identify more effective mitigations.

cs.CR↗