arXiv subjects

Nan Zhou

Publications and source records attributed to Nan Zhou.

At least 55 records · Page 3Linked to original sources

arXiv2024

China's plug-in hybrid electric vehicle transition: an operational carbon perspective

Assessing the emissions of plug-in hybrid electric vehicle (PHEV) operations is crucial for accelerating the carbon-neutral transition in the passenger car sector. This study is the first to adopt a bottom-up model to measure the real-world energy use and carbon dioxide emissions of China's top twenty selling PHEV models across different regions from 2020 to 2022. The results indicate that (1) the actual electricity intensity of the best-selling PHEV models (20.2-38.2 kWh/100 km) was 30-40% higher than the New European Driving Cycle values, and the actual gasoline intensity (4.7-23.5 L/100 km) was 3-6 times greater than the New European Driving Cycle values. (2) The overall energy use of the best-selling models varied among different regions, and the energy use from 2020 to 2022 in Southern China was double that Northern China and the Yangtze River Middle Reach. (3) The top-selling models emitted 4.7 megatons of carbon dioxide nationwide from 2020 to 2022, with 1.9 megatons released by electricity consumption and 2.8 megatons released by gasoline combustion. Furthermore, targeted policy implications for expediting the carbon-neutral transition within the passenger car sector are proposed. In essence, this study explores and compares benchmark data at both the national and regional levels, along with performance metrics associated with PHEV operations. The main objective is to aid nationwide decarbonization efforts, focusing on carbon reduction and promoting the rapid transition of road transportation toward a net-zero carbon future.

econ.GN↗

arXiv2024

Crowd-SAM: SAM as a Smart Annotator for Object Detection in Crowded Scenes

In computer vision, object detection is an important task that finds its application in many scenarios. However, obtaining extensive labels can be challenging, especially in crowded scenes. Recently, the Segment Anything Model (SAM) has been proposed as a powerful zero-shot segmenter, offering a novel approach to instance segmentation tasks. However, the accuracy and efficiency of SAM and its variants are often compromised when handling objects in crowded and occluded scenes. In this paper, we introduce Crowd-SAM, a SAM-based framework designed to enhance SAM's performance in crowded and occluded scenes with the cost of few learnable parameters and minimal labeled images. We introduce an efficient prompt sampler (EPS) and a part-whole discrimination network (PWD-Net), enhancing mask selection and accuracy in crowded scenes. Despite its simplicity, Crowd-SAM rivals state-of-the-art (SOTA) fully-supervised object detection methods on several benchmarks including CrowdHuman and CityPersons. Our code is available at https://github.com/FelixCaae/CrowdSAM.

cs.CV↗

arXiv2024

PLDNet: PLD-Guided Lightweight Deep Network Boosted by Efficient Attention for Handheld Dual-Microphone Speech Enhancement

Low-complexity speech enhancement on mobile phones is crucial in the era of 5G. Thus, focusing on handheld mobile phone communication scenario, based on power level difference (PLD) algorithm and lightweight U-Net, we propose PLD-guided lightweight deep network (PLDNet), an extremely lightweight dual-microphone speech enhancement method that integrates the guidance of signal processing algorithm and lightweight attention-augmented U-Net. For the guidance information, we employ PLD algorithm to pre-process dual-microphone spectrum, and feed the output into subsequent deep neural network, which utilizes a lightweight U-Net with our proposed gated convolution augmented frequency attention (GCAFA) module to extract desired clean speech. Experimental results demonstrate that our proposed method achieves competitive performance with recent top-performing models while reducing computational cost by over 90%, highlighting the potential for low-complexity speech enhancement on mobile phones.

eess.AS↗

arXiv2024

Estimation of Global Building Stocks by 2070: Unlocking Renovation Potential

Buildings produce one-third of carbon emissions globally, however, data absence regarding global floorspace poses challenges in advancing building carbon neutrality. We compile the measured building stocks for 14 major economies and apply our global building stock model, GLOBUS, to evaluate future trends in stock turnover. Based on a scenario not considering renovation, by 2070 the building stock in developed economies will be ~1.4 times that of 2020 (100 billion m2); in developing economies it is expected to be 2.2 times that of 2020 (313 billion m2). Based on a techno-economic potential scenario, however, stocks in developed economies will decline to approximately 0.8 times the 2020 level, while stocks in developing economies will increase to nearly twice the 2020 level due to their fewer buildings currently. Overall, GLOBUS provides a way of calculating the global building stock, helping scientists, engineers, and policymakers conduct a range of investigation across various future scenarios.

econ.GN↗

arXiv2024

GLOBUS: Global building renovation potential by 2070

Surpassing the two large emission sectors of transportation and industry, the building sector accounted for 34% and 37% of global energy consumption and carbon emissions in 2021, respectively. The building sector, the final piece to be addressed in the transition to net-zero carbon emissions, requires a comprehensive, multisectoral strategy for reducing emissions. Until now, the absence of data on global building floorspace has impeded the measurement of building carbon intensity (carbon emissions per floorspace) and the identification of ways to achieve carbon neutrality for buildings. For this study, we develop a global building stock model (GLOBUS) to fill that data gap. Our study's primary contribution lies in providing a dataset of global building stock turnover using scenarios that incorporate various levels of building renovation. By unifying the evaluation indicators, the dataset empowers building science researchers to perform comparative analyses based on floorspace. Specifically, the building stock dataset establishes a reference for measuring carbon emission intensity and decarbonization intensity of buildings within different countries. Further, we emphasize the sufficiency of existing buildings by incorporating building renovation into the model. Renovation can minimize the need to expand the building stock, thereby bolstering decarbonization of the building sector.

econ.EM↗

arXiv2024

iVPT: Improving Task-relevant Information Sharing in Visual Prompt Tuning by Cross-layer Dynamic Connection

Recent progress has shown great potential of visual prompt tuning (VPT) when adapting pre-trained vision transformers to various downstream tasks. However, most existing solutions independently optimize prompts at each layer, thereby neglecting the usage of task-relevant information encoded in prompt tokens across layers. Additionally, existing prompt structures are prone to interference from task-irrelevant noise in input images, which can do harm to the sharing of task-relevant information. In this paper, we propose a novel VPT approach, \textbf{iVPT}. It innovatively incorporates a cross-layer dynamic connection (CDC) for input prompt tokens from adjacent layers, enabling effective sharing of task-relevant information. Furthermore, we design a dynamic aggregation (DA) module that facilitates selective sharing of information between layers. The combination of CDC and DA enhances the flexibility of the attention process within the VPT framework. Building upon these foundations, iVPT introduces an attentive reinforcement (AR) mechanism, by automatically identifying salient image tokens, which are further enhanced by prompt tokens in an additive manner. Extensive experiments on 24 image classification and semantic segmentation benchmarks clearly demonstrate the advantage of the proposed iVPT, compared to the state-of-the-art counterparts.

cs.CV↗

arXiv2024

Capturing Shock Waves by Relaxation Neural Networks

In this paper, we put forward a neural network framework to solve the nonlinear hyperbolic systems. This framework, named relaxation neural networks(RelaxNN), is a simple and scalable extension of physics-informed neural networks(PINN). It is shown later that a typical PINN framework struggles to handle shock waves that arise in hyperbolic systems' solutions. This ultimately results in the failure of optimization that is based on gradient descent in the training process. Relaxation systems provide a smooth asymptotic to the discontinuity solution, under the expectation that macroscopic problems can be solved from a microscopic perspective. Based on relaxation systems, the RelaxNN framework alleviates the conflict of losses in the training process of the PINN framework. In addition to the remarkable results demonstrated in numerical simulations, most of the acceleration techniques and improvement strategies aimed at the standard PINN framework can also be applied to the RelaxNN framework.

math.NA↗

arXiv2023

DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic Calibration

The visual models pretrained on large-scale benchmarks encode general knowledge and prove effective in building more powerful representations for downstream tasks. Most existing approaches follow the fine-tuning paradigm, either by initializing or regularizing the downstream model based on the pretrained one. The former fails to retain the knowledge in the successive fine-tuning phase, thereby prone to be over-fitting, and the latter imposes strong constraints to the weights or feature maps of the downstream model without considering semantic drift, often incurring insufficient optimization. To deal with these issues, we propose a novel fine-tuning framework, namely distribution regularization with semantic calibration (DR-Tune). It employs distribution regularization by enforcing the downstream task head to decrease its classification error on the pretrained feature distribution, which prevents it from over-fitting while enabling sufficient training of downstream encoders. Furthermore, to alleviate the interference by semantic drift, we develop the semantic calibration (SC) module to align the global shape and class centers of the pretrained and downstream feature distributions. Extensive experiments on widely used image classification datasets show that DR-Tune consistently improves the performance when combing with various backbones under different pretraining strategies. Code is available at: https://github.com/weeknan/DR-Tune.

cs.CV↗

arXiv2023

Decarbonization patterns of residential building operations in China and India

As the two largest emerging emitters with the highest growth in operational carbon from residential buildings, the historical emission patterns and decarbonization efforts of China and India warrant further exploration. This study aims to be the first to present a carbon intensity model considering end-use performances, assessing the operational decarbonization progress of residential building in India and China over the past two decades using the improved decomposing structural decomposition approach. Results indicate (1) the overall operational carbon intensity increased by 1.4% and 2.5% in China and India, respectively, between 2000 and 2020. Household expenditure-related energy intensity and emission factors were crucial in decarbonizing residential buildings. (2) Building electrification played a significant role in decarbonizing space cooling (-87.7 in China and -130.2 kilograms of carbon dioxide (kgCO2) per household in India) and appliances (-169.7 in China and -43.4 kgCO2 per household in India). (3) China and India collectively decarbonized 1498.3 and 399.7 mega-tons of CO2 in residential building operations, respectively. In terms of decarbonization intensity, India (164.8 kgCO2 per household) nearly caught up with China (182.5 kgCO2 per household) in 2020 and is expected to surpass China in the upcoming years, given the country's robust annual growth rate of 7.3%. Overall, this study provides an effective data-driven tool for investigating the building decarbonization potential in China and India, and offers valuable insights for other emerging economies seeking to decarbonize residential buildings in the forthcoming COP28 age.

econ.GN↗

arXiv2023

An algebraic framework for the Drinfeld double based on infinite groupoids

The Drinfeld double associated to the weak multiplier Hopf ($*$-) algebra pairing $\left\langle A, B\right\rangle$ is constructed. We show that the Drinfeld double is again a weak multiplier Hopf ($*$-) algebra. If $A$ and $B$ are algebraic quantum groupoids, then so does the double. We also prove the correspondence between modules over the Drinfeld double and Yetter-Drinfeld modules. Finally, we prove that the double is a quasitriangular weak multiplier Hopf algebra.

math.QA↗

arXiv2023

CONA: A novel CONtext-Aware instruction paradigm for communication using large language model

We introduce CONA, a novel context-aware instruction paradigm for effective knowledge dissemination using generative pre-trained transformer (GPT) models. CONA is a flexible framework designed to leverage the capabilities of Large Language Models (LLMs) and incorporate DIKW (Data, Information, Knowledge, Wisdom) hierarchy to automatically instruct and optimise presentation content, anticipate potential audience inquiries, and provide context-aware answers that adaptive to the knowledge level of the audience group. The unique aspect of the CONA paradigm lies in its combination of an independent advisory mechanism and a recursive feedback loop rooted on the DIKW hierarchy. This synergy significantly enhances context-aware contents, ensuring they are accessible and easily comprehended by the audience. This paradigm is an early pioneer to explore new methods for knowledge dissemination and communication in the LLM era, offering effective support for everyday knowledge sharing scenarios. We conduct experiments on a range of audience roles, along with materials from various disciplines using GPT4. Both quantitative and qualitative results demonstrated that the proposed CONA paradigm achieved remarkable performance compared to the outputs guided by conventional prompt engineering.

cs.CL↗

arXiv2023

Novel Anisotropy of Upper Critical Fields in Fe$_{1+y}$Te$_{0.6}$Se$_{0.4}$

Studying the upper critical field ($μ_0$$H$$_{\rm{c2}}$) and its anisotropy of superconductors is of great importance because it can provide an unusual insight into the pair-breaking mechanism. Since Fe$_{1+y}$Te$_{1-x}$Se$_x$ exhibits the high $μ_0$$H$$_{\rm{c2}}$ and small anisotropic superconductivity, it has attracted considerable attention. However, some issues related to $μ_0$$H$$_{\rm{c2}}$ are still unknown, including the effect of excess Fe content on $μ_0$$H$$_{\rm{c2}}$ behavior and the origin of the crossover of the $μ_0H_{\rm{c2}}^c $ -- $ T$ and $μ_0H_{\rm{c2}}^{ab}$ -- $T$ curves. In this work, the value of $μ_0$$H$$_{\rm{c2}}$ of Fe$_{1+y}$Te$_{0.6}$Se$_{0.4}$ single crystals with controlled amounts of excess Fe was obtained by resistivity measurements over a wide range of temperatures down to $\sim$ 1.5 K, and magnetic fields up to $\sim$ 60 T. The crossover of the $μ_0H_{\rm{c2}}^c $ -- $ T$ and $μ_0H_{\rm{c2}}^{ab}$ -- $T$ curves was found to be independent of the excess Fe content. The angle dependence of $μ_0H_{\rm{c2}}$ was also checked. The $μ_0H_{\rm{c2}}(θ)$ symmetry at higher temperature near $T_c$ could be fitted by anisotropic G-L model, and novel fourfold symmetry of $μ_0H_{\rm{c2}}$ at lower temperature was found. Based on our spin-locking pairing model, the crossover behavior originates from the anisotropic spin-paramagnetic effect, and the novel fourfold symmetry of $μ_0H_{\rm{c2}}$ could be understood by our extended anisotropic G-L model.

cond-mat.supr-con↗

arXiv2023

Anomalous second magnetization peak in 12442-type RbCa$_2$Fe$_4$As$_4$F$_2$ superconductors

The second magnetization peak (SMP) appears in most superconductors and is crucial for the understanding of vortex physics as well as the application. Although it is well known that the SMP is related to the type and quantity of disorder/defects, the mechanism has not been universally understood. In this work, we selected three stoichiometric superconducting RbCa$_2$Fe$_4$As$_4$F$_2$ single crystals with identical superconducting critical temperature $T_c$ $\sim$ 31 K and similar self-field critical current density $J_c$, but with different amounts of disorder/defects, to study the SMP effect. It is found that only the sample S2 with a moderate disorder/defects shows a significant SMP effect. The evolution of the normalized pinning force density $f_p$ demonstrates that the dominant pinning mechanism changes from weak pinning at low temperatures to strong pinning at high temperatures. The microstructure study for sample S2 reveals some expanded Ca$_2$F$_2$ layers and dislocation defects in RbFe$_2$As$_2$ layers. The normalized magnetic relaxation results indicate that the SMP is strongly associated with the elastic to plastic (E-P) vortex transition. As temperature increases, the SMP gradually evolves into a step-like shape and then becomes a sharp peak near the irreversibility field similar to what is usually observed in low-temperature superconductors. Our findings connect the low field SMP of high-temperature superconductors and the high field peak of low-temperature superconductors, revealing the possible universal origin related to the E-P phase transition.

cond-mat.supr-con↗

arXiv2023

Significant enhancement of critical current density in H+-intercalated FeSe single crystal

Superconducting transition temperature (Tc) and critical current density (Jc) are two key factors that are not only crucial for probing high-temperature superconducting mechanisms, but also for practical applications. The simple crystal structure of FeSe is very favorable for the fabrication of thin films and wires, but its application is limited by the relatively low Tc and small Jc. A previous study has found that the Tc of FeSe can be significantly enhanced over 40 K by using the protonation method. Here, we present a systematic study of Jc and vortex properties of H+-intercalated FeSe (Hx-FeSe) single crystals. The value of Jc for Hx-FeSe single crystal is significantly enhanced, exceeding 1.3*10^6 A/cm2 at 4 K, which is more than two orders of magnitude larger than 1.1*10^4 A/cm2 of pristine FeSe. The vortex pinning mechanism of Hx-FeSe is found to be surface pinning, which is different from the dominant strong point-like pinning in pristine FeSe. Moreover, the systematic study of the vortex phase transition and the underlying mechanism provides a wealth of information for the vortex phase diagram of Hx-FeSe single crystal. Our results confirm that the introduction of H+ intercalations into FeSe not only enhances the Tc, but also significantly increases the value of Jc, which is favorable for practical applications.

cond-mat.supr-con↗

arXiv2022

Quantitative characterization of run-and-tumble statistics in bulk bacterial suspensions

We introduce a numerical method to extract the parameters of run-and-tumble dynamics from experimental measurements of the intermediate scattering function. We show that proceeding in Laplace space is unpractical and employ instead renewal processes to work directly in real time. We first validate our approach against data produced using agent-based simulations. This allows us to identify the length and time scales required for an accurate measurement of the motility parameters, including tumbling frequency and swim speed. We compare different models for the run-and-tumble dynamics by accounting for speed variability at the single-cell and population level, respectively. Finally, we apply our approach to experimental data on wild-type Escherichia coli obtained using differential dynamic microscopy.

physics.bio-ph↗

arXiv2022

Characterization and Control of the Run-and-Tumble Dynamics of {\it Escherichia Coli}

We characterize the full spatiotemporal gait of populations of swimming {\it Escherichia coli} using renewal processes to analyze the measurements of intermediate scattering functions. This allows us to demonstrate quantitatively how the persistence length of an engineered strain can be controlled by a chemical inducer and to report a controlled transition from perpetual tumbling to smooth swimming. For wild-type {\it E.~coli}, we measure simultaneously the microscopic motility parameters and the large-scale effective diffusivity, hence quantitatively bridging for the first time small-scale directed swimming and macroscopic diffusion.

cond-mat.soft↗

arXiv2022

Motion Sensitive Contrastive Learning for Self-supervised Video Representation

Contrastive learning has shown great potential in video representation learning. However, existing approaches fail to sufficiently exploit short-term motion dynamics, which are crucial to various down-stream video understanding tasks. In this paper, we propose Motion Sensitive Contrastive Learning (MSCL) that injects the motion information captured by optical flows into RGB frames to strengthen feature learning. To achieve this, in addition to clip-level global contrastive learning, we develop Local Motion Contrastive Learning (LMCL) with frame-level contrastive objectives across the two modalities. Moreover, we introduce Flow Rotation Augmentation (FRA) to generate extra motion-shuffled negative samples and Motion Differential Sampling (MDS) to accurately screen training samples. Extensive experiments on standard benchmarks validate the effectiveness of the proposed method. With the commonly-used 3D ResNet-18 as the backbone, we achieve the top-1 accuracies of 91.5\% on UCF101 and 50.3\% on Something-Something v2 for video classification, and a 65.6\% Top-1 Recall on UCF101 for video retrieval, notably improving the state-of-the-art.

cs.CV↗

arXiv2021

Protonation-induced discrete superconducting phases in bulk FeSe single crystals

The superconducting transition temperature, $T_{\rm{c}}$, of FeSe can be significantly enhanced several-fold by applying pressure, electron doping, intercalating spacing layer, and reducing dimensionality. Various ordered electronic phases, such as nematicity and spin density waves, have also been observed accompanying high-$T_{\rm{c}}$ superconductivity. Investigation on the evolution of the electronic structure with $T_{\rm{c}}$ is essential to understanding electronic behavior and high-$T_{\rm{c}}$ superconductivity in FeSe and its derived superconductors. In this report, we have found a series of discrete superconducting phases, with a maximum $T_{\rm{c}}$ up to 44 K, in H$^+$-intercalated FeSe single crystals using an ionic liquid gating method. Accompanied with the increase of $T_{\rm{c}}$, suppression of the nematic phase and evolution from non-Fermi-liquid to Fermi-liquid behavior was observed. An abrupt change in the Fermi surface topology was proposed to explain the discrete superconducting phases. A band structure that favors the high-$T_{\rm{c}}$ superconducting phase was also revealed.

cond-mat.supr-con↗