arXiv subjects

Da Huo

Publications and source records attributed to Da Huo.

17 recordsLinked to original sources

arXiv2025

Small Object Detection for Birds with Swin Transformer

Object detection is the task of detecting objects in an image. In this task, the detection of small objects is particularly difficult. Other than the small size, it is also accompanied by difficulties due to blur, occlusion, and so on. Current small object detection methods are tailored to small and dense situations, such as pedestrians in a crowd or far objects in remote sensing scenarios. However, when the target object is small and sparse, there is a lack of objects available for training, making it more difficult to learn effective features. In this paper, we propose a specialized method for detecting a specific category of small objects; birds. Particularly, we improve the features learned by the neck; the sub-network between the backbone and the prediction head, to learn more effective features with a hierarchical design. We employ Swin Transformer to upsample the image features. Moreover, we change the shifted window size for adapting to small objects. Experiments show that the proposed Swin Transformer-based neck combined with CenterNet can lead to good performance by changing the window sizes. We further find that smaller window sizes (default 2) benefit mAPs for small object detection.

cs.CV↗

arXiv2025

Toward building next-generation Geocoding systems: a systematic review

Geocoding systems are widely used in both scientific research for spatial analysis and everyday life through location-based services. The quality of geocoded data significantly impacts subsequent processes and applications, underscoring the need for next-generation systems. In response to this demand, this review first characterizes the technical requirements for next-generation geocoding inputs and outputs. We then decompose the geocoding workflow into modular functional units and survey existing implementations. For each component, we identify methodological limitations, articulate domain-specific research questions and hypotheses, and outline evaluation strategies needed. Finally, we identify opportunities to improve next-generation geocoding systems in light of recent technological advances. We envision that this review provides a technical foundation and research agenda for advancing the design, assessment, and deployment of next-generation geocoding systems.

cs.SE↗

arXiv2024

Alternating-Chiral Charge Density Waves and Hybrid Ferrimagnetism in Monolayered NbTe2

Intertwining of different quantum degrees of freedom manifests exotic quantum phenomena in many-body systems, especially in reduced dimensionality. Here we show that monolayered NbTe2 serves as an ideal platform where lattice, charge, and spin degrees of freedom manifest cooperatively, leading to a new and threading order of chirality. By using spin-polarized scanning tunneling microscopy/spectroscopy, we reveal that the root19 * root19 phase of NbTe2 is encoded with both alternating-chiral atomic displacements and charge density waves, characterized by two chiral units of opposite handedness within the reconstructed cell. We show unambiguous evidence for emergent spin polarizations spreading over the primitive cell, with the magnetization orientation synchronized with alternating handedness of chiral order. Our first-principles studies identify the origin of intertwined orders being correlation driven, with the threading order of chirality emerging when the on-site Coulomb repulsion exceeds a critical value. The spin ordering is further shown to be of hybrid ferrimagnetic nature, contributed by the itinerant electrons and localized d-orbitals. Collectively, these findings expand the realm of chiral order in correlated electron systems, and facilitate an appealing platform for chiral spintronic and related applications.

cond-mat.mtrl-sci↗

arXiv2023

Coupling liquid electrochemical TEM and mass-spectrometry to investigate electrochemical reactions occurring in a Na-ion battery anode

In this study, we propose a novel approach for investigating the formation of solid electrolyte interphase (SEI) in Na-ion batteries (NIB) through the coupling of in situ liquid electrochemical transmission electron microscopy (ec-TEM) and gas-chromatography mass-spectrometry (GC/MS). To optimize this coupling, we conducted experiments on the sodiation of hard carbon materials (HC) using two different setups: in situ ec-TEM holder (operating in an "anode free" configuration, referred to as $\mu$-battery) and ex-situ setup (Swagelok battery configuration). In the in situ TEM experiments, we intentionally degraded the electrolyte (NP30) using cyclic voltammetry (CV) and analyzed the recovered liquid product using GC/MS, while the solid product ($\mu$-chip) was analyzed using TEM techniques in a post-mortem analysis. The ex-situ experiments served as a reference to observe and detect the insertion of Na+ ions in the HC, SEI size (389 nm), SEI composition (P, Na, F, and O), and Na plating. Furthermore, the TEM analysis revealed a cyclability limitation in our in situ TEM system. This issue appears to be caused by the deposition of Na in the form of a "foam" structure, resulting from the gas release during the reaction of Na with DMC/EC electrolyte. The foam structure, subsequently transforms into a second SEI, is electrochemically inactive and reduce the cyclability of the battery. Overall, our results demonstrate the powerful synergy achieved by coupling in situ ec-TEM and GC/MS techniques, which provides a deeper understanding of the dynamics and behavior of SEI. Consequently, this knowledge contributes to the advancement of the new generation of NIB.

physics.chem-ph↗

arXiv2023

MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results

Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image appearances of small objects. This paper proposes a new SOD dataset consisting of 39,070 images including 137,121 bird instances, which is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The detail of the challenge with the SOD4SB dataset is introduced in this paper. In total, 223 participants joined this challenge. This paper briefly introduces the award-winning methods. The dataset, the baseline code, and the website for evaluation on the public testset are publicly available.

cs.CV↗

arXiv2023

Ferromagnetism and correlated insulating states in monolayer Mo33Te56

Kagome lattices have an inherent two-dimensional nature. Despite previous realizations in the monolayer limit, their abilities to drive emergent electronic states such as correlated insulators have remained unobserved. Here, we report the experimental realization of a new structural phase of monolayer Mo33Te56, characterized by its virtually global uniformity as a mirror-twin boundary loop superlattice embedded in an H-MoTe2 monolayer. Through a combination of scanning tunnelling microscopy (STM) and theoretical calculations, we unveil a kagome geometry along with multiple associated sets of kagome flat bands. Crucially, the partial filling of these kagome bands induces ferromagnetism as revealed by spin-polarized STM, and leads to a correlated insulating state exhibiting a hard gap as large as 15 meV. Our findings represent a major advance in kagome materials, offering a framework with clearer band structures and more intrinsic two-dimensional properties for exploring flat-band physics.

cond-mat.mtrl-sci↗

arXiv2023

Realization of multiple charge density waves in NbTe2 at the monolayer limit

Abstract: Layered transition-metal dichalcogenides (TMDCs) down to the monolayer (ML) limit provide a fertile platform for exploring charge-density waves (CDWs). Though bulk NbTe2 is known to harbor a single axis 3*1 CDW coexisting with non-trivial quantum properties, the scenario in the ML limit is still experimentally unknown. In this study, we unveil the richness of the CDW phases in ML NbTe2, where not only the theoretically predicted 4*4 and 4*1 phases, but also two unexpected sqrt(28)*sqrt(28) and sqrt(19)*sqrt(19) phases, can be realized. For such a complex CDW system, we establish an exhaustive growth phase diagram via systematic efforts in the material synthesis and scanning tunneling microscope characterization. Moreover, we report that the energetically stable phase is the larger scale order (sqrt(19)*sqrt(19)), which is surprisingly in contradiction to the prior prediction (4*4). These findings are confirmed using two different kinetic pathways, i.e., direct growth at proper growth temperatures (T), and low-T growth followed by high-T annealing. Our results provide a comprehensive diagram of the "zoo" of CDW orders in ML 1T-NbTe2 for the first time and offer a new material platform for studying novel quantum phases in the 2D limit.

cond-mat.mtrl-sci↗

arXiv2022

Carbon Monitor Europe, near-real-time daily CO$_2$ emissions for 27 EU countries and the United Kingdom

With the urgent need to implement the EU countries pledges and to monitor the effectiveness of Green Deal plan, Monitoring Reporting and Verification tools are needed to track how emissions are changing for all the sectors. Current official inventories only provide annual estimates of national CO$_2$ emissions with a lag of 1+ year which do not capture the variations of emissions due to recent shocks including COVID lockdowns and economic rebounds, war in Ukraine. Here we present a near-real-time country-level dataset of daily fossil fuel and cement emissions from January 2019 through December 2021 for 27 EU countries and UK, which called Carbon Monitor Europe. The data are calculated separately for six sectors: power, industry, ground transportation, domestic aviation, international aviation and residential. Daily CO$_2$ emissions are estimated from a large set of activity data compiled from different sources. The goal of this dataset is to improve the timeliness and temporal resolution of emissions for European countries, to inform the public and decision makers about current emissions changes in Europe.

physics.geo-ph↗

arXiv2022

Near-real-time global gridded daily CO$_2$ emissions 2021

We present a near-real-time global gridded daily CO$_2$ emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO$_2$ emissions at a 0.1degree*0.1degree spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from a near-real-time daily national CO$_2$ emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO$_2$ data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO$_2$ emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of 19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies' effectiveness and make adjustments quickly.

physics.ao-ph↗

arXiv2022

Learning Optimal Deterministic Auctions with Correlated Valuation Distributions

In mechanism design, it is challenging to design the optimal auction with correlated values in general settings. Although value distribution can be further exploited to improve revenue, the complex correlation structure makes it hard to acquire in practice. Data-driven auction mechanisms, powered by machine learning, enable to design auctions directly from historical auction data, without relying on specific value distributions. In this work, we design a learning-based auction, which can encode the correlation of values into the rank score of each bidder, and further adjust the ranking rule to approach the optimal revenue. We strictly guarantee the property of strategy-proofness by encoding game theoretical conditions into the neural network structure. Furthermore, all operations in the designed auctions are differentiable to enable an end-to-end training paradigm. Experimental results demonstrate that the proposed auction mechanism can represent almost any strategy-proof auction mechanism, and outperforms the auction mechanisms wildly used in the correlated value settings.

cs.GT↗

arXiv2022

Carbon Monitor-Power: near-real-time monitoring of global power generation on hourly to daily scales

We constructed a frequently updated, near-real-time global power generation dataset: Carbon Monitor-Power since January, 2016 at national levels with near-global coverage and hourly-to-daily time resolution. The data presented here are collected from 37 countries across all continents for eight source groups, including three types of fossil sources (coal, gas, and oil), nuclear energy and four groups of renewable energy sources (solar energy, wind energy, hydro energy and other renewables including biomass, geothermal, etc.). The global near-real-time power dataset shows the dynamics of the global power system, including its hourly, daily, weekly and seasonal patterns as influenced by daily periodical activities, weekends, seasonal cycles, regular and irregular events (i.e., holidays) and extreme events (i.e., the COVID-19 pandemic). The Carbon Monitor-Power dataset reveals that the COVID-19 pandemic caused strong disruptions in some countries (i.e., China and India), leading to a temporary or long-lasting shift to low carbon intensity, while it had only little impact in some other countries (i.e., Australia). This dataset offers a large range of opportunities for power-related scientific research and policy-making.

physics.data-an↗

arXiv2022

Lopsided optical diffraction in loop electromagnetically induced grating

We propose a theoretical scheme in a cold Rubidium-87 ($^{87}$Rb) atomic ensemble with a non-Hermitian optical structure, in which a lopsided optical diffraction grating can be realized just with the combination of single spatially periodic modulation and loop-phase.~Parity-time ($\mathcal{PT}$) symmetric and parity-time antisymmetric ($\mathcal{APT}$) modulation can be switched by adjusting different relative phases of the applied beams. Both $\mathcal{PT}$ symmetry and $\mathcal{PT}$ antisymmetry in our system are robust to the amplitudes of coupling fields, which allows optical response to be modulated precisely without symmetry breaking. Our scheme shows some nontrivial optical properties, such as lopsided diffraction, single-order diffraction, asymmetric Dammam-like diffraction, etc. Our work will benefit the development of versatile non-Hermitian/asymmetric optical devices.

quant-ph↗

arXiv2022

Near-real-time estimates of daily CO2 emissions from 1500 cities worldwide

Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO2) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions. Carbon Monitor Cities provides daily, city-level estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP) were performed, and we estimate the overall uncertainty to be 21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries. A more complete description of this dataset is published in Scientific Data (https://doi.org/10.1038/s41597-022-01657-z).

physics.soc-ph↗

arXiv2021

Global Gridded Daily CO$_2$ Emissions

Precise and high-resolution carbon dioxide (CO$_2$) emission data is of great importance of achieving the carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO$_2$ Emission Datasets (called GRACED) from fossil fuel and cement production with a global spatial-resolution of 0.1$^\circ$ by 0.1$^\circ$ and a temporal-resolution of 1-day. Gridded fossil emissions are computed for different sectors based on the daily national CO$_2$ emissions from near real time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Carbon Grid (GID), Emission Database for Global Atmospheric Research (EDGAR) and spatiotemporal patters of satellite nitrogen dioxide (NO$_2$) retrievals. Our study on the global CO$_2$ emissions responds to the growing and urgent need for high-quality, fine-grained near-real-time CO2 emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors between 2019 and 2020. This help us to give insights on the relative contributions of various sectors and provides a fast and fine-grained overview of where and when fossil CO$_2$ emissions have decreased and rebounded in response to emergencies (e.g. COVID-19) and other disturbances of human activities than any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will allow policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt.

physics.ao-ph↗

arXiv2021

Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising

In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue. Traditional auction mechanisms, such as GSP and VCG auctions, can be suboptimal due to their fixed allocation rules to optimize a single performance metric (e.g., revenue or social welfare). Recently, data-driven auctions, learned directly from auction outcomes to optimize multiple performance metrics, have attracted increasing research interests. However, the procedure of auction mechanisms involves various discrete calculation operations, making it challenging to be compatible with continuous optimization pipelines in machine learning. In this paper, we design \underline{D}eep \underline{N}eural \underline{A}uctions (DNAs) to enable end-to-end auction learning by proposing a differentiable model to relax the discrete sorting operation, a key component in auctions. We optimize the performance metrics by developing deep models to efficiently extract contexts from auctions, providing rich features for auction design. We further integrate the game theoretical conditions within the model design, to guarantee the stability of the auctions. DNAs have been successfully deployed in the e-commerce advertising system at Taobao. Experimental evaluation results on both large-scale data set as well as online A/B test demonstrated that DNAs significantly outperformed other mechanisms widely adopted in industry.

cs.GT↗

arXiv2020

Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics

Poisoning attacks on Reinforcement Learning (RL) systems could take advantage of RL algorithm's vulnerabilities and cause failure of the learning. However, prior works on poisoning RL usually either unrealistically assume the attacker knows the underlying Markov Decision Process (MDP), or directly apply the poisoning methods in supervised learning to RL. In this work, we build a generic poisoning framework for online RL via a comprehensive investigation of heterogeneous poisoning models in RL. Without any prior knowledge of the MDP, we propose a strategic poisoning algorithm called Vulnerability-Aware Adversarial Critic Poison (VA2C-P), which works for most policy-based deep RL agents, closing the gap that no poisoning method exists for policy-based RL agents. VA2C-P uses a novel metric, stability radius in RL, that measures the vulnerability of RL algorithms. Experiments on multiple deep RL agents and multiple environments show that our poisoning algorithm successfully prevents agents from learning a good policy or teaches the agents to converge to a target policy, with a limited attacking budget.

cs.LG↗

arXiv2020

Building Interpretable Interaction Trees for Deep NLP Models

This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing. We construct a tree to encode salient interactions extracted by the DNN. Six metrics are proposed to analyze properties of interactions between constituents in a sentence. The interaction is defined based on Shapley values of words, which are considered as an unbiased estimation of word contributions to the network prediction. Our method is used to quantify word interactions encoded inside the BERT, ELMo, LSTM, CNN, and Transformer networks. Experimental results have provided a new perspective to understand these DNNs, and have demonstrated the effectiveness of our method.

cs.CL↗