arXiv subjects

Yilong Wang

Publications and source records attributed to Yilong Wang.

At least 55 records · Page 3Linked to original sources

arXiv2024

Alterfold Theory and Topological Modular Invariance

We propose a topological paradigm in alterfold topological quantum field theory to explore various concepts, including modular invariants, $α$-induction and connections in Morita contexts within a modular fusion category of non-zero global dimension over an arbitrary field. Using our topological perspective, we provide streamlined quick proofs and broad generalizations of a wide range of results. These include all theoretical findings by Böckenhauer, Evans, and Kawahigashi on $α$-induction. Additionally, we introduce the concept of double $α$-induction for pairs of Morita contexts and define its higher-genus $Z$-transformation, which remains invariant under the action of the mapping class group. Finally, we establish a novel integral identity for modular invariance across multiple Morita contexts, unifying several known identities as special cases.

math.QA↗

arXiv2024

Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints

This study pioneers the application of Recursive Feature Machines (RFM) in QSPR modeling, introducing a tailored feature importance analysis approach to enhance interpretability. By leveraging deep feature learning through AGOP, RFM achieves state-of-the-art (SOTA) results in predicting molecular properties, as demonstrated through solubility prediction across nine benchmark datasets. To capture a wide array of structural information, we employ diverse molecular representations, including MACCS keys, Morgan fingerprints, and a custom multi-scale hybrid fingerprint (HF) derived from global descriptors and SMILES local fragmentation techniques. Notably, the HF offers significant advantages over MACCS and Morgan fingerprints in revealing structural determinants of molecular properties. The feature importance analysis in RFM provides robust local and global explanations, effectively identifying structural features that drive molecular behavior and offering valuable insights for drug development. Additionally, RFM demonstrates strong redundancy-filtering abilities, as model performance remains stable even after removing redundant features within custom fingerprints. Importantly, RFM introduces the deep feature learning capabilities of the average gradient outer product (AGOP) matrix into ultra-fast kernel machine learning, to imbue kernel machines with interpretable deep feature learning capabilities. We extend this approach beyond the Laplace Kernel to the Matern, Rational Quadratic, and Gaussian kernels, to find that the Matern and Laplace kernels deliver the best performance, thus reinforcing the flexibility and effectiveness of AGOP in RFM. Experimental results show that RFM-HF surpasses both traditional machine learning models and advanced graph neural networks.

q-bio.BM↗

arXiv2023

Alterfold Topological Quantum Field Theory

We introduce the 3-alterfold topological quantum field theory (TQFT) by extending the quantum invariant of 3-alterfolds. The bases of the TQFT are explicitly characterized and the Levin-Wen model is naturally interpreted in 3-alterfold TQFT bases. By naturally considering the RT TQFT and TV TQFT as sub-TQFTs within the 3-alterfold TQFT, we establish their equivalence. The 3-alterfold TQFT is unitary when the input fusion category is unitary. Additionally, we extend the 3-alterfold TQFT to the Morita context and demonstrate that Morita equivalent fusion categories yield equivalent TV TQFTs. We also provide a simple pictorial proof of complete positivity criteria for unitary categorization when the 3-alterfold TQFT is unitary. Expanding our scope to high-genus surfaces by replacing the torus, we introduce the high genus topological indicators and proving the equivariance under the mapping class group actions.

math-ph↗

arXiv2023

Accretion flows in the hard state of black hole X-ray binaries: the effect of hot gas condensation

It is commonly believed that accretion discs are truncated and their inner regions are described by advection dominated accretion flows (ADAFs) in the hard spectral state of black hole X-ray binaries. However, the increasing occurrence of a relativistically blurred Fe K$α$ line together with a hard continuum points to the existence of a thin disc located near the innermost stable circular orbit (ISCO). Assuming the accretion in the hard state is via an ADAF extending to near 100 Schwarzschild radii, which is supplied by either a stellar wind from a companion star or resulting from an evaporated disc, we study the possible condensation of the hot gas during its accretion towards the black hole. It is found that a small fraction of the ADAF condenses into a cold disc as a consequence of efficient radiative cooling at small distances, forming a disc-corona configuration near the ISCO. This takes place for low accretion rates corresponding to luminosities ranging from $\sim 10^{-3}$ to a few per cent of the Eddington luminosity. The coexistence of the weak inner disc and the dominant hot accretion flow provides a natural explanation of the broad K$α$ line in the hard state. Detailed computations demonstrate that such accretion flows produce a hard X-ray spectrum accompanied by a weak disc component with a negative correlation between the 2-10 keV photon index and the Eddington ratio. The predicted spectrum of Cygnus X-1 and the correlation between the photon index and the Eddington ratio are in good agreement with observations.

astro-ph.HE↗

arXiv2023

3-Alterfolds and Quantum Invariants

In this paper, we introduce the concept of 3-alterfolds with embedded separating surfaces. When the separating surface is decorated by a spherical fusion category, we obtain quantum invariants of 3-alterfold, which is consistent with many topological moves. These moves provide evaluation algorithms for various presentations of 3-alterfold, e.g. Heegaard splittings, triangulations, link surgeries. In particular, we obtain quantum invariants of 3-manifolds containing surfaces, generalizing those of 3-manifolds containing framed links. Moreover, in this framework, we topologize fundamental algebraic concepts. For instance, we implement the Drinfeld center by tube diagrams as a blow up of framed links. The topologized center leads to a quick proof of the equality between the Reshetikhin-Turaev invariants and the Turaev-Viro invariants for spherical fusion categories. In addition, we topologize half-braiding, $S$-matrix and the generalized Frobenius-Schur indicators, etc. In particular, the latter leads to a quick topological proof of the equivariance of the generalized Frobenius-Schur indicators.

math.QA↗

arXiv2023

Adversarial Laser Spot: Robust and Covert Physical-World Attack to DNNs

Most existing deep neural networks (DNNs) are easily disturbed by slight noise. However, there are few researches on physical attacks by deploying lighting equipment. The light-based physical attacks has excellent covertness, which brings great security risks to many vision-based applications (such as self-driving). Therefore, we propose a light-based physical attack, called adversarial laser spot (AdvLS), which optimizes the physical parameters of laser spots through genetic algorithm to perform physical attacks. It realizes robust and covert physical attack by using low-cost laser equipment. As far as we know, AdvLS is the first light-based physical attack that perform physical attacks in the daytime. A large number of experiments in the digital and physical environments show that AdvLS has excellent robustness and covertness. In addition, through in-depth analysis of the experimental data, we find that the adversarial perturbations generated by AdvLS have superior adversarial attack migration. The experimental results show that AdvLS impose serious interference to advanced DNNs, we call for the attention of the proposed AdvLS. The code of AdvLS is available at: https://github.com/ChengYinHu/AdvLS

cs.CV↗

arXiv2022

Handling errors in four-dimensional variational data assimilation by balancing the degrees of freedom and the model constraints: A new approach

For many years, strongly and weakly constrained approaches were the only options to deal with errors in four-dimensional variational data assimilation (4DVar), with the aim of balancing the degrees of freedom and model constraints. Strong model constraints were imposed to reduce the degrees of freedom encountered when optimizing the strongly constrained 4DVar problem, and it was assumed that the models were perfect. The weakly constrained approach sought to distinguish initial errors from model errors, and to correct them separately using weak model constraints. Our proposed i4DVar* method exploits the hidden mechanism that corrects initial and model errors simultaneously in the strongly constrained 4DVar. The i4DVar* method divides the assimilation window into several sub-windows, each of which has a unique integral and flow-dependent correction term to simultaneously handle the initial and model errors over a relatively short period. To overcome the high degrees of freedom of the weakly constrained 4DVar, for the first time we use ensemble simulations not only to solve the 4DVar optimization problem, but also to formulate this method. Thus, the i4DVar* problem is solvable even if there are many degrees of freedom. We experimentally show that i4DVar* provides superior performance with much lower computational costs than existing methods, and is simple to implement.

physics.ao-ph↗

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

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↗

arXiv2022

Higher central charges and Witt groups

In this paper, we introduce the definitions of signatures of braided fusion categories, which are proved to be invariants of their Witt equivalence classes. These signature assignments define group homomorphisms on the Witt group. The higher central charges of pseudounitary modular categories can be expressed in terms of these signatures, which are applied to prove that the Ising modular categories have infinitely many square roots in the Witt group. This result is further applied to prove a conjecture of Davydov-Nikshych-Ostrik on the super-Witt group: the torsion subgroup generated by the completely anisotropic s-simple braided fusion categories has infinite rank.

math.QA↗

arXiv2022

On symmetric representations of $\text{SL}_2(\mathbb{Z})$

We introduce the notions of symmetric and symmetrizable representations of $\text{SL}_2(\mathbb{Z})$. The linear representations of $\text{SL}_2(\mathbb{Z})$ arising from modular tensor categories are symmetric and have congruence kernel. Conversely, one may also reconstruct modular data from finite-dimensional symmetric, congruence representations of $\text{SL}_2(\mathbb{Z})$. By investigating a $\mathbb{Z}/2\mathbb{Z}$-symmetry of some Weil representations at prime power levels, we prove that all finite-dimensional congruence representations of $\text{SL}_2(\mathbb{Z})$ are symmetrizable. We also provide examples of unsymmetrizable noncongruence representations of $\text{SL}_2(\mathbb{Z})$ that are subrepresentations of a symmetric one.

math.QA↗

arXiv2022

Image Fragile Watermarking Algorithm Based on Deneighborhood Mapping

To address the security risk caused by fixed offset mapping and the limited recoverability of random mapping used in image watermarking, we propose an image self-embedding fragile watermarking algorithm based on deneighborhood mapping. First, the image is divided into several 2*2 blocks, and authentication watermark and recovery watermark are generated based on the average value of the image blocks. Then, the denighborhood mapping is implemented as, for each image block, its mapping block is randomly selected outside it's neighborhood whose size is specified by a parameter. Finally, the authentication watermark and the recovery watermark are embedded in the image block itself and its corresponding mapping block. Theoretical analysis indicates that in the case of continuous region tampering, the proposed watermarking method can achieve better the recovery rate of the tampered image block than the method based on the random mapping. The experimental results verify the rationality and effectiveness of the theoretical analysis. Moreover, compared with the existing embedding algorithms based on random mapping, chaos mapping and Arnold mapping, in the case of continuous region tampering, the average recovery rate of the tampered region achieved by the proposed algorithm is higher.

cs.CR↗

arXiv2022

The Witt classes of $SO(2r)_{2r}$

We study the Witt classes of the modular categories $SO(2r)_{2r}$ associated with quantum groups of type $D_r$ at $4r-2$th roots of unity. From these classes we derive infinitely many Witt classes of order 2 that are linearly independent modulo the subgroup generated by the pointed modular categories. In particular we produce an example of a simple, completely anisotropic modular category that is not pointed whose Witt class has order 2, answering a question of Davydov, Müger, Nikshych and Ostrik. Our results show that the trivial Witt class $[Vec]$ has infinitely many square roots modulo the pointed classes, in analogy with the recent construction of infinitely many square roots of the Ising Witt classes modulo the pointed classes constructed in a similar way from certain type $B_r$ modular categories. We compare the subgroups generated by the Ising square roots and $[Vec]$ square roots and provide evidence that they also generate linearly independent subgroups.

math.QA↗

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

Modular categories with transitive Galois actions

In this paper, we study modular categories whose Galois group actions on their simple objects are transitive. We show that such modular categories admit unique factorization into prime transitive factors. The representations of $SL_2(\mathbb{Z})$ associated with transitive modular categories are proven to be minimal and irreducible. Together with the Verlinde formula, we characterize prime transitive modular categories as the Galois conjugates of the adjoint subcategory of the quantum group modular category $\mathcal{C}(\mathfrak{sl}_2,p-2)$ for some prime $p > 3$. As a consequence, we completely classify transitive modular categories. Transitivity of super-modular categories can be similarly defined. A unique factorization of any transitive super-modular category into s-simple transitive factors is obtained, and the split transitive super-modular categories are completely classified.

math.QA↗

arXiv2021

Impact of lockdowns and winter temperatures on natural gas consumption in Europe

As the COVID-19 virus spread over the world, governments restricted mobility to slow transmission. Public health measures had different intensities across European countries but all had significant impact on peoples daily lives and economic activities, causing a drop of CO2 emissions of about 10% for the whole year 2020. Here, we analyze changes in natural gas use in the industry and built environment sectors during the first half of year 2020 with daily gas flows data from pipeline and storage facilities in Europe. We find that reductions of industrial gas use reflect decreases in industrial production across most countries. Surprisingly, natural gas use in buildings also decreased despite most people being confined at home and cold spells in March 2020. Those reductions that we attribute to the impacts of COVID-19 remain of comparable magnitude to previous variations induced by cold or warm climate anomalies in the cold season. We conclude that climate variations played a larger role than COVID-19 induced stay-home orders in natural gas consumption across Europe.

physics.ao-ph↗

arXiv2021

Global Daily CO$_2$ emissions for the year 2020

The diurnal cycle CO$_2$ emissions from fossil fuel combustion and cement production reflect seasonality, weather conditions, working days, and more recently the impact of the COVID-19 pandemic. Here, for the first time we provide a daily CO$_2$ emission dataset for the whole year of 2020 calculated from inventory and near-real-time activity data (called Carbon Monitor project: https://carbonmonitor.org). It was previously suggested from preliminary estimates that did not cover the entire year of 2020 that the pandemics may have caused more than 8% annual decline of global CO$_2$ emissions. Here we show from detailed estimates of the full year data that the global reduction was only 5.4% (-1,901 MtCO$_2$, ). This decrease is 5 times larger than the annual emission drop at the peak of the 2008 Global Financial Crisis. However, global CO$_2$ emissions gradually recovered towards 2019 levels from late April with global partial re-opening. More importantly, global CO$_2$ emissions even increased slightly by +0.9% in December 2020 compared with 2019, indicating the trends of rebound of global emissions. Later waves of COVID-19 infections in late 2020 and corresponding lockdowns have caused further CO$_2$ emissions reductions particularly in western countries, but to a much smaller extent than the declines in the first wave. That even substantial world-wide lockdowns of activity led to a one-time decline in global CO$_2$ emissions of only 5.4% in one year highlights the significant challenges for climate change mitigation that we face in the post-COVID era. These declines are significant, but will be quickly overtaken with new emissions unless the COVID-19 crisis is utilized as a break-point with our fossil-fuel trajectory, notably through policies that make the COVID-19 recovery an opportunity to green national energy and development plans.

physics.ao-ph↗