arXiv subjects

Tong Li

Publications and source records attributed to Tong Li.

At least 163 records · Page 9Linked to original sources

arXiv2023

A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities

Cities play an important role in achieving sustainable development goals (SDGs) to promote economic growth and meet social needs. Especially satellite imagery is a potential data source for studying sustainable urban development. However, a comprehensive dataset in the United States (U.S.) covering multiple cities, multiple years, multiple scales, and multiple indicators for SDG monitoring is lacking. To support the research on SDGs in U.S. cities, we develop a satellite imagery dataset using deep learning models for five SDGs containing 25 sustainable development indicators. The proposed dataset covers the 100 most populated U.S. cities and corresponding Census Block Groups from 2014 to 2023. Specifically, we collect satellite imagery and identify objects with state-of-the-art object detection and semantic segmentation models to observe cities' bird's-eye view. We further gather population, nighttime light, survey, and built environment data to depict SDGs regarding poverty, health, education, inequality, and living environment. We anticipate the dataset to help urban policymakers and researchers to advance SDGs-related studies, especially applying satellite imagery to monitor long-term and multi-scale SDGs in cities.

cs.CV↗

arXiv2023

Carbon emissions and sustainability of launching 5G mobile networks in China

Since 2021, China has deployed more than 2.1 million 5G base stations to increase the network capacity and provide ubiquitous digital connectivity for mobile terminals. However, the launch of 5G networks also exacerbates the misalignment between cellular traffic and energy consumption, which reduces carbon efficiency - the amount of network traffic that can be delivered for each unit of carbon emission. In this study, we develop a large-scale data-driven framework to estimate the carbon emissions induced by mobile networks. We show that the decline in carbon efficiency leads to a carbon efficiency trap, estimated to cause additional carbon emissions of 23.82 +- 1.07 megatons in China. To mitigate the misalignment and improve energy efficiency, we propose DeepEnergy, an energy-saving method leveraging collaborative deep reinforcement learning and graph neural networks. DeepEnergy models complex collaboration among cells, making it possible to effectively coordinate the working state of tens of thousands of cells, which could help over 71% of Chinese provinces avoid carbon efficiency traps. In addition, applying DeepEnergy is estimated to reduce 20.90 +- 0.98 megatons of carbon emissions at the national level in 2023. We further assess the effects of adopting renewable energy and discover that the mobile network could accomplish more than 50% of its net-zero goal by integrating DeepEnergy and solar energy systems. Our study provides insight into carbon emission mitigation in 5G network infrastructure launching in China and overworld, paving the way towards achieving sustainable development goals and future net-zero mobile networks.

eess.SY↗

arXiv2023

A Sequence-to-Sequence&Set Model for Text-to-Table Generation

Recently, the text-to-table generation task has attracted increasing attention due to its wide applications. In this aspect, the dominant model formalizes this task as a sequence-to-sequence generation task and serializes each table into a token sequence during training by concatenating all rows in a top-down order. However, it suffers from two serious defects: 1) the predefined order introduces a wrong bias during training, which highly penalizes shifts in the order between rows; 2) the error propagation problem becomes serious when the model outputs a long token sequence. In this paper, we first conduct a preliminary study to demonstrate the generation of most rows is order-insensitive. Furthermore, we propose a novel sequence-to-sequence&set text-to-table generation model. Specifically, in addition to a text encoder encoding the input text, our model is equipped with a table header generator to first output a table header, i.e., the first row of the table, in the manner of sequence generation. Then we use a table body generator with learnable row embeddings and column embeddings to generate a set of table body rows in parallel. Particularly, to deal with the issue that there is no correspondence between each generated table body row and target during training, we propose a target assignment strategy based on the bipartite matching between the first cells of generated table body rows and targets. Experiment results show that our model significantly surpasses the baselines, achieving state-of-the-art performance on commonly-used datasets.

cs.CL↗

arXiv2023

An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments

As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community

cs.RO↗

arXiv2023

FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection

Social bot detection is of paramount importance to the resilience and security of online social platforms. The state-of-the-art detection models are siloed and have largely overlooked a variety of data characteristics from multiple cross-lingual platforms. Meanwhile, the heterogeneity of data distribution and model architecture makes it intricate to devise an efficient cross-platform and cross-model detection framework. In this paper, we propose FedACK, a new federated adversarial contrastive knowledge distillation framework for social bot detection. We devise a GAN-based federated knowledge distillation mechanism for efficiently transferring knowledge of data distribution among clients. In particular, a global generator is used to extract the knowledge of global data distribution and distill it into each client's local model. We leverage local discriminator to enable customized model design and use local generator for data enhancement with hard-to-decide samples. Local training is conducted as multi-stage adversarial and contrastive learning to enable consistent feature spaces among clients and to constrain the optimization direction of local models, reducing the divergences between local and global models. Experiments demonstrate that FedACK outperforms the state-of-the-art approaches in terms of accuracy, communication efficiency, and feature space consistency.

cs.LG↗

arXiv2023

Solutions to axion electromagnetodynamics and new search strategies of sub-$μ$eV axion

The Witten effect implies the electromagnetic interactions between axions and magnetic monopoles. Based on the quantum electromagnetodynamics, a generic low-energy axion-photon effective field theory was built by introducing two four-potentials ($A^μ$ and $B^μ$) to describe a photon. More anomalous axion-photon interactions and couplings ($g_{aAA}$, $g_{aBB}$ and $g_{aAB}$) arise in contrary to the ordinary axion coupling $g_{aγγ}aF^{μν}\tilde{F}_{μν}$. As a consequence, the conventional axion Maxwell equations are further modified. We properly solve the new axion-modified Maxwell equations and obtain the axion-induced electromagnetic fields given a static electric or magnetic field. It turns out that the dominant couplings $g_{aAB}$ and $g_{aBB}$ can be probed in the presence of external magnetic field and electric field, respectively. The induced oscillating magnetic fields are always suppressed compared with the electric fields for the axions with large Compton wavelengths. This is contrary to the situation in conventional experiments searching for the oscillating magnetic fields induced by sub-$μ$eV axions. Thus, we propose new strategies to measure the new couplings for sub-$μ$eV axion in haloscope experiments.

hep-ph↗

arXiv2023

Revealing the origin of neutrino masses through the Type II Seesaw mechanism at high-energy muon colliders

The future muon collider can play as an ideal machine to search for new physics at high energies. In this work, we study the search potential of the heavy Higgs triplet in the Type II Seesaw mechanism at muon colliders with high collision energy and high luminosity. The latest neutrino oscillation data are taken into account for realizing the leptonic decay modes of the charged Higgs bosons $(H^{\pm\pm},~H^{\pm})$ in the Type II Seesaw. We show the impact of neutrino mass and mixing parameters on the purely leptonic decays. The pair production of doubly charged Higgs $H^{++}H^{--}$ is through direct $μ^+μ^-$ annihilation and vector boson fusion (VBF) processes at muon collider. The associated production $H^{\pm\pm}H^{\mp}$ can only be induced by VBF processes. We simulate both the purely leptonic and bosonic signal channels of charged Higgs bosons in Type II Seesaw, together with the Standard Model backgrounds. We show the required luminosity for the discovery of the charged Higgses and the reachable limits on the leptonic decay branching fractions.

hep-ph↗

arXiv2023

Evaluating Policies Early in a Pandemic: Bounding Policy Effects with Nonrandomly Missing Data

During the early part of the Covid-19 pandemic, national and local governments introduced a number of policies to combat the spread of Covid-19. In this paper, we propose a new approach to bound the effects of such early-pandemic policies on Covid-19 cases and other outcomes while dealing with complications arising from (i) limited availability of Covid-19 tests, (ii) differential availability of Covid-19 tests across locations, and (iii) eligibility requirements for individuals to be tested. We use our approach study the effects of Tennessee's expansion of Covid-19 testing early in the pandemic and find that the policy decreased Covid-19 cases.

econ.EM↗

arXiv2023

Connectivity based Real-Time fMRI Neurofeedback Training in Youth with a History of Major Depressive Disorder

Background: Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) has proven to be a powerful technique to help subjects to gauge and enhance emotional control. Traditionally, rtfMRI-nf has focused on emotional regulation through self-regulation of amygdala. Recently, rtfMRI studies have observed that regulation of a target brain region is accompanied by connectivity changes beyond the target region. Therefore, the aim of present study is to investigate the use of connectivity between amygdala and prefrontal regions as the target of neurofeedback training in healthy individuals and subjects with a life-time history of major depressive disorder (MDD) performing an emotion regulation task. Method: Ten remitted MDD subjects and twelve healthy controls (HC) performed an emotion regulation task in 4 runs of rtfMRI-nf training followed by one transfer run without neurofeedback conducted in a single session. The functional connectivity between amygdala and prefrontal cortex was presented as a feedback bar concurrent with the emotion regulation task. Participants' emotional state was measured by the Positive and Negative Affect Schedule (PANAS) prior to and following the rtfMRI-nf. Psychological assessments were used to determine subjects' history of depression. Results: Participants with a history of MDD showed a trend of decreasing functional connectivity across the four rtfMRI-nf runs, and there was a marginally significant interaction between the MDD history and number of training runs. The HC group showed a significant increase of frontal cortex activation between the second and third neurofeedback runs. Comparing PANAS scores before and after connectivity-based rtfMRI-nf, we observed a significant decrease in negative PANAS score in the whole group overall, and a significant decrease in positive PANAS score in the MDD group alone.

q-bio.NC↗

arXiv2023

Towards simultaneous coherent radiation in the visible and microwave bands with doped molecular crystals

Coherent sources exploiting the stimulated emission of non-equilibrium quantum systems, i.e. gain media, have proven indispensable for advancing fundamental research and engineering. The operating electromagnetic bands of such coherent sources have been continuously enriched for increasing demands.Nevertheless, for a single bench top coherent source, simultaneous generation of radiation in multiple bands, especially when the bands are widely separated, present formidable challenges with a single gain medium. Here, we propose a mechanism of simultaneously realizing the stimulated emission of radiation in the visible and microwave bands, i.e. lasing and masing actions, at ambient conditions by utilizing photoexcited singlet and triplet states of the pentacene molecules that are doped in p-terphenyl. The possibility is validated by the observed amplified spontaneous emission (ASE) at 645 nm with a narrow linewidth around 1 nm from the pentacene-doped p-terphenyl crystal used for masing at 1.45 GHz and consolidated by a 20 fold lower threshold of ASE compared to the reported masing threshold. The overall threshold of the pentacene-based multiband coherent source can be optimized by appropriate alignment of the pump-light polarization with the pentacene's transition dipole moment. Our work not only shows a great promise on immediate realization of multiband coherent sources but also establishes an intriguing solid-state platform for fundamental research of quantum optics in multiple frequency domains.

physics.optics↗

arXiv2022

Indirect constraints on lepton-flavour-violating quarkonium decays

Within an effective-field-theory framework, we present a model-independent analysis of the potential of discovering new physics by searching for lepton flavour violation in heavy quarkonium decays and, more in general, we study the phenomenology of lepton-flavour-violating (LFV) 2 quark - 2 lepton ($2q2\ell$) operators with two charm or bottom fields. We compute the constraints from LFV muon and tau decays on the new-physics operators that can induce LFV processes involving $c\bar c$ and $b\bar b$ systems, thus providing a comprehensive list of indirect upper limits on processes such as $J/ψ\to \ell\ell^\prime$, $Υ(nS) \to \ell\ell^\prime$, $Υ(nS) \to \ell\ell^\prime γ$ etc., which can be sought at BESIII, Belle II, and the proposed super tau-charm factory. We show that such indirect constraints are so stringent that they prevent the detection of quarkonium decays into $eμ$. In the case of decays of quarkonia into $\ellτ$ ($\ell=e,μ$), we find that an improvement by 2-3 orders of magnitude on the current sensitivities is in general required in order to discover or further constrain new physics. However, we show that cancellations among different contributions to the LFV tau decay rates are possible, such that $Υ(nS)\to \ellτ$ can saturate the present experimental bounds. We also find that, interestingly, searches for LFV $Z$ decays, $Z\to\ellτ$, at future $e^+e^-$ colliders are complementary probes of $2q2\ell$ operators with third generation quarks.

hep-ph↗

arXiv2022

UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression

Geometry problem solving is a well-recognized testbed for evaluating the high-level multi-modal reasoning capability of deep models. In most existing works, two main geometry problems: calculation and proving, are usually treated as two specific tasks, hindering a deep model to unify its reasoning capability on multiple math tasks. However, in essence, these two tasks have similar problem representations and overlapped math knowledge which can improve the understanding and reasoning ability of a deep model on both two tasks. Therefore, we construct a large-scale Unified Geometry problem benchmark, UniGeo, which contains 4,998 calculation problems and 9,543 proving problems. Each proving problem is annotated with a multi-step proof with reasons and mathematical expressions. The proof can be easily reformulated as a proving sequence that shares the same formats with the annotated program sequence for calculation problems. Naturally, we also present a unified multi-task Geometric Transformer framework, Geoformer, to tackle calculation and proving problems simultaneously in the form of sequence generation, which finally shows the reasoning ability can be improved on both two tasks by unifying formulation. Furthermore, we propose a Mathematical Expression Pretraining (MEP) method that aims to predict the mathematical expressions in the problem solution, thus improving the Geoformer model. Experiments on the UniGeo demonstrate that our proposed Geoformer obtains state-of-the-art performance by outperforming task-specific model NGS with over 5.6% and 3.2% accuracies on calculation and proving problems, respectively.

cs.AI↗

arXiv2022

Axion-like particles from primordial black hole evaporation and their detection in neutrino experiments

The primordial black holes (PBHs) play as a novel source to radiate light elementary particles of energies in the region of a few hundred MeV. We explore the possibility that the axion-like particles (ALPs) with mass less than 1 MeV are produced from PBH evaporation. The absorption of light ALPs in the underground detector targets then induces energetic photoelectron signatures in current and future neutrino experiments. Utilizing the PBH ALP event rate, we place general exclusion limits on the axion couplings at Super-K and Hyper-K. We also translate these limits into the upper bound on the fraction of DM composed of PBHs $f_{\rm PBH}$.

hep-ph↗

arXiv2022

Policy Evaluation during a Pandemic

National and local governments have implemented a large number of policies in response to the Covid-19 pandemic. Evaluating the effects of these policies, both on the number of Covid-19 cases as well as on other economic outcomes is a key ingredient for policymakers to be able to determine which policies are most effective as well as the relative costs and benefits of particular policies. In this paper, we consider the relative merits of common identification strategies that exploit variation in the timing of policies across different locations by checking whether the identification strategies are compatible with leading epidemic models in the epidemiology literature. We argue that unconfoundedness type approaches, that condition on the pre-treatment "state" of the pandemic, are likely to be more useful for evaluating policies than difference-in-differences type approaches due to the highly nonlinear spread of cases during a pandemic. For difference-in-differences, we further show that a version of this problem continues to exist even when one is interested in understanding the effect of a policy on other economic outcomes when those outcomes also depend on the number of Covid-19 cases. We propose alternative approaches that are able to circumvent these issues. We apply our proposed approach to study the effect of state level shelter-in-place orders early in the pandemic.

econ.EM↗

arXiv2022

Point Cloud Scene Completion with Joint Color and Semantic Estimation from Single RGB-D Image

We present a deep reinforcement learning method of progressive view inpainting for colored semantic point cloud scene completion under volume guidance, achieving high-quality scene reconstruction from only a single RGB-D image with severe occlusion. Our approach is end-to-end, consisting of three modules: 3D scene volume reconstruction, 2D RGB-D and segmentation image inpainting, and multi-view selection for completion. Given a single RGB-D image, our method first predicts its semantic segmentation map and goes through the 3D volume branch to obtain a volumetric scene reconstruction as a guide to the next view inpainting step, which attempts to make up the missing information; the third step involves projecting the volume under the same view of the input, concatenating them to complete the current view RGB-D and segmentation map, and integrating all RGB-D and segmentation maps into the point cloud. Since the occluded areas are unavailable, we resort to a A3C network to glance around and pick the next best view for large hole completion progressively until a scene is adequately reconstructed while guaranteeing validity. All steps are learned jointly to achieve robust and consistent results. We perform qualitative and quantitative evaluations with extensive experiments on the 3D-FUTURE data, obtaining better results than state-of-the-arts.

cs.CV↗

arXiv2022

Probing heavy triplet leptons of the type-III seesaw mechanism at future muon colliders

We study the search potential of heavy triplet leptons in Type III Seesaw mechanism at future high-energy and high-luminosity muon colliders. The impact of up-to-date neutrino oscillation results is taken into account on neutrino parameters and the consequent decay patterns of heavy leptons in Type III Seesaw. The three heavy leptons in the Casas-Ibarra parametrization with diagonal unity matrix are distinguishable in their decay modes. We consider the pair production of charged triplet leptons $E^+ E^-$ through both $μ^+μ^-$ annihilation and vector boson fusion (VBF) processes. The leading-order framework of electroweak parton distribution functions are utilized to calculate the VBF cross sections. The charged triplet leptons can be probed for masses above $\sim 1$ TeV. The $E^+E^-\to ZZ\ell^+\ell^-$ channel can be further utilized to fully reconstruct the three triplet leptons and distinguish neutrino mass patterns. The pair production of heavy neutrinos $N$ and the associated production $E^\pm N$ are only induced by VBF processes and lead to lepton-number-violating (LNV) signature. We also study the search potential of LNV processes at future high-energy muon collider with $\sqrt{s}=30$ TeV.

hep-ph↗

arXiv2022

Electron-target experiment constraints on light dark matter produced in primordial black hole evaporation

Light sub-GeV dark matter (DM) particles in the Milky Way or macroscopic objects such as primordial black holes (PBHs) become attractive DM candidates due to null results of WIMP from direct detection experiments. We explore the possibility in which the present PBHs play as a novel source to produce light boosted DM and confine light PBHs with current and future terrestrial facilities. We study the electron elastic scattering data and obtain the current constraints from Super-Kamiokande and XENON1T on the boosted DM from PBH evaporation. The prospective bounds on the sub-GeV DM-electron scattering cross section and the fraction of DM composed of PBHs $f_{\rm PBH}$ are also imposed for future Xenon experiments.

hep-ph↗

arXiv2022

DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction

Spatiotemporal activity prediction, aiming to predict user activities at a specific location and time, is crucial for applications like urban planning and mobile advertising. Existing solutions based on tensor decomposition or graph embedding suffer from the following two major limitations: 1) ignoring the fine-grained similarities of user preferences; 2) user's modeling is entangled. In this work, we propose a hypergraph neural network model called DisenHCN to bridge the above gaps. In particular, we first unify the fine-grained user similarity and the complex matching between user preferences and spatiotemporal activity into a heterogeneous hypergraph. We then disentangle the user representations into different aspects (location-aware, time-aware, and activity-aware) and aggregate corresponding aspect's features on the constructed hypergraph, capturing high-order relations from different aspects and disentangles the impact of each aspect for final prediction. Extensive experiments show that our DisenHCN outperforms the state-of-the-art methods by 14.23% to 18.10% on four real-world datasets. Further studies also convincingly verify the rationality of each component in our DisenHCN.

cs.LG↗