BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Wei Zhu

Wei Zhu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.EPastro-ph.GAMachine Learningastro-ph.HEastro-ph.SRComputer VisionComputation and Languageastro-ph.IMhep-phq-fin.STApplicationscond-mat.mtrl-sciInformation Retrievalphysics.comp-phq-fin.RMArtificial Intelligenceastro-ph.COBiomoleculesDistributed, Parallel, and Cluster Computingecon.GNeess.IVhep-exmath.NAmath.PRMolecular NetworksMultimedianlin.PSnucl-exnucl-thNumerical Analysisphysics.chem-phphysics.flu-dynq-fin.EC

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
54works
0followers
33topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

54 published item(s)

preprint2026arXiv

Noise-Started One-Step Real-World Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement

Pre-trained text-to-image (T2I) diffusion models have shown strong potential for real-world image super-resolution (Real-ISR), owing to their noise-started generation process that enables realistic texture synthesis and captures the one-to-many nature of super-resolution. However, diffusion-based Real-ISR methods still face a fundamental efficiency-quality trade-off. Multi-step methods generate high-quality results by iteratively denoising random Gaussian noise under LR conditioning, but suffer from slow sampling. Recent one-step methods greatly improve efficiency, yet they typically replace noise-started generation with direct LR-to-HR restoration, which weakens stochasticity and limits realistic detail synthesis. To address this issue, we propose SMFSR, a noise-started one-step Real-ISR framework via LR-conditioned SplitMeanFlow and GAN refinement. SMFSR preserves the random-noise starting point of diffusion models and learns a direct noise-to-HR mapping conditioned on the LR image. To this end, Interval Splitting Consistency distills the multi-step generative trajectory into a single average-velocity prediction, enabling efficient one-step generation. To compensate for the reduced opportunity for progressive refinement, we further introduce a GAN refinement stage, where a DINOv3-based discriminator enhances realistic texture synthesis and variational score distillation aligns the generated outputs with the natural image distribution under a frozen diffusion teacher. Extensive experiments demonstrate that SMFSR achieves state-of-the-art perceptual quality among one-step diffusion-based Real-ISR methods while retaining fast single-step inference.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Efficient Multi-domain Text Recognition Deep Neural Network Parameterization with Residual Adapters

Recent advancements in deep neural networks have markedly enhanced the performance of computer vision tasks, yet the specialized nature of these networks often necessitates extensive data and high computational power. Addressing these requirements, this study presents a novel neural network model adept at optical character recognition (OCR) across diverse domains, leveraging the strengths of multi-task learning to improve efficiency and generalization. The model is designed to achieve rapid adaptation to new domains, maintain a compact size conducive to reduced computational resource demand, ensure high accuracy, retain knowledge from previous learning experiences, and allow for domain-specific performance improvements without the need to retrain entirely. Rigorous evaluation on open datasets has validated the model's ability to significantly lower the number of trainable parameters without sacrificing performance, indicating its potential as a scalable and adaptable solution in the field of computer vision, particularly for applications in optical text recognition.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Text2MDT: Extracting Medical Decision Trees from Medical Texts

Knowledge of the medical decision process, which can be modeled as medical decision trees (MDTs), is critical to build clinical decision support systems. However, the current MDT construction methods rely heavily on time-consuming and laborious manual annotation. In this work, we propose a novel task, Text2MDT, to explore the automatic extraction of MDTs from medical texts such as medical guidelines and textbooks. We normalize the form of the MDT and create an annotated Text-to-MDT dataset in Chinese with the participation of medical experts. We investigate two different methods for the Text2MDT tasks: (a) an end-to-end framework which only relies on a GPT style large language models (LLM) instruction tuning to generate all the node information and tree structures. (b) The pipeline framework which decomposes the Text2MDT task to three subtasks. Experiments on our Text2MDT dataset demonstrate that: (a) the end-to-end method basd on LLMs (7B parameters or larger) show promising results, and successfully outperform the pipeline methods. (b) The chain-of-thought (COT) prompting method \cite{Wei2022ChainOT} can improve the performance of the fine-tuned LLMs on the Text2MDT test set. (c) the lightweight pipelined method based on encoder-based pretrained models can perform comparably with LLMs with model complexity two magnititudes smaller. Our Text2MDT dataset is open-sourced at \url{https://tianchi.aliyun.com/dataset/95414}, and the source codes are open-sourced at \url{https://github.com/michael-wzhu/text2dt}.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Overview of the PromptCBLUE Shared Task in CHIP2023

This paper presents an overview of the PromptCBLUE shared task (http://cips-chip.org.cn/2023/eval1) held in the CHIP-2023 Conference. This shared task reformualtes the CBLUE benchmark, and provide a good testbed for Chinese open-domain or medical-domain large language models (LLMs) in general medical natural language processing. Two different tracks are held: (a) prompt tuning track, investigating the multitask prompt tuning of LLMs, (b) probing the in-context learning capabilities of open-sourced LLMs. Many teams from both the industry and academia participated in the shared tasks, and the top teams achieved amazing test results. This paper describes the tasks, the datasets, evaluation metrics, and the top systems for both tasks. Finally, the paper summarizes the techniques and results of the evaluation of the various approaches explored by the participating teams.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation

Early exiting allows instances to exit at different layers according to the estimation of difficulty. Previous works usually adopt heuristic metrics such as the entropy of internal outputs to measure instance difficulty, which suffers from generalization and threshold-tuning. In contrast, learning to exit, or learning to predict instance difficulty is a more appealing way. Though some effort has been devoted to employing such "learn-to-exit" modules, it is still unknown whether and how well the instance difficulty can be learned. As a response, we first conduct experiments on the learnability of instance difficulty, which demonstrates that modern neural models perform poorly on predicting instance difficulty. Based on this observation, we propose a simple-yet-effective Hash-based Early Exiting approach (HashEE) that replaces the learn-to-exit modules with hash functions to assign each token to a fixed exiting layer. Different from previous methods, HashEE requires no internal classifiers nor extra parameters, and therefore is more efficient. Experimental results on classification, regression, and generation tasks demonstrate that HashEE can achieve higher performance with fewer FLOPs and inference time compared with previous state-of-the-art early exiting methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor

The AlphaFold computer program predicted protein structures for the whole human genome, which has been considered as a remarkable breakthrough both in artificial intelligence (AI) application and structural biology. Despite the varying confidence level, these predicted structures still could significantly contribute to structure-based drug design of novel targets, especially the ones with no or limited structural information. In this work, we successfully applied AlphaFold in our end-to-end AI-powered drug discovery engines constituted of a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42, to identify a first-in-class hit molecule of a novel target without an experimental structure starting from target selection towards hit identification in a cost- and time-efficient manner. PandaOmics provided the targets of interest and Chemistry42 generated the molecules based on the AlphaFold predicted structure, and the selected molecules were synthesized and tested in biological assays. Through this approach, we identified a small molecule hit compound for CDK20 with a Kd value of 8.9 +/- 1.6 uM (n = 4) within 30 days from target selection and after only synthesizing 7 compounds. Based on the available data, the second round of AI-powered compound generation was conducted and through which, a more potent hit molecule, ISM042-2 048, was discovered with a Kd value of 210.0 +/- 42.4 nM (n = 2), within 30 days and after synthesizing 6 compounds from the discovery of the first hit ISM042-2-001. To the best of our knowledge, this is the first reported small molecule targeting CDK20 and more importantly, this work is the first demonstration of AlphaFold application in the hit identification process in early drug discovery.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Continuous Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-Tuning

Since open social platforms allow for a large and continuous flow of unverified information, rumors can emerge unexpectedly and spread quickly. However, existing rumor detection (RD) models often assume the same training and testing distributions and can not cope with the continuously changing social network environment. This paper proposed a Continual Prompt-Tuning RD (CPT-RD) framework, which avoids catastrophic forgetting (CF) of upstream tasks during sequential task learning and enables bidirectional knowledge transfer between domain tasks. Specifically, we propose the following strategies: (a) Our design explicitly decouples shared and domain-specific knowledge, thus reducing the interference among different domains during optimization; (b) Several technologies aim to transfer knowledge of upstream tasks to deal with emergencies; (c) A task-conditioned prompt-wise hypernetwork (TPHNet) is used to consolidate past domains. In addition, CPT-RD avoids CF without the necessity of a rehearsal buffer.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Deep Federated Anomaly Detection for Multivariate Time Series Data

Despite the fact that many anomaly detection approaches have been developed for multivariate time series data, limited effort has been made on federated settings in which multivariate time series data are heterogeneously distributed among different edge devices while data sharing is prohibited. In this paper, we investigate the problem of federated unsupervised anomaly detection and present a Federated Exemplar-based Deep Neural Network (Fed-ExDNN) to conduct anomaly detection for multivariate time series data on different edge devices. Specifically, we first design an Exemplar-based Deep Neural network (ExDNN) to learn local time series representations based on their compatibility with an exemplar module which consists of hidden parameters learned to capture varieties of normal patterns on each edge device. Next, a constrained clustering mechanism (FedCC) is employed on the centralized server to align and aggregate the parameters of different local exemplar modules to obtain a unified global exemplar module. Finally, the global exemplar module is deployed together with a shared feature encoder to each edge device and anomaly detection is conducted by examining the compatibility of testing data to the exemplar module. Fed-ExDNN captures local normal time series patterns with ExDNN and aggregates these patterns by FedCC, and thus can handle the heterogeneous data distributed over different edge devices simultaneously. Thoroughly empirical studies on six public datasets show that ExDNN and Fed-ExDNN can outperform state-of-the-art anomaly detection algorithms and federated learning techniques.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting

Chemistry research has both high material and computational costs to conduct experiments. Institutions thus consider chemical data to be valuable and there have been few efforts to construct large public datasets for machine learning. Another challenge is that different intuitions are interested in different classes of molecules, creating heterogeneous data that cannot be easily joined by conventional distributed training. In this work, we introduce federated heterogeneous molecular learning to address these challenges. Federated learning allows end-users to build a global model collaboratively while keeping the training data distributed over isolated clients. Due to the lack of related research, we first simulate a heterogeneous federated learning benchmark (FedChem) by jointly performing scaffold splitting and latent Dirichlet allocation on existing datasets for heterogeneously distributed client data. Our results on FedChem show that significant learning challenges arise when working with heterogeneous molecules across clients. We then propose a method to alleviate the problem, namely Federated Learning by Instance reweighTing (FLIT(+)). FLIT(+) can align the local training across heterogeneous clients by improving the performance for uncertain samples. Comprehensive experiments conducted on our new benchmark FedChem validate the advantages of this method over other federated learning schemes. FedChem should enable a new type of collaboration for improving AI in chemistry that mitigates concerns about valuable chemical data.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Fluid Simulation System Based on Graph Neural Network

Traditional computational fluid dynamics calculates the physical information of the flow field by solving partial differential equations, which takes a long time to calculate and consumes a lot of computational resources. We build a fluid simulation simulator based on the graph neural network architecture. The simulator has fast computing speed and low consumption of computing resources. We regard the computational domain as a structural graph, and the computational nodes in the structural graph determine neighbor nodes through adaptive sampling. Building deep learning architectures with attention graph neural networks. The fluid simulation simulator is trained according to the simulation results of the flow field around the cylinder with different Reynolds numbers. The trained fluid simulation simulator not only has a very high accuracy for the prediction of the flow field in the training set, but also can extrapolate the flow field outside the training set. Compared to traditional CFD solvers, the fluid simulation simulator achieves a speedup of 2-3 orders of magnitude. The fluid simulation simulator provides new ideas for the rapid optimization and design of fluid mechanics models and the real-time control of intelligent fluid mechanisms.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Identification of stellar-mass black hole binaries and the validity of linear orbital motion approximation in microlensing

Gravitational microlensing is unique in detecting binary black (BH) holes with wide (a few au) separations. Models predict that about $1\%$ of microlensing binaries should be due to binary BHs, and yet zero has been robustly identified. Using simulated events with binary BH lenses, we show that the microlensing parallax effect in a typical binary BH event cannot be reliably detected. Given the crucial role of the parallax parameter in determining the mass of dark microlenses, this may explain the non-detection of binary BHs. Additionally, we show that in only a small fraction ($\lesssim7\%$) of the simulated events the full orbital motion of the binary lens cannot be modeled with the linear orbital motion approximation. This approximation has been frequently used in modelings of binary microlensing events.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Improving Micro-video Recommendation by Controlling Position Bias

As the micro-video apps become popular, the numbers of micro-videos and users increase rapidly, which highlights the importance of micro-video recommendation. Although the micro-video recommendation can be naturally treated as the sequential recommendation, the previous sequential recommendation models do not fully consider the characteristics of micro-video apps, and in their inductive biases, the role of positions is not in accord with the reality in the micro-video scenario. Therefore, in the paper, we present a model named PDMRec (Position Decoupled Micro-video Recommendation). PDMRec applies separate self-attention modules to model micro-video information and the positional information and then aggregate them together, avoid the noisy correlations between micro-video semantics and positional information being encoded into the sequence embeddings. Moreover, PDMRec proposes contrastive learning strategies which closely match with the characteristics of the micro-video scenario, thus reducing the interference from micro-video positions in sequences. We conduct the extensive experiments on two real-world datasets. The experimental results shows that PDMRec outperforms existing multiple state-of-the-art models and achieves significant performance improvements.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

KMT-2021-BLG-0171Lb and KMT-2021-BLG-1689Lb: Two Microlensing Planets in the KMTNet High-cadence Fields with Followup Observations

Follow-up observations of high-magnification gravitational microlensing events can fully exploit their intrinsic sensitivity to detect extrasolar planets, especially those with small mass ratios. To make followup more uniform and efficient, we develop a system, HighMagFinder, based on the real-time data from the Korean Microlensing Telescope Network (KMTNet) to automatically alert possible ongoing high-magnification events. We started a new phase of follow-up observations with the help of HighMagFinder in 2021. Here we report the discovery of two planets in high-magnification microlensing events, KMT-2021-BLG-0171 and KMT-2021-BLG-1689, which were identified by the HighMagFinder. We find that both events suffer the ``central-resonant'' caustic degeneracy. The planet-host mass-ratio is $q\sim4.7\times10^{-5}$ or $q\sim 2.2\times10^{-5}$ for KMT-2021-BLG-0171, and $q\sim2.5\times10^{-4}$ or $q\sim 1.8\times10^{-4}$ for KMT-2021-BLG-1689. Together with two events reported by Ryu et al. (2022), four cases that suffer such degeneracy have been discovered in the 2021 season alone, indicating that the degenerate solutions may have been missed in some previous studies. We also propose a new factor for weighting the probability of each solution from the phase-space. The resonant interpretations for the two events are disfavored under this consideration. This factor can be included in future statistical studies to weight degenerate solutions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis

Visual sentiment analysis has received increasing attention in recent years. However, the dataset's quality is a concern because the sentiment labels are crowd-sourcing, subjective, and prone to mistakes, and poses a severe threat to the data-driven models, especially the deep neural networks. The deep models would generalize poorly on the testing cases when trained to over-fit the training samples with noisy sentiment labels. Inspired by the recent progress on learning with noisy labels, we propose a robust learning method to perform robust visual sentiment analysis. Our method relies on external memory to aggregate and filters noisy labels during training. The memory is composed of the prototypes with corresponding labels, which can be updated online. The learned prototypes and their labels can be regarded as denoising features and labels for the local regions and can guide the training process to prevent the model from overfitting the noisy cases. We establish a benchmark for visual sentiment analysis with label noise using publicly available datasets. The experiment results of the proposed benchmark settings comprehensively show the effectiveness of our method.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Localized Adversarial Domain Generalization

Deep learning methods can struggle to handle domain shifts not seen in training data, which can cause them to not generalize well to unseen domains. This has led to research attention on domain generalization (DG), which aims to the model's generalization ability to out-of-distribution. Adversarial domain generalization is a popular approach to DG, but conventional approaches (1) struggle to sufficiently align features so that local neighborhoods are mixed across domains; and (2) can suffer from feature space over collapse which can threaten generalization performance. To address these limitations, we propose localized adversarial domain generalization with space compactness maintenance~(LADG) which constitutes two major contributions. First, we propose an adversarial localized classifier as the domain discriminator, along with a principled primary branch. This constructs a min-max game whereby the aim of the featurizer is to produce locally mixed domains. Second, we propose to use a coding-rate loss to alleviate feature space over collapse. We conduct comprehensive experiments on the Wilds DG benchmark to validate our approach, where LADG outperforms leading competitors on most datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

No Significant Correlation between Line-emission and Continuum Substructures in the Molecules with ALMA at Planet-forming Scales Program

Recently, the Molecules with ALMA at Planet-forming Scales (MAPS) ALMA Large Program reported a high number of line emission substructures coincident with dust rings and gaps in the continuum emission, suggesting a causal link between these axisymmetric line emission and dust continuum substructures. To test the robustness of the claimed correlation, we compare the observed spatial overlap fraction in substructures with that from the null hypothesis, in which the overlap is assumed to arise from the random placement of line emission substructures. Our results reveal that there is no statistically significant evidence for a universal correlation between line emission and continuum substructures, questioning the frequently-made link between continuum rings and pressure bumps. The analysis also clearly identifies outliers. The chemical rings and the dust gaps in MWC 480 appear to be strongly correlated (${>}4σ$), and the gaps in the CO isotopologues tend to moderately (${\sim}3σ$) correlate with dust rings.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

OGLE-2016-BLG-1093Lb: A Sub-Jupiter-mass Spitzer Planet Located in Galactic Bulge

OGLE-2016-BLG-1093 is a planetary microlensing event that is part of the statistical $Spitzer$ microlens parallax sample. The precise measurement of the microlens parallax effect for this event, combined with the measurement of finite source effects, leads to a direct measurement of the lens masses and system distance: $M_{\rm host} = 0.38$--$0.57\, M_{\odot}$, $m_p = 0.59$--$0.87\, M_{\rm Jup}$, and the system is located at the Galactic bulge ($D_L \sim 8.1$ kpc). Because this was a high-magnification event, we are also able to empirically show that the "cheap-space parallax" concept Gould & Yee (2012) produces well-constrained (and consistent) results for $|π_{\rm E}|$. This demonstrates that this concept can be extended to many two-body lenses. Finally, we briefly explore systematics in the $Spitzer$ light curve in this event and show that their potential impact is strongly mitigated by the color-constraint.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

OGLE-2018-BLG-0799Lb: a $q \sim 2.7 \times 10^{-3}$ Planet with Spitzer Parallax

We report the discovery and analysis of a planet in the microlensing event OGLE-2018-BLG-0799. The planetary signal was observed by several ground-based telescopes, and the planet-host mass ratio is $q = (2.65 \pm 0.16) \times 10^{-3}$. The ground-based observations yield a constraint on the angular Einstein radius $θ_{\rm E}$, and the microlensing parallax vector $\vecπ_{\rm E}$, is strongly constrained by the Spitzer data. However, the 2019 Spitzer baseline data reveal systematics in the Spitzer photometry, so there is ambiguity in the magnitude of the parallax. In our preferred interpretation, a full Bayesian analysis using a Galactic model indicates that the planetary system is composed of an $M_{\rm planet} = 0.26_{-0.11}^{+0.22}~M_{J}$ planet orbiting an $M_{\rm host} = 0.093_{-0.038}^{+0.082}~M_{\odot}$, at a distance of $D_{\rm L} = 3.71_{-1.70}^{+3.24}$ kpc. An alternate interpretation of the data shifts the localization of the minima along the arc-shaped microlens parallax constraints. This, in turn, yields a more massive host with median mass of $0.13 {M_{\odot}}$ at a distance of 6.3 kpc. This analysis demonstrates the robustness of the osculating circles formalism, but shows that further investigation is needed to assess how systematics affect the specific localization of the microlens parallax vector and, consequently, the inferred physical parameters.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

OGLE-2019-BLG-1470LABc: Another Microlensing Giant Planet in a Binary System?

We report the discovery and analysis of a candidate triple-lens single-source (3L1S) microlensing event, OGLE-2019-BLG-1470. This event was first classified as a normal binary-lens single-source (2L1S) event, but a careful 2L1S modelling showed that it needs an additional lens or source to fit the observed data. It is found that the 3L1S model provides the best fit, but the binary-lens binary-source (2L2S) model is only disfavoured by $Δχ^2 \simeq 18$. All of the feasible models include a planet with planet-to-host mass-ratios $10^{-3} \lesssim q \lesssim 10^{-2}$. A Bayesian analysis based on a Galactic model indicates that the planet is super-Jovian, and the projected host-planet separation is about 3 $\mathrm{au}$. Specifically, for the best-fit 3L1S model, the two stars have masses of $M_1=0.57^{+0.43}_{-0.32}M_{\odot}$, and $M_2=0.18^{+0.15}_{-0.10}M_{\odot}$, with projected separation of $1.3^{+0.5}_{-0.5}$ $\mathrm{au}$, and the planetary mass is $M_3=2.2^{+1.8}_{-1.3}M_{\rm{Jupiter}}$. For the 2L2S model, the masses of the host star and the planet are $0.55^{+0.44}_{-0.31}M_{\odot}$ and $4.6^{+3.7}_{-2.6}M_{\rm{Jupiter}}$, respectively. By investigating the properties of all known microlensing planets in binary systems, we find that all planets in binary systems published by the KMTNet survey are located inside the resonant caustics range with $q \gtrsim 2 \times 10^{-3}$, indicating the incompleteness of the KMTNet sample for planets in binary systems. Thus, planets in binary systems cannot be included in the current study of the KMTNet mass-ratio function, and a systematic search for planetary anomalies in KMTNet microlensing light curves of binary systems is needed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters

Encoding the scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many computer vision tasks especially when dealing with multiscale inputs. We study, in this paper, a scaling-translation-equivariant (ST-equivariant) CNN with joint convolutions across the space and the scaling group, which is shown to be both sufficient and necessary to achieve equivariance for the regular representation of the scaling-translation group ST . To reduce the model complexity and computational burden, we decompose the convolutional filters under two pre-fixed separable bases and truncate the expansion to low-frequency components. A further benefit of the truncated filter expansion is the improved deformation robustness of the equivariant representation, a property which is theoretically analyzed and empirically verified. Numerical experiments demonstrate that the proposed scaling-translation-equivariant network with decomposed convolutional filters (ScDCFNet) achieves significantly improved performance in multiscale image classification and better interpretability than regular CNNs at a reduced model size.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Structure-preserving GANs

Generative adversarial networks (GANs), a class of distribution-learning methods based on a two-player game between a generator and a discriminator, can generally be formulated as a minmax problem based on the variational representation of a divergence between the unknown and the generated distributions. We introduce structure-preserving GANs as a data-efficient framework for learning distributions with additional structure such as group symmetry, by developing new variational representations for divergences. Our theory shows that we can reduce the discriminator space to its projection on the invariant discriminator space, using the conditional expectation with respect to the sigma-algebra associated to the underlying structure. In addition, we prove that the discriminator space reduction must be accompanied by a careful design of structured generators, as flawed designs may easily lead to a catastrophic "mode collapse" of the learned distribution. We contextualize our framework by building symmetry-preserving GANs for distributions with intrinsic group symmetry, and demonstrate that both players, namely the equivariant generator and invariant discriminator, play important but distinct roles in the learning process. Empirical experiments and ablation studies across a broad range of data sets, including real-world medical imaging, validate our theory, and show our proposed methods achieve significantly improved sample fidelity and diversity -- almost an order of magnitude measured in Fréchet Inception Distance -- especially in the small data regime.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Systematic KMTNet Planetary Anomaly Search, Paper I: OGLE-2019-BLG-1053Lb, A Buried Terrestrial Planet

In order to exhume the buried signatures of "missing planetary caustics" in the KMTNet data, we conducted a systematic anomaly search to the residuals from point-source point-lens fits, based on a modified version of the KMTNet EventFinder algorithm. This search reveals the lowest mass-ratio planetary caustic to date in the microlensing event OGLE-2019-BLG-1053, for which the planetary signal had not been noticed before. The planetary system has a planet-host mass ratio of $q = (1.25 \pm 0.13) \times 10^{-5}$. A Bayesian analysis yields estimates of the mass of the host star, $M_{\rm host} = 0.61_{-0.24}^{+0.29}~M_\odot$, the mass of its planet, $M_{\rm planet} = 2.48_{-0.98}^{+1.19}~M_{\oplus}$, the projected planet-host separation, $a_\perp = 3.4_{-0.5}^{+0.5}$ au, and the lens distance of $D_{\rm L} = 6.8_{-0.9}^{+0.6}$ kpc. The discovery of this very low mass-ratio planet illustrates the utility of our method and opens a new window for a large and homogeneous sample to study the microlensing planet-host mass-ratio function down to $q \sim 10^{-5}$.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Systematic KMTNet Planetary Anomaly Search. IV. Complete Sample of 2019 Prime-Field

We report the complete statistical planetary sample from the prime fields ($Γ\geq 2~{\rm hr}^{-1}$) of the 2019 Korea Microlensing Telescope Network (KMTNet) microlensing survey. We develop the optimized KMTNet AnomalyFinder algorithm and apply it to the 2019 KMTNet prime fields. We find a total of 14 homogeneously selected planets and report the analysis of three planetary events, KMT-2019-BLG-(1042,1552,2974). The planet-host mass ratios, $q$, for the three planetary events are $6.34 \times 10^{-4}, 4.89 \times 10^{-3}$ and $6.18 \times 10^{-4}$, respectively. A Bayesian analysis indicates the three planets are all cold giant planets beyond the snow line of their host stars. The 13 planets are basically uniform in $\log q$ over the range $-5.0 < \log q < -1.5$. This result suggests that the planets below $q_{\rm break} = 1.7 \times 10^{-4}$ proposed by the MOA-II survey may be more common than previously believed. This work is an early component of a large project to determine the KMTNet mass-ratio function, and the whole sample of 2016--2019 KMTNet events should contain about 120 planets.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The intrinsic multiplicity distribution of exoplanets revealed from the radial velocity method

Planet multiplicities are useful in constraining the formation and evolution of planetary systems but usually difficult to constrain observationally. Here, we develop a general method that can properly take into account the survey incompleteness and recover the intrinsic planet multiplicity distribution. We then apply it to the radial velocity (RV) planet sample from the California Legacy Survey (CLS). Within the $1\,$au ($10\,$au) region, we find $21 \pm 4\%$ ($19.2 \pm 2.8\%$) of Sun-like stars host planets with masses above $10\,M_\oplus$ ($0.3\,M_{\rm J}$), about 30\% (40\%) of which are multi-planet systems; in terms of the RV semi-amplitude $K$, $33 \pm 7\%$ ($25 \pm 3\%$) of Sun-like stars contain planets of $K>1\,$m/s ($3\,$m/s), and each system hosts on average $1.8 \pm 0.4$ ($1.63 \pm 0.16$) planets. We note that the hot Jupiter rate in the CLS Sun-like sample is higher than the consensus value of $\sim$1\% by a factor of about three. We also confirm previous studies on the correlation between inner ($<1\,$au) and outer ($>1\,$au) planets.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Two Candidate KH 15D-like Systems from the Zwicky Transient Facility

KH 15D contains a circumbinary disk that is tilted relative to the orbital plane of the central binary. The precession of the disk and the orbital motion of the binary together produce rich phenomena in the photometric light curve. In this work, we present the discovery and preliminary analysis of two objects that resemble the key features of KH 15D from the Zwicky Transient Facility. These new objects, Bernhard-1 and Bernhard-2, show large-amplitude ($>1.5\,$mag), long-duration (more than tens of days), and periodic dimming events. A one-sided screen model is developed to model the photometric behaviour of these objects, the physical interpretation of which is a tilted, warped circumbinary disk occulting the inner binary. Changes in the object light curves suggest potential precession periods over timescales longer than 10 years. Additional photometric and spectroscopic observations are encouraged to better understand the nature of these interesting systems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Warning: The mini gamma-ray-bursts in planning hadron colliders beyond the LHC energies

Gluons may converge to a stable state at a critical momentum in nucleon. This gluon condensation will greatly increase the proton-proton cross section provided that the collision energies exceed the gluon condensation threshold. Based on the analyses of cosmic gamma-ray spectra, we find that the $p-Pb$ and $Pb-Pb$ collisions at the LHC are close to the energy region of the gluon condensation effect. We warn that for the next generation of hadron colliders increasing the collision energies, the extremely strong gamma-rays will be emitted in a narrow space of the accelerator due to the gluon condensation effect. Such artificial mini gamma-ray-bursts in the laboratory may damage the detectors.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Measuring microlensing parallax via simultaneous observations from Chinese Space Station Telescope and Roman Telescope

Simultaneous observations from two spatially well-separated telescopes can lead to the measurements of the microlensing parallax parameter, an important quantity toward the determinations of the lens mass. The separation between Earth and Sun-Earth L2 point, $\sim0.01$ AU, is ideal for parallax measurements of short and ultra-short ($\sim$1\,hr to 10\,days) microlensing events, which are candidates of free-floating planet (FFP) events. In this work, we study the potential of doing so in the context of two proposed space-based missions, the Chinese Space Station Telescope (CSST) in a Leo orbit and the Nancy Grace Roman Space Telescope (\emph{Roman}) at L2. We show that the joint observations of the two can directly measure the microlensing parallax of nearly all FFP events with timescales $t_{\rm E}\lesssim$ 10\,days as well as planetary (and stellar binary) events that show caustic crossing features. The potential of using CSST alone in measuring microlensing parallax is also discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Neural Networks Enforcing Physical Symmetries in Nonlinear Dynamical Lattices: The Case Example of the Ablowitz-Ladik Model

In this work we introduce symmetry-preserving, physics-informed neural networks (S-PINNs) motivated by symmetries that are ubiquitous to solutions of nonlinear dynamical lattices. Although the use of PINNs have recently attracted much attention in data-driven discovery of solutions chiefly to partial differential equations, we demonstrate that they fail at enforcing important physical laws including symmetries of solutions and conservation laws. Through the correlation of parity symmetries in both space and time of solutions to differential equations with their group equivariant representation, we construct group-equivariant NNs which respect spatio-temporal parity symmetry. Moreover, we adapt the proposed architecture to enforce different types of periodicity (or localization) of solutions to nonlinear dynamical lattices. We do so by applying S-PINNs to the completely integrable Ablowitz-Ladik model, and performing numerical experiments with a special focus on waveforms that are related to rogue structures. These include the Kuznetsov-Ma soliton, and Akhmediev breather as well as the Peregrine soliton. Our numerical results demonstrate the superiority and robustness of the proposed architecture over standard PINNs.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

OGLE-2019-BLG-0468Lb,c: two microlensing giant planets around a G-type star

With the aim of interpreting anomalous lensing events with no suggested models, we conducted a project of reinvestigating microlensing data in and before the 2019 season. In this work, we report a multi-planet system OGLE-2019-BLG-0468L found from the project. The light curve of the lensing event OGLE-2019-BLG-0468, which consists of three distinctive anomaly features, could not be explained by the usual binary-lens or binary-source interpretation. We find a solution explaining all anomaly features with a triple-lens interpretation, in which the lens is composed of two planets and their host, making the lens the fourth multi-planet system securely found by microlensing. The two planets have masses $\sim 3.4~M_{\rm J}$ and $\sim 10.2~M_{\rm J}$, and they are orbiting around a G-type star with a mass $\sim 0.9~M_\odot$ and a distance $\sim 4.4$ kpc. The host of the planets is most likely responsible for the light of the baseline object, although the possibility for the host to be a companion to the baseline object cannot be ruled out.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Systematic KMTNet Planetary Anomaly Search, Paper II: Six New $q<2\times 10^{-4}$ Mass-ratio Planets

We apply the automated AnomalyFinder algorithm of Paper I (Zang et al. 2021b) to 2018-2019 light curves from the $\simeq 13\,{\rm deg}^2$ covered by the six KMTNet prime fields, with cadences $Γ\geq 2\,{\rm hr}^{-1}$. We find a total of 11 planets with mass ratios $q<2\times 10^{-4}$, including six newly discovered planets, one planet that was reported in Paper I, and recovery of four previously discovered planets. One of the new planets, OGLE-2018-BLG-0977Lb, is in a planetary-caustic event, while the other five (OGLE-2018-BLG-0506Lb, OGLE-2018-BLG-0516Lb, OGLE-2019-BLG-1492Lb, KMT-2019-BLG-0253, and KMT-2019-BLG-0953) are revealed by a &#34;dip&#34; in the light curve as the source crosses the host-planet axis on the opposite side of the planet. These subtle signals were missed in previous by-eye searches. The planet-host separations (scaled to the Einstein radius), $s$, and planet-host mass ratios, $q$, are, respectively, $(s,q\times 10^5) = (0.88, 4.1)$, $(0.96\pm 0.10, 8.3)$, $(0.94\pm 0.07, 13)$, $(0.97\pm 0.07, 18)$, $(0.97\pm0.04,4.1)$, and $(0.74,18)$, where the &#34;$\pm$&#34; indicates a discrete degeneracy. The 11 planets are spread out over the range $-5<\log q < -3.7$. Together with the two planets previously reported with $q\sim 10^{-5}$ from the 2018-2019 non-prime KMT fields, this result suggests that planets toward the bottom of this mass-ratio range may be more common than previously believed.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

The &#39;COVID&#39; Crash of the 2020 U.S. Stock Market

We employed the log-periodic power law singularity (LPPLS) methodology to systematically investigate the 2020 stock market crash in the U.S. equities sectors with different levels of total market capitalizations through four major U.S. stock market indexes, including the Wilshire 5000 Total Market index, the S&P 500 index, the S&P MidCap 400 index, and the Russell 2000 index, representing the stocks overall, the large capitalization stocks, the middle capitalization stocks and the small capitalization stocks, respectively. During the 2020 U.S. stock market crash, all four indexes lost more than a third of their values within five weeks, while both the middle capitalization stocks and the small capitalization stocks have suffered much greater losses than the large capitalization stocks and stocks overall. Our results indicate that the price trajectories of these four stock market indexes prior to the 2020 stock market crash have clearly featured the obvious LPPLS bubble pattern and were indeed in a positive bubble regime. Contrary to the popular belief that the COVID-19 led to the 2020 stock market crash, the 2020 U.S. stock market crash was endogenous, stemming from the increasingly systemic instability of the stock market itself. We also performed the complementary post-mortem analysis of the 2020 U.S. stock market crash. Our analyses indicate that the 2020 U.S. stock market crash originated from a bubble which began to form as early as September 2018; and the bubbles in stocks with different levels of total market capitalizations have significantly different starting time profiles. This study not only sheds new light on the making of the 2020 U.S. stock market crash but also creates a novel pipeline for future real-time crash detection and mechanism dissection of any financial market and/or economic index.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization

We improve the robustness of Deep Neural Net (DNN) to adversarial attacks by using an interpolating function as the output activation. This data-dependent activation remarkably improves both the generalization and robustness of DNN. In the CIFAR10 benchmark, we raise the robust accuracy of the adversarially trained ResNet20 from $\sim 46\%$ to $\sim 69\%$ under the state-of-the-art Iterative Fast Gradient Sign Method (IFGSM) based adversarial attack. When we combine this data-dependent activation with total variation minimization on adversarial images and training data augmentation, we achieve an improvement in robust accuracy by 38.9$\%$ for ResNet56 under the strongest IFGSM attack. Furthermore, We provide an intuitive explanation of our defense by analyzing the geometry of the feature space.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification

Skin disease classification from images is crucial to dermatological diagnosis. However, identifying skin lesions involves a variety of aspects in terms of size, color, shape, and texture. To make matters worse, many categories only contain very few samples, posing great challenges to conventional machine learning algorithms and even human experts. Inspired by the recent success of Few-Shot Learning (FSL) in natural image classification, we propose to apply FSL to skin disease identification to address the extreme scarcity of training sample problem. However, directly applying FSL to this task does not work well in practice, and we find that the problem can be largely attributed to the incompatibility between Cross Entropy (CE) and episode training, which are both commonly used in FSL. Based on a detailed analysis, we propose the Query-Relative (QR) loss, which proves superior to CE under episode training and is closely related to recently proposed mutual information estimation. Moreover, we further strengthen the proposed QR loss with a novel adaptive hard margin strategy. Comprehensive experiments validate the effectiveness of the proposed FSL scheme and the possibility to diagnosis rare skin disease with a few labeled samples.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Anomalous bremsstrahlung and the structure of cosmic ray electron-positron fluxes at the GeV-TeV energy range

We reveal that the energy spectra of electrons-positrons in primary cosmic rays measured at atmosphere top have double structures: an excess component $Φ^s_{e^+}(E)=Φ^s_{e^-}(E)$ around $400 GeV$, which origins from a strong $e^+e^-$-source and the distorted background $Φ^0_{e^-}(E)$. We supposed that the difference between AMS-CALET and Fermi-LAT-DAMPE data origins from the energy loss of the fluxes due to the anomalous bremsstrahlung effect at a special window. The evolution of spectra under anomalous bremsstrahlung effect satisfies an improved electromagnetic cascade equation. The above spectra are parameterized and they can be regarded as the subjects exploring new physics. We suggest to check the previous applications of the Bethe-Heitler formula in the study of the propagation of high energy electrons and photons.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Designing Xenes with Two-Dimensional Triangular Lattice

Xenes, graphene-like two-dimensional (2D) monoelemental crystals with a honeycomb symmetry, have been the focus of numerous experimental and theoretical studies. In comparison, single-element 2D materials with a triangular lattice symmetry have not received due attention. Here, taking Pb as an example, we investigate the triangular-lattice monolayer made of group-IV atoms employing first-principles density functional theory calculations. The flat Pb monolayer supports a mirror-symmetry-protected spinless nodal line in the absence spin-orbit coupling (SOC). The introduction of an out-of-plane buckling creates a glide mirror, protecting an anisotropic Dirac nodal loop. Both flat and buckled Pb monolayers become topologically trivial after including SOC. A large buckling will make the Pb sheet a 2D semiconductor with symmetry-protected Dirac points below the Fermi level. The electronic structures of other group-IV triangular lattices such as Ge and Sn demonstrate strong similarity to Pb. We further design a quasi-3D crystal PbHfO$_2$ by alternately stacking Pb and 1T-HfO$_2$ monolayers. The new compound PbHfO$_2$ is dynamically stable and retains the properties of Pb monolayer. By applying epitaxial strains to PbHfO$_2$, it is possible to drive an insulator-to-metal transition coupled with an anti-ferroelectric-to-paraelectric phase transition. Our results suggest the potential of the 2D triangular lattice as a complimentary platform to design new type of broadly-defined Xenes.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Detecting isolated stellar-mass black holes in the absence of microlensing parallax effect

Gravitational microlensing can detect isolated stellar-mass black holes (BHs), which are believed to be the dominant form of Galactic BHs according to population synthesis models. Previous searches for BH events in microlensing data focused on long-timescale events with significant microlensing parallax detections. Here we show that, although BH events preferentially have long timescales, the microlensing parallax amplitudes are so small that in most cases the parallax signals cannot be detected statistically significantly. We then identify OGLE-2006-BLG-044 to be a candidate BH event because of its long timescale and small microlensing parallax. Our findings have implications to future BH searches in microlensing data.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Improved Bethe-Heitler formula

The Bethe-Heitler formula describes bremsstrahlung and it&#39;s a typical and important example in quantum electromagnetic dynamics (QED). This formula is widely applied in many branches of physics and astrophysics. We find that the integrated bremsstrahlung cross section at the static approximation and high energy limit has an unexpected big increment, which is missed by previous bremsstrahlung theory. This anomalous effect also exists in electron-positron pair creation. We derive the relating formulas and point out that electromagnetic cascades at the top of atmosphere can test this effect.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Looking for the possible gluon condensation signature in sub-TeV gamma-ray spectra: from active galactic nuclei to gamma ray bursts

The gluon condensation in the proton as a dynamical model is used to treat a series of unsolved puzzles in sub-TeV gamma ray spectra, they include the broken power-law of blazar&#39;s radiation, the hardening confusion of 1ES 1426+428, Mkn 501, and the recently recorded sub-TeV gamma spectra of GRB 180720B and GRB 190114C. We find that the above anomalous phenomena in gamma ray energy spectra can be understood with the simple broken power law based on a QCD gluon condensation effect.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

OGLE-2017-BLG-0406: ${\it Spitzer}$ Microlens Parallax Reveals Saturn-mass Planet orbiting M-dwarf Host in the Inner Galactic Disk

We report the discovery and analysis of the planetary microlensing event OGLE-2017-BLG-0406, which was observed both from the ground and by the ${\it Spitzer}$ satellite in a solar orbit. At high magnification, the anomaly in the light curve was densely observed by ground-based-survey and follow-up groups, and it was found to be explained by a planetary lens with a planet/host mass ratio of $q=7.0 \times 10^{-4}$ from the light-curve modeling. The ground-only and ${\it Spitzer}$-&#34;only&#34; data each provide very strong one-dimensional (1-D) constraints on the 2-D microlens parallax vector $\bf{π_{\rm E}}$. When combined, these yield a precise measurement of $\bf{π_{\rm E}}$, and so of the masses of the host $M_{\rm host}=0.56\pm0.07\,M_\odot$ and planet $M_{\rm planet} = 0.41 \pm 0.05\,M_{\rm Jup}$. The system lies at a distance $D_{\rm L}=5.2 \pm 0.5 \ {\rm kpc}$ from the Sun toward the Galactic bulge, and the host is more likely to be a disk population star according to the kinematics of the lens. The projected separation of the planet from the host is $a_{\perp} = 3.5 \pm 0.3 \ {\rm au}$, i.e., just over twice the snow line. The Galactic-disk kinematics are established in part from a precise measurement of the source proper motion based on OGLE-IV data. By contrast, the ${\it Gaia}$ proper-motion measurement of the source suffers from a catastrophic $10\,σ$ error.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

OGLE-2018-BLG-1269Lb: A Jovian Planet With A Bright, $I=16$ Host

We report the discovery of a planet in the microlensing event OGLE-2018-BLG-1269, with planet-host mass ratio $q \sim 6\times10^{-4}$, i.e., $0.6$ times smaller than the Jupiter/Sun mass ratio. Combined with the $Gaia$ parallax and proper motion, a strong one-dimensional constraint on the microlens parallax vector allows us to significantly reduce the uncertainties of lens physical parameters. A Bayesian analysis that ignores any information about light from the host yields that the planet is a cold giant $(M_{2} = 0.69_{-0.22}^{+0.44}\,M_{\rm J})$ orbiting a Sun-like star $(M_{1} = 1.13_{-0.35}^{+0.72}\,M_{\odot})$ at a distance of $D_{\rm L} = 2.56_{-0.62}^{+0.92}\,{\rm kpc}$. The projected planet-host separation is $a_{\perp} = 4.61_{-1.17}^{+1.70}\,{\rm au}$. Using {\it Gaia} astrometry, we show that the blended light lies $\lesssim 12\,$mas from the host and therefore must be either the host star or a stellar companion to the host. An isochrone analysis favors the former possibility at $>99.6\%$. The host is therefore a subgiant. For host metallicities in the range of $0.0 \leq {\rm [Fe/H]} \leq +0.3$, the host and planet masses are then in the range of $1.16 \leq M_{1}/M_{\odot} \leq 1.38$ and $0.74 \leq M_{2}/M_{\rm J} \leq 0.89$, respectively. Low host metallicities are excluded. The brightness and proximity of the lens make the event a strong candidate for spectroscopic followup both to test the microlensing solution and to further characterize the system.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On the patterns observed in Kepler multi-planet systems

Recent studies claimed that planets around the same star have similar sizes and masses and regular spacings, and that planet pairs usually show ordered sizes such that the outer planet is usually the larger one. Here I show that these patterns can be largely explained by detection biases. The \emph{Kepler} planet detections are set by the transit signal-to-noise ratio (S/N). For different stellar properties and orbital period values, the same S/N corresponds to different planetary sizes. This variation in the detection threshold naturally leads to apparent correlations in planet sizes and the observed size ordering. The apparently correlated spacings, measured in period ratios, between adjacent planet pairs in systems with at least three detected planets are partially due to the arbitrary upper limit that the earlier study imposed on the period ratio, and partially due to the varying stability threshold for different planets. After these detection biases are taken into account, we do not find strong evidence for the so-called &#34;intra-system uniformity&#34; or the size ordering effect. Instead, the physical properties of \emph{Kepler} planets are largely independent of the properties of their siblings and the parent star. It is likely that the dynamical evolution has erased the memory of \emph{Kepler} planets about their initial formation conditions. In other words, it will be difficult to infer the initial conditions from the observed properties and the architecture of \emph{Kepler} planets.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Personalized Fashion Recommendation from Personal Social Media Data: An Item-to-Set Metric Learning Approach

With the growth of online shopping for fashion products, accurate fashion recommendation has become a critical problem. Meanwhile, social networks provide an open and new data source for personalized fashion analysis. In this work, we study the problem of personalized fashion recommendation from social media data, i.e. recommending new outfits to social media users that fit their fashion preferences. To this end, we present an item-to-set metric learning framework that learns to compute the similarity between a set of historical fashion items of a user to a new fashion item. To extract features from multi-modal street-view fashion items, we propose an embedding module that performs multi-modality feature extraction and cross-modality gated fusion. To validate the effectiveness of our approach, we collect a real-world social media dataset. Extensive experiments on the collected dataset show the superior performance of our proposed approach.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Unifying Specialist Image Embedding into Universal Image Embedding

Deep image embedding provides a way to measure the semantic similarity of two images. It plays a central role in many applications such as image search, face verification, and zero-shot learning. It is desirable to have a universal deep embedding model applicable to various domains of images. However, existing methods mainly rely on training specialist embedding models each of which is applicable to images from a single domain. In this paper, we study an important but unexplored task: how to train a single universal image embedding model to match the performance of several specialists on each specialist&#39;s domain. Simply fusing the training data from multiple domains cannot solve this problem because some domains become overfitted sooner when trained together using existing methods. Therefore, we propose to distill the knowledge in multiple specialists into a universal embedding to solve this problem. In contrast to existing embedding distillation methods that distill the absolute distances between images, we transform the absolute distances between images into a probabilistic distribution and minimize the KL-divergence between the distributions of the specialists and the universal embedding. Using several public datasets, we validate that our proposed method accomplishes the goal of universal image embedding.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Wave effects in the microlensing of pulsars and FRBs by point masses

Wave effects are often neglected in microlensing studies; however, for coherent point-like sources, such as pulsars and fast radio bursts (FRBs), wave effects will become important in their gravitational lensing. In this paper, we describe the wave optics formalism, its various limits, and the conditions for which these limits hold. Using the simple point lens as an example, we will show that the frequency dependence of wave effects breaks degeneracies that are present in the usual geometric optics limit, and constructive interference results in larger magnifications further from the lens. This latter fact leads to a generic increase in cross section for microlensing events in the wave-optics regime compared to the geometric optics regime. For realistic percent-level spectral sensitivities, this leads to a relative boost in lensing cross section of more than an order of magnitude. We apply the point-lens model to the lensing of FRBs and pulsars and find that these radio sources will be lensed in the full wave-optics regime by isolated masses in the range of $0.1-100\,M_\oplus$, which includes free-floating planets (FFPs), whose Einstein radius is smaller than the Fresnel scale. More generally, the interference pattern allows an instantaneous determination of lens masses, unlike traditional microlensing techniques which only yield a mass inference from the event timescale.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Weighted Aggregating Stochastic Gradient Descent for Parallel Deep Learning

This paper investigates the stochastic optimization problem with a focus on developing scalable parallel algorithms for deep learning tasks. Our solution involves a reformation of the objective function for stochastic optimization in neural network models, along with a novel parallel strategy, coined weighted aggregating stochastic gradient descent (WASGD). Following a theoretical analysis on the characteristics of the new objective function, WASGD introduces a decentralized weighted aggregating scheme based on the performance of local workers. Without any center variable, the new method automatically assesses the importance of local workers and accepts them according to their contributions. Furthermore, we have developed an enhanced version of the method, WASGD+, by (1) considering a designed sample order and (2) applying a more advanced weight evaluating function. To validate the new method, we benchmark our schemes against several popular algorithms including the state-of-the-art techniques (e.g., elastic averaging SGD) in training deep neural networks for classification tasks. Comprehensive experiments have been conducted on four classic datasets, including the CIFAR-100, CIFAR-10, Fashion-MNIST, and MNIST. The subsequent results suggest the superiority of the WASGD scheme in accelerating the training of deep architecture. Better still, the enhanced version, WASGD+, has been shown to be a significant improvement over its basic version.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Detection of Chinese Stock Market Bubbles with LPPLS Confidence Indicator

We present an advance bubble detection methodology based on the Log Periodic Power Law Singularity (LPPLS) confidence indicator for the early causal identification of positive and negative bubbles in the Chinese stock market using the daily data on the Shanghai Shenzhen CSI 300 stock market index from January 2002 through April 2018. We account for the damping condition of LPPLS model in the search space and implement the stricter filter conditions for the qualification of the valid LPPLS fits by taking account of the maximum relative error, performing the Lomb log-periodic test of the detrended residual, and unit-root tests of the logarithmic residual based on both the Phillips-Perron test and Dickey-Fuller test to improve the performance of LPPLS confidence indicator. Our analysis shows that the LPPLS detection strategy diagnoses the positive bubbles and negative bubbles corresponding to well-known historical events, implying the detection strategy based on the LPPLS confidence indicator has an outstanding performance to identify the bubbles in advance. We find that the probability density distribution of the estimated beginning time of bubbles appears to be skewed and the mass of the distribution is concentrated on the area where the price starts to have an obvious super-exponentially growth. This study is the first work in the literature that identifies the existence of bubbles in the Chinese stock market using the daily data of CSI 300 index with the advance bubble detection methodology of LPPLS confidence indicator. We have shown that it is possible to detect the potential positive and negative bubbles and crashes ahead of time, which in turn limits the bubble sizes and eventually minimizes the damages from the bubble crash.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

KMT-2016-BLG-1836Lb: A Super-Jovian Planet From A High-Cadence Microlensing Field

We report the discovery of a super-Jovian planet in the microlensing event KMT-2016-BLG-1836, which was found by the Korea Microlensing Telescope Network&#39;s high-cadence observations (Γ~ 4~{hr}^{-1}). The planet-host mass ratio q ~ 0.004. A Bayesian analysis indicates that the planetary system is composed of a super-Jovian M_{planet} = 2.2_{-1.1}^{+1.9} M_{J} planet orbiting an M or K dwarf M_{\rm host} = 0.49_{-0.25}^{+0.38} M_{Sun}, at a distance of D_{L} = 7.1_{-2.4}^{+0.8} kpc. The projected planet-host separation is 3.5^{+1.1}_{-0.9} AU, implying that the planet is located beyond the snowline of the host star. Future high-resolution images can potentially strongly constrain the lens brightness and thus the mass and distance of the planetary system. Without considering detailed detection efficiency, selection or publication biases, we find a potential &#34;mass ratio desert&#34; at -3.7 \lesssim \log q \lesssim -3.0 for the 31 published KMTNet planets.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

OGLE-2015-BLG-1771Lb: A Microlens Planet Orbiting an Ultracool Dwarf?

We report the discovery and the analysis of the short (tE < 5 days) planetary microlensing event, OGLE-2015-BLG-1771. The event was discovered by the Optical Gravitational Lensing Experiment (OGLE), and the planetary anomaly (at I ~ 19) was captured by The Korea Microlensing Telescope Network (KMTNet). The event has three surviving planetary models that explain the observed light curves, with planet-host mass ratio q \~ 5.4 * 10^{-3}, 4.5 * 10^{-3} and 4.5 * 10^{-2}, respectively. The first model is the best-fit model, while the second model is disfavored by Δχ^2 ~ 3. The last model is strongly disfavored by Δχ^2 ~ 15 but not ruled out. A Bayesian analysis using a Galactic model indicates that the first two models are probably composed of a Saturn-mass planet orbiting a late M dwarf, while the third one could consist of a super-Jovian planet and a mid-mass brown dwarf. The source-lens relative proper motion is mu_rel ~ 9 mas/yr, so the source and lens could be resolved by current adaptive-optics (AO) instruments in 2021 if the lens is luminous.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Real-time Prediction of Bitcoin Bubble Crashes

In the past decade, Bitcoin as an emerging asset class has gained widespread public attention because of their extraordinary returns in phases of extreme price growth and their unpredictable massive crashes. We apply the log-periodic power law singularity (LPPLS) confidence indicator as a diagnostic tool for identifying bubbles using the daily data on Bitcoin price in the past two years. We find that the LPPLS confidence indicator based on the daily Bitcoin price data fails to provide effective warnings for detecting the bubbles when the Bitcoin price suffers from a large fluctuation in a short time, especially for positive bubbles. In order to diagnose the existence of bubbles and accurately predict the bubble crashes in the cryptocurrency market, this study proposes an adaptive multilevel time series detection methodology based on the LPPLS model and finer (than daily) timescale for the Bitcoin price data. We adopt two levels of time series, 1 hour and 30 minutes, to demonstrate the adaptive multilevel time series detection methodology. The results show that the LPPLS confidence indicator based on this new method is an outstanding instrument to effectively detect the bubbles and accurately forecast the bubble crashes, even if a bubble exists in a short time. In addition, we discover that the short-term LPPLS confidence indicator highly sensitive to the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale - on a day to week scale, and the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale - on a week to month scale. The adaptive multilevel time series detection methodology can provide real-time detection of bubbles and advanced forecast of crashes to warn of the imminent risk.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Spitzer + VLTI-GRAVITY Measure the Lens Mass of a Nearby Microlensing Event

We report the lens mass and distance measurements of the nearby microlensing event TCP J05074264+2447555. We measure the microlens parallax vector $π_{\rm E}$ using Spitzer and ground-based light curves with constraints on the direction of lens-source relative proper motion derived from Very Large Telescope Interferometer (VLTI) GRAVITY observations. Combining this $π_{\rm E}$ determination with the angular Einstein radius $θ_{\rm E}$ measured by VLTI GRAVITY observations, we find that the lens is a star with mass $M_{\rm L} = 0.495 \pm 0.063~M_{\odot}$ at a distance $D_{\rm L} = 429 \pm 21~{\rm pc}$. We find that the blended light basically all comes from the lens. The lens-source proper motion is $μ_{\rm rel,hel} = 26.55 \pm 0.36~{\rm mas\,yr^{-1}}$, so with currently available adaptive-optics (AO) instruments, the lens and source can be resolved in 2021. This is the first microlensing event whose lens mass is unambiguously measured by interferometry + satellite parallax observations, which opens a new window for mass measurements of isolated objects such as stellar-mass black holes.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Spitzer Microlensing parallax reveals two isolated stars in the Galactic bulge

We report the mass and distance measurements of two single-lens events from the 2017 Spitzer microlensing campaign. The ground-based observations yield the detection of finite-source effects, and the microlens parallaxes are derived from the joint analysis of ground-based observations and Spitzer observations. We find that the lens of OGLE-2017-BLG-1254 is a $0.60 \pm 0.03 M_{\odot}$ star with $D_{\rm LS} = 0.53 \pm 0.11~\text{kpc}$, where $D_{\rm LS}$ is the distance between the lens and the source. The second event, OGLE-2017-BLG-1161, is subject to the known satellite parallax degeneracy, and thus is either a $0.51^{+0.12}_{-0.10} M_{\odot}$ star with $D_{\rm LS} = 0.40 \pm 0.12~\text{kpc}$ or a $0.38^{+0.13}_{-0.12} M_{\odot}$ star with $D_{\rm LS} = 0.53 \pm 0.19~\text{kpc}$. Both of the lenses are therefore isolated stars in the Galactic bulge. By comparing the mass and distance distributions of the eight published Spitzer finite-source events with the expectations from a Galactic model, we find that the Spitzer sample is in agreement with the probability of finite-source effects occurrence in single lens events.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Electronic nature of coverage-dependent nanosurface effect by cooperative orbital redistribution

Nanomaterial surface states can effectively modify or even dominate their physical and chemical properties due to large surface-to-volume ratios. Such surface effects are highly dependent on particle size and ligand coverage, yet the underlying electronic-level mechanism still remains unknown. Using TiO2 nanosheet as a model system, we reveal the electronic nature of coverage-dependent nanosurface effects through varying ligand coverage and probing the modified surface bonding and electronic band structures with near-edge X-ray absorption fine structure. We discover experimentally that surface ligands can competitively polarize the 3d orbitals of surface Ti atoms into chemisorption states, which is cooperative with increased ligand coverages. Such coverage-dependent cooperative orbital redistribution accounts for various nanosurface effects on regulating the electronic structure, surface reactivity, optical property, and chemisorption of nanomaterials.

0 citations0 reviewsNo code linkedOpen access
preprint2017arXiv

Multi-appearance Segmentation and Extended 0-1 Program for Dense Small Object Tracking

Aiming to address the fast multi-object tracking for dense small object in the cluster background, we review track orientated multi-hypothesis tracking(TOMHT) with consideration of batch optimization. Employing autocorrelation based motion score test and staged hypotheses merging approach, we build our homologous hypothesis generation and management method. A new one-to-many constraint is proposed and applied to tackle the track exclusions during complex occlusions. Besides, to achieve better results, we develop a multi-appearance segmentation for detection, which exploits tree-like topological information and realizes one threshold for one object. Experimental results verify the strength of our methods, indicating speed and performance advantages of our tracker.

0 citations0 reviewsNo code linkedOpen access