Searcharxiv⌕ Search

arXiv subjects

Kai Li

Publications and source records attributed to Kai Li.

At least 343 records · Page 19Linked to original sources

Two models unifying warm inflation with dark matter and dark energy

Two models that unify warm inflation with dark matter and dark energy are proposed. In the models, a single scalar field is responsible for the early expansion of the universe through the process of dissipative warm inflation and then acts as both dark matter and dark energy in subsequent stages. The first model is based on a noncanonical field with the Lagrangian density $\mathcal{L}=F(X)-V(ϕ)$, where the potential is dominant at the slow-roll inflationary epoch and negligible in subsequent stages. The second model takes advantage of a $k$-essence Lagrangian density having the coupled form $\mathcal{L}=F(X)V(ϕ)$. For both models, equations of the evolution for the fields and observational constraints are presented, and an evolution law describing how the energy density $ρ$ and state parameter $w$ scale with the scale factor $a$ is obtained.

gr-qc↗

Two contact binaries with mass ratios close to the minimum mass ratio

The cut-off mass ratio is under debate for contact binaries. In this paper, we present the investigation of two contact binaries with mass ratios close to the low mass ratio limit. It is found that the mass ratios of VSX J082700.8+462850 (hereafter J082700) and 1SWASP J132829.37+555246.1 (hereafter J132829) are both less than 0.1 ($q\sim0.055$ for J082700, and $q\sim0.089$ for J132829). J082700 is a shallow contact binary with a contact degree of $\sim$19%, and J132829 is a deep contact system with a fillout factor of $\sim$70%. The $O-C$ diagram analysis indicated that both the two systems manifest long-term period decrease. In addition, J082700 exhibits a cyclic modulation which is more likely resulted from Applegate mechanism. In order to explore the properties of extremely low mass ratio contact binaries (ELMRCBs), we carried out a statistical analysis on contact binaries with mass ratios of $q\lesssim0.1$ and discovered that the values of $J_{spin}/J_{orb}$ of three systems are greater than 1/3. Two possible explanations can interpret this phenomenon. One is that some physical processes, unknown to date, are not considered when Hut presented the dynamically instability criterion. The other is that the dimensionless gyration radius ($k$) should be smaller than the value we used ($k^2=0.06$). We also found that the formation of ELMRCBs possibly has two channels. The study of evolutionary states of ELMRCBs reveals that their evolutionary states are similar with those of normal W UMa contact binaries.

astro-ph.SR↗

TopoShot: Uncovering Ethereum's Network Topology Leveraging Replacement Transactions

Ethereum relies on a peer-to-peer overlay network to propagate information. The knowledge of Ethereum network topology holds the key to understanding Ethereum's security, availability, and user anonymity. From a measurement perspective, an Ethereum network's topology is routing-table information hidden inside individual Ethereum nodes, measuring which poses challenges and remains an open research problem in the existing literature. This paper presents TopoShot, a new method uniquely repurposing Ethereum's transaction replacement/eviction policies for topology measurement. TopoShot can be configured to support Geth, Parity, and other major Ethereum clients. As validated on local nodes, TopoShot achieves 100% measurement precision and high recall 88% - 97%. To efficiently measure the large Ethereum networks in the wild, we propose a non-trivial schedule to run pair-wise measurements in parallel. To enable ethical measurement on Ethereum mainnet, we propose workload-adaptive configurations of TopoShot to minimize the service interruption to target nodes/network. We systematically measure a variety of Ethereum networks and obtain new knowledge including the full-network topology in major testnets (Ropsten, Rinkeby and Goerli) and critical sub-network topology in the mainnet. The results on testnets show interesting graph-theoretic properties, such as all testnets exhibit graph modularity significantly lower than random graphs, implying resilience to network partitions. The mainnet results show biased neighbor selection strategies adopted by critical Ethereum services such as mining pools and transaction relays, implying a degree of centralization in real Ethereum networks.

cs.NI↗

Deep Learning for Multi-Fidelity Aerodynamic Distribution Modeling from Experimental and Simulation Data

The wind-tunnel experiment plays a critical role in the design and development phases of modern aircraft, which is limited by prohibitive cost. In contrast, numerical simulation, as an important alternative paradigm, mimics complex flow behaviors but is less accurate compared to experiment. This leads to the recent development and emerging interest in applying data fusion for aerodynamic prediction. In particular, the accurate prediction of aerodynamic with lower computational cost can be achieved by fusing experimental (high-fidelity) and computational (low-fidelity) aerodynamic data. Currently, existing works on aerodynamic model using data fusion mainly concern integral data (lift, drag, etc.). In this paper, a multi-fidelity aerodynamic model based on Deep Neural Network (DNN), where both numerical and experimental data are introduced in the loss function with proper weighting factor to balance the overall accuracy, is developed for aerodynamic distribution over the wing surface. Specially, the proposed approach is illustrated by modeling the surface pressure distribution of ONERA M6 wing in transonic flow, including different secnarios where the flow condition varies or there is less high-fidelity data. The results demonstrate that the proposed approach with decent number of low-fidelity data and a few high-fidelity data can accurately predict the surface pressure distribution on transonic wing. The outperformance of the proposed model over other DNNs from only high fidelity or low-fidelity, has been reported.

physics.flu-dyn↗

Cooperation Method of Connected and Automated Vehicles at Unsignalized Intersections: Lane Changing and Arrival Scheduling

The cooperation of connected and automated vehicles (CAVs) has shown great potential in improving traffic efficiency during intersection management. Existing research mainly focuses on intersections where lane changing is prohibited, which is impractical for real-life implementation. This paper proposes a two-stage cooperation framework, which decouples the longitudinal and lateral control of CAVs, allowing them to change to their preferred lanes. Based on formation control, an iterative framework is initially proposed to solve the target assignment and path planning problem for multiple CAVs on multiple lanes. A graph-based minimum clique cover method is then applied to obtain the optimal scheduling plan for the CAVs. Extensive numerical simulations for different numbers of vehicles and traffic volumes validate the effectiveness of the proposed algorithm.

math.OC↗

EMA: Auditing Data Removal from Trained Models

Data auditing is a process to verify whether certain data have been removed from a trained model. A recently proposed method (Liu et al. 20) uses Kolmogorov-Smirnov (KS) distance for such data auditing. However, it fails under certain practical conditions. In this paper, we propose a new method called Ensembled Membership Auditing (EMA) for auditing data removal to overcome these limitations. We compare both methods using benchmark datasets (MNIST and SVHN) and Chest X-ray datasets with multi-layer perceptrons (MLP) and convolutional neural networks (CNN). Our experiments show that EMA is robust under various conditions, including the failure cases of the previously proposed method. Our code is available at: https://github.com/Hazelsuko07/EMA.

cs.LG↗

Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function

Numerous detection problems in computer vision, including road crack detection, suffer from exceedingly foreground-background imbalance. Fortunately, modification of loss function appears to solve this puzzle once and for all. In this paper, we propose a pixel-based adaptive weighted cross-entropy loss in conjunction with Jaccard distance to facilitate high-quality pixel-level road crack detection. Our work profoundly demonstrates the influence of loss functions on detection outcomes, and sheds light on the sophisticated consecutive improvements in the realm of crack detection. Specifically, to verify the effectiveness of the proposed loss, we conduct extensive experiments on four public databases, i.e., CrackForest, AigleRN, Crack360, and BJN260. Compared with the vanilla weighted cross-entropy, the proposed loss significantly speeds up the training process while retaining the test accuracy.

cs.CV↗

DSAM: A Distance Shrinking with Angular Marginalizing Loss for High Performance Vehicle Re-identificatio

Vehicle Re-identification (ReID) is an important yet challenging problem in computer vision. Compared to other visual objects like faces and persons, vehicles simultaneously exhibit much larger intraclass viewpoint variations and interclass visual similarities, making most exiting loss functions designed for face recognition and person ReID unsuitable for vehicle ReID. To obtain a high-performance vehicle ReID model, we present a novel Distance Shrinking with Angular Marginalizing (DSAM) loss function to perform hybrid learning in both the Original Feature Space (OFS) and the Feature Angular Space (FAS) using the local verification and the global identification information. Specifically, it shrinks the distance between samples of the same class locally in the Original Feature Space while keeps samples of different classes far away in the Feature Angular Space. The shrinking and marginalizing operations are performed during each iteration of the training process and are suitable for different SoftMax based loss functions. We evaluate the DSAM loss function on three large vehicle ReID datasets with detailed analyses and extensive comparisons with many competing vehicle ReID methods. Experimental results show that our DSAM loss enhances the SoftMax loss by a large margin on the PKU-VD1-Large dataset: 10.41% for mAP, 5.29% for cmc1, and 4.60% for cmc5. Moreover, the mAP is increased by 9.34% on the PKU-VehicleID dataset and 6.13% on the VeRi-776 dataset. Source code will be released to facilitate further studies in this research direction.

cs.CV↗

iBatch: Saving Ethereum Fees via Secure and Cost-Effective Batching of Smart-Contract Invocations

This paper presents iBatch, a middleware system running on top of an operational Ethereum network to enable secure batching of smart-contract invocations against an untrusted relay server off-chain. iBatch does so at a low overhead by validating the server's batched invocations in smart contracts without additional states. The iBatch mechanism supports a variety of policies, ranging from conservative to aggressive batching, and can be configured adaptively to the current workloads. iBatch automatically rewrites smart contracts to integrate with legacy applications and support large-scale deployment. For cost evaluation, we develop a platform with fast and cost-accurate transaction replaying, build real transaction benchmarks on popular Ethereum applications, and build a functional prototype of iBatch on Ethereum. The evaluation results show that iBatch saves 14.6%-59.1% Gas cost per invocation with a moderate 2-minute delay and 19.06%-31.52% Ether cost per invocation with a delay of 0.26-1.66 blocks.

cs.CR↗

ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation

This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled samples and a few labeled samples in the target domain, with the help of labeled samples from a source domain. Several SSDA methods have been proposed recently, which however fail to fully exploit the value of the few labeled target samples. In this paper, we propose Enhanced Categorical Alignment and Consistency Learning (ECACL), a holistic SSDA framework that incorporates multiple mutually complementary domain alignment techniques. ECACL includes two categorical domain alignment techniques that achieve class-level alignment, a strong data augmentation based technique that enhances the model's generalizability and a consistency learning based technique that forces the model to be robust with image perturbations. These techniques are applied on one or multiple of the three inputs (labeled source, unlabeled target, and labeled target) and align the domains from different perspectives. ECACL unifies them together and achieves fairly comprehensive domain alignments that are much better than the existing methods: For example, ECACL raises the state-of-the-art accuracy from 68.4 to 81.1 on VisDA2017 and from 45.5 to 53.4 on DomainNet for the 1-shot setting. Our code is available at \url{https://github.com/kailigo/pacl}.

cs.CV↗

Spectroscopic evidence for a large spot on the dimming Betelgeuse

During October 2019 and March 2020, the luminous red supergiant Betelgeuse demonstrated an unusually deep minimum of its brightness. It became fainter by more than one magnitude and this is the most significant dimming observed in the recent decades. While the reason for the dimming is debated, pre-phase of supernova explosion, obscuring dust, or changes in the photosphere of the star were suggested scenarios. Here, we present spectroscopic studies of Betelgeuse using high-resolution and high signal-to- noise ratio near-infrared spectra obtained at Weihai Observatory on four epochs in 2020 covering the phases of during and after dimming. We show that the dimming episode is caused by the dropping of its effective temperature by at least 170 K on 2020 January 31, that can be attributed to the emergence of a large dark spot on the surface of the star.

astro-ph.SR↗

Plot2Spectra: an Automatic Spectra Extraction Tool

Different types of spectroscopies, such as X-ray absorption near edge structure (XANES) and Raman spectroscopy, play a very important role in analyzing the characteristics of different materials. In scientific literature, XANES/Raman data are usually plotted in line graphs which is a visually appropriate way to represent the information when the end-user is a human reader. However, such graphs are not conducive to direct programmatic analysis due to the lack of automatic tools. In this paper, we develop a plot digitizer, named Plot2Spectra, to extract data points from spectroscopy graph images in an automatic fashion, which makes it possible for large scale data acquisition and analysis. Specifically, the plot digitizer is a two-stage framework. In the first axis alignment stage, we adopt an anchor-free detector to detect the plot region and then refine the detected bounding boxes with an edge-based constraint to locate the position of two axes. We also apply scene text detector to extract and interpret all tick information below the x-axis. In the second plot data extraction stage, we first employ semantic segmentation to separate pixels belonging to plot lines from the background, and from there, incorporate optical flow constraints to the plot line pixels to assign them to the appropriate line (data instance) they encode. Extensive experiments are conducted to validate the effectiveness of the proposed plot digitizer, which shows that such a tool could help accelerate the discovery and machine learning of materials properties.

cs.CV↗

The first photometric and spectroscopic analysis of the extremely low mass ratio contact binary NSVS 5029961

We performed photometric and spectroscopic investigations of NSVS 5029961 for the first time. The new BV(RI)$_c$-band light curves were obtained with the 1.0-m telescope at Weihai Observatory of Shandong University. Applying the Wilson-Devinney program, we found that NSVS 5029961 is an A-subtype shallow contact binary with extremely low mass ratio (q = 0.1515, f = 19.1\%). Six spectra have been obtained by LAMOST, and many chromospheric activity emission line indicators were detected in the spectra, revealing that the target exhibits strong chromospheric activity. We calculated the absolute parameters with the photometric solutions and Gaia distance, and estimated the initial masses of the two components and the age of the binary. The evolutionary status was discussed by using the mass-radius and mass-luminosity diagrams. The result shows the primary component is a little evolved star and the secondary component has evolved away from the main sequence. The formation and evolution investigations of NSVS 5029661 indicate that it may have evolved from a detached binary with short period and low mass ratio by angular momentum loss via magnetic braking and case A mass transfer, and is in a stable contact stage at present.

astro-ph.SR↗

Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment

Extending transfer learning to cooperative multi-agent reinforcement learning (MARL) has recently received much attention. In contrast to the single-agent setting, the coordination indispensable in cooperative MARL constrains each agent's policy. However, existing transfer methods focus exclusively on agent policy and ignores coordination knowledge. We propose a new architecture that realizes robust coordination knowledge transfer through appropriate decomposition of the overall coordination into several coordination patterns. We use a novel mixing network named level-adaptive QTransformer (LA-QTransformer) to realize agent coordination that considers credit assignment, with appropriate coordination patterns for different agents realized by a novel level-adaptive Transformer (LA-Transformer) dedicated to the transfer of coordination knowledge. In addition, we use a novel agent network named Population Invariant agent with Transformer (PIT) to realize the coordination transfer in more varieties of scenarios. Extensive experiments in StarCraft II micro-management show that LA-QTransformer together with PIT achieves superior performance compared with state-of-the-art baselines.

cs.AI↗

Photometric study and absolute parameters estimation of six totally eclipsing contact binaries

High precision CCD observations of six totally eclipsing contact binaries were presented and analyzed. It is found that only one target is an A-type contact binary (V429 Cam), while the others are W-type contact ones. By analyzing the times of light minima, we discovered that two of them exhibit secular period increase while three manifest long-term period decrease. For V1033 Her, a cyclic variation superimposed on the long-term increase was discovered. By comparing the Gaia distances with those calculated by the absolute parameters of 173 contact binaries, we found that Gaia distance can be applied to estimate absolute parameters for most contact binaries. The absolute parameters of our six targets were estimated by using their Gaia distances. The evolutionary status of contact binaries was studied, we found that the A- and W- subtype contact binaries may have different formation channels. The relationship between the spectroscopic and photometric mass ratios for 101 contact binaries was presented. It is discovered that the photometric mass ratios are in good agreement with the spectroscopic ones for almost all the totally eclipsing systems, which is corresponding to the results derived by Pribulla et al. and Terrell & Wilson.

astro-ph.SR↗

The data paper as a socio-linguistic epistemic object: A content analysis on the rhetorical moves used in data paper abstracts

The data paper is an emerging academic genre that focuses on the description of research data objects. However, there is a lack of empirical knowledge about this rising genre in quantitative science studies, particularly from the perspective of its linguistic features. To fill this gap, this research aims to offer a first quantitative examination of which rhetorical moves-rhetorical units performing a coherent narrative function-are used in data paper abstracts, as well as how these moves are used. To this end, we developed a new classification scheme for rhetorical moves in data paper abstracts by expanding a well-received system that focuses on English-language research article abstracts. We used this expanded scheme to classify and analyze rhetorical moves used in two flagship data journals, Scientific Data and Data in Brief. We found that data papers exhibit a combination of IMRaD- and data-oriented moves and that the usage differences between the journals can be largely explained by journal policies concerning abstract and paper structure. This research offers a novel examination of how the data paper, a novel data-oriented knowledge representation, is composed, which greatly contributes to a deeper understanding of data and data publication in the scholarly communication system.

cs.DL↗

RPCL: A Framework for Improving Cross-Domain Detection with Auxiliary Tasks

Cross-Domain Detection (XDD) aims to train an object detector using labeled image from a source domain but have good performance in the target domain with only unlabeled images. Existing approaches achieve this either by aligning the feature maps or the region proposals from the two domains, or by transferring the style of source images to that of target image. Contrasted with prior work, this paper provides a complementary solution to align domains by learning the same auxiliary tasks in both domains simultaneously. These auxiliary tasks push image from both domains towards shared spaces, which bridges the domain gap. Specifically, this paper proposes Rotation Prediction and Consistency Learning (PRCL), a framework complementing existing XDD methods for domain alignment by leveraging the two auxiliary tasks. The first one encourages the model to extract region proposals from foreground regions by rotating an image and predicting the rotation angle from the extracted region proposals. The second task encourages the model to be robust to changes in the image space by optimizing the model to make consistent class predictions for region proposals regardless of image perturbations. Experiments show the detection performance can be consistently and significantly enhanced by applying the two proposed tasks to existing XDD methods.

cs.CV↗

Light Field View Synthesis via Aperture Disparity and Warping Confidence Map

This paper presents a learning-based approach to synthesize the view from an arbitrary camera position given a sparse set of images. A key challenge for this novel view synthesis arises from the reconstruction process, when the views from different input images may not be consistent due to obstruction in the light path. We overcome this by jointly modeling the epipolar property and occlusion in designing a convolutional neural network. We start by defining and computing the aperture disparity map, which approximates the parallax and measures the pixel-wise shift between two views. While this relates to free-space rendering and can fail near the object boundaries, we further develop a warping confidence map to address pixel occlusion in these challenging regions. The proposed method is evaluated on diverse real-world and synthetic light field scenes, and it shows better performance over several state-of-the-art techniques.

cs.CV↗