arXiv subjects

Teng Wang

Publications and source records attributed to Teng Wang.

At least 127 records · Page 7Linked to original sources

arXiv2022

In-plane electronic anisotropy revealed by interlayer resistivity measurements on the iron-based superconductor parent compound CaFeAsF

Both cuprates and iron-based superconductors demonstrate nematicity, defined as the spontaneous breaking of rotational symmetry in electron systems. The nematic state can play a role in the high-transition-temperature superconductivity of these compounds. However, the microscopic mechanism responsible for the transport anisotropy in iron-based compounds remains debatable. Here, we investigate the electronic anisotropy of CaFeAsF by measuring its interlayer resistivity under magnetic fields with varying field directions. Counterintuitively, the interlayer resistivity was larger in the longitudinal configuration ($B \parallel I \parallel c$) than in the transverse one ($B \perp I \parallel c$). The interlayer resistivity exhibited a so-called coherence peak under in-plane fields and was highly anisotropic with respect to the in-plane field direction. At $T$ = 4 K and $B$ = 14 T, the magnetoresistance $Δρ/ρ_0$ was seven times larger in the $B \parallel b_o$ than in the $B \parallel a_o$ configuration. Our theoretical calculations of the conductivity based on the first-principles electronic band structure qualitatively reproduced the above observations but underestimated the magnitudes of the observed features. The proposed methodology can be a powerful tool for probing the nematic electronic state in various materials.

cond-mat.supr-con↗

arXiv2022

RescureService: A Benchmark Microservice System for the Research of Mobile Edge and Cloud Computing

The dramatic development of cloud and edge computing allows for better Quality of Service (QoS) in many scenarios by deploying services on cloud and edge servers. Microservice technology is also adopted in these scenarios to decompose complex business logic into many small independent services. Meanwhile, as microservice systems continue to grow, providing stable QoS in these systems becomes a challenge, and many different approaches have been proposed for stable QoS. However, the microservice systems used in the experiments of these work have problems such as the low number of services and a single type of service. Therefore, we developed the open-source benchmark microservice system RescureService with 20+ services, including database, front-end, business logic, data processing, and artificial intelligence services in the disaster relief scenario. Measuring tools are provided to measure the service properties to help researchers prepare experimental data, and the service properties pre-measured are also presented. Meanwhile, the fulfillment of benchmark requirements is detailed, and the results show that our RescureService meets the requirements of a benchmark system in research. Moreover, instructions are given to describe adopting our system in service computing as examples.

cs.DC↗

arXiv2022

Exploiting Context Information for Generic Event Boundary Captioning

Generic Event Boundary Captioning (GEBC) aims to generate three sentences describing the status change for a given time boundary. Previous methods only process the information of a single boundary at a time, which lacks utilization of video context information. To tackle this issue, we design a model that directly takes the whole video as input and generates captions for all boundaries parallelly. The model could learn the context information for each time boundary by modeling the boundary-boundary interactions. Experiments demonstrate the effectiveness of context information. The proposed method achieved a 72.84 score on the test set, and we reached the $2^{nd}$ place in this challenge. Our code is available at: \url{https://github.com/zjr2000/Context-GEBC}

cs.CV↗

arXiv2022

VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix

Existing vision-language pre-training (VLP) methods primarily rely on paired image-text datasets, which are either annotated by enormous human labors, or crawled from the internet followed by elaborate data cleaning techniques. To reduce the dependency on well-aligned image-text pairs, it is promising to directly leverage the large-scale text-only and image-only corpora. This paper proposes a data augmentation method, namely cross-modal CutMix (CMC), for implicit cross-modal alignment learning in unpaired VLP. Specifically, CMC transforms natural sentences from the textual view into a multi-modal view, where visually-grounded words in a sentence are randomly replaced by diverse image patches with similar semantics. There are several appealing proprieties of the proposed CMC. First, it enhances the data diversity while keeping the semantic meaning intact for tackling problems where the aligned data are scarce; Second, by attaching cross-modal noise on uni-modal data, it guides models to learn token-level interactions across modalities for better denoising. Furthermore, we present a new unpaired VLP method, dubbed as VLMixer, that integrates CMC with contrastive learning to pull together the uni-modal and multi-modal views for better instance-level alignments among different modalities. Extensive experiments on five downstream tasks show that VLMixer could surpass previous state-of-the-art unpaired VLP methods.

cs.CV↗

arXiv2022

Anomalous High-Field Magnetotransport in CaFeAsF due to the Quantum Hall Effect

CaFeAsF is an iron-based superconductor parent compound whose Fermi surface is quasi-two dimensional, composed of Dirac-electron and Schrödinger-hole cylinders elongated along the $c$ axis. We measured the longitudinal and Hall resistivities in CaFeAsF with the electrical current in the $ab$ plane in magnetic fields up to 45 T applied along the $c$ axis and obtained the corresponding conductivities via tensor inversion. We found that both the longitudinal and Hall conductivities approached zero above $\sim$40 T as the temperature was lowered to 0.4 K. Our analysis indicates that the Landau-level filling factor is $ν$ = 2 for both electrons and holes at these high field strengths, resulting in a total filling factor $ν$ = $ν_{hole} - ν_{electron}$ = 0. We therefore argue that the $ν$ = 0 quantum Hall state emerges under these conditions.

cond-mat.supr-con↗

arXiv2022

Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, and urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. Besides, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset. Code is available at https://github.com/WangLibo1995/GeoSeg.

cs.CV↗

arXiv2022

Transformer-Guided Convolutional Neural Network for Cross-View Geolocalization

Ground-to-aerial geolocalization refers to localizing a ground-level query image by matching it to a reference database of geo-tagged aerial imagery. This is very challenging due to the huge perspective differences in visual appearances and geometric configurations between these two views. In this work, we propose a novel Transformer-guided convolutional neural network (TransGCNN) architecture, which couples CNN-based local features with Transformer-based global representations for enhanced representation learning. Specifically, our TransGCNN consists of a CNN backbone extracting feature map from an input image and a Transformer head modeling global context from the CNN map. In particular, our Transformer head acts as a spatial-aware importance generator to select salient CNN features as the final feature representation. Such a coupling procedure allows us to leverage a lightweight Transformer network to greatly enhance the discriminative capability of the embedded features. Furthermore, we design a dual-branch Transformer head network to combine image features from multi-scale windows in order to improve details of the global feature representation. Extensive experiments on popular benchmark datasets demonstrate that our model achieves top-1 accuracy of 94.12\% and 84.92\% on CVUSA and CVACT_val, respectively, which outperforms the second-performing baseline with less than 50% parameters and almost 2x higher frame rate, therefore achieving a preferable accuracy-efficiency tradeoff.

cs.CV↗

arXiv2022

Nonlinear stability of planar viscous shock wave to three-dimensional compressible Navier-Stokes equations

We prove the nonlinear stability of the planar viscous shock up to a time-dependent shift for the three-dimensional (3D) compressible Navier-Stokes equations under the generic perturbations, in particular, without zero mass conditions. Moreover, the time-dependent shift function keeps the shock profile shape time-asymptotically. Our stability result is unconditional for the weak planar Navier-Stokes shock. Our proof is motivated by the $a$-contraction method (a kind of weighted $L^2$-relative entropy method) with time-dependent shift introduced in [10,11,13] for the stability of viscous shock in one-dimensional (1D) case. Instead of the classical anti-derivative techniques, we perform the stability analysis of planar Navier-Stokes shock in original $H^2$-perturbation framework and therefore zero mass conditions are not necessarily needed, which, in turn, brings out the essential difficulties due to the compressibility of viscous shock. Furthermore, compared with 1D case, there are additional difficulties coming from the wave propagation along the multi-dimensional transverse directions and their interactions with the viscous shock. To overcome these difficulties, a multi-dimensional version sharp weighted Poincar${\rm \acute{e}}$ inequality (see Lemma 3.1), $a$-contraction techniques with time-dependent shift, and some essential physical structures of the multi-dimensional Navier-Stokes system are fully used.

math.AP↗

arXiv2022

Semantic-Aware Pretraining for Dense Video Captioning

This report describes the details of our approach for the event dense-captioning task in ActivityNet Challenge 2021. We present a semantic-aware pretraining method for dense video captioning, which empowers the learned features to recognize high-level semantic concepts. Diverse video features of different modalities are fed into an event captioning module to generate accurate and meaningful sentences. Our final ensemble model achieves a 10.00 METEOR score on the test set.

cs.CV↗

arXiv2022

Topological frequency shift of quantum oscillation in CaFeAsF

Guo, Alexandradinata, \textit{et al.} have recently proposed that quantum-oscillation frequencies from Dirac/Weyl fermions exhibit a negative shift proportional to $T^2$ because of the energy dependence of the effective mass peculiar to a linear band-dispersion. We have measured Shubnikov--de Haas oscillation in CaFeAsF up to $T$ = 9 K. The frequency of the $α$ Dirac electron exhibits a negative shift with increasing $T$, while that of the $β$ Schrödinger hole does not. For $T \geqslant 5$ K where $β$ is negligible, the $α$-frequency shift is proportional to $T^2$ and its rate agrees with the theoretical prediction within experimental accuracy. At lower temperatures, the shifts of $α$ and $β$ deviate from theoretical expectations, which we ascribe to the inaccuracy in the frequency determination due to unfavorable interference between frequencies. Our results confirm that the topological frequency shift can be utilized to identify Dirac/Weyl fermions when quantum-oscillation frequencies can be determined accurately.

cond-mat.str-el↗

arXiv2022

Biasing Like Human: A Cognitive Bias Framework for Scene Graph Generation

Scene graph generation is a sophisticated task because there is no specific recognition pattern (e.g., "looking at" and "near" have no conspicuous difference concerning vision, whereas "near" could occur between entities with different morphology). Thus some scene graph generation methods are trapped into most frequent relation predictions caused by capricious visual features and trivial dataset annotations. Therefore, recent works emphasized the "unbiased" approaches to balance predictions for a more informative scene graph. However, human's quick and accurate judgments over relations between numerous objects should be attributed to "bias" (i.e., experience and linguistic knowledge) rather than pure vision. To enhance the model capability, inspired by the "cognitive bias" mechanism, we propose a novel 3-paradigms framework that simulates how humans incorporate the label linguistic features as guidance of vision-based representations to better mine hidden relation patterns and alleviate noisy visual propagation. Our framework is model-agnostic to any scene graph model. Comprehensive experiments prove our framework outperforms baseline modules in several metrics with minimum parameters increment and achieves new SOTA performance on Visual Genome dataset.

cs.CV↗

arXiv2022

Lattice QCD calculation of $K\to \ellν_\ell \ell'^+ \ell'^-$ decay width

We develop a methodology for the computation of the $K\to \ellν_\ell \ell'^+ \ell'^-$ decay width using lattice QCD and present an exploratory study here. We use a scalar function method to account for the momentum dependence of the decay amplitude and adopt the infinite volume reconstruction method to reduce the systematic errors such as the temporal truncation effects and the finite-volume effects. We then perform a four-body phase-space integral to obtain the decay width. The only remaining technical problem is the possible power-law finite-volume effects associated with the process of $K\toππ\ellν_\ell\to \ellν_\ell \ell'^+ \ell'^-$, where the intermediate state involves multiple hadrons. In this work, we use a gauge ensemble of twisted mass fermion with a pion mass $m_π=352$ MeV and a nearly-physical kaon mass. At this kinematics, the $ππ$ in the intermediate state cannot be on shell simultaneously as $2m_π>m_K$ and the finite-volume effects associated with $ππ$ state are exponentially suppressed. Using the developed methods mentioned above, we calculate the branching ratios for four channels of $K\to \ellν_\ell\ell'^+ \ell'^-$, and obtain the results comparable to the experimental measurements and ChPT predictions. Our work demonstrates the capability of lattice QCD to improve Standard Model prediction in $K\to \ellν_\ell \ell'^+ \ell'^-$ decay width.

hep-lat↗

arXiv2022

Multi-modal Visual Place Recognition in Dynamics-Invariant Perception Space

Visual place recognition is one of the essential and challenging problems in the fields of robotics. In this letter, we for the first time explore the use of multi-modal fusion of semantic and visual modalities in dynamics-invariant space to improve place recognition in dynamic environments. We achieve this by first designing a novel deep learning architecture to generate the static semantic segmentation and recover the static image directly from the corresponding dynamic image. We then innovatively leverage the spatial-pyramid-matching model to encode the static semantic segmentation into feature vectors. In parallel, the static image is encoded using the popular Bag-of-words model. On the basis of the above multi-modal features, we finally measure the similarity between the query image and target landmark by the joint similarity of their semantic and visual codes. Extensive experiments demonstrate the effectiveness and robustness of the proposed approach for place recognition in dynamic environments.

cs.CV↗

arXiv2021

End-to-End Dense Video Captioning with Parallel Decoding

Dense video captioning aims to generate multiple associated captions with their temporal locations from the video. Previous methods follow a sophisticated "localize-then-describe" scheme, which heavily relies on numerous hand-crafted components. In this paper, we proposed a simple yet effective framework for end-to-end dense video captioning with parallel decoding (PDVC), by formulating the dense caption generation as a set prediction task. In practice, through stacking a newly proposed event counter on the top of a transformer decoder, the PDVC precisely segments the video into a number of event pieces under the holistic understanding of the video content, which effectively increases the coherence and readability of predicted captions. Compared with prior arts, the PDVC has several appealing advantages: (1) Without relying on heuristic non-maximum suppression or a recurrent event sequence selection network to remove redundancy, PDVC directly produces an event set with an appropriate size; (2) In contrast to adopting the two-stage scheme, we feed the enhanced representations of event queries into the localization head and caption head in parallel, making these two sub-tasks deeply interrelated and mutually promoted through the optimization; (3) Without bells and whistles, extensive experiments on ActivityNet Captions and YouCook2 show that PDVC is capable of producing high-quality captioning results, surpassing the state-of-the-art two-stage methods when its localization accuracy is on par with them. Code is available at https://github.com/ttengwang/PDVC.

cs.CV↗

arXiv2021

Enhancement of the superconductivity and quantum metallic state in the thin film of superconducting Kagome metal KV$_3$Sb$_5$

Recently V-based Kagome metal attracted intense attention due to the emergence of superconductivity in the low temperature. Here we report the fabrication and physical investigations of the high quality single-crystalline thin films of the Kagome metal KV$_3$Sb$_5$. For the sample with the thickness of about 15 nm, the temperature dependent resistance reveals a Berezinskii-Kosterlitz-Thouless (BKT) type behavior, indicating the presence of two-dimensional superconductivity. Compared with the bulk sample, the onset transition temperature $T^{onset}_{c}$ and the out-of-plane upper critical field $H_{c2}$ are enhanced by 15\% and more than 10 times respectively. Moreover, the zero-resistance state is destroyed by a magnetic field as low as 50 Oe. Meanwhile, the temperature-independent resistance is observed in a wide field region, which is the hallmark of quantum metallic state. Our results provide evidences for the existence of unconventional superconductivity in this material.

cond-mat.supr-con↗

arXiv2021

ConfInLog: Leveraging Software Logs to Infer Configuration Constraints

Misconfigurations have become the dominant causes of software failures in recent years, drawing tremendous attention for their increasing prevalence and severity. Configuration constraints can preemptively avoid misconfiguration by defining the conditions that configuration options should satisfy. Documentation is the main source of configuration constraints, but it might be incomplete or inconsistent with the source code. In this regard, prior researches have focused on obtaining configuration constraints from software source code through static analysis. However, the difficulty in pointer analysis and context comprehension prevents them from collecting accurate and comprehensive constraints. In this paper, we observed that software logs often contain configuration constraints. We conducted an empirical study and summarized patterns of configuration-related log messages. Guided by the study, we designed and implemented ConfInLog, a static tool to infer configuration constraints from log messages. ConfInLog first selects configuration-related log messages from source code by using the summarized patterns, then infers constraints from log messages based on the summarized natural language patterns. To evaluate the effectiveness of ConfInLog, we applied our tool on seven popular open-source software systems. ConfInLog successfully inferred 22 to 163 constraints, in which 59.5% to 61.6% could not be inferred by the state-of-the-art work. Finally, we submitted 67 documentation patches regarding the constraints inferred by ConfInLog. The constraints in 29 patches have been confirmed by the developers, among which 10 patches have been accepted.

cs.SE↗

arXiv2021

A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis

Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) has been proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user's data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. What's more, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

cs.CR↗

arXiv2020

Local Differential Privacy based Federated Learning for Internet of Things

Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users' location information, which raises severe location privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The Three-Outputs mechanism is proposed which introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We further propose a suboptimal mechanism (PM-SUB) with a simple formula and comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB.

cs.CR↗