arXiv subjects

Peng Wang

Publications and source records attributed to Peng Wang.

At least 829 records · Page 46Linked to original sources

arXiv2020

Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs

Humans are able to describe image contents with coarse to fine details as they wish. However, most image captioning models are intention-agnostic which can not generate diverse descriptions according to different user intentions initiatively. In this work, we propose the Abstract Scene Graph (ASG) structure to represent user intention in fine-grained level and control what and how detailed the generated description should be. The ASG is a directed graph consisting of three types of \textbf{abstract nodes} (object, attribute, relationship) grounded in the image without any concrete semantic labels. Thus it is easy to obtain either manually or automatically. From the ASG, we propose a novel ASG2Caption model, which is able to recognise user intentions and semantics in the graph, and therefore generate desired captions according to the graph structure. Our model achieves better controllability conditioning on ASGs than carefully designed baselines on both VisualGenome and MSCOCO datasets. It also significantly improves the caption diversity via automatically sampling diverse ASGs as control signals.

cs.CV↗

arXiv2020

Cops-Ref: A new Dataset and Task on Compositional Referring Expression Comprehension

Referring expression comprehension (REF) aims at identifying a particular object in a scene by a natural language expression. It requires joint reasoning over the textual and visual domains to solve the problem. Some popular referring expression datasets, however, fail to provide an ideal test bed for evaluating the reasoning ability of the models, mainly because 1) their expressions typically describe only some simple distinctive properties of the object and 2) their images contain limited distracting information. To bridge the gap, we propose a new dataset for visual reasoning in context of referring expression comprehension with two main features. First, we design a novel expression engine rendering various reasoning logics that can be flexibly combined with rich visual properties to generate expressions with varying compositionality. Second, to better exploit the full reasoning chain embodied in an expression, we propose a new test setting by adding additional distracting images containing objects sharing similar properties with the referent, thus minimising the success rate of reasoning-free cross-domain alignment. We evaluate several state-of-the-art REF models, but find none of them can achieve promising performance. A proposed modular hard mining strategy performs the best but still leaves substantial room for improvement. We hope this new dataset and task can serve as a benchmark for deeper visual reasoning analysis and foster the research on referring expression comprehension.

cs.CV↗

arXiv2020

Chaotic Motion around a Black Hole under Minimal Length Effects

We use the Melnikov method to identify chaotic behavior in geodesic motion perturbed by the minimal length effects around a Schwarzschild black hole. Unlike the integrable unperturbed geodesic motion, our results show that the perturbed homoclinic orbit, which is a geodesic joining the unstable circular orbit to itself, becomes chaotic in the sense that Smale horseshoes chaotic structure is present in phase space.

gr-qc↗

arXiv2020

NAS-FCOS: Fast Neural Architecture Search for Object Detection

The success of deep neural networks relies on significant architecture engineering. Recently neural architecture search (NAS) has emerged as a promise to greatly reduce manual effort in network design by automatically searching for optimal architectures, although typically such algorithms need an excessive amount of computational resources, e.g., a few thousand GPU-days. To date, on challenging vision tasks such as object detection, NAS, especially fast versions of NAS, is less studied. Here we propose to search for the decoder structure of object detectors with search efficiency being taken into consideration. To be more specific, we aim to efficiently search for the feature pyramid network (FPN) as well as the prediction head of a simple anchor-free object detector, namely FCOS, using a tailored reinforcement learning paradigm. With carefully designed search space, search algorithms and strategies for evaluating network quality, we are able to efficiently search a top-performing detection architecture within 4 days using 8 V100 GPUs. The discovered architecture surpasses state-of-the-art object detection models (such as Faster R-CNN, RetinaNet and FCOS) by 1.5 to 3.5 points in AP on the COCO dataset, with comparable computation complexity and memory footprint, demonstrating the efficacy of the proposed NAS for object detection.

cs.CV↗

arXiv2020

Sympathetic eruptions of two filaments with an identifiable causal link observed by the Solar Dynamics Observatory

Filament eruptions occurring at different places within a relatively short time internal, but with a certain physical causal connection are usually known as sympathetic eruption. Studies on sympathetic eruptions are not uncommon. However, in the existed reports, the causal links between sympathetic eruptions remain rather speculative. In this work, we present detailed observations of a sympathetic filament eruption event, where an identifiable causal link between two eruptive filaments is observed. On 2015 November 15, two filaments (F1 in the north and F2 in the south) were located at the southwestern quadrant of solar disk. The main axes of them were almost parallel to each other. Around 22:20 UT, F1 began to erupt, forming two flare ribbons. The southwestern ribbon apparently moved to southwest and intruded southeast part of F2. This continuous intrusion caused F2's eventual eruption. Accompanying the eruption of F2, flare ribbons and post-flare loops appeared in northwest region of F2. Meanwhile, neither flare ribbons nor post-flare loops could be observed in southeastern area of F2. In addition, the nonlinear force-free field (NLFFF) extrapolations show that the magnetic fields above F2 in the southeast region are much weaker than that in the northwest region. These results imply that the overlying magnetic fields of F2 were not uniform. So we propose that the southwest ribbon formed by eruptive F1 invaded F2 from its southeast region with relatively weaker overlying magnetic fields in comparison with its northwest region, disturbing F2 and leading F2 to erupt eventually.

astro-ph.SR↗

arXiv2020

On symmetric Willmore surfaces in spheres II: the orientation reversing case

In this paper we provide a systematic treatment of Willmore surfaces with orientation reversing symmetries and illustrate the theory by (old and new) examples. We apply our theory to isotropic Willmore two-spheres in $S^4$ and derive a necessary condition for such ( possibly branched) isotropic surfaces to descend to (possibly branched) maps from $\mathbb{R} P^2$ to $S^4$. The Veronese sphere and several other examples of non-branched Willmore immersions from $\mathbb{R} P^2$ to $S^4$ are derived as an illustration of the general theory. The Willmore immersions of $\mathbb{R} P^2$, just mentioned and different from the Veronese sphere, are new to the authors' best knowledge.

math.DG↗

arXiv2020

Using Sampled Network Data With The Autologistic Actor Attribute Model

Social science research increasingly benefits from statistical methods for understanding the structured nature of social life, including for social network data. However, the application of statistical network models within large-scale community research is hindered by too little understanding of the validity of their inferences under realistic data collection conditions, including sampled or missing network data. The autologistic actor attribute model (ALAAM) is a statistical model based on the well-established exponential random graph model (ERGM) for social networks. ALAAMs can be regarded as a social influence model, predicting an individual-level outcome based on the actor's network ties, concurrent outcomes of his/her network partners, and attributes of the actor and his/her network partners. In particular, an ALAAM can be used to measure contagion effects, that is, the propensity of two actors connected by a social network tie to both have the same value of an attribute. We investigate the effect of using simple random samples and snowball samples of network data on ALAAM parameter inference, and find that parameter inference can still work well even with a nontrivial fraction of missing nodes. However it is safer to take a snowball sample of the network and estimate conditional on the snowball sampling structure.

cs.SI↗

arXiv2020

Dislocation Slip or Phase Transformation Lead to Room-Temperature Plasticity in Diamond: Comment on Plastic Deformation of Single-Crystal Diamond Nanopillars

Despite decades of extensive research on mechanical properties of diamond, much remains to be understood in term of plastic deformation mechanisms due to the poor deformability at room temperature. In a recent work in Advanced Materials, it was claimed that room-temperature plasticity occurred in <001>-oriented single-crystal diamond nanopillars based on observation of unrecovered deformation inside scanning electron microscope. The plastic deformation was suggested to be mediated by a phase transition from sp3 carbon to an O8-carbon phase by molecular dynamics simulations. By comparison, our in-situ transmission electron microscopy study reveals that the room-temperature plasticity can be carried out by dislocation slip in both <100> and <111>-oriented diamond nanopillars. The brittle-to-ductile transition is highly dependent on the stress state. We note that the surface structure may play a significant role in the deformation mechanisms as the incipient plasticity always occurs from the surface region in nanoscale diamonds.

cond-mat.mtrl-sci↗

arXiv2020

Construct $α^{\prime}$ corrected or loop corrected solutions without curvature singularities

For the bosonic gravi-dilaton system, we provide systematical approaches to construct non-perturbative string cosmological solutions without curvature singularities, which can match the perturbative solution to any order in $α^{\prime}$ expansion. When higher order $α^{\prime}$ corrections are calculated, they can be straightforwardly plugged in to generate compatible non-perturbative evolutions without curvature singularities. We also give a (phenomenological) map between $α^{\prime}$ corrected EOM and loop corrected EOM. This map enables us to easily generate a loop corrected solution from an $α^{\prime}$ corrected solution, and vice versa, therefore substantially enlarges the solution space.

hep-th↗

arXiv2020

Wettability and surface energy of parylene F

Parylenes are barrier materials employed as protective layers. However, many parylenes are unsuitable for applications under harsh conditions. A new material, parylene F, demonstrates considerable potential for a wide range of applications due to its high temperature and UV resistance. For the first time, the wettability and surface energy of parylene F were investigated to determine the feasibility of parylene F as an alternative to the commonly employed parylene C. The results show that parylene F has a hydrophobic surface with a water contact angle of 109.63 degrees. We found that 3.5 ul probe liquid is an optimal value for the contact angle measurement of parylene F. Moreover, we found that the Owens-Wendt-Kaelble and the Lifshitz-van der Waals/acid-base approaches are unsuitable for determining the surface energy of parylene F, whereas an approach based on the limitless liquid-solid interface wetting system is compatible. Furthermore, the results show that parylene F has a surface energy of 39.05 mJ/m2. Considering the improved resistance, relatively low cost, and the desirable properties, parylene F can replace parylene C for applications under harsh conditions.

physics.chem-ph↗

arXiv2020

Real-time Segmentation and Facial Skin Tones Grading

Modern approaches for semantic segmention usually pay too much attention to the accuracy of the model, and therefore it is strongly recommended to introduce cumbersome backbones, which brings heavy computation burden and memory footprint. To alleviate this problem, we propose an efficient segmentation method based on deep convolutional neural networks (DCNNs) for the task of hair and facial skin segmentation, which achieving remarkable trade-off between speed and performance on three benchmark datasets. As far as we know, the accuracy of skin tones classification is usually unsatisfactory due to the influence of external environmental factors such as illumination and background noise. Therefore, we use the segmentated face to obtain a specific face area, and further exploit the color moment algorithm to extract its color features. Specifically, for a 224 x 224 standard input, using our high-resolution spatial detail information and low-resolution contextual information fusion network (HLNet), we achieve 90.73% Pixel Accuracy on Figaro1k dataset at over 16 FPS in the case of CPU environment. Additional experiments on CamVid dataset further confirm the universality of the proposed model. We further use masked color moment for skin tones grade evaluation and approximate 80% classification accuracy demonstrate the feasibility of the proposed scheme.Code is available at https://github.com/JACKYLUO1991/Face-skin-hair-segmentaiton-and-skin-color-evaluation.

cs.CV↗

arXiv2019

Strong cosmic censorship for a scalar field in an Einstein-Maxwell-Gauss-Bonnet-de Sitter black hole

It has been shown that the Christodoulou version of the Strong Cosmic Censorship (SCC) conjecture can be violated for a scalar field in a near-extremal Reissner-Nordstrom-de Sitter black hole. In this paper, we investigate the effects of higher derivative corrections to the Einstein-Hilbert action on the validity of SCC, by considering a neutral massless scalar perturbation in 5- and 6-dimensional Einstein-Maxwell-Gauss-Bonnet-de Sitter black holes. Our numerical results exhibit that the higher derivative term plays a different role in the d = 5 case than it does in the d = 6 case. For d = 5, the SCC violation region increases as the strength of the higher derivative term increases. For d = 6, the SCC violation region first increases and then decreases as the higher derivative correction becomes stronger, and SCC can always be restored for a black hole with a fixed charge ratio when the higher derivative correction is strong enough. Finally, we find that the C2 version of SCC is respected in the d = 6 case, but can be violated in some near-extremal regime in the d = 5 case.

gr-qc↗

arXiv2019

AutoRemover: Automatic Object Removal for Autonomous Driving Videos

Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm \emph{AutoRemover}, designed specifically for generating street-view videos without any moving objects. In our setup we have two challenges: the first is the shadow, shadows are usually unlabeled but tightly coupled with the moving objects. The second is the large ego-motion in the videos. To deal with shadows, we build up an autonomous driving shadow dataset and design a deep neural network to detect shadows automatically. To deal with large ego-motion, we take advantage of the multi-source data, in particular the 3D data, in autonomous driving. More specifically, the geometric relationship between frames is incorporated into an inpainting deep neural network to produce high-quality structurally consistent video output. Experiments show that our method outperforms other state-of-the-art (SOTA) object removal algorithms, reducing the RMSE by over $19\%$.

cs.CV↗

arXiv2019

Thermodynamic Geometry of AdS Black Holes and Black Holes in a Cavity

The thermodynamic geometry has been proved to be quite useful in understanding the microscopic structure of black holes. We investigate the phase structure, thermodynamic geometry and critical behavior of a Reissner-Nordstrom-AdS black hole and a Reissner-Nordstrom black hole in a cavity, which can reach equilibrium in a canonical ensemble. Although the phase structure and critical behavior of both cases show striking resemblance, we find that there exist significant differences between the thermodynamic geometry of these two cases. Our results imply that there may be a connection between the black hole microstates and its boundary condition.

gr-qc↗

arXiv2019

CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion

Depth Completion deals with the problem of converting a sparse depth map to a dense one, given the corresponding color image. Convolutional spatial propagation network (CSPN) is one of the state-of-the-art (SoTA) methods of depth completion, which recovers structural details of the scene. In this paper, we propose CSPN++, which further improves its effectiveness and efficiency by learning adaptive convolutional kernel sizes and the number of iterations for the propagation, thus the context and computational resources needed at each pixel could be dynamically assigned upon requests. Specifically, we formulate the learning of the two hyper-parameters as an architecture selection problem where various configurations of kernel sizes and numbers of iterations are first defined, and then a set of soft weighting parameters are trained to either properly assemble or select from the pre-defined configurations at each pixel. In our experiments, we find weighted assembling can lead to significant accuracy improvements, which we referred to as "context-aware CSPN", while weighted selection, "resource-aware CSPN" can reduce the computational resource significantly with similar or better accuracy. Besides, the resource needed for CSPN++ can be adjusted w.r.t. the computational budget automatically. Finally, to avoid the side effects of noise or inaccurate sparse depths, we embed a gated network inside CSPN++, which further improves the performance. We demonstrate the effectiveness of CSPN++on the KITTI depth completion benchmark, where it significantly improves over CSPN and other SoTA methods.

cs.CV↗

arXiv2019

Discriminative and Robust Online Learning for Siamese Visual Tracking

The problem of visual object tracking has traditionally been handled by variant tracking paradigms, either learning a model of the object's appearance exclusively online or matching the object with the target in an offline-trained embedding space. Despite the recent success, each method agonizes over its intrinsic constraint. The online-only approaches suffer from a lack of generalization of the model they learn thus are inferior in target regression, while the offline-only approaches (e.g., convolutional siamese trackers) lack the target-specific context information thus are not discriminative enough to handle distractors, and robust enough to deformation. Therefore, we propose an online module with an attention mechanism for offline siamese networks to extract target-specific features under L2 error. We further propose a filter update strategy adaptive to treacherous background noises for discriminative learning, and a template update strategy to handle large target deformations for robust learning. Effectiveness can be validated in the consistent improvement over three siamese baselines: SiamFC, SiamRPN++, and SiamMask. Beyond that, our model based on SiamRPN++ obtains the best results over six popular tracking benchmarks and can operate beyond real-time.

cs.CV↗

arXiv2019

Resilient Unit Commitment for Day-ahead Market Considering Probabilistic Impacts of Hurricanes

In the face of extreme events, e.g., hurricanes, the transmission systems, especially the transmission lines, are affected across time and space. To mitigate these impacts on the day-ahead market from a probabilistic perspective, a resilient unit commitment (UC) problem is formulated as a two-stage robust optimization (RO) problem. In the first stage, the status, energy, and reserves of generators are pre-scheduled to minimize the operational cost, responding to the worst line failure scenario in the operating day. The failure, operation status, and repair of transmission lines are depicted by a novel robust uncertainty set with chance constraint considering the repair of failed lines. This chance constraint is reformulated to its deterministic equivalence. Using both load shedding and generation curtailment, a recourse problem is formulated in the second stage considering the time-varying transmission lines operation status. The formulated RO problem is solved using a column-and-constraint generation scheme. Simulations are conducted on IEEE-24 and two-area IEEE reliability test system-1996 under hurricanes and results verify the effectiveness of the proposed method on the conservation of uncertainty set, worst-case line failure scenario detection and repair preparedness.

math.OC↗

arXiv2019

Single-shot fourth-order auto-correlator

The temporal contrast is one of the most important parameters of an ultra-high intense laser pulse. Third-order auto-correlator or cross-correlator have been widely used to characterize the temporal contrast of an ultra-intense laser pulse in the past decades. Here, a novel and simple single-shot fourth-order auto-correlator to characterize the temporal contrast with higher time resolution and better pulse contrast fidelity in comparison to third-order correlators is proposed. The single-shot fourth-order autocorrelation consists of a frequency degenerate four-wave mixing process and a sum-frequency mixing process. The proof-of-principle experiments show that a dynamic range of approximately 10^11 compared with the noise level, a time resolution of approximately 160 fs, and a time window of 65 ps can be successfully obtained using the novel single-shot fourth-order auto-correlator, which is the highest dynamic range with simultaneous high time resolution for single-shot temporal contrast measurement so far. Furthermore, the temporal contrast of laser pulse from a PW laser system is successfully measured in single-shot with a dynamic range of about 2*10^10 and simultaneous a time resolution of 160 fs.

physics.optics↗