arXiv subjects

Xin Qin

Publications and source records attributed to Xin Qin.

45 records · Page 3Linked to original sources

arXiv2021

Composite Localization for Human Pose Estimation

The existing human pose estimation methods are confronted with inaccurate long-distance regression or high computational cost due to the complex learning objectives. This work proposes a novel deep learning framework for human pose estimation called composite localization to divide the complex learning objective into two simpler ones: a sparse heatmap to find the keypoint's approximate location and two short-distance offsetmaps to obtain its final precise coordinates. To realize the framework, we construct two types of composite localization networks: CLNet-ResNet and CLNet-Hourglass. We evaluate the networks on three benchmark datasets, including the Leeds Sports Pose dataset, the MPII Human Pose dataset, and the COCO keypoints detection dataset. The experimental results show that our CLNet-ResNet50 outperforms SimpleBaseline by 1.14% with about 1/2 GFLOPs. Our CLNet-Hourglass outperforms the original stacked-hourglass by 4.45% on COCO.

cs.CV↗

arXiv2021

FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

With the rapid development of computing technology, wearable devices such as smart phones and wristbands make it easy to get access to people's health information including activities, sleep, sports, etc. Smart healthcare achieves great success by training machine learning models on a large quantity of user data. However, there are two critical challenges. Firstly, user data often exists in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Secondly, the models trained on the cloud fail on personalization. In this paper, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds personalized models by transfer learning. It is able to achieve accurate and personalized healthcare without compromising privacy and security. Experiments demonstrate that FedHealth produces higher accuracy (5.3% improvement) for wearable activity recognition when compared to traditional methods. FedHealth is general and extensible and has the potential to be used in many healthcare applications.

cs.LG↗

arXiv2021

Image of a regular phantom compact object and its luminosity under spherical accretions

We have studied the shadow and its luminosity for a static and regular phantom compact object under the static spherical accretion and the infalling spherical accretion, respectively. Comparing with the usual Schwarzschild black hole, the presence of phantom hair yields the larger black hole shadow and the darker image. In both spherical accretion models, with the increase of phantom parameter, the maximum luminosity occurred at photon ring and the brightness of the central region in the shadow decrease, but in the region far from the shadow, the luminosity of image slightly increases. The image of a phantom wormhole and its luminosity have similar behaviors. Moreover, as the phantom charge parameter $α$ increases up to the critical value at where the compact object changes from black hole to wormhole, there exists a jump for the specific intensity, which also appears in the slowly rotating case. This implies that the phantom hair is imprinted on both the shadow radius and the intensity of the electromagnetic flux radiation around compacted object.

gr-qc↗

arXiv2021

PcmNet: Position-Sensitive Context Modeling Network for Temporal Action Localization

Temporal action localization is an important and challenging task that aims to locate temporal regions in real-world untrimmed videos where actions occur and recognize their classes. It is widely acknowledged that video context is a critical cue for video understanding, and exploiting the context has become an important strategy to boost localization performance. However, previous state-of-the-art methods focus more on exploring semantic context which captures the feature similarity among frames or proposals, and neglect positional context which is vital for temporal localization. In this paper, we propose a temporal-position-sensitive context modeling approach to incorporate both positional and semantic information for more precise action localization. Specifically, we first augment feature representations with directed temporal positional encoding, and then conduct attention-based information propagation, in both frame-level and proposal-level. Consequently, the generated feature representations are significantly empowered with the discriminative capability of encoding the position-aware context information, and thus benefit boundary detection and proposal evaluation. We achieve state-of-the-art performance on both two challenging datasets, THUMOS-14 and ActivityNet-1.3, demonstrating the effectiveness and generalization ability of our method.

cs.CV↗

arXiv2020

Increasing the Flexibility of Combined Heat and Power Systems through Optimal Dispatch with Variable Mass Flow

In combined heat and power systems, varying mass flow can better make use of the heating system inertia to increase the flexibility of electric power systems. This is challenging, however, due to integer variables and bilinear constraints in existing optimal dispatch models. In this paper, we incorporate an improved heat pipeline model to eliminate complexity from integer variables without compromise on accuracy. Subsequently, the resulting optimal dispatch model with bilinear constraints is solved by the proposed modified Generalized Benders Decomposition method, which decomposes the optimal dispatch model into a convex sub-problem with the fixed mass flow and a simple upper-level problem searching for the optimal mass flow. Comparisons with existing benchmarks show that the proposed method can achieve lower operation costs with outstanding computational efficiency.

eess.SY↗

arXiv2020

FOCUS: Dealing with Label Quality Disparity in Federated Learning

Ubiquitous systems with End-Edge-Cloud architecture are increasingly being used in healthcare applications. Federated Learning (FL) is highly useful for such applications, due to silo effect and privacy preserving. Existing FL approaches generally do not account for disparities in the quality of local data labels. However, the clients in ubiquitous systems tend to suffer from label noise due to varying skill-levels, biases or malicious tampering of the annotators. In this paper, we propose Federated Opportunistic Computing for Ubiquitous Systems (FOCUS) to address this challenge. It maintains a small set of benchmark samples on the FL server and quantifies the credibility of the client local data without directly observing them by computing the mutual cross-entropy between performance of the FL model on the local datasets and that of the client local FL model on the benchmark dataset. Then, a credit weighted orchestration is performed to adjust the weight assigned to clients in the FL model based on their credibility values. FOCUS has been experimentally evaluated on both synthetic data and real-world data. The results show that it effectively identifies clients with noisy labels and reduces their impact on the model performance, thereby significantly outperforming existing FL approaches.

cs.LG↗

arXiv2019

Distributed Optimal Frequency Control for Integrated Energy Systems with Electricity and Heat

With more and more distributed energy resources (DERs) deployed in Integrated Energy Systems (IESs), frequency stability challenges the pursuit of reliability and efficiency. This paper proposes a fully-distributed frequency control method for load-side DERs, in which the optimality can be guaranteed in an IES where electricity and heat are coupled. Moreover, the global asymptotic stability of the closed-loop system is proved and the robustness with respect to inaccurate coefficients is shown. Case studies demonstrate the effectiveness of proposed method.

eess.SY↗

arXiv2019

Superconductivity in single-crystalline, aluminum- and gallium-hyperdoped germanium

Superconductivity in group IV semiconductors is desired for hybrid devices combining both semiconducting and superconducting properties. Following boron doped diamond and Si, superconductivity has been observed in gallium doped Ge, however the obtained specimen is in polycrystalline form [Herrmannsdörfer et al., Phys. Rev. Lett. 102, 217003 (2009)]. Here, we present superconducting single-crystalline Ge hyperdoped with gallium or aluminium by ion implantation and rear-side flash lamp annealing. The maximum concentration of Al and Ga incorporated into substitutional positions in Ge is eight times higher than the equilibrium solid solubility. This corresponds to a hole concentration above 10^21 cm-3. Using density functional theory in the local density approximation and pseudopotential plane-wave approach, we show that the superconductivity in p-type Ge is phonon-mediated. According to the ab initio calculations the critical superconducting temperature for Al- and Ga-doped Ge is in the range of 0.45 K for 6.25 at.% of dopant concentration being in a qualitative agreement with experimentally obtained values.

cond-mat.mtrl-sci↗

arXiv2019

Joint Probability Distribution of Prediction Errors of ARIMA

Producing probabilistic guarantee for several steps of a predicted signal follow a temporal logic defined behavior has its rising importance in monitoring. In this paper, we derive a method to compute the joint probability distribution of prediction errors of multiple steps based on Autoregressive Integrated Moving Average(ARIMA) model. We cover scenarios in stationary process and intrinsically stationary process for univariate and multivariate.

eess.SY↗