arXiv subjects

Chenhao Zhang

Publications and source records attributed to Chenhao Zhang.

61 records · Page 4Linked to original sources

arXiv2023

FuseFPS: Accelerating Farthest Point Sampling with Fusing KD-tree Construction for Point Clouds

Point cloud analytics has become a critical workload for embedded and mobile platforms across various applications. Farthest point sampling (FPS) is a fundamental and widely used kernel in point cloud processing. However, the heavy external memory access makes FPS a performance bottleneck for real-time point cloud processing. Although bucket-based farthest point sampling can significantly reduce unnecessary memory accesses during the point sampling stage, the KD-tree construction stage becomes the predominant contributor to execution time. In this paper, we present FuseFPS, an architecture and algorithm co-design for bucket-based farthest point sampling. We first propose a hardware-friendly sampling-driven KD-tree construction algorithm. The algorithm fuses the KD-tree construction stage into the point sampling stage, further reducing memory accesses. Then, we design an efficient accelerator for bucket-based point sampling. The accelerator can offload the entire bucket-based FPS kernel at a low hardware cost. Finally, we evaluate our approach on various point cloud datasets. The detailed experiments show that compared to the state-of-the-art accelerator QuickFPS, FuseFPS achieves about 4.3$\times$ and about 6.1$\times$ improvements on speed and power efficiency, respectively.

cs.AR↗

arXiv2023

Accessibility optimization of public transportation in historical districts:a study of Belin District, Xian

With the continuous improvement of urbanization and motorization, travel demand in historical blocks is higher than before. The contradiction between supply of transportation facilities and environmental protection is more serious. Traditional public transport planning methods aim to improve mobility. However, several existing studies do not put travelers in the first place, which ignore quantitative description of land uses and the characteristics of travelers. This paper intends to improve individual accessibility of public transportation in historical districts. Based on POI data, the calculation of accessibility combines the utility model and spatial interaction model. To optimize public transportation, the goal of improving individual accessibility is transformed into reducing residents' travel negative utility. Using a real-world dataset in Beilin District of Xian, the performance of the proposed model is evaluated. This paper calculates the present accessibility and uses ant colony algorithm to optimize public transportation. The results demonstrate that the calculation and optimization of public transportation accessibility are practical, which are also valuable to public transportation planning and organization in historical blocks.

physics.soc-ph↗

arXiv2022

A Boosting Algorithm for Positive-Unlabeled Learning

Positive-unlabeled (PU) learning deals with binary classification problems when only positive (P) and unlabeled (U) data are available. Many recent PU methods are based on neural networks, but little has been done to develop boosting algorithms for PU learning, despite boosting algorithms' strong performance on many fully supervised classification problems. In this paper, we propose a novel boosting algorithm, AdaPU, for PU learning. Similarly to AdaBoost, AdaPU aims to optimize an empirical exponential loss, but the loss is based on the PU data, rather than on positive-negative (PN) data. As in AdaBoost, we learn a weighted combination of weak classifiers by learning one weak classifier and its weight at a time. However, AdaPU requires a very different algorithm for learning the weak classifiers and determining their weights. This is because AdaPU learns a weak classifier and its weight using a weighted positive-negative (PN) dataset with some negative data weights $-$ the dataset is derived from the original PU data, and the data weights are determined by the current weighted classifier combination, but some data weights are negative. Our experiments showed that AdaPU outperforms neural networks on several benchmark PU datasets, including a large-scale challenging cyber security dataset.

cs.LG↗

arXiv2021

SnapCheck: Automated Testing for Snap Programs

Programming environments such as Snap, Scratch, and Processing engage learners by allowing them to create programming artifacts such as apps and games, with visual and interactive output. Learning programming with such a media-focused context has been shown to increase retention and success rate. However, assessing these visual, interactive projects requires time and laborious manual effort, and it is therefore difficult to offer automated or real-time feedback to students as they work. In this paper, we introduce SnapCheck, a dynamic testing framework for Snap that enables instructors to author test cases with Condition-Action templates. The goal of SnapCheck is to allow instructors or researchers to author property-based test cases that can automatically assess students' interactive programs with high accuracy. Our evaluation of SnapCheck on 162 code snapshots from a Pong game assignment in an introductory programming course shows that our automated testing framework achieves at least 98% accuracy over all rubric items, showing potentials to use SnapCheck for auto-grading and providing formative feedback to students.

cs.SE↗

arXiv2020

Optimal Budget-Feasible Mechanisms for Additive Valuations

In this paper, we show a tight approximation guarantee for budget-feasible mechanisms with an additive buyer. We propose a new simple randomized mechanism with approximation ratio of $2$, improving the previous best known result of $3$. Our bound is tight with respect to either the optimal offline benchmark, or its fractional relaxation. We also present a simple deterministic mechanism with the tight approximation guarantee of $3$ against the fractional optimum, improving the best known result of $(2+ \sqrt{2})$ for the weaker integral benchmark.

cs.GT↗

arXiv2016

A Chandra Study of the Image Power Spectra of 41 Cool Core and Non-Cool Core Galaxy Clusters

In this work we propose a new diagnostic to segregate cool core (CC) clusters from non-cool core (NCC) clusters by studying the two-dimensional power spectra of the X-ray images observed with the Chandra X-ray observatory. Our sample contains 41 members ($z=0.01\sim 0.54$), which are selected from the Chandra archive when a high photon count, an adequate angular resolution, a relatively complete detector coverage, and coincident CC-NCC classifications derived with three traditional diagnostics are simultaneously guaranteed. We find that in the log-log space the derived image power spectra can be well represented by a constant model component at large wavenumbers, while at small wavenumbers a power excess beyond the constant component appears in all clusters, with a clear tendency that the excess is stronger in CC clusters. By introducing a new CC diagnostic parameter, i.e., the power excess index (PEI), we classify the clusters in our sample and compare the results with those obtained with three traditional CC diagnostics. We find that the results agree with each other very well. By calculating the PEI values of the simulated clusters, we find that the new diagnostic works well at redshifts up to 0.5 for intermediately sized and massive clusters with a typical Chandra or XMM pointing observation. The new CC diagnostic has several advantages over its counterparts, e.g., it is free of the effects of the commonly seen centroid shift of the X-ray halo caused by merger event, and the corresponding calculation is straightforward, almost irrelevant to the complicated spectral analysis.

astro-ph.CO↗

arXiv2015

A Chandra Study of Radial Temperature Profiles of the Intra-Cluster Medium in 50 Galaxy Clusters

In order to investigate the spatial distribution of the ICM temperature in galaxy clusters in a quantitative way and probe the physics behind, we analyze the X-ray spectra of a sample of 50 galaxy clusters, which were observed with the Chandra ACIS instrument in the past 15 years, and measure the radial temperature profiles out to $0.45r_{500}$. We construct a physical model that takes into account the effects of gravitational heating, thermal history (such as radiative cooling, AGN feedback, and thermal conduction) and work done via gas compression, and use it to fit the observed temperature profiles by running Bayesian regressions. The results show that in all cases our model provides an acceptable fit at the 68% confidence level. To further validate this model we select nine clusters that have been observed with both Chandra (out to $\gtrsim 0.3r_{500}$) and Suzaku (out to $\gtrsim 1.5r_{500}$), fit their Chandra spectra with our model, and compare the extrapolation of the best-fits with the Suzaku measurements. We find that the model profiles agree with the Suzaku results very well in seven clusters. In the rest two clusters the difference between the model and observation is possibly caused by local thermal substructures. Our study also implies that for most of the clusters the assumption of hydrostatic equilibrium is safe out to at least $0.5r_{500}$, and the non-gravitational interactions between dark matter and its luminous counterpart is consistent with zero.

astro-ph.CO↗