arXiv subjects

Ru Jia

Publications and source records attributed to Ru Jia.

5 recordsLinked to original sources

arXiv2025

CylinderPlane: Nested Cylinder Representation for 3D-aware Image Generation

While the proposal of the Tri-plane representation has advanced the development of the 3D-aware image generative models, problems rooted in its inherent structure, such as multi-face artifacts caused by sharing the same features in symmetric regions, limit its ability to generate 360$^\circ$ view images. In this paper, we propose CylinderPlane, a novel implicit representation based on Cylindrical Coordinate System, to eliminate the feature ambiguity issue and ensure multi-view consistency in 360$^\circ$. Different from the inevitable feature entanglement in Cartesian coordinate-based Tri-plane representation, the cylindrical coordinate system explicitly separates features at different angles, allowing our cylindrical representation possible to achieve high-quality, artifacts-free 360$^\circ$ image synthesis. We further introduce the nested cylinder representation that composites multiple cylinders at different scales, thereby enabling the model more adaptable to complex geometry and varying resolutions. The combination of cylinders with different resolutions can effectively capture more critical locations and multi-scale features, greatly facilitates fine detail learning and robustness to different resolutions. Moreover, our representation is agnostic to implicit rendering methods and can be easily integrated into any neural rendering pipeline. Extensive experiments on both synthetic dataset and unstructured in-the-wild images demonstrate that our proposed representation achieves superior performance over previous methods.

cs.CV↗

arXiv2025

An ab initio dataset of size-dependent effective thermal conductivity for advanced technology transistors

As the size of transistors shrinks and power density increases, thermal simulation has become an indispensable part of the device design procedure. However, existing works for advanced technology transistors use simplified empirical models to calculate effective thermal conductivity in the simulations. In this work, we present a dataset of size-dependent effective thermal conductivity with electron and phonon properties extracted from ab initio computations. Absolute in-plane and cross-plane thermal conductivity data of eight semiconducting materials (Si, Ge, GaN, AlN, 4H-SiC, GaAs, InAs, BAs) and four metallic materials (Al, W, TiN, Ti) with the characteristic length ranging from 5 to 50 nanometers have been provided. Besides the absolute value, normalized effective thermal conductivity is also given, in case it needs to be used with updated bulk thermal conductivity in the future. The dataset presented in this paper are openly available at https://doi.org/10.57760/sciencedb.j00113.00154.

cond-mat.mtrl-sci↗

arXiv2024

A Distance Metric Learning Model Based On Variational Information Bottleneck

In recent years, personalized recommendation technology has flourished and become one of the hot research directions. The matrix factorization model and the metric learning model which proposed successively have been widely studied and applied. The latter uses the Euclidean distance instead of the dot product used by the former to measure the latent space vector. While avoiding the shortcomings of the dot product, the assumption of Euclidean distance is neglected, resulting in limited recommendation quality of the model. In order to solve this problem, this paper combines the Variationl Information Bottleneck with metric learning model for the first time, and proposes a new metric learning model VIB-DML (Variational Information Bottleneck Distance Metric Learning) for rating prediction, which limits the mutual information of the latent space feature vector to improve the robustness of the model and satisfiy the assumption of Euclidean distance by decoupling the latent space feature vector. In this paper, the experimental results are compared with the root mean square error (RMSE) on the three public datasets. The results show that the generalization ability of VIB-DML is excellent. Compared with the general metric learning model MetricF, the prediction error is reduced by 7.29%. Finally, the paper proves the strong robustness of VIBDML through experiments.

cs.IR↗

arXiv2023

GiftBTE: An efficient deterministic solver for non-gray phonon Boltzmann transport equation

Advances in nanotechnology have facilitated the exploration of submicron thermal transport. At this scale, Fourier's law is no longer applicable, and the governing equation for thermal transport is the phonon Boltzmann transport equation (BTE). However, the availability of open-source solvers for the phonon BTE is limited, impeding progress in this field. This study introduces an open-source package, GiftBTE, for numerically solving the non-gray phonon BTE. GiftBTE employs deterministic solutions and provides both steady-state and transient solvers. For the steady-state solver, GiftBTE employs the implicit discrete ordinates method (DOM) with second-order spatial accuracy and the synthetic iterative scheme. For the transient solver, GiftBTE employs the explicit DOM with second-order spatial accuracy. This package demonstrates excellent computational efficiency, enabling realistic three-dimensional simulations of devices and materials. By interfacing with first-principles calculations, this solver enables parameter-free computation of submicron thermal transport. The application of GiftBTE includes, but is not limited to, computing the thermal conductivity of nanostructures, predicting temperature rises in transistors, and simulating laser heating processes.

cond-mat.mes-hall↗

arXiv2022

Enabling Fast and Flexible Distributed Deep Learning with Programmable Switches

Deep learning has been used in a wide range of areas and made a huge breakthrough. With the ever-increasing model size and train-ing data volume, distributed deep learning emerges which utilizes a cluster to train a model in parallel. Unfortunately, the performance is often far from linear speedup due to the communication overhead between cluster nodes. To address this challenge, this paper designs and implements Libra, a network aggregator, that utilizes in-network computation to optimize the communication for distributed DL training in two aspects: 1) reduce active connections and 2) aggregate exchanged network packets. We implemented our Libra on Intel Tofino switches, customized a lightweight host stack and integrated it into an open-source training framework PS-lite. The experimental result shows that our Libra can achieve 1.5~4 times speedup.

cs.NI↗