694 research outputs found
LATTE: Application Oriented Social Network Embedding
In recent years, many research works propose to embed the network structured
data into a low-dimensional feature space, where each node is represented as a
feature vector. However, due to the detachment of embedding process with
external tasks, the learned embedding results by most existing embedding models
can be ineffective for application tasks with specific objectives, e.g.,
community detection or information diffusion. In this paper, we propose study
the application oriented heterogeneous social network embedding problem.
Significantly different from the existing works, besides the network structure
preservation, the problem should also incorporate the objectives of external
applications in the objective function. To resolve the problem, in this paper,
we propose a novel network embedding framework, namely the "appLicAtion
orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network
structure can be applied to compute the node "diffusive proximity" scores,
which capture both local and global network structures. Based on these computed
scores, Latte learns the network representation feature vectors by extending
the autoencoder model model to the heterogeneous network scenario, which can
also effectively unite the objectives of network embedding and external
application tasks. Extensive experiments have been done on real-world
heterogeneous social network datasets, and the experimental results have
demonstrated the outstanding performance of Latte in learning the
representation vectors for specific application tasks.Comment: 11 Pages, 12 Figures, 1 Tabl
Statistical Methods for Wearable Devices with Applications to Epidemiological Studies
Monitoring and assessing the level of physical activity has been an important part of research in many public health and medical studies. However, the conventional self-reports of physical activity were found unreliable due to various reasons. Recent advances of wearable computing technology enabled researchers to deploy accelerometers in health studies as a physical activity assessment tool. Such devices are able to provide objective and continuous measurement of physical activity for as long as a few months. Common types of data collected by accelerometers include high-frequency tri-axial acceleration time series (raw data) and summarized metrics in epochs (count data). Size of the data and uniqueness of the data structure called for development of new statistical methods, which include topics such as analysis of raw or count data and processing of raw data. The purpose of this dissertation is to provide solutions to three types of questions in accelerometry research. First, a dictionary-based classification method was proposed to predict the type of physical activity performed by elderly adults using raw accelerometry data. The classification method decomposed movements into short components called "movelets" and built a reference for each activity type. Unknown activities were predicted by matching new movelets to the reference. The movelet method was able to identify a variety of household activities including short activities, such as chair-stands. Second, a set of explicit and open-source metrics for physical activity was introduced to summarize raw accelerometry data into count data. One of the metrics, the Activity Index, was compared with several existing summary metrics and showed to be more sensitive to sedentary and light activities and better associated with energy expenditure. Third, a two-stage regression model was proposed to study the association between minute-by-minute activity count and human demographics. The model allows for both time-varying parameters and time-invariant parameters, which helps capture both the transition dynamics between active/inactive periods (Stage 1) and the activity intensity dynamics during active periods (Stage 2)
Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis
This paper presents ER-NeRF, a novel conditional Neural Radiance Fields
(NeRF) based architecture for talking portrait synthesis that can concurrently
achieve fast convergence, real-time rendering, and state-of-the-art performance
with small model size. Our idea is to explicitly exploit the unequal
contribution of spatial regions to guide talking portrait modeling.
Specifically, to improve the accuracy of dynamic head reconstruction, a compact
and expressive NeRF-based Tri-Plane Hash Representation is introduced by
pruning empty spatial regions with three planar hash encoders. For speech
audio, we propose a Region Attention Module to generate region-aware condition
feature via an attention mechanism. Different from existing methods that
utilize an MLP-based encoder to learn the cross-modal relation implicitly, the
attention mechanism builds an explicit connection between audio features and
spatial regions to capture the priors of local motions. Moreover, a direct and
fast Adaptive Pose Encoding is introduced to optimize the head-torso separation
problem by mapping the complex transformation of the head pose into spatial
coordinates. Extensive experiments demonstrate that our method renders better
high-fidelity and audio-lips synchronized talking portrait videos, with
realistic details and high efficiency compared to previous methods.Comment: Accepted by ICCV 202
PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization
Few-shot abstractive summarization has become a challenging task in natural
language generation. To support it, we designed a novel soft prompts
architecture coupled with a prompt pre-training plus fine-tuning paradigm that
is effective and tunes only extremely light parameters. The soft prompts
include continuous input embeddings across an encoder and a decoder to fit the
structure of the generation models. Importantly, a novel inner-prompt placed in
the text is introduced to capture document-level information. The aim is to
devote attention to understanding the document that better prompts the model to
generate document-related content. The first step in the summarization
procedure is to conduct prompt pre-training with self-supervised pseudo-data.
This teaches the model basic summarizing capabilities. The model is then
fine-tuned with few-shot examples. Experimental results on the CNN/DailyMail
and XSum datasets show that our method, with only 0.1% of the parameters,
outperforms full-model tuning where all model parameters are tuned. It also
surpasses Prompt Tuning by a large margin and delivers competitive results
against Prefix-Tuning with 3% of the parameters.Comment: 12 page
MOVELETS: A DICTIONARY OF MOVEMENT
Recent technological advances provide researchers a way of gathering real-time information on an individual’s movement through the use of wearable devices that record acceleration. In this paper, we propose a method for identifying activity types, like walking, standing, and resting, from acceleration data. Our approach decomposes movements into short components called “movelets”, and builds a reference for each activity type. Unknown activities are predicted by matching new movelets to the reference. We apply our method to data collected from a single, three-axis accelerometer and focus on activities of interest in studying physical function in elderly populations. An important technical advantage of our methods is that they allow identification of short activities, such as taking two or three steps and then stopping, as well as low frequency rare activities, such as sitting on a chair. Based on our results we provide simple and actionable recommendations for the design and implementation of large epidemiological studies that could collect accelerometry data for the purpose of predicting the time series of activities and connecting it to health outcomes
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