128 research outputs found
Efficient Spatial Keyword Search in Trajectory Databases
An increasing amount of trajectory data is being annotated with text
descriptions to better capture the semantics associated with locations. The
fusion of spatial locations and text descriptions in trajectories engenders a
new type of top- queries that take into account both aspects. Each
trajectory in consideration consists of a sequence of geo-spatial locations
associated with text descriptions. Given a user location and a
keyword set , a top- query returns trajectories whose text
descriptions cover the keywords and that have the shortest match
distance. To the best of our knowledge, previous research on querying
trajectory databases has focused on trajectory data without any text
description, and no existing work has studied such kind of top- queries on
trajectories. This paper proposes one novel method for efficiently computing
top- trajectories. The method is developed based on a new hybrid index,
cell-keyword conscious B-tree, denoted by \cellbtree, which enables us to
exploit both text relevance and location proximity to facilitate efficient and
effective query processing. The results of our extensive empirical studies with
an implementation of the proposed algorithms on BerkeleyDB demonstrate that our
proposed methods are capable of achieving excellent performance and good
scalability.Comment: 12 page
Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning
Recent progress has been made in using attention based encoder-decoder
framework for video captioning. However, most existing decoders apply the
attention mechanism to every generated word including both visual words (e.g.,
"gun" and "shooting") and non-visual words (e.g. "the", "a"). However, these
non-visual words can be easily predicted using natural language model without
considering visual signals or attention. Imposing attention mechanism on
non-visual words could mislead and decrease the overall performance of video
captioning. To address this issue, we propose a hierarchical LSTM with adjusted
temporal attention (hLSTMat) approach for video captioning. Specifically, the
proposed framework utilizes the temporal attention for selecting specific
frames to predict the related words, while the adjusted temporal attention is
for deciding whether to depend on the visual information or the language
context information. Also, a hierarchical LSTMs is designed to simultaneously
consider both low-level visual information and high-level language context
information to support the video caption generation. To demonstrate the
effectiveness of our proposed framework, we test our method on two prevalent
datasets: MSVD and MSR-VTT, and experimental results show that our approach
outperforms the state-of-the-art methods on both two datasets
TLM: Token-Level Masking for Transformers
Structured dropout approaches, such as attention dropout and DropHead, have
been investigated to regularize the multi-head attention mechanism in
Transformers. In this paper, we propose a new regularization scheme based on
token-level rather than structure-level to reduce overfitting. Specifically, we
devise a novel Token-Level Masking (TLM) training strategy for Transformers to
regularize the connections of self-attention, which consists of two masking
techniques that are effective and easy to implement. The underlying idea is to
manipulate the connections between tokens in the multi-head attention via
masking, where the networks are forced to exploit partial neighbors'
information to produce a meaningful representation. The generality and
effectiveness of TLM are thoroughly evaluated via extensive experiments on 4
diversified NLP tasks across 18 datasets, including natural language
understanding benchmark GLUE, ChineseGLUE, Chinese Grammatical Error
Correction, and data-to-text generation. The results indicate that TLM can
consistently outperform attention dropout and DropHead, e.g., it increases by
0.5 points relative to DropHead with BERT-large on GLUE. Moreover, TLM can
establish a new record on the data-to-text benchmark Rotowire (18.93 BLEU). Our
code will be publicly available at https://github.com/Young1993/tlm.Comment: 13 pages. Accepted by EMNLP2023 main conferenc
Analysis of Nonlinear Dynamics for Abrupt Change of Interphase Structure in Liquid-Liquid Mass Transfer
As a liquid-liquid system is far from equilibrium state, the phase thickness is variable when mass transfer process with chemical reaction occurs in interphase zone, and a dispersible transitional layer called the interphase dispersed zone (IDZ) is formed. The IZD model composed of thermodynamically instable O/W or W/O microemulsion has reasonably explained enormous experimental phenomena in nonlinear mass transfer. To forecast the possible parameter ranges of IDZ process and abrupt change of liquid-liquid mass transfer rate, the dynamic characteristics of a molecular diffusion model are considered in this paper. We applied the bifurcation theory of planar dynamical system, Laplace transform, and maple software to investigate the model, and obtain different phase portraits of the system in different regions. The results obtained will play an important directive role in the study of IDZ model
Enhancing Balanced Graph Edge Partition with Effective Local Search
Graph partition is a key component to achieve workload balance and reduce job
completion time in parallel graph processing systems. Among the various
partition strategies, edge partition has demonstrated more promising
performance in power-law graphs than vertex partition and thereby has been more
widely adopted as the default partition strategy by existing graph systems. The
graph edge partition problem, which is to split the edge set into multiple
balanced parts to minimize the total number of copied vertices, has been widely
studied from the view of optimization and algorithms. In this paper, we study
local search algorithms for this problem to further improve the partition
results from existing methods. More specifically, we propose two novel
concepts, namely adjustable edges and blocks. Based on these, we develop a
greedy heuristic as well as an improved search algorithm utilizing the property
of the max-flow model. To evaluate the performance of our algorithms, we first
provide adequate theoretical analysis in terms of the approximation quality. We
significantly improve the previously known approximation ratio for this
problem. Then we conduct extensive experiments on a large number of benchmark
datasets and state-of-the-art edge partition strategies. The results show that
our proposed local search framework can further improve the quality of graph
partition by a wide margin.Comment: To appear in AAAI 202
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