144 research outputs found
A note on nowhere-zero 3-flow and Z_3-connectivity
There are many major open problems in integer flow theory, such as Tutte's
3-flow conjecture that every 4-edge-connected graph admits a nowhere-zero
3-flow, Jaeger et al.'s conjecture that every 5-edge-connected graph is
-connected and Kochol's conjecture that every bridgeless graph with at
most three 3-edge-cuts admits a nowhere-zero 3-flow (an equivalent version of
3-flow conjecture). Thomassen proved that every 8-edge-connected graph is
-connected and therefore admits a nowhere-zero 3-flow. Furthermore,
Lovsz, Thomassen, Wu and Zhang improved Thomassen's result to
6-edge-connected graphs. In this paper, we prove that: (1) Every
4-edge-connected graph with at most seven 5-edge-cuts admits a nowhere-zero
3-flow. (2) Every bridgeless graph containing no 5-edge-cuts but at most three
3-edge-cuts admits a nowhere-zero 3-flow. (3) Every 5-edge-connected graph with
at most five 5-edge-cuts is -connected. Our main theorems are partial
results to Tutte's 3-flow conjecture, Kochol's conjecture and Jaeger et al.'s
conjecture, respectively.Comment: 10 pages. Typos correcte
Explicit gain equations for hybrid graphene-quantum-dot photodetectors
Graphene is an attractive material for broadband photodetection but suffers
from weak light absorption. Coating graphene with quantum dots can
significantly enhance light absorption and create extraordinarily high photo
gain. This high gain is often explained by the classical gain theory which is
unfortunately an implicit function and may even be questionable. In this work,
we managed to derive explicit gain equations for hybrid graphene-quantum-dot
photodetectors. Due to the work function mismatch, lead sulfide (PbS) quantum
dots coated on graphene will form a surface depletion region near the interface
of quantum dots and graphene. Light illumination narrows down the surface
depletion region, creating a photovoltage that gates the graphene. As a result,
high photo gain in graphene is observed. The explicit gain equations are
derived from the theoretical gate transfer characteristics of graphene and the
correlation of the photovoltage with the light illumination intensity. The
derived explicit gain equations fit well with the experimental data, from which
physical parameters are extracted.Comment: 14 pages, 6 figure
Optimizing Feature Set for Click-Through Rate Prediction
Click-through prediction (CTR) models transform features into latent vectors
and enumerate possible feature interactions to improve performance based on the
input feature set. Therefore, when selecting an optimal feature set, we should
consider the influence of both feature and its interaction. However, most
previous works focus on either feature field selection or only select feature
interaction based on the fixed feature set to produce the feature set. The
former restricts search space to the feature field, which is too coarse to
determine subtle features. They also do not filter useless feature
interactions, leading to higher computation costs and degraded model
performance. The latter identifies useful feature interaction from all
available features, resulting in many redundant features in the feature set. In
this paper, we propose a novel method named OptFS to address these problems. To
unify the selection of feature and its interaction, we decompose the selection
of each feature interaction into the selection of two correlated features. Such
a decomposition makes the model end-to-end trainable given various feature
interaction operations. By adopting feature-level search space, we set a
learnable gate to determine whether each feature should be within the feature
set. Because of the large-scale search space, we develop a
learning-by-continuation training scheme to learn such gates. Hence, OptFS
generates the feature set only containing features which improve the final
prediction results. Experimentally, we evaluate OptFS on three public datasets,
demonstrating OptFS can optimize feature sets which enhance the model
performance and further reduce both the storage and computational cost.Comment: Accepted by WWW 2023 Research Track
Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network
Deep sparse networks are widely investigated as a neural network architecture
for prediction tasks with high-dimensional sparse features, with which feature
interaction selection is a critical component. While previous methods primarily
focus on how to search feature interaction in a coarse-grained space, less
attention has been given to a finer granularity. In this work, we introduce a
hybrid-grained feature interaction selection approach that targets both feature
field and feature value for deep sparse networks. To explore such expansive
space, we propose a decomposed space which is calculated on the fly. We then
develop a selection algorithm called OptFeature, which efficiently selects the
feature interaction from both the feature field and the feature value
simultaneously. Results from experiments on three large real-world benchmark
datasets demonstrate that OptFeature performs well in terms of accuracy and
efficiency. Additional studies support the feasibility of our method.Comment: NeurIPS 2023 poste
Result Diversification in Search and Recommendation: A Survey
Diversifying return results is an important research topic in retrieval
systems in order to satisfy both the various interests of customers and the
equal market exposure of providers. There has been growing attention on
diversity-aware research during recent years, accompanied by a proliferation of
literature on methods to promote diversity in search and recommendation.
However, diversity-aware studies in retrieval systems lack a systematic
organization and are rather fragmented. In this survey, we are the first to
propose a unified taxonomy for classifying the metrics and approaches of
diversification in both search and recommendation, which are two of the most
extensively researched fields of retrieval systems. We begin the survey with a
brief discussion of why diversity is important in retrieval systems, followed
by a summary of the various diversity concerns in search and recommendation,
highlighting their relationship and differences. For the survey's main body, we
present a unified taxonomy of diversification metrics and approaches in
retrieval systems, from both the search and recommendation perspectives. In the
later part of the survey, we discuss the open research questions of
diversity-aware research in search and recommendation in an effort to inspire
future innovations and encourage the implementation of diversity in real-world
systems.Comment: 20 page
Towards Benchmarking GUI Compatibility Testing on Mobile Applications
GUI is a bridge connecting user and application. Existing GUI testing tasks
can be categorized into two groups: functionality testing and compatibility
testing. While the functionality testing focuses on detecting application
runtime bugs, the compatibility testing aims at detecting bugs resulting from
device or platform difference. To automate testing procedures and improve
testing efficiency, previous works have proposed dozens of tools. To evaluate
these tools, in functionality testing, researchers have published testing
benchmarks. Comparatively, in compatibility testing, the question of ``Do
existing methods indeed effectively assist test cases replay?'' is not well
answered. To answer this question and advance the related research in GUI
compatibility testing, we propose a benchmark of GUI compatibility testing. In
our experiments, we compare the replay success rate of existing tools. Based on
the experimental results, we summarize causes which may lead to ineffectiveness
in test case replay and propose opportunities for improving the
state-of-the-art
Negative Perceptions of Urban Tourism Community in Beijing: Based on Online Comments
The development of urban tourism community (UTC) will bring new vigor and vitality into the urban sustainable development. There are abundant tourists’ comments about personal experience in UTC in online travel communities, which provide good access to the knowledge of negative perceptions of urban tourism community. Based on online comments, this paper used content analysis method to research tourists’ negative perceptions about five typical tourism communities in Beijing, whereby the common problems and special problems of UTC were identified. According to the research results, the authors made suggestions to the sustainable development of UTC in Beijing
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