323 research outputs found
EXPLORATION ON THE PATH OF ENVIRONMENTAL ART TEACHING REFORM IN COLLEGES AND UNIVERSITIES FROM THE PERSPECTIVE OF EDUCATIONAL PSYCHOLOGY
EXPLORATION ON THE PATH OF ENVIRONMENTAL ART TEACHING REFORM IN COLLEGES AND UNIVERSITIES FROM THE PERSPECTIVE OF EDUCATIONAL PSYCHOLOGY
A Tensor-Based Framework for Studying Eigenvector Multicentrality in Multilayer Networks
Centrality is widely recognized as one of the most critical measures to
provide insight in the structure and function of complex networks. While
various centrality measures have been proposed for single-layer networks, a
general framework for studying centrality in multilayer networks (i.e.,
multicentrality) is still lacking. In this study, a tensor-based framework is
introduced to study eigenvector multicentrality, which enables the
quantification of the impact of interlayer influence on multicentrality,
providing a systematic way to describe how multicentrality propagates across
different layers. This framework can leverage prior knowledge about the
interplay among layers to better characterize multicentrality for varying
scenarios. Two interesting cases are presented to illustrate how to model
multilayer influence by choosing appropriate functions of interlayer influence
and design algorithms to calculate eigenvector multicentrality. This framework
is applied to analyze several empirical multilayer networks, and the results
corroborate that it can quantify the influence among layers and multicentrality
of nodes effectively.Comment: 57 pages, 10 figure
Wireless Transmission of Images With The Assistance of Multi-level Semantic Information
Semantic-oriented communication has been considered as a promising to boost
the bandwidth efficiency by only transmitting the semantics of the data. In
this paper, we propose a multi-level semantic aware communication system for
wireless image transmission, named MLSC-image, which is based on the deep
learning techniques and trained in an end to end manner. In particular, the
proposed model includes a multilevel semantic feature extractor, that extracts
both the highlevel semantic information, such as the text semantics and the
segmentation semantics, and the low-level semantic information, such as local
spatial details of the images. We employ a pretrained image caption to capture
the text semantics and a pretrained image segmentation model to obtain the
segmentation semantics. These high-level and low-level semantic features are
then combined and encoded by a joint semantic and channel encoder into symbols
to transmit over the physical channel. The numerical results validate the
effectiveness and efficiency of the proposed semantic communication system,
especially under the limited bandwidth condition, which indicates the
advantages of the high-level semantics in the compression of images
Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank
Leveraging biased click data for optimizing learning to rank systems has been
a popular approach in information retrieval. Because click data is often noisy
and biased, a variety of methods have been proposed to construct unbiased
learning to rank (ULTR) algorithms for the learning of unbiased ranking models.
Among them, automatic unbiased learning to rank (AutoULTR) algorithms that
jointly learn user bias models (i.e., propensity models) with unbiased rankers
have received a lot of attention due to their superior performance and low
deployment cost in practice. Despite their differences in theories and
algorithm design, existing studies on ULTR usually use uni-variate ranking
functions to score each document or result independently. On the other hand,
recent advances in context-aware learning-to-rank models have shown that
multivariate scoring functions, which read multiple documents together and
predict their ranking scores jointly, are more powerful than uni-variate
ranking functions in ranking tasks with human-annotated relevance labels.
Whether such superior performance would hold in ULTR with noisy data, however,
is mostly unknown. In this paper, we investigate existing multivariate scoring
functions and AutoULTR algorithms in theory and prove that permutation
invariance is a crucial factor that determines whether a context-aware
learning-to-rank model could be applied to existing AutoULTR framework. Our
experiments with synthetic clicks on two large-scale benchmark datasets show
that AutoULTR models with permutation-invariant multivariate scoring functions
significantly outperform those with uni-variate scoring functions and
permutation-variant multivariate scoring functions.Comment: 4 pages, 2 figures. It has already been accepted and will show in
Proceedings of the 29th ACM International Conference on Information and
Knowledge Management (CIKM '20), October 19--23, 202
Design of microstrip-line coupled kinetic inductance detectors for near infrared astronomy
Kinetic inductance detectors (KID) have great potential in astronomical
observation, such as searching for exoplanets, because of their low noise, fast
response and photon counting characteristics. In this paper, we present the
design process and simulation results of a microstrip line coupled KIDs array
for near-infrared astronomical observation. Compared with coplanar waveguide
(CPW) feedlines, microstrip feedlines do not require air bridges, which
simplify fabrication process. In the design part, we mainly focus on the
impedance transforming networks, the KID structure, and the frequency crosstalk
simulations. The test array has a total of 104 resonators with 8 rows and 13
columns, which ranges from 4.899~GHz to 6.194~GHz. The pitch size is about
200~m and the frequency crosstalk is less than 50~kHz in simulation
Two Mutations Were Critical for Bat-to-Human Transmission of Middle East Respiratory Syndrome Coronavirus
To understand how Middle East respiratory syndrome coronavirus (MERS-CoV) transmitted from bats to humans, we compared the virus surface spikes of MERS-CoV and a related bat coronavirus, HKU4. Although HKU4 spike cannot mediate viral entry into human cells, two mutations enabled it to do so by allowing it to be activated by human proteases. These mutations are present in MERS-CoV spike, explaining why MERS-CoV infects human cells. These mutations therefore played critical roles in the bat-to-human transmission of MERS-CoV, either directly or through intermediate hosts
Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Transformer-based large language models (LLMs) have demonstrated impressive
capabilities in a variety of natural language processing (NLP) tasks.
Nonetheless, it is challenging to deploy and fine-tune LLMs on mobile edge
devices with limited computing, memory, and energy budgets. In this paper, we
propose Confidant, a multi-backend collaborative training framework for
customizing state-of-the-art LLMs on commodity mobile devices like smartphones.
Confidant partitions an LLM into several sub-models so that each fits into a
mobile device's memory. A pipeline parallel training mechanism is further
developed to ensure fast and efficient distributed training. In addition, we
propose a novel backend scheduler to allocate different attention heads to
heterogeneous compute hardware, including mobile CPU and GPUs, to maximize the
compute resource utilization on each edge device. Our preliminary experimental
results show that Confidant achieves at most 45.3% memory reduction and 8.03x
inference speedup in practical settings.Comment: 6 pages, 7 figures; Submitted to HotMobile 202
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