49 research outputs found
Influence and Role of Social Practice on the Development of Comprehensive Quality of University Students
With the development of society and the popularization of higher education, the cultivation of comprehensive quality of college students has become the focus of attention of the education sector and the community. This paper researches and discusses the influence and role of social practice on the development of comprehensive quality of college students. Firstly, it discusses the importance of social practice activities in enhancing the comprehensive quality of college students, including the role of cultivating practical ability, enhancing the sense of social responsibility and teamwork spirit. Secondly, it analyzes the influence of social practice on the cognitive level, emotional attitude and values of college students, and points out that social practice can promote the overall development of college students. Finally, the problems and challenges of the current social practice activities are discussed, and corresponding countermeasures and suggestions are put forward to further play the role of social practice in the comprehensive quality cultivation of college students, and to provide better support and guarantee for the growth and development of college students. (Bai, 2020
Grounded Image Text Matching with Mismatched Relation Reasoning
This paper introduces Grounded Image Text Matching with Mismatched Relation
(GITM-MR), a novel visual-linguistic joint task that evaluates the relation
understanding capabilities of transformer-based pre-trained models. GITM-MR
requires a model to first determine if an expression describes an image, then
localize referred objects or ground the mismatched parts of the text. We
provide a benchmark for evaluating pre-trained models on this task, with a
focus on the challenging settings of limited data and out-of-distribution
sentence lengths. Our evaluation demonstrates that pre-trained models lack data
efficiency and length generalization ability. To address this, we propose the
Relation-sensitive Correspondence Reasoning Network (RCRN), which incorporates
relation-aware reasoning via bi-directional message propagation guided by
language structure. RCRN can be interpreted as a modular program and delivers
strong performance in both length generalization and data efficiency
Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction
Online Social Networks (OSNs) evolve through two pervasive behaviors: follow
and unfollow, which respectively signify relationship creation and relationship
dissolution. Researches on social network evolution mainly focus on the follow
behavior, while the unfollow behavior has largely been ignored. Mining unfollow
behavior is challenging because user's decision on unfollow is not only
affected by the simple combination of user's attributes like informativeness
and reciprocity, but also affected by the complex interaction among them.
Meanwhile, prior datasets seldom contain sufficient records for inferring such
complex interaction. To address these issues, we first construct a large-scale
real-world Weibo dataset, which records detailed post content and relationship
dynamics of 1.8 million Chinese users. Next, we define user's attributes as two
categories: spatial attributes (e.g., social role of user) and temporal
attributes (e.g., post content of user). Leveraging the constructed dataset, we
systematically study how the interaction effects between user's spatial and
temporal attributes contribute to the unfollow behavior. Afterwards, we propose
a novel unified model with heterogeneous information (UMHI) for unfollow
prediction. Specifically, our UMHI model: 1) captures user's spatial attributes
through social network structure; 2) infers user's temporal attributes through
user-posted content and unfollow history; and 3) models the interaction between
spatial and temporal attributes by the nonlinear MLP layers. Comprehensive
evaluations on the constructed dataset demonstrate that the proposed UMHI model
outperforms baseline methods by 16.44% on average in terms of precision. In
addition, factor analyses verify that both spatial attributes and temporal
attributes are essential for mining unfollow behavior.Comment: 8 pages, 7 figures, Accepted by AAAI 202
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning
Real-Time Bidding (RTB) is an important mechanism in modern online
advertising systems. Advertisers employ bidding strategies in RTB to optimize
their advertising effects subject to various financial requirements, especially
the return-on-investment (ROI) constraint. ROIs change non-monotonically during
the sequential bidding process, and often induce a see-saw effect between
constraint satisfaction and objective optimization. While some existing
approaches show promising results in static or mildly changing ad markets, they
fail to generalize to highly dynamic ad markets with ROI constraints, due to
their inability to adaptively balance constraints and objectives amidst
non-stationarity and partial observability. In this work, we specialize in
ROI-Constrained Bidding in non-stationary markets. Based on a Partially
Observable Constrained Markov Decision Process, our method exploits an
indicator-augmented reward function free of extra trade-off parameters and
develops a Curriculum-Guided Bayesian Reinforcement Learning (CBRL) framework
to adaptively control the constraint-objective trade-off in non-stationary ad
markets. Extensive experiments on a large-scale industrial dataset with two
problem settings reveal that CBRL generalizes well in both in-distribution and
out-of-distribution data regimes, and enjoys superior learning efficiency and
stability.Comment: Accepted by SIGKDD 202
Improving GAN Training via Feature Space Shrinkage
Due to the outstanding capability for data generation, Generative Adversarial
Networks (GANs) have attracted considerable attention in unsupervised learning.
However, training GANs is difficult, since the training distribution is dynamic
for the discriminator, leading to unstable image representation. In this paper,
we address the problem of training GANs from a novel perspective, \emph{i.e.,}
robust image classification. Motivated by studies on robust image
representation, we propose a simple yet effective module, namely AdaptiveMix,
for GANs, which shrinks the regions of training data in the image
representation space of the discriminator. Considering it is intractable to
directly bound feature space, we propose to construct hard samples and narrow
down the feature distance between hard and easy samples. The hard samples are
constructed by mixing a pair of training images. We evaluate the effectiveness
of our AdaptiveMix with widely-used and state-of-the-art GAN architectures. The
evaluation results demonstrate that our AdaptiveMix can facilitate the training
of GANs and effectively improve the image quality of generated samples. We also
show that our AdaptiveMix can be further applied to image classification and
Out-Of-Distribution (OOD) detection tasks, by equipping it with
state-of-the-art methods. Extensive experiments on seven publicly available
datasets show that our method effectively boosts the performance of baselines.
The code is publicly available at
https://github.com/WentianZhang-ML/AdaptiveMix.Comment: Accepted by CVPR'2023. Code and Demo are available at
https://github.com/WentianZhang-ML/AdaptiveMi
Assess and Summarize: Improve Outage Understanding with Large Language Models
Cloud systems have become increasingly popular in recent years due to their
flexibility and scalability. Each time cloud computing applications and
services hosted on the cloud are affected by a cloud outage, users can
experience slow response times, connection issues or total service disruption,
resulting in a significant negative business impact. Outages are usually
comprised of several concurring events/source causes, and therefore
understanding the context of outages is a very challenging yet crucial first
step toward mitigating and resolving outages. In current practice, on-call
engineers with in-depth domain knowledge, have to manually assess and summarize
outages when they happen, which is time-consuming and labor-intensive. In this
paper, we first present a large-scale empirical study investigating the way
on-call engineers currently deal with cloud outages at Microsoft, and then
present and empirically validate a novel approach (dubbed Oasis) to help the
engineers in this task. Oasis is able to automatically assess the impact scope
of outages as well as to produce human-readable summarization. Specifically,
Oasis first assesses the impact scope of an outage by aggregating relevant
incidents via multiple techniques. Then, it generates a human-readable summary
by leveraging fine-tuned large language models like GPT-3.x. The impact
assessment component of Oasis was introduced in Microsoft over three years ago,
and it is now widely adopted, while the outage summarization component has been
recently introduced, and in this article we present the results of an empirical
evaluation we carried out on 18 real-world cloud systems as well as a
human-based evaluation with outage owners. The results show that Oasis can
effectively and efficiently summarize outages, and lead Microsoft to deploy its
first prototype which is currently under experimental adoption by some of the
incident teams
Superconductivity above 30 K achieved in dense scandium
Superconductivity is one of most intriguing quantum phenomena, and the quest
for elemental superconductors with high critical temperature (Tc) is of great
scientific significance due to their relatively simple material composition and
the underlying mechanism. Here we report the experimental discovery of densely
compressed scandium (Sc) becoming the first elemental superconductor with Tc
breaking into 30 K range, which is comparable to the Tc values of the classic
La-Ba-Cu-O or LaFeAsO superconductors. Our results show that Tconset of Sc
increases from ~3 K at around 43 GPa to ~32 K at about 283 GPa (Tczero ~ 31 K),
which is well above liquid neon temperature. Interestingly measured Tc shows no
sign of saturation up to the maximum pressure achieved in our experiments,
indicating that Tc might be even higher upon further compression.Comment: 22 pages, 16 figure
Superconductivity above 70 K observed in lutetium polyhydrides
The binary polyhydrides of heavy rare earth lutetium that shares a similar
valence electron configuration to lanthanum have been experimentally discovered
to be superconductive. The lutetium polyhydrides were successfully synthesized
at high pressure and high temperature conditions using a diamond anvil cell in
combinations with the in-situ high pressure laser heating technique. The
resistance measurements as a function of temperature were performed at the same
pressure of synthesis in order to study the transitions of superconductivity
(SC). The superconducting transition with a maximum onset temperature (Tc) 71 K
was observed at pressure of 218 GPa in the experiments. The Tc decreased to 65
K when pressure was at 181 GPa. From the evolution of SC at applied magnetic
fields, the upper critical field at zero temperature {\mu}0Hc2(0) was obtained
to be ~36 Tesla. The in-situ high pressure X-ray diffraction experiments imply
that the high Tc SC should arise from the Lu4H23 phase with Pm-3n symmetry that
forms a new type of hydrogen cage framework different from those reported for
previous light rare earth polyhydride superconductors