81 research outputs found
Asian ELL (English Language Learners) International Students’ Learning Adjustment
Studying outside one's home country can be exciting while posing many adjustment challenges simultaneously. Like me, a lot of Asian ELL (English language learners) international students are both thrilled and struggling. Linguistic and cultural barriers cause a lot of challenges and stress that significantly affect our overseas study experiences. Aiming to help instructors better support their Asian ELL international students in classrooms, I have conducted a focus group to collect empirical descriptions of some adjustment issues of studying abroad and I offer advice for teaching interventions. Six participants from three Asian countries have participated in four activities. Through each activity, participants were gradually stimulated to open their minds, share experiences and ideas, and make new suggestions. The findings showed that all the student participants were supportive of the various teaching interventions to support Asian ELL students, but their feelings about a few specific approaches varied. They also came up with many new teaching intervention proposals
FedDisco: Federated Learning with Discrepancy-Aware Collaboration
This work considers the category distribution heterogeneity in federated
learning. This issue is due to biased labeling preferences at multiple clients
and is a typical setting of data heterogeneity. To alleviate this issue, most
previous works consider either regularizing local models or fine-tuning the
global model, while they ignore the adjustment of aggregation weights and
simply assign weights based on the dataset size. However, based on our
empirical observations and theoretical analysis, we find that the dataset size
is not optimal and the discrepancy between local and global category
distributions could be a beneficial and complementary indicator for determining
aggregation weights. We thus propose a novel aggregation method, Federated
Learning with Discrepancy-aware Collaboration (FedDisco), whose aggregation
weights not only involve both the dataset size and the discrepancy value, but
also contribute to a tighter theoretical upper bound of the optimization error.
FedDisco also promotes privacy-preservation, communication and computation
efficiency, as well as modularity. Extensive experiments show that our FedDisco
outperforms several state-of-the-art methods and can be easily incorporated
with many existing methods to further enhance the performance. Our code will be
available at https://github.com/MediaBrain-SJTU/FedDisco.Comment: Accepted by International Conference on Machine Learning (ICML2023
Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction
Exploring spatial-temporal dependencies from observed motions is one of the
core challenges of human motion prediction. Previous methods mainly focus on
dedicated network structures to model the spatial and temporal dependencies.
This paper considers a new direction by introducing a model learning framework
with auxiliary tasks. In our auxiliary tasks, partial body joints' coordinates
are corrupted by either masking or adding noise and the goal is to recover
corrupted coordinates depending on the rest coordinates. To work with auxiliary
tasks, we propose a novel auxiliary-adapted transformer, which can handle
incomplete, corrupted motion data and achieve coordinate recovery via capturing
spatial-temporal dependencies. Through auxiliary tasks, the auxiliary-adapted
transformer is promoted to capture more comprehensive spatial-temporal
dependencies among body joints' coordinates, leading to better feature
learning. Extensive experimental results have shown that our method outperforms
state-of-the-art methods by remarkable margins of 7.2%, 3.7%, and 9.4% in terms
of 3D mean per joint position error (MPJPE) on the Human3.6M, CMU Mocap, and
3DPW datasets, respectively. We also demonstrate that our method is more robust
under data missing cases and noisy data cases. Code is available at
https://github.com/MediaBrain-SJTU/AuxFormer.Comment: Accpeted to ICCV202
Unrolled Graph Learning for Multi-Agent Collaboration
Multi-agent learning has gained increasing attention to tackle distributed
machine learning scenarios under constrictions of data exchanging. However,
existing multi-agent learning models usually consider data fusion under fixed
and compulsory collaborative relations among agents, which is not as flexible
and autonomous as human collaboration. To fill this gap, we propose a
distributed multi-agent learning model inspired by human collaboration, in
which the agents can autonomously detect suitable collaborators and refer to
collaborators' model for better performance. To implement such adaptive
collaboration, we use a collaboration graph to indicate the pairwise
collaborative relation. The collaboration graph can be obtained by graph
learning techniques based on model similarity between different agents. Since
model similarity can not be formulated by a fixed graphical optimization, we
design a graph learning network by unrolling, which can learn underlying
similar features among potential collaborators. By testing on both regression
and classification tasks, we validate that our proposed collaboration model can
figure out accurate collaborative relationship and greatly improve agents'
learning performance
EVALUATION OF HEAVY METALS AROUND THE MINING OF DECORATIVE STONE ORE IN SUSONG COUNTY LIAOHE RIVER
In order to study the pollution of heavy metals around Liaohe Fender stone mine in Susong County, the soils at six points and the sediment at four points were selected. The effects of heavy metals Cu, Zn, Pb, Cd, Cr , Ni ,Hg and As were measured, the single factor index and the Nemero index method were used to evaluate the heavy metal elements in soil and sediment. The results showed that the values of heavy metal elements in the soil and sediment were less than 1 and the Pintegrated values were less than 0.85,the mine area was not polluted by heavy metals and belonged to the clean area within the grade â…
Malicious Agent Detection for Robust Multi-Agent Collaborative Perception
Recently, multi-agent collaborative (MAC) perception has been proposed and
outperformed the traditional single-agent perception in many applications, such
as autonomous driving. However, MAC perception is more vulnerable to
adversarial attacks than single-agent perception due to the information
exchange. The attacker can easily degrade the performance of a victim agent by
sending harmful information from a malicious agent nearby. In this paper, we
extend adversarial attacks to an important perception task -- MAC object
detection, where generic defenses such as adversarial training are no longer
effective against these attacks. More importantly, we propose Malicious Agent
Detection (MADE), a reactive defense specific to MAC perception that can be
deployed by each agent to accurately detect and then remove any potential
malicious agent in its local collaboration network. In particular, MADE
inspects each agent in the network independently using a semi-supervised
anomaly detector based on a double-hypothesis test with the Benjamini-Hochberg
procedure to control the false positive rate of the inference. For the two
hypothesis tests, we propose a match loss statistic and a collaborative
reconstruction loss statistic, respectively, both based on the consistency
between the agent to be inspected and the ego agent where our detector is
deployed. We conduct comprehensive evaluations on a benchmark 3D dataset
V2X-sim and a real-road dataset DAIR-V2X and show that with the protection of
MADE, the drops in the average precision compared with the best-case "oracle"
defender against our attack are merely 1.28% and 0.34%, respectively, much
lower than 8.92% and 10.00% for adversarial training, respectively
EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
Learning to predict agent motions with relationship reasoning is important
for many applications. In motion prediction tasks, maintaining motion
equivariance under Euclidean geometric transformations and invariance of agent
interaction is a critical and fundamental principle. However, such equivariance
and invariance properties are overlooked by most existing methods. To fill this
gap, we propose EqMotion, an efficient equivariant motion prediction model with
invariant interaction reasoning. To achieve motion equivariance, we propose an
equivariant geometric feature learning module to learn a Euclidean
transformable feature through dedicated designs of equivariant operations. To
reason agent's interactions, we propose an invariant interaction reasoning
module to achieve a more stable interaction modeling. To further promote more
comprehensive motion features, we propose an invariant pattern feature learning
module to learn an invariant pattern feature, which cooperates with the
equivariant geometric feature to enhance network expressiveness. We conduct
experiments for the proposed model on four distinct scenarios: particle
dynamics, molecule dynamics, human skeleton motion prediction and pedestrian
trajectory prediction. Experimental results show that our method is not only
generally applicable, but also achieves state-of-the-art prediction
performances on all the four tasks, improving by 24.0/30.1/8.6/9.2%. Code is
available at https://github.com/MediaBrain-SJTU/EqMotion.Comment: Accepted to CVPR 202
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