281 research outputs found
Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis
Improving model robustness against potential modality noise, as an essential
step for adapting multimodal models to real-world applications, has received
increasing attention among researchers. For Multimodal Sentiment Analysis
(MSA), there is also a debate on whether multimodal models are more effective
against noisy features than unimodal ones. Stressing on intuitive illustration
and in-depth analysis of these concerns, we present Robust-MSA, an interactive
platform that visualizes the impact of modality noise as well as simple defence
methods to help researchers know better about how their models perform with
imperfect real-world data.Comment: Accept by AAAI 2023. Code is available at
https://github.com/thuiar/Robust-MS
Detection and Genetic Analysis of Porcine Bocavirus
Porcine Bocavirus (PBoV) has been reported to be associated with postweaning multisystemic wasting syndrome and pneumonia in pigs. In this study, a survey was conducted to evaluate the prevalence of PBoV in slaughter pigs, sick pigs, asymptomatic pigs and classical swine fever virus (CSFV) eradication plan herds in five provinces of China (Henan, Liaoning, Shandong, Hebei and Tianjin) by means of PCR targeting NS1 gene of PBoV. Among the total of 403 tissue samples, 11.41% were positive for PBoV. The positive rates of spleen (20.75%) and inguinal lymph node (27.18%) are higher than those of other organs. PCR products of twenty PBoV positive samples from slaughter pigs were sequenced for phylogenetic analysis. The result revealed that PBoV could be divided into 6 groups (PBoV-a~PBoV-f). All PBoV sequenced in this study belong to PBoV-a–PBoV-d with 90.1% to 99% nucleotide identities. Our results exhibited significant genetic diversity of PBoV and suggested a complex prevalence of PBoV in Chinese swine herds. Whether this diversity of PBoV has a significance to pig production or even public health remains to be further studied
Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
Deep cooperative multi-agent reinforcement learning has demonstrated its
remarkable success over a wide spectrum of complex control tasks. However,
recent advances in multi-agent learning mainly focus on value decomposition
while leaving entity interactions still intertwined, which easily leads to
over-fitting on noisy interactions between entities. In this work, we introduce
a novel interactiOn Pattern disenTangling (OPT) method, to disentangle not only
the joint value function into agent-wise value functions for decentralized
execution, but also the entity interactions into interaction prototypes, each
of which represents an underlying interaction pattern within a subgroup of the
entities. OPT facilitates filtering the noisy interactions between irrelevant
entities and thus significantly improves generalizability as well as
interpretability. Specifically, OPT introduces a sparse disagreement mechanism
to encourage sparsity and diversity among discovered interaction prototypes.
Then the model selectively restructures these prototypes into a compact
interaction pattern by an aggregator with learnable weights. To alleviate the
training instability issue caused by partial observability, we propose to
maximize the mutual information between the aggregation weights and the history
behaviors of each agent. Experiments on both single-task and multi-task
benchmarks demonstrate that the proposed method yields results superior to the
state-of-the-art counterparts. Our code is available at
https://github.com/liushunyu/OPT
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?
Centralized Training with Decentralized Execution (CTDE) has recently emerged
as a popular framework for cooperative Multi-Agent Reinforcement Learning
(MARL), where agents can use additional global state information to guide
training in a centralized way and make their own decisions only based on
decentralized local policies. Despite the encouraging results achieved, CTDE
makes an independence assumption on agent policies, which limits agents to
adopt global cooperative information from each other during centralized
training. Therefore, we argue that existing CTDE methods cannot fully utilize
global information for training, leading to an inefficient joint-policy
exploration and even suboptimal results. In this paper, we introduce a novel
Centralized Advising and Decentralized Pruning (CADP) framework for multi-agent
reinforcement learning, that not only enables an efficacious message exchange
among agents during training but also guarantees the independent policies for
execution. Firstly, CADP endows agents the explicit communication channel to
seek and take advices from different agents for more centralized training. To
further ensure the decentralized execution, we propose a smooth model pruning
mechanism to progressively constraint the agent communication into a closed one
without degradation in agent cooperation capability. Empirical evaluations on
StarCraft II micromanagement and Google Research Football benchmarks
demonstrate that the proposed framework achieves superior performance compared
with the state-of-the-art counterparts. Our code will be made publicly
available
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
Value Decomposition (VD) aims to deduce the contributions of agents for
decentralized policies in the presence of only global rewards, and has recently
emerged as a powerful credit assignment paradigm for tackling cooperative
Multi-Agent Reinforcement Learning (MARL) problems. One of the main challenges
in VD is to promote diverse behaviors among agents, while existing methods
directly encourage the diversity of learned agent networks with various
strategies. However, we argue that these dedicated designs for agent networks
are still limited by the indistinguishable VD network, leading to homogeneous
agent behaviors and thus downgrading the cooperation capability. In this paper,
we propose a novel Contrastive Identity-Aware learning (CIA) method, explicitly
boosting the credit-level distinguishability of the VD network to break the
bottleneck of multi-agent diversity. Specifically, our approach leverages
contrastive learning to maximize the mutual information between the temporal
credits and identity representations of different agents, encouraging the full
expressiveness of credit assignment and further the emergence of
individualities. The algorithm implementation of the proposed CIA module is
simple yet effective that can be readily incorporated into various VD
architectures. Experiments on the SMAC benchmarks and across different VD
backbones demonstrate that the proposed method yields results superior to the
state-of-the-art counterparts. Our code is available at
https://github.com/liushunyu/CIA
Macrophage inhibits the osteogenesis of fibroblasts in ultrahigh molecular weight polyethylene (UHMWPE) wear particle-induced osteolysis
Background
In the ultrahigh molecular weight polyethylene (UHMWPE) prosthetic environment, fibroblasts affected by wear particles have the capacity of osteogenesis to reduce osteolysis. We aimed to assess the effects of macrophages on the osteogenic capability of fibroblasts treated with UHMWPE wear particles.
Methods
The effect of different concentrations of UHMWPE (0, 0.01, 0.1, and 1 mg/ml, respectively) on macrophage proliferation were validated by MTT assay to determine the optimum one. The fibroblasts viability was further determined in the co-culture system of UHMWPE particles and macrophage supernatants. The experiment was designed as seven groups: (A) fibroblasts only; (B) fibroblasts + 1 mg/ml UHMWPE particles; and (C1–C5) fibroblasts + 1/16, 1/8, 1/4, 1/2, and 1/1 supernatants of macrophage cultures stimulated by 1 mg/ml UHMWPE particles vs. fibroblast complete media, respectively. Alizarin red staining was used to detect calcium accumulation. The expression levels of osteogenic proteins were detected by Western blot and ELISA, including alkaline phosphatase (ALP) and osteocalcin (OCN).
Results
The concentration of 0.1 mg/ml was considered as the optimum concentration for macrophage proliferation due to the survival rate and was highest among the four concentrations. Fibroblast viability was better in the group of fibroblasts + 1/16 ratio of macrophage supernatants stimulated by 1 mg/ml of UHMWPE particles than the other groups (1:8, 1:4, 1:2, 1:1). ALP and OCN expressions were significantly decreased in the group of fibroblasts + 1/4, 1/2, and 1/1 supernatants stimulated by 1 mg/ml of UHMWPE particles compared with other groups (1/8, 1/16) and the group of fibroblasts + 1 mg/ml UHMWPE (p < 0.5).
Conclusions
Macrophages are potentially involved in the periprosthetic osteolysis by reducing the osteogenic capability of fibroblasts treated with wear particles generated from UHMWPE materials in total hip arthroplasty.China Sholarship Council (Grant 201506370173)China. National Natural Science Foundation (Grant 31200739)China. National Natural Science Foundation (Grant 31470948
The Effects of Storage Conditions on Lycopene Content and Color of Tomato Hot Pot Sauce
Tomato hot pot sauce (THPS) at different storage temperatures (0, 25, and 37°C) and with two kinds of packaging for 120 days was investigated in this study. High performance liquid chromatography was employed for detecting lycopene and 5-hydroxymethylfurfural (HMF). The changes of lycopene and HMF during storage were regressed with kinetic equation of both zero-order and first-order models, and the latter fitted better. The kinetic equation constant (k value) of lycopene or HMF at 37°C was higher than that at 25°C. The k value of lycopene of PET/PE (P1) packaged THPS was 1.60 times of that of PET/Al/EAA/PE (P2) packaged at 37°C, while it was 2.12 times at 25°C. The k value of HMF of P1 packaged THPS was 1.69 times of that of P2 packaged at 37°C, while it was 1.01 times at 25°C. Significant correlations between color index of L⁎, a⁎, and a⁎/b⁎ and lycopene or HMF were found at storage temperature. Browning color was attributed to both Maillard reaction and degradation of lycopene. In conclusion, lower storage temperature and stronger oxygen barrier property of package could maintain color stability and extend shelf life
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