97 research outputs found
Structured Memetic Automation for Online Human-like Social Behavior Learning
Meme automaton is an adaptive entity that autonomously acquires an increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper begins a study on memetic multiagent system (MeMAS) toward human-like social agents with memetic automaton. We introduce a potentially rich meme-inspired design and operational model, with Darwin's theory of natural selection and Dawkins' notion of a meme as the principal driving forces behind interactions among agents, whereby memes form the fundamental building blocks of the agents' mind universe. To improve the efficiency and scalability of MeMAS, we propose memetic agents with structured memes in this paper. Particularly, we focus on meme selection design where the commonly used elitist strategy is further improved by assimilating the notion of like-attracts-like in the human learning. We conduct experimental study on multiple problem domains and show the performance of the proposed MeMAS on human-like social behavior
A study on like-attracts-like versus elitist selection criterion for human-like social behavior of memetic mulitagent systems
Memetic multi agent system emerges as an enhanced version of multiagent systems with the implementation of meme-inspired computational agents. It aims to evolve human-like behavior of multiple agents by exploiting the Dawkins' notion of a meme and Universal Darwinism. Previous research has developed a computational framework in which a series of memetic operations have been designed for implementing humanlike agents. This paper will focus on improving the human-like behavior of multiple agents when they are engaged in social interactions. The improvement is mainly on how an agent shall learn from others and adapt its behavior in a complex dynamic environment. In particular, we design a new mechanism that supervises how the agent shall select one of the other agents for the learning purpose. The selection is a trade-off between the elitist and like-attracts-like principles. We demonstrate the desirable interactions of multiple agents in two problem domains
On Information Coverage for Location Category Based Point-of-Interest Recommendation
Point-of-interest(POI) recommendation becomes a valuable service in location-based social networks. Based on the norm that similar users are likely to have similar preference of POIs, the current recommendation techniques mainly focus on users' preference to provide accurate recommendation results. This tends to generate a list of homogeneous POIs that are clustered into a narrow band of location categories(like food, museum, etc.) in a city. However, users are more interested to taste a wide range of flavors that are exposed in a global set of location categories in the city.In this paper, we formulate a new POI recommendation problem, namely top-K location category based POI recommendation, by introducing information coverage to encode the location categories of POIs in a city.The problem is NP-hard. We develop a greedy algorithm and further optimization to solve this challenging problem. The experimental results on two real-world datasets demonstrate the utility of new POI recommendations and the superior performance of the proposed algorithms
Influence Maximization with Novelty Decay in Social Networks
Influence maximization problem is to find a set of seed nodes in a social network such that their influence spread is maximized under certain propagation models. A few algorithms have been proposed for solving this problem. However, they have not considered the impact of novelty decay on influence propagation, i.e., repeated exposures will have diminishing influence on users. In this paper, we consider the problem of influence maximization with novelty decay (IMND). We investigate the effect of novelty decay on influence propagation on real-life datasets and formulate the IMND problem. We further analyze the problem properties and propose an influence estimation technique. We demonstrate the performance of our algorithms on four social networks
Extending the Propagation Distance of a Silver Nanowire Plasmonic Waveguide with a Dielectric Multilayer Substrate
Chemical synthesized silver nanowires have been proved to be the efficient
architecture for Plasmonic waveguides, but the high propagation loss prevents
their widely applications. Here, we demonstrate that the propagation distance
of the plasmons along the Ag NW can be extended if the Ag NW was placed on a
dielectric multilayer substrate containing a photonic band gap, but not placed
on a commonly used glass substrate. The propagation distance at 630 nm
wavelength can reach 16 um even that the Ag NW is as thin as 90 nm in diameter.
Experimental and simulation results further show that the polarization of this
propagating plasmon mode was nearly parallel to the surface of the dielectric
multilayer, so it was excited by a transverse-electric polarized Bloch surface
wave propagating along a polymer nanowire with diameter at only about 170 nm on
the same dielectric multilayer. Numerical simulations were also carried out and
consistent with the experiment results. Our work provides a platform to extend
the propagation distance of plasmonic waveguide and also for the integration
between photonic and plasmonic waveguides on the nanometre scale.Comment: 5 pages, 4 figure
Refined Temporal Pyramidal Compression-and-Amplification Transformer for 3D Human Pose Estimation
Accurately estimating the 3D pose of humans in video sequences requires both
accuracy and a well-structured architecture. With the success of transformers,
we introduce the Refined Temporal Pyramidal Compression-and-Amplification
(RTPCA) transformer. Exploiting the temporal dimension, RTPCA extends
intra-block temporal modeling via its Temporal Pyramidal
Compression-and-Amplification (TPCA) structure and refines inter-block feature
interaction with a Cross-Layer Refinement (XLR) module. In particular, TPCA
block exploits a temporal pyramid paradigm, reinforcing key and value
representation capabilities and seamlessly extracting spatial semantics from
motion sequences. We stitch these TPCA blocks with XLR that promotes rich
semantic representation through continuous interaction of queries, keys, and
values. This strategy embodies early-stage information with current flows,
addressing typical deficits in detail and stability seen in other
transformer-based methods. We demonstrate the effectiveness of RTPCA by
achieving state-of-the-art results on Human3.6M, HumanEva-I, and MPI-INF-3DHP
benchmarks with minimal computational overhead. The source code is available at
https://github.com/hbing-l/RTPCA.Comment: 11 pages, 5 figure
DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving
Real-time perception, or streaming perception, is a crucial aspect of
autonomous driving that has yet to be thoroughly explored in existing research.
To address this gap, we present DAMO-StreamNet, an optimized framework that
combines recent advances from the YOLO series with a comprehensive analysis of
spatial and temporal perception mechanisms, delivering a cutting-edge solution.
The key innovations of DAMO-StreamNet are: (1) A robust neck structure
incorporating deformable convolution, enhancing the receptive field and feature
alignment capabilities. (2) A dual-branch structure that integrates short-path
semantic features and long-path temporal features, improving motion state
prediction accuracy. (3) Logits-level distillation for efficient optimization,
aligning the logits of teacher and student networks in semantic space. (4) A
real-time forecasting mechanism that updates support frame features with the
current frame, ensuring seamless streaming perception during inference. Our
experiments demonstrate that DAMO-StreamNet surpasses existing state-of-the-art
methods, achieving 37.8% (normal size (600, 960)) and 43.3% (large size (1200,
1920)) sAP without using extra data. This work not only sets a new benchmark
for real-time perception but also provides valuable insights for future
research. Additionally, DAMO-StreamNet can be applied to various autonomous
systems, such as drones and robots, paving the way for real-time perception.
The code is available at https://github.com/zhiqic/DAMO-StreamNet
Insulin Attenuates Beta-Amyloid-Associated Insulin/Akt/EAAT Signaling Perturbations in Human Astrocytes
The excitatory amino acid transporters 1 and 2 (EAAT1 and EAAT2), mostly located on astrocytes, are the main mediators for glutamate clearance in humans. Malfunctions of these transporters may lead to excessive glutamate accumulation and subsequent excitotoxicity to neurons, which has been implicated in many kinds of neurodegenerative disorders including Alzheimer’s disease (AD). Yet, the specific mechanism of the glutamate system dysregulation remains vague. To explore whether the insulin/protein kinase B (Akt)/EAAT signaling in human astrocytes could be disturbed by beta-amyloid protein (Aβ) and be protected by insulin, we incubated HA-1800 cells with varying concentrations of Aβ1–42 oligomers and insulin. Then the alterations of several key substrates in this signal transduction pathway were determined. Our results showed that expressions of insulin receptor, phospho-insulin receptor, phospho-protein kinase B, phospho-mammalian target of rapamycin, and EAAT1 and EAAT2 were decreased by the Aβ1–42 oligomers in a dose-dependent manner (p 0.05), and the mRNA levels of EAAT1 and EAAT2 were also unchanged (p > 0.05). Taken together, this study indicates that Aβ1–42 oligomers could cause disturbances in insulin/Akt/EAAT signaling in astrocytes, which might be responsible for AD onset and progression. Additionally, insulin can exert protective functions to the brain by modulating protein modifications or expressions
PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation
Existing 3D human pose estimators face challenges in adapting to new datasets
due to the lack of 2D-3D pose pairs in training sets. To overcome this issue,
we propose \textit{Multi-Hypothesis \textbf{P}ose \textbf{Syn}thesis
\textbf{D}omain \textbf{A}daptation} (\textbf{PoSynDA}) framework to bridge
this data disparity gap in target domain. Typically, PoSynDA uses a
diffusion-inspired structure to simulate 3D pose distribution in the target
domain. By incorporating a multi-hypothesis network, PoSynDA generates diverse
pose hypotheses and aligns them with the target domain. To do this, it first
utilizes target-specific source augmentation to obtain the target domain
distribution data from the source domain by decoupling the scale and position
parameters. The process is then further refined through the teacher-student
paradigm and low-rank adaptation. With extensive comparison of benchmarks such
as Human3.6M and MPI-INF-3DHP, PoSynDA demonstrates competitive performance,
even comparable to the target-trained MixSTE model\cite{zhang2022mixste}. This
work paves the way for the practical application of 3D human pose estimation in
unseen domains. The code is available at https://github.com/hbing-l/PoSynDA.Comment: Accepted to ACM Multimedia 2023; 10 pages, 4 figures, 8 tables; the
code is at https://github.com/hbing-l/PoSynD
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