273 research outputs found
Music Composition from the Brain Signal: Representing the Mental State by Music
This paper proposes a method to translate human EEG into music, so as to represent mental state by music. The arousal levels of the brain mental state and music emotion are implicitly used as the bridge between the mind world and the music. The arousal level of the brain is based on the EEG features extracted mainly by wavelet analysis, and the music arousal level is related to the musical parameters such as pitch, tempo, rhythm, and tonality. While composing, some music principles (harmonics and structure) were taken into consideration. With EEGs during various sleep stages as an example, the music generated from them had different patterns of pitch, rhythm, and tonality. 35 volunteers listened to the music pieces, and significant difference in music arousal levels was found. It implied that different mental states may be identified by the corresponding music, and so the music from EEG may be a potential tool for EEG monitoring, biofeedback therapy, and so forth
Simplified Neutrosophic Sets Based on Interval Dependent Degree for Multi-Criteria Group Decision-Making Problems
In this paper, a new approach and framework based on the interval dependent degree for multi-criteria group decision-making (MCGDM) problems with simplified neutrosophic sets (SNSs) is proposed. Firstly, the simplified dependent function and distribution function are defined. Then, they are integrated into the interval dependent function which contains interval computing and distribution information of the intervals
Entanglement Entropy of Topological Orders with Boundaries
In this paper we explore how non trivial boundary conditions could influence
the entanglement entropy in a topological order in 2+1 dimensions. Specifically
we consider the special class of topological orders describable by the quantum
double. We will find very interesting dependence of the entanglement entropy on
the boundary conditions particularly when the system is non-Abelian. Along the
way, we demonstrate a streamlined procedure to compute the entanglement
entropy, which is particularly efficient when dealing with systems with
boundaries. We also show how this method efficiently reproduces all the known
results in the presence of anyonic excitations.Comment: 29 pages, 11 figure
Exploring Annotation-free Image Captioning with Retrieval-augmented Pseudo Sentence Generation
Training an image captioner without annotated image-sentence pairs has gained
traction in recent years. Previous approaches can be categorized into two
strategies: crawling sentences from mismatching corpora and aligning them with
the given images as pseudo annotations, or pre-training the captioner using
external image-text pairs. However, the aligning setting seems to reach its
performance limit due to the quality problem of pairs, and pre-training
requires significant computational resources. To address these challenges, we
propose a new strategy ``LPM + retrieval-augmented learning" where the prior
knowledge from large pre-trained models (LPMs) is leveraged as supervision, and
a retrieval process is integrated to further reinforce its effectiveness.
Specifically, we introduce Retrieval-augmented Pseudo Sentence Generation
(RaPSG), which adopts an efficient approach to retrieve highly relevant short
region descriptions from the mismatching corpora and use them to generate a
variety of pseudo sentences with distinct representations as well as high
quality via LPMs. In addition, a fluency filter and a CLIP-guided training
objective are further introduced to facilitate model optimization. Experimental
results demonstrate that our method surpasses the SOTA pre-training model
(Flamingo3B) by achieving a CIDEr score of 78.1 (+5.1) while utilizing only
0.3% of its trainable parameters (1.3B VS 33M). Importantly, our approach
eliminates the need of computationally expensive pre-training processes on
external datasets (e.g., the requirement of 312M image-text pairs for
Flamingo3B). We further show that with a simple extension, the generated pseudo
sentences can be deployed as weak supervision to boost the 1% semi-supervised
image caption benchmark up to 93.4 CIDEr score (+8.9) which showcases the
versatility and effectiveness of our approach.Comment: 10 pages 5 figure
Information Flow Topology in Mixed Traffic: A Comparative Study between "Looking Ahead" and "Looking Behind"
The emergence of connected and automated vehicles (CAVs) promises smoother
traffic flow. In mixed traffic where human-driven vehicles (HDVs) also exist,
existing research mostly focuses on "looking ahead" (i.e., the CAVs receive
information from preceding vehicles) strategies for CAVs, while recent work
reveals that "looking behind" (i.e., the CAVs receive information from their
rear vehicles) strategies might provide more possibilities for CAV longitudinal
control. This paper presents a comparative study between these two types of
information flow topology (IFT) from the string stability perspective, with the
role of maximum platoon size (MPS) also under investigation. Precisely, we
provide a dynamical modeling framework for the mixed platoon under the
multi-predecessor-following (MPF) topology and the multi-successor-leading
(MSL) topology. Then, a unified method for string stability analysis is
presented, with explicit consideration of both IFT and MPS. Numerical results
suggest that MSL ("looking behind") outperforms MPF ("looking ahead" ) in
mitigating traffic perturbations. In addition, increasing MPS could further
improve string stability of mixed traffic flow.Comment: This paper has been accepted by 26th IEEE International Conference on
Intelligent Transportation Systems ITSC 202
SymmNeRF: Learning to Explore Symmetry Prior for Single-View View Synthesis
We study the problem of novel view synthesis of objects from a single image.
Existing methods have demonstrated the potential in single-view view synthesis.
However, they still fail to recover the fine appearance details, especially in
self-occluded areas. This is because a single view only provides limited
information. We observe that manmade objects usually exhibit symmetric
appearances, which introduce additional prior knowledge. Motivated by this, we
investigate the potential performance gains of explicitly embedding symmetry
into the scene representation. In this paper, we propose SymmNeRF, a neural
radiance field (NeRF) based framework that combines local and global
conditioning under the introduction of symmetry priors. In particular, SymmNeRF
takes the pixel-aligned image features and the corresponding symmetric features
as extra inputs to the NeRF, whose parameters are generated by a hypernetwork.
As the parameters are conditioned on the image-encoded latent codes, SymmNeRF
is thus scene-independent and can generalize to new scenes. Experiments on
synthetic and real-world datasets show that SymmNeRF synthesizes novel views
with more details regardless of the pose transformation, and demonstrates good
generalization when applied to unseen objects. Code is available at:
https://github.com/xingyi-li/SymmNeRF.Comment: Accepted by ACCV 202
Crystallographic Interdigitation in Oyster Shell Folia Enhances Material Strength
Shells of oyster species belonging to the genus Crassostrea have similar shell microstructural features comprising well-ordered calcite folia. However, the mechanical strengths of folia differ dramatically between closely related species. For example, the calcareous shells of the Hong Kong oyster Crassostrea hongkongensis are stronger than those of its closest relative, the Portuguese oyster, Crassostrea angulata. Specifically, after removal of organic content, the folia of C. hongkongensis are 200% tougher and able to withstand a 100% higher crushing force than that of C. angulata. Detailed analyses of shell structural and mechanical features support the hypothesis that crystallographic interdigitations confer elevated mechanical strength in C. hongkongensis oyster shells compared to C. angulata shells. Consequently, the folia of C. hongkongensis are structurally equipped to withstand a higher external load compared to C. angulata. The observed relationships between oyster shell structure, crystallography, and mechanical properties provided an insightful context in which to consider the likely fate of these two species in future climate change scenarios. Furthermore, the interdisciplinary approach developed in this study through integrating electron backscatter diffraction (EBSD) data into finite element analysis (FEA) could be applied to other biomineral systems to investigate the relationship between crystallography and mechanical behavior
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