That crisp signal was once a really big deal. The film was made for the military in 1944 and released to the public five years later. Breakthroughs, discoveries, and DIY tips sent six days a week. While fewer and fewer people are listening to FM radio today, it was hot stuff amid its widespread rollout during the late 1930s and early 40s. Short for frequency modulation, FM's appeal compared to AM (amplitude modulation) were immediately apparent: a clearer sound, less static, and more reliable transmissions.

--Molecular odor prediction has great potential across diverse fields such as chemistry, pharmaceuticals, and environmental science, enabling the rapid design of new materials and enhancing environmental monitoring. However, current methods face two main challenges: First, existing models struggle with non-smooth objective functions and the complexity of mixed feature dimensions; Second, datasets suffer from severe label imbalance, which hampers model training, particularly in learning minority class labels. T o address these issues, we introduce a novel feature mapping method and a molecular ensemble optimization loss function. By incorporating feature importance learning and frequency modulation, our model adaptively adjusts the contribution of each feature, efficiently capturing the intricate relationship between molecular structures and odor descriptors. Our feature mapping preserves feature independence while enhancing the model's efficiency in utilizing molecular features through frequency modulation. Furthermore, the proposed loss function dynamically adjusts label weights, improves structural consistency, and strengthens label correlations, effectively addressing data imbalance and label co-occurrence challenges. Experimental results show that our method significantly can improves the accuracy of molecular odor prediction across various deep learning models, demonstrating its promising potential in molecular structure representation and chemoinformatics. Molecular odor prediction from structure is a critical task with diverse applications in fragrance design, chemical production, and environmental monitoring [1].

The accelerated MRI reconstruction process presents a challenging ill-posed inverse problem due to the extensive under-sampling in k-space. Recently, Vision Transformers (ViTs) have become the mainstream for this task, demonstrating substantial performance improvements. However, there are still three significant issues remain unaddressed: (1) ViTs struggle to capture high-frequency components of images, limiting their ability to detect local textures and edge information, thereby impeding MRI restoration; (2) Previous methods calculate multi-head self-attention (MSA) among both related and unrelated tokens in content, introducing noise and significantly increasing computational burden; (3) The naive feed-forward network in ViTs cannot model the multi-scale information that is important for image restoration. In this paper, we propose FPS-Former, a powerful ViT-based framework, to address these issues from the perspectives of frequency modulation, spatial purification, and scale diversification. Specifically, for issue (1), we introduce a frequency modulation attention module to enhance the self-attention map by adaptively re-calibrating the frequency information in a Laplacian pyramid. For issue (2), we customize a spatial purification attention module to capture interactions among closely related tokens, thereby reducing redundant or irrelevant feature representations. For issue (3), we propose an efficient feed-forward network based on a hybrid-scale fusion strategy. Comprehensive experiments conducted on three public datasets show that our FPS-Former outperforms state-of-the-art methods while requiring lower computational costs.

It is now possible to reconstruct dynamic human motion and shape from a sparse set of cameras using Neural Radiance Fields (NeRF) driven by an underlying skeleton. However, a challenge remains to model the deformation of cloth and skin in relation to skeleton pose. Unlike existing avatar models that are learned implicitly or rely on a proxy surface, our approach is motivated by the observation that different poses necessitate unique frequency assignments. We develop a two-branch neural network that is adaptive and explicit in the frequency domain. The first branch is a graph neural network that models correlations among body parts locally, taking skeleton pose as input. The second branch combines these correlation features to a set of global frequencies and then modulates the feature encoding. Our experiments demonstrate that our network outperforms state-of-the-art methods in terms of preserving details and generalization capabilities. Human avatar modeling has garnered significant attention as enabling 3D telepresence and digitization with applications ranging from computer graphics (Wu et al., 2019; Bagautdinov et al., 2021; Peng et al., 2021a; Lombardi et al., 2021) to medical diagnosis (Hu et al., 2022). To tackle this challenge, the majority of approaches start from a skeleton structure that rigs a surface mesh equipped with a neural texture (Bagautdinov et al., 2021; Liu et al., 2021) or learnable vertex features (Kwon et al., 2021; Peng et al., 2021a;b). Although this enables reconstructing intricate details with high precision (Liu et al., 2021; Thies et al., 2019) in controlled conditions, artifacts remain when learning the pose-dependent deformation from sparse examples. To counteract, existing methods typically rely on a parametric template obtained from a large number of laser scans, which still limits the variety of the human shape and pose.

Version identification (VI) has seen substantial progress over the past few years. On the one hand, the introduction of the metric learning paradigm has favored the emergence of scalable yet accurate VI systems. On the other hand, using features focusing on specific aspects of musical pieces, such as melody, harmony, or lyrics, yielded interpretable and promising performances. In this work, we build upon these recent advances and propose a metric learning-based system systematically leveraging four dimensions commonly admitted to convey musical similarity between versions: melodic line, harmonic structure, rhythmic patterns, and lyrics. We describe our deliberately simple model architecture, and we show in particular that an approximated representation of the lyrics is an efficient proxy to discriminate between versions and non-versions. We then describe how these features complement each other and yield new state-of-the-art performances on two publicly available datasets. We finally suggest that a VI system using a combination of melodic, harmonic, rhythmic and lyrics features could theoretically reach the optimal performances obtainable on these datasets.

I sit at my desk at least eight hours a day. Between the steady pings from Slack, my trusty group chat and the siren song of the greater internet, staying focused can be difficult. As I write this, I'm swiping between a full-screen Scrivener window and a full-screen Chrome window because... Some people listen to podcasts at work to make mindless tasks go by. Unfortunately, I can't simultaneously pay attention to a conversation and write, so to help combat the incessant distractions, I listen to music -- a lot of it. Usually I turn to original scores from movies and video games, but I switch it up with instrumental hip-hop, industrial and downtempo electronic. A side effect of my efforts to fight against distraction is that I'm always on the lookout for new music to help me focus. Tune into ChilledCow's "lofi hip hop radio - beats to relax/study to" on YouTube at any given moment and you'll find thousands of people watching simultaneously.