2,190 research outputs found
Wrapped Cauchy Distributed Angular Softmax for Long-Tailed Visual Recognition
Addressing imbalanced or long-tailed data is a major challenge in visual
recognition tasks due to disparities between training and testing distributions
and issues with data noise. We propose the Wrapped Cauchy Distributed Angular
Softmax (WCDAS), a novel softmax function that incorporates data-wise
Gaussian-based kernels into the angular correlation between feature
representations and classifier weights, effectively mitigating noise and sparse
sampling concerns. The class-wise distribution of angular representation
becomes a sum of these kernels. Our theoretical analysis reveals that the
wrapped Cauchy distribution excels the Gaussian distribution in approximating
mixed distributions. Additionally, WCDAS uses trainable concentration
parameters to dynamically adjust the compactness and margin of each class.
Empirical results confirm label-aware behavior in these parameters and
demonstrate WCDAS's superiority over other state-of-the-art softmax-based
methods in handling long-tailed visual recognition across multiple benchmark
datasets. The code is public available.Comment: accepted by ICML 202
GFM: Building Geospatial Foundation Models via Continual Pretraining
Geospatial technologies are becoming increasingly essential in our world for
a wide range of applications, including agriculture, urban planning, and
disaster response. To help improve the applicability and performance of deep
learning models on these geospatial tasks, various works have begun
investigating foundation models for this domain. Researchers have explored two
prominent approaches for introducing such models in geospatial applications,
but both have drawbacks in terms of limited performance benefit or prohibitive
training cost. Therefore, in this work, we propose a novel paradigm for
building highly effective geospatial foundation models with minimal resource
cost and carbon impact. We first construct a compact yet diverse dataset from
multiple sources to promote feature diversity, which we term GeoPile. Then, we
investigate the potential of continual pretraining from large-scale
ImageNet-22k models and propose a multi-objective continual pretraining
paradigm, which leverages the strong representations of ImageNet while
simultaneously providing the freedom to learn valuable in-domain features. Our
approach outperforms previous state-of-the-art geospatial pretraining methods
in an extensive evaluation on seven downstream datasets covering various tasks
such as change detection, classification, multi-label classification, semantic
segmentation, and super-resolution
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
Earth system forecasting has traditionally relied on complex physical models
that are computationally expensive and require significant domain expertise. In
the past decade, the unprecedented increase in spatiotemporal Earth observation
data has enabled data-driven forecasting models using deep learning techniques.
These models have shown promise for diverse Earth system forecasting tasks but
either struggle with handling uncertainty or neglect domain-specific prior
knowledge, resulting in averaging possible futures to blurred forecasts or
generating physically implausible predictions. To address these limitations, we
propose a two-stage pipeline for probabilistic spatiotemporal forecasting: 1)
We develop PreDiff, a conditional latent diffusion model capable of
probabilistic forecasts. 2) We incorporate an explicit knowledge control
mechanism to align forecasts with domain-specific physical constraints. This is
achieved by estimating the deviation from imposed constraints at each denoising
step and adjusting the transition distribution accordingly. We conduct
empirical studies on two datasets: N-body MNIST, a synthetic dataset with
chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset.
Specifically, we impose the law of conservation of energy in N-body MNIST and
anticipated precipitation intensity in SEVIR. Experiments demonstrate the
effectiveness of PreDiff in handling uncertainty, incorporating domain-specific
prior knowledge, and generating forecasts that exhibit high operational
utility.Comment: Technical repor
Prevalent presence of periodic actin-spectrin-based membrane skeleton in a broad range of neuronal cell types and animal species
Actin, spectrin, and associated molecules form a periodic, submembrane cytoskeleton in the axons of neurons. For a better understanding of this membrane-associated periodic skeleton (MPS), it is important to address how prevalent this structure is in different neuronal types, different subcellular compartments, and across different animal species. Here, we investigated the organization of spectrin in a variety of neuronal- and glial-cell types. We observed the presence of MPS in all of the tested neuronal types cultured from mouse central and peripheral nervous systems, including excitatory and inhibitory neurons from several brain regions, as well as sensory and motor neurons. Quantitative analyses show that MPS is preferentially formed in axons in all neuronal types tested here: Spectrin shows a long-range, periodic distribution throughout all axons but appears periodic only in a small fraction of dendrites, typically in the form of isolated patches in subregions of these dendrites. As in dendrites, we also observed patches of periodic spectrin structures in a small fraction of glial-cell processes in four types of glial cells cultured from rodent tissues. Interestingly, despite its strong presence in the axonal shaft, MPS is disrupted in most presynaptic boutons but is present in an appreciable fraction of dendritic spine necks, including some projecting from dendrites where such a periodic structure is not observed in the shaft. Finally, we found that spectrin is capable of adopting a similar periodic organization in neurons of a variety of animal species, including Caenorhabditis elegans, Drosophila, Gallus gallus, Mus musculus, and Homo sapiens
Recovering Protein-Protein and Domain-Domain Interactions from Aggregation of IP-MS Proteomics of Coregulator Complexes
Coregulator proteins (CoRegs) are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/
- …