Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning a 3D Morphable Face Reflectance Model from Low-Cost Data

Modeling non-Lambertian effects such as facial specularity leads to a more realistic 3D Morphable Face Model. Existing works build parametric models for diffuse and specular albedo using Light Stage data. However, only diffuse and specular albedo cannot determine the full BRDF. In addition, the requirement of Light Stage data is hard to fulfill for the research communities. This paper proposes the first 3D morphable face reflectance model with spatially varying BRDF using only low-cost publicly-available data. We apply linear shiness weighting into parametric modeling to represent spatially varying specular intensity and shiness. Then an inverse rendering algorithm is developed to reconstruct the reflectance parameters from non-Light Stage data, which are used to train an initial morphable reflectance model. To enhance the model's generalization capability and expressive power, we further propose an update-by-reconstruction strategy to finetune it on an in-the-wild dataset. Experimental results show that our method obtains decent rendering results with plausible facial specularities. Our code is released here.
Multi-modal Prompting for Low-Shot Temporal Action Localization

In this paper, we consider the problem of temporal action localization under low-shot (zero-shot&few-shot) scenario, with the goal of detecting and classifying the action instances from arbitrary categories within some untrimmed videos, even not seen at training time. We adopt a Transformer-based two-stage action localization architecture with class-agnostic action proposal, followed by open-vocabulary classification. We make the following contributions. First, to compensate image-text foundation models with temporal motions, we improve category-agnostic action proposal by explicitly aligning embeddings of optical flows, RGB and texts, which has largely been ignored in existing low-shot methods. Second, to improve open-vocabulary action classification, we construct classifiers with strong discriminative power, i.e., avoid lexical ambiguities. To be specific, we propose to prompt the pre-trained CLIP text encoder either with detailed action descriptions (acquired from large-scale language models), or visually-conditioned instance-specific prompt vectors. Third, we conduct thorough experiments and ablation studies on THUMOS14 and ActivityNet1.3, demonstrating the superior performance of our proposed model, outperforming existing state-of-the-art approaches by one significant margin.
Electromechanical response of ionic polymer-metal composites

An ionic polymer-metal composite (IPMC) consisting of a thin Nafion sheet, platinum plated on both faces, undergoes large bending motion when an electric field is applied across its thickness. Conversely, a voltage is produced across its faces when it is suddenly bent. A micromechanical model is developed which accounts for the coupled ion transport, electric field, and elastic deformation to predict the response of the IPMC, qualitatively and quantitatively. First, the basic three-dimensional coupled field equations are presented, and then the results are applied to predict the response of a thin sheet of an IPMC. Central to the theory is the recognition that the interaction between an imbalanced charge density and the backbone polymer can be presented by an eigenstress field (Nemat-Nasser and Hori, Micromechanics, Overall Properties of Heterogeneous Materials, 2nd Ed., Elsevier, Amsterdam, 1999). The constitutive parameter connecting the eigenstress to the charge density is calculated directly using a s.
A fuzzy adaptive evolutionary-based feature selection and machine learning framework for single and multi-objective body fat prediction

Predicting body fat can provide medical practitioners and users with essential information for preventing and diagnosing heart diseases. Hybrid machine learning models offer better performance than simple regression analysis methods by selecting relevant body measurements and capturing complex nonlinear relationships among selected features in modelling body fat prediction problems. There are, however, some disadvantages to them. Current machine learning. Modelling body fat prediction as a combinatorial single- and multi-objective optimisation problem often gets stuck in local optima. When multiple feature subsets produce similar or close predictions, avoiding local optima becomes more complex. Evolutionary feature selection has been used to solve several machine-learning-based optimisation problems. A fuzzy set theory determines appropriate levels of exploration and exploitation while managing parameterisation and computational costs. A weighted-sum body fat prediction approach was explored using evolutionary feature selection, fuzzy set theory, and machine learning algorithms, integrating contradictory metrics into a single composite goal optimised by fuzzy adaptive evolutionary feature selection. Hybrid fuzzy adaptive global learning local search universal diversity-based feature selection is applied to this single-objective feature selection-machine learning framework (FAGLSUD-based FS-ML). While using fewer features, this model achieved a more accurate and stable estimate of body fat percentage than other hybrid and state-of-the-art machine learning models. A multi-objective FAGLSUD-based FS-MLP is also proposed to analyse accuracy, stability, and dimensionality conflicts simultaneously. To make informed decisions about fat deposits in the most vital body parts and blood lipid levels, medical practitioners and users can use a well-distributed Pareto set of trade-off solutions.
Closed-form summations of certain hypergeometric-type series containing the digamma function

Recently, interesting novel summation formulae for hypergeometric-type series containing a digamma function as a factor have been established by Miller (2006 J. Phys. A: Math. Gen. 39 3011–20) mainly by exploiting already known results and using certain transformation and reduction formulae in the theory of the Kampé de Fériet double hypergeometric function. It is shown here that these, and several other series of this type, can be closed-form summed by simpler and more direct arguments based only on a derivative formula for the Pochhammer symbol and the theory of the digamma (or ψ) function and generalized hypergeometric function. In addition, a new reduction formula for the Kampé de Fériet function is obtained.
Degenerate Cahn-Hilliard systems: From nonlocal to local

We provide a rigorous mathematical framework to establish the limit of a nonlocal model of cell-cell adhesion system to a local model. When the parameter of the nonlocality goes to 0, the system tends to a Cahn-Hilliard system with degenerate mobility and cross interaction forces. Our analysis relies on a priori estimates and compactness properties.
Adhesive Release via Elasto-Osmotic Stress Driven Surface Instability

Recent studies demonstrated that an elastomer containing hygroscopic inclusions absorbs moisture and swell. Here we show that a thin film of such an elastomer bonded to a rigid substrate undergoes morphological instability upon absorption of water, the wavelength of which increases linearly with its thickness. As the driving force for such a morphological instability arises from the difference of the chemical potential of water between its source and that in the film, its development is slowed down as the salinity of the water increases. Nonetheless, the wavelength of the fully developed morphology, but not its amplitude, is independent of the salinity. We also demonstrate that if a domed disk shaped adherent is attached to the hygro-elastomeric film before moisture absorption, the elastic force generated during the morphological transition is able to dislodge it completely without the need of any external force. These patterns, once developed in pure water, is subdued when the salinity of water increases or if it is exposed to dry air. They re-emerge when the film is immersed in water again. Such an active response could be important in fouling release when a ship coated with such a hygro-elastomer changes its location during its long travel through sea, where salinity varies from place to place.
On the Existence and Information of Orthogonal Moments

Locally Robust (LR)/Orthogonal/Debiased moments have proven useful with machine learning first steps, but their existence has not been investigated for general parameters. In this paper, we provide a necessary and sufficient condition, referred to as Restricted Local Non-surjectivity (RLN), for the existence of such orthogonal moments to conduct robust inference on general parameters of interest in regular semiparametric models. Importantly, RLN does not require either identification of the parameters of interest or the nuisance parameters. However, for orthogonal moments to be informative, the efficient Fisher Information matrix for the parameter must be non-zero (though possibly singular). Thus, orthogonal moments exist and are informative under more general conditions than previously recognized. We demonstrate the utility of our general results by characterizing orthogonal moments in a class of models with Unobserved Heterogeneity (UH). For this class of models our method delivers functional differencing as a special case. Orthogonality for general smooth functionals of the distribution of UH is also characterized. As a second major application, we investigate the existence of orthogonal moments and their relevance for models defined by moment restrictions with possibly different conditioning variables. We find orthogonal moments for the fully saturated two stage least squares, for heterogeneous parameters in treatment effects, for sample selection models, and for popular models of demand for differentiated products. We apply our results to the Oregon Health Experiment to study heterogeneous treatment effects of Medicaid on different health outcomes.
Assessor-Guided Learning for Continual Environments

This paper proposes an assessor-guided learning strategy for continual learning where an assessor guides the learning process of a base learner by controlling the direction and pace of the learning process thus allowing an efficient learning of new environments while protecting against the catastrophic interference problem. The assessor is trained in a meta-learning manner with a meta-objective to boost the learning process of the base learner. It performs a soft-weighting mechanism of every sample accepting positive samples while rejecting negative samples. The training objective of a base learner is to minimize a meta-weighted combination of the cross entropy loss function, the dark experience replay (DER) loss function and the knowledge distillation loss function whose interactions are controlled in such a way to attain an improved performance. A compensated over-sampling (COS) strategy is developed to overcome the class imbalanced problem of the episodic memory due to limited memory budgets. Our approach, Assessor-Guided Learning Approach (AGLA), has been evaluated in the class-incremental and task-incremental learning problems. AGLA achieves improved performances compared to its competitors while the theoretical analysis of the COS strategy is offered. Source codes of AGLA, baseline algorithms and experimental logs are shared publicly in \url{https://github.com/anwarmaxsum/AGLA} for further study.
Accurate Dust Temperature and Star Formation Rate in the Most Luminous z > 6 Quasar in the Hyperluminous Quasars at the Epoch of Reionization (HYPERION) Sample

We present ALMA Band 9 continuum observation of the ultraluminous quasi-stellar object (QSO) SDSS J0100+2802 providing a ∼10σ detection at ∼670 GHz. SDSS J0100+2802 is the brightest QSO with the most massive supermassive black hole (SMBH) known at z > 6, and we study its dust spectral energy distribution in order to determine the dust properties and the star formation rate (SFR) of its host galaxy. We obtain the most accurate estimate so far of the temperature, mass, and emissivity index of the dust, which are T dust = 48.4 ± 2.3 K, M dust = (2.29 ± 0.83) × 107 M ⊙, and β = 2.63 ± 0.23, respectively. This allows us to measure the SFR with the smallest statistical error for this QSO, SFR = 265 ± 32 M ⊙yr−1. Our results enable us to evaluate the relative growth of the SMBH and host galaxy of J0100+2802. We find that the SMBH is dominating the process of black-hole galaxy growth in this QSO at z = 6.327, when the universe was 865 Myr old. Such unprecedented constraints on the host-galaxy SFR and dust temperature can only be obtained through high-frequency observations and highlight the importance of ALMA Band 9 to obtain a robust overview of the buildup of the first quasars’ host galaxies at z > 6.
Waves in the ocean and atmosphere : introduction to wave dynamics

1 Introduction. - 2 Kinematic Generalization. - 3 Equations of Motion Surface Gravity Waves. - 4 Fields of Motion in Gravity Waves and Energy. - 5 The Initial Value Problem. - 6 Discussion of Initial Value Problem [Continued). - 7 Internal Gravity Waves. - 8 Internal Waves, Group Velocity and Reflection. - 9 WKB Theory for Internal Gravity Waves. - 10 Vertical Propagation of Waves: Steady Flow and the Radiation Condition. - 11 Rotation and Potential Vorticity. - 12 Large-Scale Hydrostatic Motions. - 13 Shallow Water Waves in a Rotating Fluid Poincare and Kelvin Waves. - 14 Rossby Waves. - 15 Rossby Waves (Continued), Quasi-Geostrophy. - 16 Energy and Energy Flux in Rossby Waves. - 17 Laplace Tidal Equations and the Vertical Structure Equation. - 18 Equatorial Beta-Plane and Equatorial Waves. - 19 Stratified Quasi-Geostrophic Motion and Instability Waves. - 20 Energy Equation and Necessary Conditions for Instability. - 21 Wave-Mean Flow Interaction. - Problems. - References.
NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from Multi-View Images

Recent progress in neural implicit functions has set new state-of-the-art in reconstructing high-fidelity 3D shapes from a collection of images. However, these approaches are limited to closed surfaces as they require the surface to be represented by a signed distance field. In this paper, we propose NeAT, a new neural rendering framework that can learn implicit surfaces with arbitrary topologies from multi-view images. In particular, NeAT represents the 3D surface as a level set of a signed distance function (SDF) with a validity branch for estimating the surface existence probability at the query positions. We also develop a novel neural volume rendering method, which uses SDF and validity to calculate the volume opacity and avoids rendering points with low validity. NeAT supports easy field-to-mesh conversion using the classic Marching Cubes algorithm. Extensive experiments on DTU [20], MGN [4], and Deep Fashion 3D [19] datasets indicate that our approach is able to faithfully reconstruct both watertight and non-watertight surfaces. In particular, NeAT significantly outperforms the state-of-the-art methods in the task of open surface reconstruction both quantitatively and qualitatively.
A Tale of Two Circuits: Grokking as Competition of Sparse and Dense Subnetworks

Grokking is a phenomenon where a model trained on an algorithmic task first overfits but, then, after a large amount of additional training, undergoes a phase transition to generalize perfectly. We empirically study the internal structure of networks undergoing grokking on the sparse parity task, and find that the grokking phase transition corresponds to the emergence of a sparse subnetwork that dominates model predictions. On an optimization level, we find that this subnetwork arises when a small subset of neurons undergoes rapid norm growth, whereas the other neurons in the network decay slowly in norm. Thus, we suggest that the grokking phase transition can be understood to emerge from competition of two largely distinct subnetworks: a dense one that dominates before the transition and generalizes poorly, and a sparse one that dominates afterwards.
CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation

Existing works on open-vocabulary semantic segmentation have utilized large-scale vision-language models, such as CLIP, to leverage their exceptional open-vocabulary recognition capabilities. However, the problem of transferring these capabilities learned from image-level supervision to the pixel-level task of segmentation and addressing arbitrary unseen categories at inference makes this task challenging. To address these issues, we aim to attentively relate objects within an image to given categories by leveraging relational information among class categories and visual semantics through aggregation, while also adapting the CLIP representations to the pixel-level task. However, we observe that direct optimization of the CLIP embeddings can harm its open-vocabulary capabilities. In this regard, we propose an alternative approach to optimize the image-text similarity map, i.e. the cost map, using a novel cost aggregation-based method. Our framework, namely CAT-Seg, achieves state-of-the-art performance across all benchmarks. We provide extensive ablation studies to validate our choices. Project page: https://ku-cvlab.github. io/CAT-Seg/.
3D-CLFusion: Fast Text-to-3D Rendering with Contrastive Latent Diffusion

We tackle the task of text-to-3D creation with pre-trained latent-based NeRFs (NeRFs that generate 3D objects given input latent code). Recent works such as DreamFusion and Magic3D have shown great success in generating 3D content using NeRFs and text prompts, but the current approach of optimizing a NeRF for every text prompt is 1) extremely time-consuming and 2) often leads to low-resolution outputs. To address these challenges, we propose a novel method named 3D-CLFusion which leverages the pre-trained latent-based NeRFs and performs fast 3D content creation in less than a minute. In particular, we introduce a latent diffusion prior network for learning the w latent from the input CLIP text/image embeddings. This pipeline allows us to produce the w latent without further optimization during inference and the pre-trained NeRF is able to perform multi-view high-resolution 3D synthesis based on the latent. We note that the novelty of our model lies in that we introduce contrastive learning during training the diffusion prior which enables the generation of the valid view-invariant latent code. We demonstrate through experiments the effectiveness of our proposed view-invariant diffusion process for fast text-to-3D creation, e.g., 100 times faster than DreamFusion. We note that our model is able to serve as the role of a plug-and-play tool for text-to-3D with pre-trained NeRFs.
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration

Inversion by Direct Iteration (InDI) is a new formulation for supervised image restoration that avoids the so-called"regression to the mean"effect and produces more realistic and detailed images than existing regression-based methods. It does this by gradually improving image quality in small steps, similar to generative denoising diffusion models. Image restoration is an ill-posed problem where multiple high-quality images are plausible reconstructions of a given low-quality input. Therefore, the outcome of a single step regression model is typically an aggregate of all possible explanations, therefore lacking details and realism. The main advantage of InDI is that it does not try to predict the clean target image in a single step but instead gradually improves the image in small steps, resulting in better perceptual quality. While generative denoising diffusion models also work in small steps, our formulation is distinct in that it does not require knowledge of any analytic form of the degradation process. Instead, we directly learn an iterative restoration process from low-quality and high-quality paired examples. InDI can be applied to virtually any image degradation, given paired training data. In conditional denoising diffusion image restoration the denoising network generates the restored image by repeatedly denoising an initial image of pure noise, conditioned on the degraded input. Contrary to conditional denoising formulations, InDI directly proceeds by iteratively restoring the input low-quality image, producing high-quality results on a variety of image restoration tasks, including motion and out-of-focus deblurring, super-resolution, compression artifact removal, and denoising.
NASDM: Nuclei-Aware Semantic Histopathology Image Generation Using Diffusion Models

In recent years, computational pathology has seen tremendous progress driven by deep learning methods in segmentation and classification tasks aiding prognostic and diagnostic settings. Nuclei segmentation, for instance, is an important task for diagnosing different cancers. However, training deep learning models for nuclei segmentation requires large amounts of annotated data, which is expensive to collect and label. This necessitates explorations into generative modeling of histopathological images. In this work, we use recent advances in conditional diffusion modeling to formulate a first-of-its-kind nuclei-aware semantic tissue generation framework (NASDM) which can synthesize realistic tissue samples given a semantic instance mask of up to six different nuclei types, enabling pixel-perfect nuclei localization in generated samples. These synthetic images are useful in applications in pathology pedagogy, validation of models, and supplementation of existing nuclei segmentation datasets. We demonstrate that NASDM is able to synthesize high-quality histopathology images of the colon with superior quality and semantic controllability over existing generative methods.
Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery

ABSTRACT The responsible and sustainable agave-tequila production chain is fundamental for the social, environmental, and economic development of Mexico’s agave regions. It is therefore relevant to develop new tools for large-scale automatic agave region monitoring. In this work, we present an Agave tequilana Weber azul crop segmentation and maturity classification using very high-resolution satellite imagery, which could be useful for this task. To achieve this, we solve real-world deep learning problems in the very specific context of agave crop segmentation such as a lack of data, low-quality labels, highly imbalanced data, and low model performance. The proposed strategies go beyond data augmentation and data transfer combining active learning and the creation of synthetic images with human supervision. As a result, the segmentation performance evaluated with the Intersection over Union (IoU) value increased from 0.72 to 0.90 in the test set. The authors also propose a method for classifying agave crop maturity with 95% accuracy. With the resulting accurate models, agave production forecasting can be available for large regions. In addition, some supply-demand problems such as excessive supplies of agave or, deforestation, could be detected early.