Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Geometry and Reflectance Disentanglement for 3D Face Reconstruction from Sparse-view Images

This paper presents a novel two-stage approach for reconstructing human faces from sparse-view images, a task made challenging by the unique geometry and complex skin reflectance of each individual. Our method focuses on decomposing key facial attributes, including geometry, diffuse reflectance, and specular reflectance, from ambient light. Initially, we create a general facial template from a diverse collection of individual faces, capturing essential geometric and reflectance characteristics. Guided by this template, we refine each specific face model in the second stage, which further considers the interaction between geometry and reflectance, as well as the subsurface scattering effects on facial skin. Our method enables the reconstruction of high-quality facial representations from as few as three images, offering improved geometric accuracy and reflectance detail. Through comprehensive evaluations and comparisons, our method demonstrates superiority over existing techniques. Our method effectively disentangles geometry and reflectance components, leading to enhanced quality in synthesizing new views and opening up possibilities for applications such as relighting and reflectance editing. We will make the code publicly available.
Minimum-Time Trajectory Optimization With Data-Based Models: A Linear Programming Approach

In this paper, we develop a computationally-efficient approach to minimum-time trajectory optimization using input-output data-based models, to produce an end-to-end data-to-control solution to time-optimal planning/control of dynamic systems and hence facilitate their autonomous operation. The approach integrates a non-parametric data-based model for trajectory prediction and a continuous optimization formulation based on an exponential weighting scheme for minimum-time trajectory planning. The optimization problem in its final form is a linear program and is easy to solve. We validate the approach and illustrate its application with a spacecraft relative motion planning problem.
Effects of Grain Boundaries and Surfaces on Electronic and Mechanical Properties of Solid Electrolytes

Extended defects, including exposed surfaces and grain boundaries (GBs), are critical to the properties of polycrystalline solid electrolytes in all‐solid‐state batteries (ASSBs). These defects can alter the mechanical and electronic properties of solid electrolytes, with direct manifestations in the performance of ASSBs. Here, by building a library of 590 surfaces and grain boundaries of 11 relevant solid electrolytes—including halides, oxides, and sulfides— their electronic, mechanical, and thermodynamic characteristics are linked to the functional properties of polycrystalline solid electrolytes. It is found that the energy required to mechanically “separate” grain boundaries can be significantly lower than in the bulk region of materials, which can trigger preferential cracking of solid electrolyte particles in the grain boundary regions. The brittleness of ceramic solid electrolytes, inferred from the predicted low fracture toughness at the grain boundaries, contributes to their cracking under local pressure imparted by lithium (sodium) penetration in the grain boundaries. Extended defects of solid electrolytes introduce new electronic interfacial states within bandgaps of solid electrolytes. These states alter and possibly increase locally the availability of free electrons and holes in solid electrolytes. Factoring effects arising from extended defects appear crucial to explain electrochemical and mechanical observations in ASSBs.
Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation

Nowadays, the rapid development of mobile economy has promoted the flourishing of online marketing campaigns, whose success greatly hinges on the efficient matching between user preferences and desired marketing campaigns where a well-established Marketing-oriented Knowledge Graph (dubbed as MoKG) could serve as the critical"bridge"for preference propagation. In this paper, we seek to carefully prompt a Large Language Model (LLM) with domain-level knowledge as a better marketing-oriented knowledge miner for marketing-oriented knowledge graph construction, which is however non-trivial, suffering from several inevitable issues in real-world marketing scenarios, i.e., uncontrollable relation generation of LLMs,insufficient prompting ability of a single prompt, the unaffordable deployment cost of LLMs. To this end, we propose PAIR, a novel Progressive prompting Augmented mIning fRamework for harvesting marketing-oriented knowledge graph with LLMs. In particular, we reduce the pure relation generation to an LLM based adaptive relation filtering process through the knowledge-empowered prompting technique. Next, we steer LLMs for entity expansion with progressive prompting augmentation,followed by a reliable aggregation with comprehensive consideration of both self-consistency and semantic relatedness. In terms of online serving, we specialize in a small and white-box PAIR (i.e.,LightPAIR),which is fine-tuned with a high-quality corpus provided by a strong teacher-LLM. Extensive experiments and practical applications in audience targeting verify the effectiveness of the proposed (Light)PAIR.
Variational Auto-Encoder Based Deep Learning Technique For Filling Gaps in Reacting PIV Data

In this study, a deep learning based conditional density estimation technique known as conditional variational auto-encoder (CVAE) is used to fill gaps typically observed in particle image velocimetry (PIV) measurements in combustion systems. The proposed CVAE technique is trained using time resolved gappy PIV fields, typically observed in industrially relevant combustors. Stereo-PIV (SPIV) data from a swirl combustor with very a high vector yield is used to showcase the accuracy of the proposed CVAE technique. Various error metrics evaluated on the reconstructed velocity field in the gaps are presented from data sets corresponding to three sets of combustor operating conditions. In addition to accurate data reproduction, the proposed CVAE technique offers data compression by reducing the latent space dimension, enabling the efficient processing of large-scale PIV data.
Functional Data Analysis: An Introduction and Recent Developments

Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry, and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a data set on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available on Github: https://github.com/davidruegamer/FDA_tutorial .
Radical pairs and superoxide amplification can explain magnetic field effects on planarian regeneration

Weak magnetic field exposure can affect many biological processes across a wide range of living organisms. Recently, it has been observed that weak magnetic fields can modulate reactive oxygen species (ROS) concentration, affecting regeneration in planaria. These effects show unusual nonlinear dependence on magnetic field strength, including a sign change. In another study by the same group, superoxide is identified as the particular ROS being modulated. We propose a radical pair mechanism based on a flavin-superoxide radical pair to explain the modulation of superoxide production and its effect on planarian regeneration. The results of our calculations favor a triplet-born radical pair. Our yield calculations can reproduce the observed magnetic field dependence, including the sign change. Moreover, to explain the size of the effect on ROS concentration, we suggest a simple amplification model inspired by known biochemical mechanisms and lay out the conditions for such a model to work. Further, we also make empirical predictions concerning the hypomagnetic field effects on planarian regeneration.
Uncovering high-dimensional phase space and the application of Mixture of Experts (MoE) on building the Large CALPHAD Model (LCM)

This study presents a novel approach for analyzing and establishing Large CALPHAD model (LCM) in complex alloy systems. Through the introduction of"composition space volume", a multi-dimensional metric which allows to quatitatively define alloy composition variations. Utilizing stochastic methods, the study quantifies phase space complexity through phase density, and model training costs through data density. This leads to a strategic segmentation of the entire composition space, tailored to the complexity of each segment, thereby reducing computational efforts in model training. A significant advancement is the integration of segmented models using a Mixture of Experts (MoE) approach, ensuring accurate portrayal of phase behaviors across diverse composition spaces. This technique is demonstrated in establishing a high-dimensional phase diagram for the FeCoNiTi system, highlighting its efficiency and accuracy. The study's methodologies offer a systematic and cost-effective framework for modeling complex alloy systems, marking a step forward in the field of alloy design and analysis.
3D Hand Pose Estimation in Egocentric Images in the Wild

We present WildHands, a method for 3D hand pose estimation in egocentric images in the wild. This is challenging due to (a) lack of 3D hand pose annotations for images in the wild, and (b) a form of perspective distortion-induced shape ambiguity that arises in the analysis of crops around hands. For the former, we use auxiliary supervision on in-the-wild data in the form of segmentation masks&grasp labels in addition to 3D supervision available in lab datasets. For the latter, we provide spatial cues about the location of the hand crop in the camera's field of view. Our approach achieves the best 3D hand pose on the ARCTIC leaderboard and outperforms FrankMocap, a popular and robust approach for estimating hand pose in the wild, by 45.3% when evaluated on 2D hand pose on our EPIC-HandKps dataset.
SECNN: Squeeze-and-Excitation Convolutional Neural Network for Sentence Classification

Sentence classification is one of the basic tasks of natural language processing. Convolution neural network (CNN) has the ability to extract n-grams features through convolutional filters and capture local correlations between consecutive words in parallel, so CNN is a popular neural network architecture to dealing with the task. But restricted by the width of convolutional filters, it is difficult for CNN to capture long term contextual dependencies. Attention is a mechanism that considers global information and pays more attention to keywords in sentences, thus attention mechanism is cooperated with CNN network to improve performance in sentence classification task. In our work, we don't focus on keyword in a sentence, but on which CNN's output feature map is more important. We propose a Squeeze-and-Excitation Convolutional neural Network (SECNN) for sentence classification. SECNN takes the feature maps from multiple CNN as different channels of sentence representation, and then, we can utilize channel attention mechanism, that is SE attention mechanism, to enable the model to learn the attention weights of different channel features. The results show that our model achieves advanced performance in the sentence classification task.
Diseño de Mecanismos de Grano Fino para la Gestión Eficiente de Consumo y Temperatura en Procesadores Multinúcleo

En la ultima decada los ingenieros informaticos se han enfrentado a profundos cambios en el modo en que se disenan y fabrican los microprocesadores. Los nuevos procesadores no solo deben ser mas rapidos que los anteriores, tambien deben ser factibles en terminos de energia y disipacion termica, sobre todo en dispositivos que trabajan con baterias. Los problemas relacionados con consumo y temperatura son muy comunes en estos procesadores y son habitualmente detectados y tratados por un mecanismo de gestion dinamica de temperatura o Dynamic Thermal Management (DTM). La politica mas comun suele ser limitar la cantidad de energia que el procesador o nucleos del procesador pueden consumir (estableciendo un limite de consumo). En cualquier caso, la mayoria de los procesadores actuales no disponen de los medios para medir el consumo actual en tiempo real. En esta Tesis presentamos los denominados Power Tokens, los cuales permiten estimar la energia con mayor precision. Tambien analizamos el rendimiento, disipacion de potencia, consumo energetico y precision de diferentes mecanismos de reduccion de. Tras este analisis descubrimos que dichos mecanismos no son suficientemente buenos para adaptarse a un limite de consumo preestablecido con una penalizacion de rendimiento razonable. Para solucionar este problema proponemos diversas tecnicas a nivel de microarquitectura que combinan de manera dinamica varios mecanismos de reduccion de consumo para obtener una aproximacion al limite de consumo mucho mas precisa con una penalizacion de rendimiento minima.
ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network

Graph Comparative Learning (GCL) is a self-supervised method that combines the advantages of Graph Convolutional Networks (GCNs) and comparative learning, making it promising for learning node representations. However, the GCN encoders used in these methods rely on the Fourier transform to learn fixed graph representations, which is inherently limited by the uncertainty principle involving spatial and spectral localization trade-offs. To overcome the inflexibility of existing methods and the computationally expensive eigen-decomposition and dense matrix multiplication, this paper proposes an Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network (ASWT-SGNN). The proposed method employs spectral adaptive polynomials to approximate the filter function and optimize the wavelet using contrast loss. This design enables the creation of local filters in both spectral and spatial domains, allowing flexible aggregation of neighborhood information at various scales and facilitating controlled transformation between local and global information. Compared to existing methods, the proposed approach reduces computational complexity and addresses the limitation of graph convolutional neural networks, which are constrained by graph size and lack flexible control over the neighborhood aspect. Extensive experiments on eight benchmark datasets demonstrate that ASWT-SGNN accurately approximates the filter function in high-density spectral regions, avoiding costly eigen-decomposition. Furthermore, ASWT-SGNN achieves comparable performance to state-of-the-art models in node classification tasks.
SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolution

Depth super-resolution (DSR) aims to restore high-resolution (HR) depth from low-resolution (LR) one, where RGB image is often used to promote this task. Recent image guided DSR approaches mainly focus on spatial domain to rebuild depth structure. However, since the structure of LR depth is usually blurry, only considering spatial domain is not very sufficient to acquire satisfactory results. In this paper, we propose structure guided network (SGNet), a method that pays more attention to gradient and frequency domains, both of which have the inherent ability to capture high-frequency structure. Specifically, we first introduce the gradient calibration module (GCM), which employs the accurate gradient prior of RGB to sharpen the LR depth structure. Then we present the Frequency Awareness Module (FAM) that recursively conducts multiple spectrum differencing blocks (SDB), each of which propagates the precise high-frequency components of RGB into the LR depth. Extensive experimental results on both real and synthetic datasets demonstrate the superiority of our SGNet, reaching the state-of-the-art (see Fig. 1). Codes and pre-trained models are available at https://github.com/yanzq95/SGNet.
Improving the performance of weak supervision searches using transfer and meta-learning

Abstract Weak supervision searches have in principle the advantages of both being able to train on experimental data and being able to learn distinctive signal properties. However, the practical applicability of such searches is limited by the fact that successfully training a neural network via weak supervision can require a large amount of signal. In this work, we seek to create neural networks that can learn from less experimental signal by using transfer and meta-learning. The general idea is to first train a neural network on simulations, thereby learning concepts that can be reused or becoming a more efficient learner. The neural network would then be trained on experimental data and should require less signal because of its previous training. We find that transfer and meta-learning can substantially improve the performance of weak supervision searches.
Decision and control in uncertain resource systems

The best ebooks about Decision And Control In Uncertain Resource Systems that you can get for free here by download this Decision And Control In Uncertain Resource Systems and save to your desktop. This ebooks is under topic such as marc mangel: decision and control in uncertain resource chapter 1: what is adaptive management? project management decisions with uncertain targets adaptation of water resource systems to an uncertain future application of uncertain variables totaskand resource confronting dynamics and uncertainty in optimal decision adaptation of water resource systems to an uncertain future climate change, uncertainty, and natural resource management adaptive resource management for uncertain execution platforms i decision-theoretic control of problem solving: i agent-based systems for supply chain management uncertain supply chain management growing science decision making under interval uncertainty Ã¢Â€Â“ selected monitoring in the context of structured decision-making human-automation collaboration in complex multivariate revenue management for cloud computing providers: decision dynamic power management under uncertain information decision support systems in water resources management fully integrated web-based risk management systems for designing a decision support system for production a convex optimization-based approach to control of robust optimal policies for markov decision processes with power, organization design and managerial behaviour ideas and technology of control systems mathku panaceas, uncertainty, and the robust control framework in computational challenges and analysis under increasingly natural resources analysis and decision making decision making under uncertainty: an assessment of ssdc 20 uncertain optimal path for agv system accepting uncertainty, assessing risk: decision quality in
Spin noise spectroscopy of an alignment-based atomic magnetometer

Optically pumped magnetometers (OPMs) are revolutionising the task of magnetic-field sensing due to their extremely high sensitivity combined with technological improvements in miniaturisation which have led to compact and portable devices. OPMs can be based on spin-oriented or spin-aligned atomic ensembles which are spin-polarized through optical pumping with circular or linear polarized light, respectively. Characterisation of OPMs and the dynamical properties of their noise is important for applications in real-time sensing tasks. In our work, we experimentally perform spin noise spectroscopy of an alignment-based magnetometer. Moreover, we propose a stochastic model that predicts the noise power spectra exhibited by the device when, apart from the strong magnetic field responsible for the Larmor precession of the spin, white noise is applied in the perpendicular direction aligned with the pumping-probing beam. By varying the strength of the noise applied as well as the linear-polarisation angle of incoming light, we verify the model to accurately predict the heights of the Larmor-induced spectral peaks and their corresponding line-widths. Our work paves the way for alignment-based magnetometers to become operational in real-time sensing tasks.
A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis

Nailfold capillaroscopy is widely used in assessing health conditions, highlighting the pressing need for an automated nailfold capillary analysis system. In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset-321 images, 219 videos from 68 subjects, with clinic reports and expert annotations-that serves as a crucial resource for training deep-learning models. Leveraging this dataset, we finetuned three deep learning models with expert annotations as supervised labels and integrated them into a novel end-to-end nailfold capillary analysis pipeline. This pipeline excels in automatically detecting and measuring a wide range of size factors, morphological features, and dynamic aspects of nailfold capillaries. We compared our outcomes with clinical reports. Experiment results showed that our automated pipeline achieves an average of sub-pixel level precision in measurements and 89.9% accuracy in identifying morphological abnormalities. These results underscore its potential for advancing quantitative medical research and enabling pervasive computing in healthcare. Our data and code are available at https://github.com/THU-CS-PI-LAB/ANFC-Automated-Nailfold-Capillary.
A Video is Worth 256 Bases: Spatial-Temporal Expectation-Maximization Inversion for Zero-Shot Video Editing

This paper presents a video inversion approach for zero-shot video editing, which aims to model the input video with low-rank representation during the inversion process. The existing video editing methods usually apply the typical 2D DDIM inversion or na\"ive spatial-temporal DDIM inversion before editing, which leverages time-varying representation for each frame to derive noisy latent. Unlike most existing approaches, we propose a Spatial-Temporal Expectation-Maximization (STEM) inversion, which formulates the dense video feature under an expectation-maximization manner and iteratively estimates a more compact basis set to represent the whole video. Each frame applies the fixed and global representation for inversion, which is more friendly for temporal consistency during reconstruction and editing. Extensive qualitative and quantitative experiments demonstrate that our STEM inversion can achieve consistent improvement on two state-of-the-art video editing methods.