We discuss recently emerging applications of the state-of-art deep learning methods on optical microscopy and microscopic image reconstruction, which enable new transformations among different modes and modalities of microscopic imaging, driven entirely by image data. We believe that deep learning will fundamentally ch…
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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
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48 results for “Microscopic Images”
Framework detects and classifies multi-label RBC images from microscopic images.
problem Challenges in separating touching or overlapping cells for classification.
method Region proposal model + CNN feature extraction + multi-label prediction networks.
result Framework achieves good performance in automatic cell detection and classification.
Deep learning model detects and classifies marine microfossils.
problem Manual identification of microfossils is time-consuming and error-prone.
method Transfer learning from ImageNet dataset to classify foraminifera.
result Proposed model achieves high accuracy on foraminifera classification.
Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learningcs.CV
A-MIL improves histopathology image classification and localization.
problem Improving diagnosis of breast cancer through better interpretation of histopathology images.
method Frame image classification as multiple instance learning, use attention-based learning for localization.
result A-MIL achieves better localization without compromising classification accuracy.
Improved self-supervised denoising for Poisson-Gaussian noise.
problem Handling Poisson-Gaussian noise in self-supervised denoising.
method Extended blindspot model, improved training scheme without hyperparameters.
result Improved denoising performance on microscope image benchmarks.
Novel framework for whole-slide blood cell segmentation.
problem Semantic segmentation of whole-slide blood cell images.
method Convolutional encoder-decoder framework with VGG-16 feature extraction.
result Outstanding accuracy (97.18% global accuracy) in classwise segmentation.
A microscopic model is established for financial Brownian motion from the direct observation of the dynamics of high-frequency traders (HFTs) in a foreign exchange market. Furthermore, a theoretical framework parallel to molecular kinetic theory is developed for the systematic description of the financial market from m…
Macroscopic price evolution models are commonly used for investment strategies. There are first promising achievements in defining microscopic agent based models for the same purpose. Microscopic models allow a deeper understanding of mechanisms in the market than the purely phenomenological macroscopic models, and thu…
To alleviate the burden of gathering detailed expert annotations when training deep neural networks, we propose a weakly supervised learning approach to recognize metastases in microscopic images of breast lymph nodes. We describe an alternative training loss which clusters weakly labeled bags in latent space to inform…
A microscopic model of triangular arbitragephysics.soc-ph
We introduce a microscopic model which describes the dynamics of each dealer in multiple foreign exchange markets, taking account of the triangular arbitrage transaction. The model reproduces the interaction among the markets well. We explore the relation between the parameters of the present microscopic model and the …
(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de S…
Microscopic Models of Financial Marketscond-mat.stat-mech
Submitted to F. Schweitzer (ed.), Microscopic Models for Economic Dynamics, Lecture notes in physics, Springer, Berlin-Heidelberg 2002.kiel.tex
We establish a microscopic convexity principle for nonlinear elliptic and parabolic partial differential equations in general form.
An important part of Digital Pathology is the analysis of multiple digitised whole slide images from differently stained tissue sections. It is common practice to mount consecutive sections containing corresponding microscopic structures on glass slides, and to stain them differently to highlight specific tissue compon…
Deep learning automates bacterial image classification.
problem Manual bacterial classification is time-consuming and error-prone.
method ResNet-50 pre-trained CNN architecture with transfer learning.
result Average classification accuracy of 99.2%.
Study validates Lillo-Mike-Farmer model predicting financial market long-range correlations.
problem Quantifying long-range correlations in financial markets.
method Analyzed nine years of market data to classify traders as order-splitting or random, measured metaorder-length distributions, and compared to LMF model predictions.
result Agreement between LMF model predictions and actual data, validating the model.
This study compares microscopic and macroscopic models for commodity index derivatives pricing.
problem Lack of accurate futures curve dynamics in macroscopic models for real scenarios.
method Calibrated both microscopic and macroscopic models using S\&P GSCI Crude Oil excess-return index derivatives.
result Macroscopic models struggle to capture futures curve dynamics, affecting pricing and sensitivities.
Enhances high-throughput imaging of microtubule networks, improving clarity and consistency.
problem Fluorescence noise obscures microtubule structures in high-throughput imaging.
method CycleGAN learning to enhance low-resolution images of microtubule networks.
result CycleGAN effectively identifies microtubules with high accuracy (0.93+ AUC-ROC).
New models explain multidimensional rough volatility from microscopic price dynamics.
problem Designing new rough stochastic volatility models for multi-asset scenarios.
method Using Hawkes processes to model microstructural interactions and investigate scaling limits.
result Multivariate rough volatility models arise naturally from microscopic price dynamics.
Methodology to analyze traffic accidents using microscopic models.
problem Understanding and predicting traffic accidents and their impact.
method Developed a statistical approach using microscopic traffic models and SUMO.
result Approximate distribution of total losses as a mean-variance mixture.
Fluorescence microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, fluorescence microscopy is not only much noisier than photography, but also presented with Poisson-Gaussian noise where Poisson noise, or shot noise, is the dominating noise source. To get clean fluorescence…
Derives mean field game equations from microscopic agent dynamics.
problem Modeling behavior of large interacting agents in games.
method Derives mean field game PDEs from deterministic agent dynamics using Nash equilibria and dynamic programming.
result Derives mean field game limits and scales from agent-based financial market model.
The idea is considered that a quantum wormhole in a spacetime foam can be described as a Ricci flow. In this interpretation the Ricci flow is a statistical system and every metric in the Ricci flow is a microscopical state. The probability density of the microscopical state is connected with a Perelman's functional of …
Microscopic (pore-scale) properties of porous media affect and often determine their macroscopic (continuum- or Darcy-scale) counterparts. Understanding the relationship between processes on these two scales is essential to both the derivation of macroscopic models of, e.g., transport phenomena in natural porous media,…
Paper proves existence of unique constant scalar curvature Kähler metric under certain conditions.
problem Existence of constant scalar curvature Kähler metrics on polarized manifolds.
method Direct proof using microscopic stability thresholds and conditions on the limit.
result Existence of a unique constant scalar curvature Kähler metric under specific conditions.
Tying knots and linking microscopic loops of polymers, macromolecules, or defect lines in complex materials is a challenging task for material scientists. We demonstrate the knotting of microscopic topological defect lines in chiral nematic liquid crystal colloids into knots and links of arbitrary complexity by using l…
Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observationsstat.ML
Generative model for high-dimensional categorical data using Gaussian-Dirichlet fields.
problem Efficiently modeling and predicting high-dimensional categorical data.
method Combines Dirichlet and Gaussian processes for spatio-temporal modeling.
result Model accurately approximates categorical data in unobserved locations.
Deep feature fusion improves mitosis counting accuracy.
problem Manual mitosis counting by pathologists is time-consuming and inconsistent.
method Combines Faster R-CNN for object detection with UNet segmentation features and RGB image features.
result Achieved an F-score of 0.508 on mitosis counting challenge dataset, outperforming state-of-the-art methods.
Background: For complex financial systems, the negative and positive return-volatility correlations, i.e., the so-called leverage and anti-leverage effects, are particularly important for the understanding of the price dynamics. However, the microscopic origination of the leverage and anti-leverage effects is still not…
Digital Rock Imaging is constrained by detector hardware, and a trade-off between the image field of view (FOV) and the image resolution must be made. This can be compensated for with super resolution (SR) techniques that take a wide FOV, low resolution (LR) image, and super resolve a high resolution (HR), high FOV ima…
Separation of dissipation from diffusioncond-mat.stat-mech
We study velocity correlations induced by diffusion and dissipation in a simple dissipative dynamical system. We observe that diffusion, as a result of time reversible microscopic processes, leads to correlations with different spatial parity from those caused by dissipation, consisting of time irreversible microscopic…
Automatic post-picking using MAPPOS improves particle image detection from Cryo-EM micrographsstat.ML
Cryo-electron microscopy (cryo-EM) studies using single particle reconstruction are extensively used to reveal structural information on macromolecular complexes. Aiming at the highest achievable resolution, state of the art electron microscopes automatically acquire thousands of high-quality micrographs. Particles are…
Study compares DL models for medical image segmentation, finds synergistic ensemble strategies improve performance.
problem Improving DL models for specialized medical image segmentation using transfer learning.
method Detailed comparisons of TII and LMI models for binary segmentation of medical images.
result Ensemble strategies improve performance by 10% in certain scenarios.
Extracts coarse-grained PDEs from microscopic simulations.
problem Discovering effective PDEs for macro-scale processes from microscopic data.
method Combining neural networks with equation-free numerics and data-driven approaches.
result Efficiently discovers macro-scale PDEs from microscopic simulations.
We present examples of agent-based and stochastic models of competition and business processes in economics and finance. We start from as simple as possible models, which have microscopic, agent-based, versions and macroscopic treatment in behavior. Microscopic and macroscopic versions of herding model proposed by Kirm…
Far from Asymptopiastat.OT
New optimal prior avoids bias in complex models with limited data.
problem Bias in inference from limited data using Jeffreys prior.
method Developed a principled choice of measure that avoids bias, dependent on data quantity.
result Optimal prior leads to unbiased inference in complex models.
We study the distributions of event-time returns and clock-time returns at different microscopic timescales using ultra-high-frequency data extracted from the limit-order books of 23 stocks traded in the Chinese stock market in 2003. We find that the returns at the one-trade timescale obey the inverse cubic law. For la…
Malaria is a serious infectious disease that is responsible for over half million deaths yearly worldwide. The major cause of these mortalities is late or inaccurate diagnosis. Manual microscopy is currently considered as the dominant diagnostic method for malaria. However, it is time consuming and prone to human error…
Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized Gaussian Process Approachstat.ML
A new model captures car-following and lane-changing behaviors in traffic.
problem Modeling stochastic microscopic traffic behaviors.
method Physics regularized Gaussian process (PRGP) approach.
result The proposed model outperforms previous methods in estimation precision.
Detection of cell nuclei in microscopic images is a challenging research topic, because of limitations in cellular image quality and diversity of nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success sho…
Profiling cellular phenotypes from microscopic imaging can provide meaningful biological information resulting from various factors affecting the cells. One motivating application is drug development: morphological cell features can be captured from images, from which similarities between different drug compounds appli…
A prognosis oriented microscopic stock market modelcond-mat.stat-mech
We present a new microscopic stochastic model for an ensemble of interacting investors that buy and sell stocks in discrete time steps via limit orders based on individual forecasts about the price of the stock. These orders determine the supply and demand fixing after each round (time step) the new price of the stock …
An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detectioncs.CV
This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks. The crux of the idea is to train a deep convolutional autoencoder to suppress undesired parts of an image frame while allowing the desired parts resulting in efficient object detection. The efficacy of the fr…
We propose a class of Markovian agent based models for the time evolution of a share price in an interactive market. The models rely on a microscopic description of a market of buyers and sellers who change their opinion about the stock value in a stochastic way. The actual price is determined in realistic way by match…
Motivated by a zero-intelligence approach, the aim of this paper is to connect the microscopic (discrete price and volume), mesoscopic (discrete price and continuous volume) and macroscopic (continuous price and volume) frameworks for the modelling of limit order books, with a view to providing a natural probabilistic …
We infer both microscopic and macroscopic behaviors of a three-dimensional chaotic fluid flow using reservoir computing. In our procedure of the inference, we assume no prior knowledge of a physical process of a fluid flow except that its behavior is complex but deterministic. We present two ways of inference of the co…
In complex financial systems, the sector structure and volatility clustering are respectively important features of the spatial and temporal correlations. However, the microscopic generation mechanism of the sector structure is not yet understood. Especially, how to produce these two features in one model remains chall…
In this paper we study the possible microscopic origin of heavy-tailed probability density distributions for the price variation of financial instruments. We extend the standard log-normal process to include another random component in the so-called stochastic volatility models. We study these models under an assumptio…