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.
A novel approach for augmenting histopathological images by blending Gaussian-Laplacian pyramids.
The paper analyzes Laplacian pyramids for extending and denoising discrete functions.
Improves instance segmentation accuracy by integrating low-level features.
Pyramid Attention Networks improve image restoration by leveraging self-similarities across scales.
Study on convergence of transformed metric spaces as dimensions grow.
Simulation reveals relationships in stock market pyramid schemes.
A new pyramidal diffusion model speeds up image generation.
Single wide layer followed by a pyramidal structure ensures global convergence in deep networks.
We generalize the observable diameter and the separation distance for metric measure spaces to those for pyramids, and prove some limit formulas for these invariants for a convergent sequence of pyramids. We obtain various applications of our limit formulas as follows. We have a criterion of the phase transition proper…
Reduced CNN complexity for exoplanet detection without significant loss in accuracy.
By formulating N = 1, 2, 4, 8, D = 3, Yang-Mills with a single Lagrangian and single set of transformation rules, but with fields valued respectively in R,C,H,O, it was recently shown that tensoring left and right multiplets yields a Freudenthal-Rosenfeld-Tits magic square of D = 3 supergravities. This was subsequently…
TP-AIS improves sampling efficiency over existing methods.
Study smoothings of ellipsoid intersections with singularities.
Improves speaker verification for variable-duration utterances using a feature pyramid module.
The paper develops a new Ricci flow method in higher dimensions.
We define invariants for colored oriented spatial graphs by generalizing CM invariants, which were defined via non-integral highest weight representations of . We apply the same method to define Yokota's invariants, and we call these invariants Yokota type invariants. Then we propose a volume conjecture of t…
PyFi uses adversarial agents to train VLMs on financial image understanding.
SPF uses a hierarchical approach to efficiently emulate climate changes.
Enhances neural networks' robustness against adversarial samples without sacrificing clean sample generalization.
Pyramidal GNN combines RC and pooling for efficient graph embeddings.
Deep neural network detects changepoints at multiple scales in multivariate time series.
Detects parking spaces in parcels using satellite images.
Proposes SPE for robust speaker verification.
We introduce a wavelet-domain functional analysis of variance (fANOVA) method based on a Bayesian hierarchical model. The factor effects are modeled through a spike-and-slab mixture at each location-scale combination along with a normal-inverse-Gamma (NIG) conjugate setup for the coefficients and errors. A graphical mo…
This paper focuses on a class of linear Hawkes processes with general immigrants. These are counting processes with shot noise intensity, including self-excited and externally excited patterns. For such processes, we introduce the concept of age pyramid which evolves according to immigration and births. The virtue if t…
Biological neural network mimics CCA for multi-channel data.
PC-RNN reconstructs MRI images from undersampled data with more details.
The challenge of object categorization in images is largely due to arbitrary translations and scales of the foreground objects. To attack this difficulty, we propose a new approach called collaborative receptive field learning to extract specific receptive fields (RF's) or regions from multiple images, and the selected…
GSANet improves semantic segmentation accuracy with selective and global attention.
Advances conditions for neural networks to learn connected decision regions.
S&P 500 index data sampled at one-minute intervals over the course of 11.5 years (January 1989- May 2000) is analyzed, and in particular the Hurst parameter over segments of stationarity (the time period over which the Hurst parameter is almost constant) is estimated. An asymptotically unbiased and efficient estimator …
Non-linear dimensionality reduction techniques such as manifold learning algorithms have become a common way for processing and analyzing high-dimensional patterns that often have attached a target that corresponds to the value of an unknown function. Their application to new points consists in two steps: first, embedd…
A deep learning framework assesses physical rehabilitation exercises.
We construct a global homeomorphism from any 3D Ricci limit space to a smooth manifold, that is locally bi-Holder. This extends the recent work of Miles Simon and the second author, and we build upon their techniques. A key step in our proof is the construction of local "pyramid Ricci flows", existing on uniform region…
Paper proposes using NLPD for cGANs to improve image realism and segmentation.
Hyperbolic links in thickened torus decompose into angled tetrahedra.
The predictions of the S&P 500 returns made in 2007 have been tested and the underlying models amended. The period between 2003 and 2008 should be described by the dependence of the S&P 500 stock market index on real GDP because the population pyramid was highly inaccurate. The 2008 trough and 2009 rally are well predi…
Develops LSTM for predicting neuronal dynamics over long time-horizons.
A family of algorithms for time series classification (TSC) involve running a sliding window across each series, discretising the window to form a word, forming a histogram of word counts over the dictionary, then constructing a classifier on the histograms. A recent evaluation of two of this type of algorithm, Bag of …
ADAVI tackles variational inference for large HBM models in neuroimaging.
While the optimization problem behind deep neural networks is highly non-convex, it is frequently observed in practice that training deep networks seems possible without getting stuck in suboptimal points. It has been argued that this is the case as all local minima are close to being globally optimal. We show that thi…
MRCNet tackles crowd counting and density mapping in aerial imagery.
We present a machine learning-based approach to lossy image compression which outperforms all existing codecs, while running in real-time. Our algorithm typically produces files 2.5 times smaller than JPEG and JPEG 2000, 2 times smaller than WebP, and 1.7 times smaller than BPG on datasets of generic images across all …
In the recent literature the important role of depth in deep learning has been emphasized. In this paper we argue that sufficient width of a feedforward network is equally important by answering the simple question under which conditions the decision regions of a neural network are connected. It turns out that for a cl…
In Maslov (2003), a two level model of the occurrence of financial pyramid (bubbles) has been considered. We also considered the mathematical analogy of this model to Bose condensation. In the present paper, we explain why Ponzi schemes and bubbles result in a crisis in real economics. In Maslov (2005), the law of incr…
Reflective of income and wealth distributions, philanthropic gifting appears to follow an approximate power-law size distribution as measured by the size of gifts received by individual institutions. We explore the ecology of gifting by analysing data sets of individual gifts for a diverse group of institutions dedicat…
DefogGAN predicts hidden RTS game information to aid strategic decision-making.
The signed volume function for polyhedra can be generalized to a mean volume function for volume elements by averaging over the triangulations of the underlying polyhedron. If we consider these up to translation and scaling, the resulting quotient space is diffeomorphic to a sphere. The mean volume function restricted …