arXiv research

In this paper, we provide an alternative proof of Donaldson's almost-holomorphic section theorem and symplectic Lefschetz pencil theorem, through constructions of certain special kind of Donaldson-type sections of the line bundle based on properties of exponential sums.

Study on convergence rate of Bergman metrics on Kähler manifolds.

Geometrically constructs representations for quantization on Kähler manifolds.

Study Bergman kernels on complex hyperbolic cusps, generalizing previous results.

Method finds compatible features for subsets of data.

Associated with a smooth, $d$-closed $(1, 1)$-form $α$ of possibly non-rational De Rham cohomology class on a compact complex manifold $X$ is a sequence of asymptotically holomorphic complex line bundles $L_k$ on $X$ equipped with $(0, 1)$-connections $\bar\partial_k$ for which $\bar\partial_k^2\neq 0$. Their study was…

We give a direct proof for the asymptotic faithfulness of the quantum $SU(n)$ representations of the mapping class groups using peak sections in Kodaira embedding. We give also estimates on the norm of the parallell transport of the projective connection on the Verlinde bundle. The faithfulness has been proved earlier …

A geometric account explains why 'The Dress' is ambiguous, predicting observable signatures in image processing.

A robust method for decomposing spectral peaks robust to distortion and interference.

SPADE improves demand forecasting accuracy by 4.5% for post-promotion periods.

We find empirically a characteristic sharp peak-flat trough pattern in a large set of commodity prices. We argue that the sharp peak structure reflects an endogenous inter-market organization, and that peaks may be seen as local ``singularities'' resulting from imitation and herding. These findings impose a novel strin…

Joint peak detection is a central problem when comparing samples in genomic data analysis, but current algorithms for this task are unsupervised and limited to at most 2 sample types. We propose PeakSegJoint, a new constrained maximum likelihood segmentation model for any number of sample types. To select the number of…

Mass spectrometry (MS) is an important technique for chemical profiling which calculates for a sample a high dimensional histogram-like spectrum. A crucial step of MS data processing is the peak picking which selects peaks containing information about molecules with high concentrations which are of interest in an MS in…

Study on-chain peak shaving to reduce Ethereum transaction costs.

The paper explains two distinct peaks in generalization error for neural networks and simpler models, each governed by different factors.

The heuristic identification of peaks from noisy complex spectra often leads to misunderstanding of the physical and chemical properties of matter. In this paper, we propose a framework based on Bayesian inference, which enables us to separate multipeak spectra into single peaks statistically and consists of two steps.…

Bayesian framework integrates spectral deconvolution with expert reasoning for robust peak estimation.

FLOPART solves peak detection by creating accurate train and test set predictions.

The paper shows how the generalization curve can have multiple peaks, influenced by data and learning algorithm biases.

The paper links labor income risk to stock returns using industry portfolio returns.

PEAKS selects key training examples incrementally based on prediction error and kernel similarity.

PEAK tests means of multiple data streams with sequential betting.

Data analysis in high-dimensional spaces aims at obtaining a synthetic description of a data set, revealing its main structure and its salient features. We here introduce an approach providing this description in the form of a topography of the data, namely a human-readable chart of the probability density from which t…

LLMs learn peaked distributions slowly due to power-law losses.

Finite-time queue peaks in stochastic networks have logarithmic scaling after geometric thresholds.

Bayesian Quadrature improves ensembling for neural networks with dispersed likelihood peaks.

During a stock market peak the price of a given stock ($i$) jumps from an initial level $p_1(i)$ to a peak level $p_2(i)$ before falling back to a bottom level $p_3(i)$. The ratios $A(i) = p_2(i)/p_1(i)$ and $B(i)= p_3(i)/p_1(i)$ are referred to as the peak- and bottom-amplitude respectively. The paper show…

In this paper, the fractional order curvature equation $(-Δ)^γu = (1 + \varepsilon K(x))u^{\frac{N + 2γ}{N - 2γ}}$ in $\mathbb{R}^N$ is considered. Assuming $K(x)$ has two critical points satisfying certain local conditions, we prove the existence of two-peak solutions.

A nonparametric method for time series analysis extracts envelopes, detects peaks, and clusters data.

Populations of species in ecosystems are often constrained by availability of resources within their environment. In effect this means that a growth of one population, needs to be balanced by comparable reduction in populations of others. In neutral models of biodiversity all populations are assumed to change increment…

Paper proposes a network framework for prosumers to manage peak loads in Iran.

Traditionally in regression one minimizes the number of fitting parameters or uses smoothing/regularization to trade training (TE) and generalization error (GE). Driving TE to zero by increasing fitting degrees of freedom (dof) is expected to increase GE. However modern big-data approaches, including deep nets, seem to…

New algorithm tackles constrained Markov decision processes with peak constraints.

We win EVA2025 by estimating extreme precipitation events using Peaks Over Thresholds and martingale testing.

This paper explains why double descent sometimes occurs weakly or not at all from an optimization perspective.

Support Vector Data Description (SVDD) provides a useful approach to construct a description of multivariate data for single-class classification and outlier detection with various practical applications. Gaussian kernel used in SVDD formulation allows flexible data description defined by observations designated as sup…

A new clustering algorithm reduces density peaks clustering's computational complexity.

Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.

We study the dynamics of order flows around large intraday price changes using ultra-high-frequency data from the Shenzhen Stock Exchange. We find a significant reversal of price for both intraday price decreases and increases with a permanent price impact. The volatility, the volume of different types of orders, the b…

Improved peak detection in ChIP-seq data reduces over-dispersion.

New model improves volatility forecasting by reducing overestimation and underestimation.

Extracts important peaks from XRD spectra using Attention mechanism.

During a speculative episode the price of an item jumps from an initial level p_1 to a peak level p_2 before more or less returning to level p_1. The ratio p_2/p_1 is referred to as the amplitude A of the peak. This paper shows that for a given market the peak amplitude is a linear function of the logarithm of the pric…

Efficient neural Bayes estimators for censored peaks-over-threshold models improve inference speed and accuracy.

Novel graph-based method detects R-peaks in noisy ECG signals without preprocessing.

Method identifies financial rogue waves close to their onset.

Optimal scheduling of hydrogen production in dynamic pricing power market can maximize the profit of hydrogen producer; however, it highly depends on the accurate forecast of hydrogen consumption. In this paper, we propose a deep leaning based forecasting approach for predicting hydrogen consumption of fuel cell vehicl…

Using publicly available traffic camera data in New York City, we quantify time-dependent patterns in aggregate pedestrian foot traffic. These patterns exhibit repeatable diurnal behaviors that differ for weekdays and weekends but are broadly consistent across neighborhoods in the borough of Manhattan. Weekday patterns…