arXiv research

Generative model gradients enhance MS/MS peptide identification.

We show that $|MS(L_1 # L_2)|=|MS(L_1)|\times|MS(L_2)|\times\mathbb{R}$ when $L_1$ and $L_2$ are any non-split and non-fibred links. Here $MS(L)$ denotes the Kakimizu complex of a link $L$, which records the taut Seifert surfaces for $L$. We also show that the analogous result holds if we study incompressible Seifert s…

Pricing and hedging rainbow options using Bayesian MS-VAR process.

Deep learning diagnoses MS from smartphone data.

Improved analysis shows Maillard sampling achieves optimal regret bounds.

Maps dBKP solutions to MS system solutions, defining Einstein-Weyl structures.

Model uses smartphone data to assess MS trajectories.

Bayesian MS-VAR process improves option pricing models.

In this work, we present direction-of-arrival (DoA) estimation algorithms based on the Krylov subspace that effectively exploit prior knowledge of the signals that impinge on a sensor array. The proposed multi-step knowledge-aided iterative conjugate gradient (CG) (MS-KAI-CG) algorithms perform subtraction of the unwan…

MS-CASTLE learns causal structures across multiple time scales.

The paper determines the structure of Kakimizu complexes for genus one hyperbolic knots.

Selective prediction framework reduces errors in molecular structure identification from MS/MS.

We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from diffe…

Improved inter-scanner MS lesion segmentation through adversarial training.

This study investigates the content of the published scientific literature in the fields of operations research and management science (OR/MS) since the early 1950s. Our study is based on 80,757 published journal abstracts from 37 of the leading OR/MS journals. We have developed a topic model, using Latent Dirichlet Al…

CRBM generates digital twins for MS patients, aiding in disease progression analysis.

The paper studies the topology of spherical tori with one conical point.

KL-MS improves regret bounds for multi-armed bandits with bounded rewards.

Deep learning complements OR/MS for decision-making under uncertainty.

Fast, accurate thalamus segmentation method for MS and ET.

A new method selects regions of interest in GC-MS data without prior target selection.

There are different problems for resolution of complex LC-MS or GC-MS data, such as the existence of embedded chromatographic peaks, continuum background and overlapping in mass channels for different components. These problems cause rotational ambiguity in recovered profiles calculated using multivariate curve resolut…

A new Multi-Stream VAE separates multiple sources in images and audio.

Let $(M,g,\si)$ be a compact spin manifold of dimension $n \geq 2$. Let $λ_1^+(\tilde{g})$ be the smallest positive eigenvalue of the Dirac operator in the metric $\tilde{g} \in [g]$ conformal to $g$. We then define $\lamin(M,[g],\si) = \inf_{\tilde{g} \in [g]} λ_1^+(\tilde{g}) \Vol(M,\tilde{g})^{1/n} $. We show that $…

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…

Bayesian MS-VAR model for pricing equity-linked life insurance products.

Proposes LRR and LRLR for improving stock prediction accuracy.

Paper uses Gromov-Hausdorff convergence to re-examine surface classification.

Many proteoforms - arising from alternative splicing, post-translational modifications (PTMs), or paralogous genes - have distinct biological functions, such as histone PTM proteoforms. However, their quantification by existing bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific biases. To avoid …

Study guarantees convergence of mean shift mode estimation.

Classifiers and beamforming algorithms improved audio surveillance detection accuracy.

Graph Attention Networks predict disease state from single-cell data.

In this work, we aim to gain a better understanding of the volatility smile observed in options markets through microsimulation (MS). We adopt two types of active traders in our MS model: speculators and arbitrageurs, and call and put options on one underlying asset. Speculators make decisions based on their expectatio…

Paper proposes MS-k-NN for improved convergence rate in k-NN classification.

Study a specific line arrangement and compute its fundamental group via braid monodromy.

This study presents a new lossy image compression method that utilizes the multi-scale features of natural images. Our model consists of two networks: multi-scale lossy autoencoder and parallel multi-scale lossless coder. The multi-scale lossy autoencoder extracts the multi-scale image features to quantized variables a…

GT estimator shows convergence for Markov samples, improving i.i.d. results.

Todays interactive devices such as smart-phone assistants and smart speakers often deal with short-duration speech segments. As a result, speaker recognition systems integrated into such devices will be much better suited with models capable of performing the recognition task with short-duration utterances. In this pap…

Recent deep learning approaches have achieved impressive performance on speech enhancement and separation tasks. However, these approaches have not been investigated for separating mixtures of arbitrary sounds of different types, a task we refer to as universal sound separation, and it is unknown how performance on spe…

Let $M$ be a smooth compact manifold and $P$ be either $R^1$ or $S^1$. There is a natural action of the groups $Diff(M)$ and $Diff(M) \times Diff(P)$ on the space of smooth mappings $C^{\infty}(M,P)$. For $f\in C^{\infty}(M,P)$ let $S_f$, $S_{MP}$, $O_f$, and $O_{MP}$ be the stabilizers and orbits of $f$ under these ac…

Learning the minimum/maximum mean among a finite set of distributions is a fundamental sub-task in planning, game tree search and reinforcement learning. We formalize this learning task as the problem of sequentially testing how the minimum mean among a finite set of distributions compares to a given threshold. We deve…

Develops an MS-inspired algorithm for regression mode finding and space partitioning.

Real-time semantic segmentation for autonomous vehicles on FPGA reduces latency and power consumption.

The morphological structure of left ventricle segmented from cardiac magnetic resonance images can be used to calculate key clinical parameters, and it is of great significance to the accurate and efficient diagnosis of cardiovascular diseases. Compared with traditional methods, the segmentation algorithms based on ful…

Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales. Fortunately, most music is also highly structured and can be represented as discrete note events played on musical instruments. Herein, we show that by using no…

Classifies spatial graphs with finite N-quandles.

Humans take advantage of real world symmetries for various tasks, yet capturing their superb symmetry perception mechanism with a computational model remains elusive. Motivated by a new study demonstrating the extremely high inter-person accuracy of human perceived symmetries in the wild, we have constructed the first …

New video compression method outperforms traditional approaches.