arXiv research

The study develops a logic reasoner to verify MS case management specifications.

MS-CASTLE learns causal structures across multiple time scales.

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

Model uses smartphone data to assess MS trajectories.

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…

The study compares MS-GARCH and SARV models for Bitcoin volatility forecasting.

In this paper, we combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisions in OR/MS problems. In a departure from other work on data-driven optimization and reflecting our practic…

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

Bayesian MS-VAR process improves option pricing models.

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…

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

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

Financial time-series classification (FTC) is extremely valuable for investment management. In past decades, it draws a lot of attention from a wide extent of research areas, especially Artificial Intelligence (AI). Existing researches majorly focused on exploring the effects of the Multi-Scale (MS) property or the Tem…

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.

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…

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 …

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.

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…

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

Improved inter-scanner MS lesion segmentation through adversarial training.

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

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

Tandem mass spectrometry (MS/MS) is a high-throughput technology used toidentify the proteins in a complex biological sample, such as a drop of blood. A collection of spectra is generated at the output of the process, each spectrum of which is representative of a peptide (protein subsequence) present in the original co…

Due to the growing demand for improving surveillance capabilities in smart cities, systems need to be developed to provide better monitoring capabilities to competent authorities, agencies responsible for strategic resource management, and emergency call centers. This work assumes that, as a complementary monitoring so…

Fast, accurate thalamus segmentation method for MS and ET.

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…

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

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

Classifies spatial graphs with finite N-quandles.

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 $…

Estimates variance function using aggregation methods in regression models.

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.

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.

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…

The paper presents a method for personalized exercise recommendations that improves learner skill gain.

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

The paper analyzes performance criteria for competing fund managers in Ito-diffusion markets.