Staffing & Scheduling

MSBooster: improving peptide identification rates using deep learning-based features.

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based pre...

Dissociable default-mode subnetworks subserve childhood attention and cognitive flexibility: Evidence from deep learning and stereotactic electroencephalography.

Cognitive flexibility encompasses the ability to efficiently shift focus and forms a critical component of goal-directed attention. The neural substrates of this process are incompletely understood in part due to difficulties in sampling the involved circuitry. We leverage stereotactic intracranial recordings to directly resolve local-field potentials from otherwise inaccessible structures to stud...

Prediction sets adaptive to unknown covariate shift.

Predicting sets of outcomes-instead of unique outcomes-is a promising solution to uncertainty quantification in statistical learning. Despite a rich l...

A simple and reliable instance selection for fast training support vector machine: Valid Border Recognition.

Support vector machines (SVMs) are powerful statistical learning tools, but their application to large datasets can cause time-consuming training comp...

Experience matters for robotic assistance: an analysis of case data.

Many robotic procedures require active participation by assistants. Most prior work on assistants' effect on outcomes has been limited in procedural f...

Athlete body fat rate monitoring and motion image simulation based on SDN data center network and sensors.

With the development of artificial intelligence technology, new software is also emerging in an endless stream. On the basis of sensors, the new softw...

Enhanced regularization for on-chip training using analog and temporary memory weights.

In-memory computing techniques are used to accelerate artificial neural network (ANN) training and inference tasks. Memory technology and architectura...

Propensity matched analysis of robotic and laparoscopic operations for mid-low rectal cancer: short-term comparison of anal function and oncological outcomes.

Laparoscopic surgery for rectal cancer, while in some respects equivalent or even preferable to open surgery, is challenged in specific conditions whe...

Predicting Neuromuscular Engagement to Improve Gait Training with a Robotic Ankle Exoskeleton.

The clinical efficacy of robotic rehabilitation interventions hinges on appropriate neuromuscular recruitment from the patient. The first purpose of t...

Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning.

This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain an...

Accurate prediction of isothermal gas chromatographic Kováts retention indices.

We describe a freely available web server called Retention Index Predictor (RIpred) (https://ripred.ca) that rapidly and accurately predicts Gas Chrom...

Discovery of a Novel DCAF1 Ligand Using a Drug-Target Interaction Prediction Model: Generalizing Machine Learning to New Drug Targets.

DCAF1 functions as a substrate recruitment subunit for the RING-type CRL4 and the HECT family EDVP E3 ubiquitin ligases. The WDR domain of DCAF1 serve...

The comparison data forest: A new comparison data approach to determine the number of factors in exploratory factor analysis.

Developing psychological assessment instruments often involves exploratory factor analyses, during which one must determine the number of factors to r...

Current trends in chromatographic prediction using artificial intelligence and machine learning.

Artificial intelligence (AI) and machine learning (ML) gained tremendous growth and are rapidly becoming popular in various fields of prediction due t...

No person is an island: Unpacking the work and after-work consequences of interacting with artificial intelligence.

The artificial intelligence (AI) revolution has arrived, as AI systems are increasingly being integrated across organizational functions into the work...

SENet: A deep learning framework for discriminating super- and typical enhancers by sequence information.

Super-enhancers are large domains on the genome where multiple short typical enhancers within a specific genomic distance are stitched together. Typic...

A memristor fingerprinting and characterisation methodology for hardware security.

The modern IC supply chain encompasses a large number of steps and manufacturers. In many applications it is critically important that chips are of th...

LSTM-AE for Domain Shift Quantification in Cross-Day Upper-Limb Motion Estimation Using Surface Electromyography.

Although deep learning (DL) techniques have been extensively researched in upper-limb myoelectric control, system robustness in cross-day applications...

Deep learning-based Lorentzian fitting of water saturation shift referencing spectra in MRI.

PURPOSE: Water saturation shift referencing (WASSR) Z-spectra are used commonly for field referencing in chemical exchange saturation transfer (CEST) ...

Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study.

BACKGROUND: Free flap monitoring is essential for postmicrosurgical management and outcomes but traditionally relies on human observers; the process i...