A supervised recalibration protocol for unbiased BCI

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Computational protein design has emerged as a powerful tool capable of identifying sequences compatible with pre-defined protein structures. The sequence design protocols, implemented in the Rosetta suite, have become widely used in the protein engineering ...

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject da ...

Training models that perform well under distribution shifts is a central challenge in machine learning. In this paper, we introduce a modeling framework where, in addition to training data, we have partial structural knowledge of the shifted test distribut ...

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...

Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to s ...

Neurofeedback based on real-time functional MRI is an emerging technique to train voluntary control over brain activity in healthy and disease states. Recent developments even allow for training of brain networks using connectivity feedback based on dynami ...

When moving, the spatiotemporal unfolding of events is bound to our physical trajectory, and time and space become entangled in episodic memory. When imagining past or future events, or being in different geographical locations, the temporal and spatial di ...

One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental da ...

Humans use their hands mainly for grasping and manipulating objects, performing simple and dexterous tasks. The loss of a hand may significantly affect one's working status and independence in daily life. A restoration of the grasping ability is important ...

The use of drones in search and rescue (SAR) missions can be very cognitively demanding. Since high levels of cognitive workload can negatively affect human performance, there is a risk of compromising the mission and leading to failure with catastrophic o ...

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

This paper considers regression tasks involving high-dimensional multivariate processes whose structure is dependent on some known graph topology. We put forth a new definition of time-vertex wide-sense stationarity, or joint stationarity for short, that g ...

The hypervalent iodine reagent 1-[(TriIsoPropylSilyl)Ethynyl]-1,2-BenziodoXol-3(1H)-one (TIPS-EBX) is among the most useful electrophilic and somophilic alkynylation reagents. Herein, we report a new synthetic protocol to access this reagent in one step fr ...

This paper is concerned with frequency domain theory for functional time series, which are temporally dependent sequences of functions in a Hilbert space. We consider a variance decomposition, which is more suitable for such a data structure than the varia ...

A supervised recalibration protocol for unbiased BCI

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A Rosetta-based protein design protocol converging to natural sequences

Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Robust Generalization despite Distribution Shift via Minimum Discriminating Information

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Data-driven tensor independent component analysis for model-based connectivity neurofeedback

Building the Arrow of Time ... Over Time: A Sequence of Brain Activity Mapping Imagined Events in Time and Space

Multi-ion-sensing emulator and multivariate calibration optimization by machine learning models

Improving the arm-hand coordination in neuroprosthetics control with prior information from muscle activity

Cognitive workload monitoring in virtual reality based rescue missions with drones

Wasserstein Distributionally Robust Learning

Stationary time-vertex signal processing

One-Pot Synthesis of 1-[(Triisopropylsilyl)ethynyl]-1,2-benziodoxol-3(1H)-one (TIPS-EBX): Process Safety Assessment and Impact of Impurities on Product Stability

Frequency domain theory for functional time series: Variance decomposition and an invariance principle