Toward a structural identification of metastable molecular conformations.

Interpreting high-dimensional data from molecular dynamics simulations is a persistent challenge. In this paper, we show that for a small peptide, deca-alanine, metastable states can be identified through a neural net based on structural information alone. While processing molecular dynamics data, dimensionality reduction is a necessary step that projects high-dimensional data onto a low-dimensional representation that, ideally, captures the conformational changes in the underlying data.

Reagent prediction with a molecular transformer improves reaction data quality.

Automated synthesis planning is key for efficient generative chemistry. Since reactions of given reactants may yield different products depending on conditions such as the chemical context imposed by specific reagents, computer-aided synthesis planning should benefit from recommendations of reaction conditions. Traditional synthesis planning software, however, typically proposes reactions without specifying such conditions, relying on human organic chemists who know the conditions to carry out suggested reactions.

Future Speech Interfaces with Sensors and Machine Intelligence.

Speech is the most spontaneous and natural means of communication. Speech is also becoming the preferred modality for interacting with mobile or fixed electronic devices. However, speech interfaces have drawbacks, including a lack of user privacy; non-inclusivity for certain users; poor robustness in noisy conditions; and the difficulty of creating complex man-machine interfaces.

User training for machine learning controlled upper limb prostheses: a serious game approach.

Background: Upper limb prosthetics with multiple degrees of freedom (DoFs) are still mostly operated through the clinical standard Direct Control scheme. Machine learning control, on the other hand, allows controlling multiple DoFs although it requires separable and consistent electromyogram (EMG) patterns. Whereas user training can improve EMG pattern quality, conventional training methods might limit user potential.

Temporal dependency in automatic sleep scoring via deep learning based architectures: An empirical study.

The present study evaluates how effectively a deep learning based sleep scoring system does encode the temporal dependency from raw polysomnography signals. An exhaustive range of neural networks, including state of the art architecture, have been used in the evaluation. The architectures have been assessed using a single-channel EEG Fpz-Cz from the open source Sleep-EDF expanded database.

Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides.

Background: Muscle-invasive bladder cancer (MIBC) is the second most common genitourinary malignancy, and is associated with high morbidity and mortality. Recently, molecular subtypes of MIBC have been identified, which have important clinical implications.

Objective: In the current study, we tried to predict the molecular subtype of MIBC samples from conventional histomorphology alone using deep learning.

Electroclinic effect in a chiral carbosilane-terminated 5-phenylpyrimidine liquid crystal with 'de Vries-like' properties.

The chiral carbosilane-terminated liquid crystal 2-[(2S,3S)-2,3-difluorohexyloxy]-5-[4-(12,12,14,14,16,16-hexamethyl-12,14,16-trisilaheptadecyloxy)phenyl]pyrimidine () undergoes a smectic A*-smectic C* phase transition with a maximum layer contraction of only 0.2%. It exhibits an electroclinic effect (ECE) comparable to that reported for the 'de Vries-like' liquid crystal and shows no appreciable optical stripe defects due to horizontal chevron formation.

Pattern learning with deep neural networks in EMG-based speech recognition.

We report on classification of phones and phonetic features from facial electromyographic (EMG) data, within the context of our EMG-based Silent Speech interface. In this paper we show that a Deep Neural Network can be used to perform this classification task, yielding a significant improvement over conventional Gaussian Mixture models. Our central contribution is the visualization of patterns which are learned by the neural network.

Tackling speaking mode varieties in EMG-based speech recognition.

An electromyographic (EMG) silent speech recognizer is a system that recognizes speech by capturing the electric potentials of the human articulatory muscles, thus enabling the user to communicate silently. After having established a baseline EMG-based continuous speech recognizer, in this paper, we investigate speaking mode variations, i.e.

Artifact removal algorithm for an EMG-based Silent Speech Interface.

An electromygraphic (EMG) Silent Speech Interface is a system which recognizes speech by capturing the electric potentials of the human articulatory muscles, thus enabling the user to communicate silently. This study deals with improving the EMG signal quality by removing artifacts: The EMG signals are captured by electrode arrays with multiple measuring points. On the resulting high-dimensional signal, Independent Component Analysis is performed, and artifact components are automatically detected and removed.

Meshless Modeling of Deformable Shapes and their Motion.

We present a new framework for interactive shape deformation modeling and key frame interpolation based on a meshless finite element formulation. Starting from a coarse nodal sampling of an object's volume, we formulate rigidity and volume preservation constraints that are enforced to yield realistic shape deformations at interactive frame rates. Additionally, by specifying key frame poses of the deforming shape and optimizing the nodal displacements while targeting smooth interpolated motion, our algorithm extends to a motion planning framework for deformable objects.

Ferroelectric liquid crystals induced by dopants with axially chiral 2,2'-spirobiindan-1,1'-dione cores.

The axially chiral dopants (R)-5,5'-, 5,6'-, and 6,6'-diheptyloxy-2,2'-spirobiindan-1,1'-dione ((R)-2, -3, and -4) were synthesized in optically pure form, and their absolute configurations were assigned by the exciton chirality method using circular dichroism spectroscopy. These new compounds were doped in four achiral liquid crystal hosts to give chiral smectic C* (SmC*) phases with spontaneous polarizations (Ps) that vary with the core structure of the host. The spontaneous polarization induced by the 5,5'-dialkoxy derivative (R)-2 is uniformly positive, whereas that induced by the 6,6'-dialkoxy derivative (R)-4 is uniformly negative and shows a different trend in host dependence.

Photoinduced polarization inversion in a ferroelectric liquid crystal using an ambidextrous chiral thioindigo dopant.

In this Communication, we report the first example of photoswitching of a ferroelectric SmC* liquid crystal based on a photoinduced sign inversion of the spontaneous polarization (PS) induced by a single chiral dopant. This is achieved without concomitant destabilization of the SmC* phase using the "ambidextrous" dopant 6-((R,R)-2,3-difluorooct-1-yloxy)-5'-nitro-6'-((R)-2-octyloxy)thioindigo. The (R)-2-octyloxy side chain is sterically coupled to the thioindigo core via the nitro substituent and induces a positive polarization, whereas the (R,R)-2,3-difluorooct-1-yloxy side chain is decoupled from the core and induces a negative polarization.

Detection of chiral perturbations in ferroelectric liquid crystals induced by an atropisomeric biphenyl dopant.

The atropisomeric dopant 2,2',6,6'-tetramethyl-3,3'-dinitro-4,4'-bis[(4-nonyloxybenzoyl)oxy]biphenyl (1) induces a ferroelectric SmC phase when doped into the SmC liquid crystal hosts 2-(4-butyloxyphenyl)-5-octyloxypyrimidine (PhP1) and (+/-)-4-[(4-methylhexyl)oxy]phenyl 4-decyloxybenzoate (PhB). The propensity of dopant 1 to induce a spontaneous polarization (polarization power) is much higher in PhP1 than in PhB (1555 nC/cm(2) vs <35 nC/cm(2)), which is attributed to a greater propensity of 1 to undergo chirality transfer via core-core interactions with PhP1. In previous work, we postulated that a chiral perturbation exerted by 1 in PhP1 amplifies the polarization power of the dopant by causing a chiral distortion of the mean field potential (binding site) constraining the dopant in the SmC host, as described by the Chirality Transfer Feedback (CTF) model.

Bistable ferroelectric liquid crystal photoswitch triggered by a dithienylethene dopant.

The spontaneous polarization (PS) of a ferroelectric liquid crystal is modulated reversibly by photocyclization of the dopant 1,2-bis[5'-(4' '-heptyloxyphenyl)-2'-methylthien-3'-yl]perfluorocyclopentene. The magnitude of PS photomodulation increases with dopant concentration up to 3 mol %, and the resulting photoswitch is fatigue resistant and bistable. To the best of our knowledge, this is the first example of a bistable ferroelectric liquid crystal photoswitch to be reported in the literature.