Real-time low latency estimation of brain rhythms with deep neural networks.

Neurofeedback and brain-computer interfacing technology open the exciting opportunity for establishing interactive closed-loop real-time communication with the human brain. This requires interpreting brain's rhythmic activity and generating timely feedback to the brain. Lower delay between neuronal events and the appropriate feedback increases the efficacy of such interaction.

Electronic Structure and Minimal Models for Flat and Corrugated CuO Monolayers: An Ab Initio Study.

CuO atomic thin monolayer (mlCuO) was synthesized recently. Interest in the mlCuO is based on its close relation to CuO2 layers in typical high temperature cuprate superconductors. Here, we present the calculation of the band structure, the density of states and the Fermi surface of the flat mlCuO as well as the corrugated mlCuO within the density functional theory (DFT) in the generalized gradient approximation (GGA).

Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition.

While spatial proteomics by fluorescence imaging has quickly become an essential discovery tool for researchers, fast and scalable methods to classify and embed single-cell protein distributions in such images are lacking. Here, we present the design and analysis of the results from the competition Human Protein Atlas - Single-Cell Classification hosted on the Kaggle platform. This represents a crowd-sourced competition to develop machine learning models trained on limited annotations to label single-cell protein patterns in fluorescent images.

Self-supervised recurrent depth estimation with attention mechanisms.

Depth estimation has been an essential task for many computer vision applications, especially in autonomous driving, where safety is paramount. Depth can be estimated not only with traditional supervised learning but also via a self-supervised approach that relies on camera motion and does not require ground truth depth maps. Recently, major improvements have been introduced to make self-supervised depth prediction more precise.

Temporal network embedding framework with causal anonymous walks representations.

Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding. Though there exists a lot of network embeddings for static graphs, the task becomes much more complicated when the dynamic ( temporal) network is analyzed.

Fusion of text and graph information for machine learning problems on networks.

Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space. A network embedding constructed this way aims to preserve nodes similarity and other specific network properties. Embedding vectors can later be used for downstream machine learning problems, such as node classification, link prediction and network visualization.

Survey on graph embeddings and their applications to machine learning problems on graphs.

Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties.

Online supervised attention-based recurrent depth estimation from monocular video.

Autonomous driving highly depends on depth information for safe driving. Recently, major improvements have been taken towards improving both supervised and self-supervised methods for depth reconstruction. However, most of the current approaches focus on single frame depth estimation, where quality limit is hard to beat due to limitations of supervised learning of deep neural networks in general.

A Predictive Model Towards Site-Selective Metalations of Functionalized Heterocycles, Arenes, Olefins, and Alkanes using TMPZnCl⋅LiCl.

The development of a predictive model towards site-selective deprotometalation reactions using TMPZnCl⋅LiCl is reported (TMP=2,2,6,6-tetramethylpiperidinyl). The pK values of functionalized N-, S-, and O-heterocycles, arenes, alkenes, or alkanes were calculated and compared to the experimental deprotonation sites. Large overlap (>80 %) between the calculated and empirical deprotonation sites was observed, showing that thermodynamic factors strongly govern the metalation regioselectivity.

Dual network embedding for representing research interests in the link prediction problem on co-authorship networks.

We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use the link prediction (LP) model for constructing a recommender system for searching collaborators with similar research interests. Extracting topics for each paper, we construct keywords co-occurrence network and use its embedding for further generalizing author attributes.

Synthesis of Bicyclo[1.1.1]pentane Bioisosteres of Internal Alkynes and para-Disubstituted Benzenes from [1.1.1]Propellane.

We report a general preparation of arylated bicyclo[1.1.1]pentanes through the opening of [1.

Zn-, Mg-, and Li-TMP Bases for the Successive Regioselective Metalations of the 1,5-Naphthyridine Scaffold (TMP=2,2,6,6-Tetramethylpiperidyl).

A set of successive regioselective metalations and functionalizations of the 1,5-naphthyridine scaffold are described. A combination of Zn-, Mg-, and Li-TMP (TMP=2,2,6,6-tetramethylpiperidyl) bases and the presence or absence of a Lewis acid (BF ⋅OEt ) allows the introduction of up to three substituents to the 1,5-naphthyridine core. Also, a novel "halogen dance" reaction was discovered upon metalation of an 8-iodo-2,4-trifunctionalized 1,5-naphthyridine allowing a fourth regioselective functionalization.

Synthetic Applications and Mechanistic Studies of the Hydroxide-Mediated Cleavage of Carbon-Carbon Bonds in Ketones.

The hydroxide-mediated cleavage of ketones into alkanes and carboxylic acids has been reinvestigated and the substrate scope extended to benzyl carbonyl compounds. The transformation is performed with a 0.05 M ketone solution in refluxing xylene in the presence of 10 equiv of potassium hydroxide.

Dehydrogenative Synthesis of Carboxylic Acids from Primary Alcohols and Hydroxide Catalyzed by a Ruthenium N-Heterocyclic Carbene Complex.

Primary alcohols have been reacted with hydroxide and the ruthenium complex [RuCl(IiPr)(p-cymene)] to afford carboxylic acids and dihydrogen. The dehydrogenative reaction is performed in toluene, which allows for a simple isolation of the products by precipitation and extraction. The transformation can be applied to a range of benzylic and saturated aliphatic alcohols containing halide and (thio)ether substituents, while olefins and ester groups are not compatible with the reaction conditions.

Regioselective Transition-Metal-Free Allyl-Allyl Cross-Couplings.

Readily prepared allylic zinc halides undergo SN 2-type substitutions with allylic bromides in a 1:1 mixture of THF and DMPU providing 1,5-dienes regioselectively. The allylic zinc species reacts at the most branched end (γ-position) of the allylic system furnishing exclusively γ,α'-allyl-allyl cross-coupling products. Remarkably, the double bond stereochemistry of the allylic halide is maintained during the cross-coupling process.

Palladium-catalyzed carbonylative couplings of vinylogous enolates: application to statin structures.

The first Pd-catalyzed carbonylative couplings of aryl and vinyl halides with vinylogous enolates are reported generating products derived from C-C bond formation exclusively at the γ-position. Good results were obtained with a dienolate derivative of acetoacetate (1,3-dioxin-4-one). These transformations occurred at room temperature and importantly with only stoichiometric carbon monoxide in a two-chamber reactor.

Efficient fluoride-catalyzed conversion of CO2 to CO at room temperature.

A protocol for the efficient and selective reduction of carbon dioxide to carbon monoxide has been developed. Remarkably, this oxygen abstraction step can be performed with only the presence of catalytic cesium fluoride and a stoichiometric amount of a disilane in DMSO at room temperature. Rapid reduction of CO2 to CO could be achieved in only 2 h, which was observed by pressure measurements.

Ruthenium-catalyzed self-coupling of primary and secondary alcohols with the liberation of dihydrogen.

The dehydrogenative self-condensation of primary and secondary alcohols has been studied in the presence of RuCl2(IiPr)(p-cymene). The conversion of primary alcohols into esters has been further optimized by using magnesium nitride as an additive, which allows the reaction to take place at a temperature and catalyst loading lower than those described previously. Secondary alcohols were dimerized into racemic ketones by a dehydrogenative Guerbet reaction with potassium hydroxide as the additive.

Mechanistic investigation of the ruthenium-N-heterocyclic-carbene-catalyzed amidation of amines with alcohols.

The mechanism of the ruthenium-N-heterocyclic-carbene-catalyzed formation of amides from alcohols and amines was investigated by experimental techniques (Hammett studies, kinetic isotope effects) and by a computational study with dispersion-corrected density functional theory (DFT/M06). The Hammett study indicated that a small positive charge builds-up at the benzylic position in the transition state of the turnover-limiting step. The kinetic isotope effect was determined to be 2.