Technical University of Berlin

About: Technical University of Berlin is a education organization based out in Berlin, Germany. It is known for research contribution in the topics: Quantum dot & Laser. The organization has 27292 authors who have published 59342 publications receiving 1414623 citations. The organization is also known as: Technische Universität Berlin & TU Berlin.

How to Explain Individual Classification Decisions

Abstract: After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted a particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.

Antibacterial action of chitosan

Abstract: The antibacterial action of chitosan hydroglutamate (CH), chitosan lactate (CL) and chitosan derived from fungal mycelia was examined against both gram‐negative and gram‐positive bacteria. Plate counts indicated inactivation rates of one‐ to five‐log‐cycles within one hour. Fungal chitosan had significantly less antibiotic effect than CH and CL. The antibacterial action of CH and CL was very similar and shown to be concentration dependent with 0.1 mg/mL more effective than 2.0 and 5.0 mg/mL. When CH (or CL) and polygalacturonate were added to cell suspensions, death was prevented, possibly indicating that chitosan complexed with polygalacturonate could not penetrate the cell or disrupt the membrane. Leakage of intracellular components caused by chitosan was determined by exposing lactose‐induced Escherichia coli to chitosan with assay for s‐galactosidase activity indicating that cell permeabilization occurred more extensively at the low chitosan concentrations. Microscopic examination showed that...

Monolithic perovskite/silicon tandem solar cell with >29% efficiency by enhanced hole extraction

Abstract: Tandem solar cells that pair silicon with a metal halide perovskite are a promising option for surpassing the single-cell efficiency limit. We report a monolithic perovskite/silicon tandem with a certified power conversion efficiency of 29.15%. The perovskite absorber, with a bandgap of 1.68 electron volts, remained phase-stable under illumination through a combination of fast hole extraction and minimized nonradiative recombination at the hole-selective interface. These features were made possible by a self-assembled, methyl-substituted carbazole monolayer as the hole-selective layer in the perovskite cell. The accelerated hole extraction was linked to a low ideality factor of 1.26 and single-junction fill factors of up to 84%, while enabling a tandem open-circuit voltage of as high as 1.92 volts. In air, without encapsulation, a tandem retained 95% of its initial efficiency after 300 hours of operation.

How do humans sketch objects

Abstract: Humans have used sketching to depict our visual world since prehistoric times. Even today, sketching is possibly the only rendering technique readily available to all humans. This paper is the first large scale exploration of human sketches. We analyze the distribution of non-expert sketches of everyday objects such as 'teapot' or 'car'. We ask humans to sketch objects of a given category and gather 20,000 unique sketches evenly distributed over 250 object categories. With this dataset we perform a perceptual study and find that humans can correctly identify the object category of a sketch 73% of the time. We compare human performance against computational recognition methods. We develop a bag-of-features sketch representation and use multi-class support vector machines, trained on our sketch dataset, to classify sketches. The resulting recognition method is able to identify unknown sketches with 56% accuracy (chance is 0.4%). Based on the computational model, we demonstrate an interactive sketch recognition system. We release the complete crowd-sourced dataset of sketches to the community.