Technische Universität Darmstadt

About: Technische Universität Darmstadt is a education organization based out in Darmstadt, Germany. It is known for research contribution in the topics: Neutron & Finite element method. The organization has 17316 authors who have published 40619 publications receiving 937916 citations. The organization is also known as: Darmstadt University of Technology & University of Darmstadt.

Identification of strontium in the merger of two neutron stars

Abstract: Half of all of the elements in the Universe that are heavier than iron were created by rapid neutron capture. The theory underlying this astrophysical r-process was worked out six decades ago, and requires an enormous neutron flux to make the bulk of the elements1. Where this happens is still debated2. A key piece of evidence would be the discovery of freshly synthesized r-process elements in an astrophysical site. Existing models3-5 and circumstantial evidence6 point to neutron-star mergers as a probable r-process site; the optical/infrared transient known as a 'kilonova' that emerges in the days after a merger is a likely place to detect the spectral signatures of newly created neutron-capture elements7-9. The kilonova AT2017gfo-which was found following the discovery of the neutron-star merger GW170817 by gravitational-wave detectors10-was the first kilonova for which detailed spectra were recorded. When these spectra were first reported11,12, it was argued that they were broadly consistent with an outflow of radioactive heavy elements; however, there was no robust identification of any one element. Here we report the identification of the neutron-capture element strontium in a reanalysis of these spectra. The detection of a neutron-capture element associated with the collision of two extreme-density stars establishes the origin of r-process elements in neutron-star mergers, and shows that neutron stars are made of neutron-rich matter13.

A Performance Evaluation of Single and Multi-feature People Detection

Abstract: Over the years a number of powerful people detectors have been proposed. While it is standard to test complete detectors on publicly available datasets, it is often unclear how the different components (e.g. features and classifiers) of the respective detectors compare. Therefore, this paper contributes a systematic comparison of the most prominent and successful people detectors. Based on this evaluation we also propose a new detector that outperforms the state-of-art on the INRIA person dataset by combining multiple features.

On fuzzy logic applications for automatic control, supervision, and fault diagnosis

Abstract: The degree of vagueness of variables, process description, and automation functions is considered and is shown. Where quantitative and qualitative knowledge is available for design and information processing within automation systems. Fuzzy-rule-based systems with several levels of rules form the basis for different automation functions. Fuzzy control can be used in many ways, for normal and for special operating conditions. Experience with the design of fuzzy controllers in the basic level is summarized, as well as criteria for efficient applications. Different fuzzy control schemes are considered, including cascade, feedforward, variable structure, self-tuning, adaptive and quality control leading to hybrid classical/fuzzy control systems. It is then shown how fuzzy logic approaches can be applied to process supervision and to fault diagnosis with approximate reasoning on observed symptoms. Based on the properties of fuzzy logic approaches the contribution gives a review and classification of the potentials of fuzzy logic in process automation.

Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

Abstract: We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of generic SFE protocols with the ones that are based upon additive secret sharing. In particular, the framework performs linear operations in the ring $\mathbbZ _2^l $ using additively secret shared values and nonlinear operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson protocol. Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase. Almost all of the heavy cryptographic operations are precomputed in an offline phase which substantially reduces the communication overhead. Chameleon is both scalable and significantly more efficient than the ABY framework (NDSS'15) it is based on. Our framework supports signed fixed-point numbers. In particular, Chameleon's vector dot product of signed fixed-point numbers improves the efficiency of mining and classification of encrypted data for algorithms based upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer convolutional deep neural network shows 133x and 4.2x faster executions than Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively.