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Resonant optical nanoantennas hold great promise for applications in physics and chemistry 1 , 2 , 3 , 4 , 5 , 6 . Their operation relies on their ability to concentrate light on spatial scales much smaller than the wavelength. In this work, we mechanically tune the length and gap between two triangles comprising a single gold bow-tie antenna by precise nanomanipulation with the tip of an atomic force microscope. At the same time, the optical response of the nanostructure is determined by means of dark-field scattering spectroscopy. We find no unique single ‘antenna resonance’. Instead, the plasmon mode splits into two dipole resonances for gap sizes on the order of a few tens of nanometres, governed by the full three-dimensional shape of the antenna arms. This result opens the door to new nano-optomechanical devices, where mechanical changes on the nanometre scale control the optical properties of artificial structures. Optical antennas are able to concentrate light on a scale much smaller then the wavelength. Bow–tie–shape nanostructures are one example. It is now possible to tune the response of such an antenna by precisely moving one half of the bow tie.

Laser-based additive manufacturing enables the production of complex geometries via layer-wise cladding. Laser metal deposition (LMD) uses a scanning laser source to fuse in situ deposited metal powder layer by layer. However, due to the excessive number of influential factors in the physical transformation of the metal powder and the highly dynamic temperature fields caused by the melt pool dynamics and phase transitions, the quality and repeatability of parts built by this process is still challenging. In order to analyze and/or predict the spatially varying and time dependent thermal behavior in LMD, extensive work has been done to develop predictive models usually by using finite element method (FEM). From a control-oriented perspective, simulations based on these models are computationally too expensive and are thus not suitable for real-time control applications. In this contribution, a spatio-temporal input–output model based on the heat equation is proposed. In contrast to other works, the parameters of the model are directly estimated from measurements of the LMD process acquired with an infrared (IR) camera during processing specimens using AISI 316 L stainless steel. In order to deal with noisy data, system identification techniques are used taking different disturbing noise into account. By doing so, spatio-temporal models are developed, enabling the prediction of the thermal behavior by means of the radiance measured by the IR camera in the range of the considered processing parameters. Furthermore, in the considered modeling framework, the computational effort for thermal prediction is reduced compared to FEM, thus enabling the use in real-time control applications.

Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply. For the aspiration of these objectives, good population data is essential. The traditional method of collecting population data through the census is expensive and tedious. In recent years, statistical and machine learning methods have been developed to estimate population distribution. Most of the methods use data sets that are either developed on a small scale or not publicly available yet. Thus, the development and evaluation of new methods become challenging. We fill this gap by providing a comprehensive data set for population estimation in 98 European cities. The data set comprises a digital elevation model, local climate zone, land use proportions, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative. We anticipate that it would be a valuable addition to the research community for the development of sophisticated approaches in the field of population estimation.Measurement(s)human population distributionTechnology Type(s)remote sensingFactor Type(s)remote sensing dataSample Characteristic - LocationEurope

We analyze why firms use nonintermediated short-term debt by studying the commercial paper (CP) market. Using a comprehensive database of CP issuers and issuance activity, we show that firms use CP to provide start-up financing for capital investment. Firms' CP issuance is driven by a desire to minimize transaction costs associated with raising capital for new investment. We show that firms with high rollover risk are less likely to enter the CP market, borrow less CP, and borrow more from bank credit lines. Further, CP is often refinanced with long-term bond issuance to reduce rollover risk.

Agency problems in firms are prevalent because of a scarcity of wealthy principals with corporate governance ability, whom we call “restructuring specialists.” We investigate how this scarce resource, “agency cost-free capital,” is allocated. We show that the restructuring specialists may acquire blocks only in those states of the worls in which they can increase firm value the most, which corresponds to a takeover. Firms with dispersed ownership and firms with a financial intermediary as a blockholder can coexist, although they are otherwise identical. The moderl can explain differences in corporate ownership structures and restructuring mechanisms across economies.

We propose a theory of mergers that combines managerial merger motives with an industry-level regime shift that may lead to value-increasing merger opportunities. Anticipation of these merger opportunities can lead to defensive acquisitions, where managers acquire other firms to avoid losing private benefits if their firms are acquired, or \"positioning\" acquisitions, where firms position themselves as more attractive takeover targets to earn takeover premia. The identity of acquirers and targets and the profitability of acquisitions depend on the distribution of firm sizes within an industry, among other factors. We find empirical support for some unique predictions of our theory.

Many firms emerging from a debt restructuring remain highly leveraged, continue to invest little, perform poorly, and often reenter financial distress. The existing literature interprets these findings as inefficiencies arising from coordination problems among many creditors or an inefficient design of bankruptcy law. In contrast, this paper emphasizes that creditors lack the information that is needed to make quick and correct liquidation decisions. It can explain the long-term nature of financial distress solely as the result of dynamic learning strategies of creditors and suggests that it may be an unavoidable byproduct of an efficient resolution of financial distress.

In this paper, a novel method to enhance Frequency Modulated Continuous Wave (FMCW) THz imaging resolution beyond its diffraction limit is proposed. Our method comprises two stages. Firstly, we reconstruct the signal in depth-direction using a sinc-envelope, yielding a significant improvement in depth estimation and signal parameter extraction. The resulting high-precision depth estimate is used to deduce an accurate reflection intensity THz image. This image is fed in the second stage of our method to a 2D blind deconvolution procedure, adopted to enhance the lateral THz image resolution beyond the diffraction limit. Experimental data acquired with a FMCW system operating at 577 GHz with a bandwidth of 126 GHz shows that the proposed method enhances the lateral resolution by a factor of 2.29 to 346.2 μm with respect to the diffraction limit. The depth accuracy is 91 μm. Interestingly, the lateral resolution enhancement achieved with this blind deconvolution concept leads to better results in comparison with conventional gaussian deconvolution. Experimental data on a PCB resolution target is presented, in order to quantify the resolution enhancement and to compare the performance with established image enhancement approaches. The presented technique allows exposure of the interwoven fiber reinforced embedded structures of the PCB test sample.

Tag-based approaches were originally designed to increase the throughput of capillary sequencing, where concatemers of short sequences were first used in expression profiling. New Next Generation Sequencing methods largely extended the use of tag-based approaches as the tag lengths perfectly match with the short read length of highly parallel sequencing reactions. Tag-based approaches will maintain their important role in life and biomedical science, because longer read lengths are often not required to obtain meaningful data for many applications. Whereas genome re-sequencing and de novo sequencing will benefit from ever more powerful sequencing methods, analytical applications can be performed by tag-based approaches, where the focus shifts from 'sequencing power' to better means of data analysis and visualization for common users. Today Next Generation Sequence data require powerful bioinformatics expertise that has to be converted into easy-to-use data analysis tools. The book's intention is to give an overview on recently developed tag-based approaches along with means of their data analysis together with introductions to Next-Generation Sequencing Methods, protocols and user guides to be an entry for scientists to tag-based approaches for Next Generation Sequencing.

Non-intrusive load monitoring (NILM) aims at energy consumption and appliance state information retrieval from aggregated consumption measurements, with the help of signal processing and machine learning algorithms. Representation learning with deep neural networks is successfully applied to several related disciplines. The main advantage of representation learning lies in replacing an expert-driven, hand-crafted feature extraction with hierarchical learning from many representations in raw data format. In this paper, we show how the NILM processing-chain can be improved, reduced in complexity and alternatively designed with recent deep learning algorithms. On the basis of an event-based appliance recognition approach, we evaluate seven different classification models: a classical machine learning approach that is based on a hand-crafted feature extraction, three different deep neural network architectures for automated feature extraction on raw waveform data, as well as three baseline approaches for raw data processing. We evaluate all approaches on two large-scale energy consumption datasets with more than 50,000 events of 44 appliances. We show that with the use of deep learning, we are able to reach and surpass the performance of the state-of-the-art classical machine learning approach for appliance recognition with an F-Score of 0.75 and 0.86 compared to 0.69 and 0.87 of the classical approach.