Search

We present electron energy-loss spectroscopic measurements of potassium (K) intercalated tungsten diselenide (WSe

Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks. In this paper, we review several possibilites to reverse map these architectures to biologically more realistic spiking networks with the aim of emulating them on fast, low-power neuromorphic hardware. Since many of these devices employ analog components, which cannot be perfectly controlled, finding ways to compensate for the resulting effects represents a key challenge. Here, we discuss three different strategies to address this problem: the addition of auxiliary network components for stabilizing activity, the utilization of inherently robust architectures and a training method for hardware-emulated networks that functions without perfect knowledge of the system's dynamics and parameters. For all three scenarios, we corroborate our theoretical considerations with experimental results on accelerated analog neuromorphic platforms., Comment: accepted at ISCAS 2017

The problem of automatically estimating the creation date of photos has been addressed rarely in the past. In this paper, we introduce a novel dataset Date Estimation in the Wild for the task of predicting the acquisition year of images captured in the period from 1930 to 1999. In contrast to previous work, the dataset is neither restricted to color photography nor to specific visual concepts. The dataset consists of more than one million images crawled from Flickr and contains a large number of different motives. In addition, we propose two baseline approaches for regression and classification, respectively, relying on state-of-the-art deep convolutional neural networks. Experimental results demonstrate that these baselines are already superior to annotations of untrained humans.

Some recent approaches for character identification in movies and TV broadcasts are realized in a semi-supervised manner by assigning transcripts and/or subtitles to the speakers. However, the labels obtained in this way achieve only an accuracy of $80\% - 90\%$ and the number of training examples for the different actors is unevenly distributed. In this paper, we propose a novel approach for person identification in video by correcting and extending the training data with reliable predictions to reduce the number of annotation errors. Furthermore, the intra-class diversity of rarely speaking characters is enhanced. To address the imbalance of training data per person, we suggest two complementary prediction scores. These scores are also used to recognize whether or not a face track belongs to a (supporting) character whose identity does not appear in the transcript etc. Experimental results demonstrate the feasibility of the proposed approach, outperforming the current state of the art.

Bei der vierten Aufgabe der 2. Runde des Bundeswettbewerbs Mathematik 1990, die mehrheitlich als die schonste aller jemals gestellten Aufgaben beurteilt worden ist, war zu zeigen, dass man jeden Wurm der Lange 1 in der Ebene mit einer Halbkreisscheibe mit dem Durchmesser 1 zudecken kann. Der Beitrag stellt einen kurzen Beweis dieser Aussage mit Hilfe des Spiegelungsprinzips vor und schlagt eine Brucke zu verwandten Problemen, u. a. dem ungelosten Wurmproblem von Leo Moser, und enthalt eine entsprechende Aussage fur Wurmer im Raum.

In der vierten Aufgabe der 1. Runde des Bundeswettbewerbs Mathematik 2001 war zu zeigen, dass jede naturliche Zahl mindestens so viele Teiler mit der Endziffer 1 oder 9 wie Teiler mit den Endziffern 3 oder 7 hat. Der Artikel beweist daneben eine entsprechende Aussage bezuglich der Endziffern 2, 8, 4, 6 und bestimmt allgemeiner – zunachst experimentell computerunterstutzt und dann durch Beweise, wie haufig Teiler mit gewisser Endziffer unter den Teilern einer naturlichen Zahl durchschnittlich vorkommen. Die Beweise enthalten zum Teil ungewohnliche und interessante Anwendungen hoherer Hilfsmittel wie der Potenzreihe fur den naturlichen Logarithmus und dem Satz von Dirichlet uber Primzahlen in arithmetischen Folgen.

A polynomially recursive sequence satisfies a recursive relation with variable coefficients. The set of these sequences has the structure of a topological bialgebra. If such a sequence is of a combinatorial nature, a formula for its coproduct can (upon appropriate evaluation) be interpreted as a combinatorial identity. Here we give a coproduct formula for each sequence , one for each t ≥ 0, and its interpretation as a combinatorial identity. We also obtain a q-version of this coproduct formula and combinatorial identity.

In current organic photovoltaic devices, the loss in energy caused by the inevitable charge transfer step leads to a low open circuit voltage, which is one of the main reasons for rather low power conversion efficiencies. A possible approach to avoid these losses is to tune the exciton binding energy below 25 meV, which would lead to free charges upon absorption of a photon, and therefore increase the power conversion efficiency towards the Shockley Queisser limit for inorganic solar cells. We determine the size of the excitons for different one-dimensional organic small molecules or polymers by electron energy loss spectroscopy (EELS) measurements and by DFT calculations. Using the measured dielectric constant and exciton extension, the exciton binding energy is calculated for the investigated molecules, leading to a lower limit of the exciton binding energy for ladder-type polymers. We discuss and propose potential ways to increase the ionic and electronic part of the dielectric function in order to further lower the limit of the exciton binding energy in organic materials. Furthermore, the influence of charge transfer states on the exciton size and its binding energy is calculated with DFT methods for the ladder-type polymer poly(benzimidazobenzophenanthroline) (BBL) in a dimer configuration.

This book constitutes the proceedings of the 14th International Conference on Relational and Algebraic Methods in Computer Science, RAMiCS 2014 held in Marienstatt, Germany, in April/May 2014. The 25 revised full papers presented were carefully selected from 37 submissions. The papers are structured in specific fields on concurrent Kleene algebras and related formalisms, reasoning about computations and programs, heterogeneous and categorical approaches, applications of relational and algebraic methods and developments related to modal logics and lattices.

A recursive sequence is an infinite sequence of elements of some fixed ground field which satisfies a recursion relation of finite order. We shall investigate certain bialgebra structures on linear spaces of recursive sequences. By choosing appropriate bases for these bialgebras we show how an explicit formula for the coproduct can imply interesting combinatorial identities.

An ASIC is developed to control and data quantization for large format NIR/SWIR detector arrays. Both cryogenic and space radiation environment issue are considered during the design. Therefore it can be integrated in the cryogenic chamber, which reduces significantly the vast amount of long wires going in and out the cryogenic chamber, i.e. benefits EMI and noise concerns, as well as the power consumption of cooling system and interfacing circuits. In this paper, we will describe the development of this prototype ASIC for image sensor driving and signal processing as well as the testing in both room and cryogenic temperature.

Since the quantum groups have been invented by Drinfel'd [5], they have mainly been considered from two directions: deformations (or quantizations) of universal enveloping algebras of complex semis...

We compute the coradical filtration and the group of Hopf algebra automorphisms of the non-restricted specialization of the quantized universal enveloping algebra of a finite-dimensional semisimple Lie algebra.

We discuss a method to classify all Hopf subalgebras of a certain class of pointed Hopf algebras including quantized universal enveloping algebras and Frobenius–Lusztig kernels. We find factor Hopf algebras of the dual quotients of the coordinate rings of quantumGL(n). We consider a large class of quasitriangular structures for the Frobenius–Lusztig kernels and compute ribbon elements

This chapter provides an overview of the potential utilisation of RFID in the manufacturing context. More specifically the specificity of RFID applications, in terms of application fields, objectives, drivers, potential benefits, are analysed for various manufacturing strategies. The characteristics of the market context and of the structure of the manufacturing/assembly system having a significant impact on RFID application are identified and their influence described. An extensive review of industrial case studies classified according to the various manufacturing typologies (ETO, MTO, ATO, MTS, Retailing & Distribution) is provided. This allows to better assess the steps to be undertaken for developing a successful RFID applications, understand the activities to be included, identify the challenges, estimate the potential benefits and the required investments.

International audience; In this article, we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results.

In this paper we describe our approach towards highly configurable neuromorphic hardware systems that serve as useful and flexible tools in modeling neuroscience. We utilize a mixed-signal VLSI model that implements a massively accelerated network of spiking neurons, and we describe a novel methodological framework that allows to exploit both the speed and the programmability of this device for the systematic and simulator-like exploration of cortical network architectures. We present a variety of experimental results that illustrate the functionality of our modeling platform, and we verify all hardware measurements with reference software simulations. Especially on the network level these comparison studies are unique in terms of the quantitative correspondence between the data. The presented hardware experiments include high-conductance states in hardware neurons and the application of synaptic depression and facilitation for self-adjusting network architectures.

Lentigo maligna denotes flat, pigmented lesions predominantly in areas of actinic damage that have the propensity to become malignant. More than 10 years may pass before lentigo maligna evolves into an invasive neoplasma. As an invasive process, it is termed lentigo maligna melanoma (LMM), and it has the potential for both lymphatic and hematogenic metastases. Because of the size and location of the lesions, cosmetically unsatisfactory scars may result from conventional surgery. Therefore, alternative means of treatment, including cryosurgery, have been employed. We report on 12 patients suffering from lentigo maligna who had been treated successfully by cryosurgery between 1984 and 1990. The average follow-up period was 51.4 months, and the recurrence rate was 8.3 percent. Knowing that microinvasive components can be demonstrated in 15 percent of lentigo maligna lesions, we retrospectively reassessed our patients by immunohistochemical procedures with S-100 protein. Although intradermal microinvasion could be confirmed in one patient, no recurrence had been observed within 61 months of follow-up. Provided that patients are selected properly and extension of cryonecrosis is monitored, cryosurgery may prove an efficient alternative to conventional surgery in the treatment of lentigo maligna.

International audience; Neuromorphic hardware systems provide new possibilities for the neuroscience modeling community. Due to the intrinsic parallelism of the micro-electronic emulation of neural computation, such models are highly scalable without a loss of speed. However, the communities of software simulator users and neuromorphic engineering in neuroscience are rather disjoint. We present a software concept that provides the possibility to establish such hardware devices as valuable modeling tools. It is based on the integration of the hardware interface into a simulator-independent language which allows for unified experiment descriptions that can be run on various simulation platforms without modification, implying experiment portability and a huge simplification of the quantitative comparison of hardware and simulator results. We introduce an accelerated neuromorphic hardware device and describe the implementation of the proposed concept for this system. An example setup and results acquired by utilizing both the hardware system and a software simulator are demonstrated.

Ehrenreich, D.et al., Ultrahot giant exoplanets receive thousands of times Earth’s insolation. Their high-temperature atmospheres (greater than 2,000 kelvin) are ideal laboratories for studying extreme planetary climates and chemistry. Daysides are predicted to be cloud-free, dominated by atomic species and much hotter than nightsides. Atoms are expected to recombine into molecules over the nightside, resulting in different day and night chemistries. Although metallic elements and a large temperature contrast have been observed, no chemical gradient has been measured across the surface of such an exoplanet. Different atmospheric chemistry between the day-to-night (‘evening’) and night-to-day (‘morning’) terminators could, however, be revealed as an asymmetric absorption signature during transit. Here we report the detection of an asymmetric atmospheric signature in the ultrahot exoplanet WASP-76b. We spectrally and temporally resolve this signature using a combination of high-dispersion spectroscopy with a large photon-collecting area. The absorption signal, attributed to neutral iron, is blueshifted by −11 ± 0.7 kilometres per second on the trailing limb, which can be explained by a combination of planetary rotation and wind blowing from the hot dayside. In contrast, no signal arises from the nightside close to the morning terminator, showing that atomic iron is not absorbing starlight there. We conclude that iron must therefore condense during its journey across the nightside., With funding from the Spanish government through the "María de Maeztu Unit of Excellence" accreditation (MDM-2017-0737)

An alternative description of the Frobenius–Lusztig kernels and restricted specializations is presented and used to find their coradical filtrations, automorphisms, and imbeddings. Moreover, factor Hopf algebras of the Frobenius–Lusztig kernels are determined.

The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits as well as two versatile digital microprocessors. Primarily designed to emulate spiking neural networks, the system can also operate in a vector-matrix multiplication and accumulation mode for artificial neural networks. Analog multiplication is carried out in the synapse circuits, while the results are accumulated on the neurons’ membrane capacitors. Designed as an analog, in-memory computing device, it promises high energy efficiency. Fixed-pattern noise and trial-to-trial variations, however, require the implemented networks to cope with a certain level of perturbations. Further limitations are imposed by the digital resolution of the input values (5 bit), matrix weights (6 bit) and resulting neuron activations (8 bit). In this paper, we discuss BrainScaleS-2 as an analog inference accelerator and present calibration as well as optimization strategies, highlighting the advantages of training with hardware in the loop. Among other benchmarks, we classify the MNIST handwritten digits dataset using a two-dimensional convolution and two dense layers. We reach 98.0% test accuracy, closely matching the performance of the same network evaluated in software.