Special stock market segments for small company shares in Europe - What went wrong?

Special stock market segments for small company shares were established by all major European stock exchanges during the 1980's. After showing a remarkable success during the first years of their existence - both in the primary as well as in the secondary market - these segments today suffer from increasing illiquidity. The stock exchange authorities in London and in Amsterdam have therefore decided to close down their stock markets for smaller companies. This paper takes a closer look at the current situation of these stock market segments throughout Europe and discusses the causes for their decline. --

Resilience, collapse and reorganization of a rangeland socio-ecological system in South Africa

Communal rangelands in semi-arid areas are complex socio-ecological systems (SES). Their complexity arises from non-linear feedbacks between the social- and the ecosystem. To understand the social system requires tackling institutional issues associated with common pool resource governance. Moreover, assessing ecosystem dynamics commands to acknowledge high climatic variability in semi-arid areas. This thesis quantifies the dynamics of a communal livestock production SES in a former homeland of South Africa using a SES modelling approach. Here, a social agent based model is combined with a biomass growth model of the rangeland. The coupling of both models is achieved by full integration on software (Java) level. Accordingly, the resulting model does account for ecological complexity. The latter constitutes a contribution to the methodological advancement of bio-economic modelling insofar as bio-economic models strongly simplify ecological processes. The SES model is specified based on primary data from a case study. On a conceptual level, the three main chapters in this thesis investigate aspects of SES resilience, collapse and reorganization. Specifically, chapter two assesses social welfare impacts from reorganizing resource use by the adjustment of stocking rates and alterations of spatio-temporal grazing patterns. Chapter 3 explores the effect of a local norm on SES dynamics with a focus on collapse vs. stability. Finally, chapter 4 quantifies the resilience on multiple scales of the SES towards droughts, a loss of social embededdness and a significant change in subsidization. We found that the adjustment of stocking rates yields higher social benefits compared to the (re)-introduction of rotational grazing in a system assumed to be void of institutional arrangements. In a second step, we identified the existence of a local norm indirectly impacting resource use by endogenous stocking rate adjustments. The existence of the informal institution significantly contributes to the long-term stability of the SES by reducing the chance for collapse. The emergence of norm-following behaviour is fostered by climatic variability. The SES was resilient towards droughts and a change in subsidization. It was however not resilient towards a loss in social embededdness. At another level, only the introduction of a basic income grant was able to stop a process of structural change eroding household resilience. The introduction of a basic income grant enabled poorer households to successfully compete with richer ones without jeopardizing the resilience of the coupled system

Aspekte der Agrarpolitik 2007

Agricultural and Food Policy, Community/Rural/Urban Development, International Relations/Trade,

Möglichkeiten und Maßnahmen zur Wahrung und Steigerung der Wettbewerbsfähigkeit der Baden-Württembergischen Wertpapierbörse zu Stuttgart

Die Börsenstrukturen befinden sich international in einer Phase dynamischer Veränderungen. Der härter gewordene Wettbewerb hinterläßt bei den großen nationalen Börsen tiefe Spuren und führt zu Anpassungsmaßnahmen, die vor allem in der technischen Neuorganisation des Börsenhandels und in der Erweiterung der Produktpalette ihren Ausdruck finden. Regionalbörsen wie die Baden-Württembergische Wertpapierbörse zu Stuttgart sind dabei einem noch stärkeren Wettbewerbsdruck ausgesetzt, da neben die internationale Konkurrenz noch die Konkurrenz der deutschen Börsen untereinander tritt. Das vorliegende Gutachten, das vom Staatsministerium Baden-Württemberg in Auftrag gegeben wurde, widmet sich speziell der Erarbeitung von Vorschlägen, die der Wahrung und Steigerung der Wettbewerbsfähigkeit der Stuttgarter Wertpapierbörse dienen

Möglichkeiten und Maßnahmen zur Wahrung und Steigerung der Wettbewerbsfähigkeit der Baden-Württembergischen Wertpapierbörse zu Stuttgart

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A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays

We introduce the IBM Analog Hardware Acceleration Kit, a new and first of a kind open source toolkit to simulate analog crossbar arrays in a convenient fashion from within PyTorch (freely available at https://github.com/IBM/aihwkit). The toolkit is under active development and is centered around the concept of an "analog tile" which captures the computations performed on a crossbar array. Analog tiles are building blocks that can be used to extend existing network modules with analog components and compose arbitrary artificial neural networks (ANNs) using the flexibility of the PyTorch framework. Analog tiles can be conveniently configured to emulate a plethora of different analog hardware characteristics and their non-idealities, such as device-to-device and cycle-to-cycle variations, resistive device response curves, and weight and output noise. Additionally, the toolkit makes it possible to design custom unit cell configurations and to use advanced analog optimization algorithms such as Tiki-Taka. Moreover, the backward and update behavior can be set to "ideal" to enable hardware-aware training features for chips that target inference acceleration only. To evaluate the inference accuracy of such chips over time, we provide statistical programming noise and drift models calibrated on phase-change memory hardware. Our new toolkit is fully GPU accelerated and can be used to conveniently estimate the impact of material properties and non-idealities of future analog technology on the accuracy for arbitrary ANNs.Comment: Submitted to AICAS202

Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference

Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics, and the non-ideal peripheral circuitry in AIMC chips, require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. The AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, that provides the benefits of using the AIHWKit simulation platform in a fully managed cloud setting. Finally, we show examples on how users can expand and customize AIHWKit for their own needs. This tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

Analog in-memory computing (AIMC) -- a promising approach for energy-efficient acceleration of deep learning workloads -- computes matrix-vector multiplications (MVMs) but only approximately, due to nonidealities that often are non-deterministic or nonlinear. This can adversely impact the achievable deep neural network (DNN) inference accuracy as compared to a conventional floating point (FP) implementation. While retraining has previously been suggested to improve robustness, prior work has explored only a few DNN topologies, using disparate and overly simplified AIMC hardware models. Here, we use hardware-aware (HWA) training to systematically examine the accuracy of AIMC for multiple common artificial intelligence (AI) workloads across multiple DNN topologies, and investigate sensitivity and robustness to a broad set of nonidealities. By introducing a new and highly realistic AIMC crossbar-model, we improve significantly on earlier retraining approaches. We show that many large-scale DNNs of various topologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, can in fact be successfully retrained to show iso-accuracy on AIMC. Our results further suggest that AIMC nonidealities that add noise to the inputs or outputs, not the weights, have the largest impact on DNN accuracy, and that RNNs are particularly robust to all nonidealities.Comment: 35 pages, 7 figures, 5 table

Incommensurate and multiple-q\boldsymbol{q} magnetic misfit order in the frustrated quantum spin ladder material antlerite, Cu3_3SO4_4(OH)4_4

In frustrated magnetic systems, the competition amongst interactions can introduce extremely high degeneracy and prevent the system from readily selecting a unique ground state. In such cases, the magnetic order is often exquisitely sensitive to the balance among the interactions, allowing tuning among novel magnetically ordered phases. In antlerite, Cu$_3$SO$_4$(OH)$_4$, Cu$^{2+}$ ($S=1/2$) quantum spins populate three-leg zigzag ladders in a highly frustrated quasi-one-dimensional structural motif. We demonstrate that at zero applied field, in addition to its recently reported low-temperature phase of coupled ferromagnetic and antiferromagnetic spin chains, this mineral hosts an incommensurate helical+cycloidal state, an idle-spin state, and a multiple-$q$ phase which is the magnetic analog of misfit crystal structures. The antiferromagnetic order on the central leg is reentrant. The high tunability of the magnetism in antlerite makes it a particularly promising platform for pursuing exotic magnetic order.Comment: 18.3 pages, 16 Figures, follow-up paper to arXiv:2203.1534