119 research outputs found
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
A deep learning model enables accurate prediction and quantification of pulmonary edema from chest X-rays
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- magnetic misfit order in the frustrated quantum spin ladder material antlerite, CuSO(OH)
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, CuSO(OH),
Cu () 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-
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
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