Hybrid Branching-Time Logics

Hybrid branching-time logics are introduced as extensions of CTL-like logics with state variables and the downarrow-binder. Following recent work in the linear framework, only logics with a single variable are considered. The expressive power and the complexity of satisfiability of the resulting logics is investigated. As main result, the satisfiability problem for the hybrid versions of several branching-time logics is proved to be 2EXPTIME-complete. These branching-time logics range from strict fragments of CTL to extensions of CTL that can talk about the past and express fairness-properties. The complexity gap relative to CTL is explained by a corresponding succinctness result. To prove the upper bound, the automata-theoretic approach to branching-time logics is extended to hybrid logics, showing that non-emptiness of alternating one-pebble Buchi tree automata is 2EXPTIME-complete.Comment: An extended abstract of this paper was presented at the International Workshop on Hybrid Logics (HyLo 2007

Learning Fault-tolerant Speech Parsing with SCREEN

This paper describes a new approach and a system SCREEN for fault-tolerant speech parsing. SCREEEN stands for Symbolic Connectionist Robust EnterprisE for Natural language. Speech parsing describes the syntactic and semantic analysis of spontaneous spoken language. The general approach is based on incremental immediate flat analysis, learning of syntactic and semantic speech parsing, parallel integration of current hypotheses, and the consideration of various forms of speech related errors. The goal for this approach is to explore the parallel interactions between various knowledge sources for learning incremental fault-tolerant speech parsing. This approach is examined in a system SCREEN using various hybrid connectionist techniques. Hybrid connectionist techniques are examined because of their promising properties of inherent fault tolerance, learning, gradedness and parallel constraint integration. The input for SCREEN is hypotheses about recognized words of a spoken utterance potentially analyzed by a speech system, the output is hypotheses about the flat syntactic and semantic analysis of the utterance. In this paper we focus on the general approach, the overall architecture, and examples for learning flat syntactic speech parsing. Different from most other speech language architectures SCREEN emphasizes an interactive rather than an autonomous position, learning rather than encoding, flat analysis rather than in-depth analysis, and fault-tolerant processing of phonetic, syntactic and semantic knowledge.Comment: 6 pages, postscript, compressed, uuencoded to appear in Proceedings of AAAI 9

SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

In this paper, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, learned and flat analysis. In this paper, we focus on a detailed description of SCREEN's architecture, the flat syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the influence of noisy or incomplete input. The main result of this paper is that flat representations allow more robust processing of spontaneous spoken language than deeply structured representations. In particular, we show how the fault-tolerance and learning capability of connectionist networks can support a flat analysis for providing more robust spoken-language processing within an overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial Intelligence Research 6(1), 199

Payout Policy and Owners? Interests: Evidence from German Savings Banks

The savings banks? decision to distribute profits among their public owners is strongly regulated by law in order to guarantee their adequate funding via retained profits. However, the legal scope is reluctantly exhausted. In this study we examine the determinants of the savings banks? payout decision in more detail. We find that besides internal determinants also external factors regarding the savings bank?s public owner have strong explanatory power. The better the financial situation of the public owner, the less likely is the savings bank to distribute profits and to increase payouts, respectively. --Savings Banks,Germany,Payout Policy

How Do Banks Determine Capital? Empirical Evidence for Germany

This paper examines how capital is determined by German banks. We analyse whether the determinants found in the previous empirical literature hold for the special German banking sector with its three characteristic banking groups of savings banks, cooperative banks and other banks. On the basis of a unique data set of nearly all German banks between 1992 and 2001 provided by the Deutsche Bundesbank, we apply the generalised method of moments (GMM) within a dynamic panel data framework. The results largely confirm the findings for other countries, but show considerable differences between the three German banking groups. --Bank capital,portfolio risk,banking regulation,panel data,GMM

How Do Banks Determine Capital? Evidence from Germany

We analyse whether the determinants of capital found in the previous literature hold for the special German banking sector comprising of three characteristic banking groups including savings banks, cooperative banks and other banks, which greatly differ regarding their ownership and their access to the capital market. Through the use of accounting data from German banks between 1992 and 2001 we find evidence in accordance with the buffer theory of capital for all German banking groups. Nevertheless, we also detect some remarkable differences between the three banking groups regarding their determinantion of capital due to institutional characteristics

Payout Policy and Owners' Interests – Evidence from German Savings Banks

The savings banks' decision to distribute profits among their public owners is strongly regulated by law in order to guarantee their adequate funding via retained profits. However, the legal scope is reluctantly exhausted. In this study we examine the determinants of the savings banks' payout decision in more detail. We find that besides internal determinants also external factors regarding the savings bank's public owner have strong explanatory power. The better the financial situation of the public owner, the less likely is the savings bank to distribute profits and to increase payouts, respectively

How Do Banks Determine Capital? - Empirical Evidence for Germany

This paper examines how capital is determined by German banks. We analyse whether the determinants found in the previous empirical literature hold for the special German banking sector with its three characteristic banking groups of savings banks, cooperative banks and other banks. On the basis of a unique data set of nearly all German banks between 1992 and 2001 provided by the Deutsche Bundesbank, we apply the generalised method of moments (GMM) within a dynamic panel data framework. The results largely confirm the findings for other countries, but show considerable differences between the three German banking groups

Spoken language processing in the hybrid connectionist architecture SCREEN

In this paper we describe a robust, learning approach to spoken language understanding. Since interactively spoken and computationally analyzed language often contains many errors, robust connectionist networks are used for providing a flat screening analysis. A screening analysis is a shallow flat analysis based on category sequences at various syntactic, semantic and dialog levels. Rather than using tree or graph representations a screening analysis uses category sequences in order to support robustness and learning. This flat screening analysis is examined in the context of the system SCREEN (Symbolic Connectionist Robust EnterprisE for Natural language). Starting with the word hypotheses generated by a speech recognizer, we give an overview of the architecture, and illustrate the flat robust processing at the levels of syntax, semantics, and dialog acts. While early connectionist models were often limited to a single network and a small task, the hybrid connectionist SCREEN system is an important step towards exploring connectionist techniques in larger hybrid symbolic/connectionist environments and for real-world problemsBased on our experience with SCREEN, hybrid connectionist techniques show a lot of potential for supporting robustness in interactive spoken language processing