Bill 25 proposed by the Texas Senate in 2017 was created to eliminate the so-called ‘wrongful birth’ cause of action. This plan raised some questions about the ‘right to know’ and indirectly about rights in general. We provide a preliminary logical analysis investigating these questions by using deontic and epistemic logics within the theory of normative positions. This work contributes to the logic-based legal knowledge representation tradition, and to the formal conceptual analysis of legal rights studying the cause of action’s role in the debated relation between the Hohfeldian categories ‘claim-right’ and ‘power’.

This is a correction notice for the Editorial exy006 (DOI: https://doi.org/10.1093/logcom/exy006), published on 20 March 2018. The publisher regrets to inform that an incorrect email address was listed for Allard Tamminga, whose corresponding email address should instead have been given as [email protected]. This has been corrected in the published article. The publisher apologizes for this error.

This special issue of the Journal of Logic and Computation puts together contributions that were first presented at the 13th Deontic Logic and Normative Systems (DEON) conference, formerly known as ‘Deontic Logic in Computer Science’, that took place in Bayreuth in July 2016. The conference proceedings were published by College Publications as the first volume of their DEON series. This special issue is based on selected papers from the conference. The authors have been asked to revise and significantly extend their conference contributions, and each paper went through the usual rigourous journal review process. Of the twelve papers submitted, five made it through that process and are presented in this issue. DEON 2016’s special focus was on ‘Reasons, argumentation and justification’. Reasons play a prominent role in the normative study of action, belief, intention and emotions, as well as in everyday justification and argumentation. Recent years have seen numerous fruitful exchanges between deontic logicians, computer scientists and philosophers on the nature of reasons and their role in practical and theoretical deliberation. There have also been multiple applications of formal frameworks for the study of reasons in areas of interest to linguists and philosophers of language. The goal of DEON 2016’s special focus was to continue this positive trend by encouraging submissions that explore the significance of deontic logic for the study of reasons and their connection with justification and argumentation (and vice versa).

The fourth international workshop on Logic Rationality and Interaction (LORI-4) took place in Hangzhou, China on October 9th–12th 2013, hosted by the Center for the Study of Language and Cognition (CSLC) of Zhejiang University. 1 The event brought together an international group of logicians, computer scientists, decision and game theorists, linguists and AI researchers. Along with the contributed papers and posters, the workshop featured seven invited talks—by Giuseppe Dari-Mattiacci, Valentin Goranko, Hannes Leitgeb, Beishui Liao, Christian List, Sonja Smets and Dongmo Zhang. 2 The articles in this special issue were selected among the papers of the workshop proceedings [ 2 ]. The selected papers were revised and extended, and went through the same extensive review process as all regular submissions to the JLC . The result is a collection of papers that accurately represents the width and depth of the ideas contributed to LORI-4. We are confident that you will appreciate them.

This special issue arose out of two workshops held in Prague and Munich in 2011 and 2013.1 It puts together a number of contributions combining substructrual logic (SL) and dynamic epistemic logic (DEL) in order to address, first, the problem of logical omniscience [10], secondly, the question of interpreting the Routley–Meyer semantics for substructural logic [5], and finally, the challenge of providing a proof theory that enjoys normalization or cut-elimination for DEL [9]. This issue is of course not the first encounter between substructural logic and logical models of information dynamics, but it shows that their relationship is much more than a one-night stand. On the one hand, substructural systems have been extensively used to tackle the logical omniscience problem, in both classical, static epistemic logic and in its dynamic extensions [6]. The first two contributions of this issue are by key contributors in this tradition. Sedlár's paper opens by providing a very general substructural framework for modelling knowledge, its dynamics and its relation to non-mononotic reasoning. Bílkova et al. follow a similar thread, this time generalizing previous work of theirs which makes explicit the sources of one's knowledge and belief.

This article looks at so-called dynamic consequence relations for models of soft information change. We provide a sound, complete calculus for one-step soft dynamic consequence relations. We then study a generalization to sequences of updates, for which we show a number of valid and invalid structural rules.

This paper describes a multi-FPGA architecture dedicated for the real-time imaging using the Total Focusing Method (TFM) and an advanced acquisition technique called the Full Matrix Capture (FMC). The maximum operating frequency is 147.3 MHz for the control blocks and 161.3 MHz for the acquisition blocks. The architecture is able to perform real-time image reconstruction at a maximum frame rate of 73 fps and a maximum resolution of 128 × 128 pixels, which is adequate as a performance for a real-time imaging system with a good characterization of defects.

We consider the task of recovering correlated vectors at a central decoder based on fixed linear measurements obtained by distributed sensors. A general formulation of the problem is proposed, under both a universal and an almost sure reconstruction requirement. We then study a specific correlation model which involves a filter that is sparse in the time domain. While this sparsity assumption does not allow reducing the description cost in the universal case, we show that large gains can be achieved in the almost sure scenario by means of a novel distributed scheme based on annihilating filters. The robustness of the proposed method is also investigated.

In this paper we study Aumann's Agreement Theorem in dynamic-epistemic logic. We show that common belief of posteriors is sufficient for agreements in "epistemic-plausibility models", under common and well-founded priors, from which the usual form of agreement results follows, using common knowledge. We do not restrict ourselves to the finite case, and show that in countable structures such results hold if and only if the underlying "plausibility ordering" is well-founded. We look at these results from a syntactic point of view, showing that neither well-foundedness nor common priors are expressible in a commonly used language, but that the static agreement result is finitely derivable in an extended modal logic. We finally consider "dynamic" agreement results, show they have a counterpart in epistemic-plausibility models, and provide a new form of agreements via "public announcements". Comparison of the two types of dynamic agreement reveals that they can indeed be different.

Distributed estimation of an unknown signal is a common task in sensor networks. The scenario usually envisioned consists of several nodes, each making an observation correlated with the signal of interest. The acquired data is then wirelessly transmitted to a fusion center that aims at estimating the desired signal within a prescribed accuracy. Motivated by the obvious processing limitations inherent to such distributed infrastructures, we seek to find efficient compression schemes that account for limited available power and communication bandwidth. In this paper, we propose a transform-based approach to this problem where each sensor provides the fusion center with a low-dimensional approximation of its local observation by means of a suitable linear transform. Under the mean squared error criterion, we derive the optimal solution to apply at one sensor assuming all else being fixed. This naturally leads to an iterative algorithm whose optimality properties are exemplified using a simple though illustrative correlation model. The stationarity issue is also investigated. Under restrictive assumptions, we then provide an asymptotic distortion analysis, as the size of the observed vectors becomes large. Our derivation relies on a variation of the Toeplitz distribution theorem, which allows us to provide a reverse ldquowater-fillingrdquo perspective to the problem of optimal dimensionality reduction. We illustrate, with a first-order Gauss-Markov model, how our findings allow for the computation of analytical closed-form distortion formulas that provide an accurate estimation of the reconstruction error obtained in the finite-dimensional regime.

We address the problem of distributed compression in acoustic sensor networks. A typical scenario consists of a set of microphones that record a sound source located at some unknown position. The goal then is to convey the corresponding audio signals to a central base station for processing. We thus aim at designing efficient distributed communication schemes that minimize the overall bit-rate needed in order to achieve a given reconstruction accuracy. In this paper, we show how the a priori knowledge of the system's geometry can be beneficially used in order to lower the transmission rates. We propose a distributed coding scheme for simple synthetic sources. These results are then applied to more general signals by means of oversampled analog-to-digital conversion. Simulation results confirm the effectiveness of our approach

Hearing aids are audio capture devices which aim at providing the hearing impaired with better audibility. Most of the state-of-the-art systems involve sensing devices that work independently. However, the availability of a wireless communication link between two hearing aids would allow to perform spatial beamforming such as to provide better rejection of interfering signals. In this paper, we identify and study the above scenario from an information-theoretic viewpoint. We explore the gain provided by collaborating hearing aids as a function of the communication rate. In particular, we derive a closed-form gain-rate formula in the case where a sound source has to be extracted from ambient noise. A similar analysis is provided in the presence of an interfering point source and the corresponding optimal rate allocation is discussed

Most space-time coding schemes can be classified either as noncoherent or coherent. In this paper we prove that optimal noncoherent decoding can always be decomposed into a channel estimation step followed by coherent decoding step. Surprisingly the required estimators do not in general minimise the mean squared error between the estimated and actual channel.