Probabilistic Consensus of the Blockchain Protocol

We introduce a temporal epistemic logic with probabilities as an extension of temporal epistemic logic. This extension enables us to reason about properties that characterize the uncertain nature of knowledge, like “agent a will with high probability know after time s same fact”. To define semantics for the logic we enrich temporal epistemic Kripke models with probability functions defined on sets of possible worlds. We use this framework to model and reason about probabilistic properties of the blockchain protocol, which is in essence probabilistic since ledgers are immutable with high probabilities. We prove the probabilistic convergence for reaching the consensus of the protocol

Explicit Evidence Systems with Common Knowledge

Justification logics are epistemic logics that explicitly include justifications for the agents' knowledge. We develop a multi-agent justification logic with evidence terms for individual agents as well as for common knowledge. We define a Kripke-style semantics that is similar to Fitting's semantics for the Logic of Proofs LP. We show the soundness, completeness, and finite model property of our multi-agent justification logic with respect to this Kripke-style semantics. We demonstrate that our logic is a conservative extension of Yavorskaya's minimal bimodal explicit evidence logic, which is a two-agent version of LP. We discuss the relationship of our logic to the multi-agent modal logic S4 with common knowledge. Finally, we give a brief analysis of the coordinated attack problem in the newly developed language of our logic

Incremental Medians via Online Bidding

In the k-median problem we are given sets of facilities and customers, and distances between them. For a given set F of facilities, the cost of serving a customer u is the minimum distance between u and a facility in F. The goal is to find a set F of k facilities that minimizes the sum, over all customers, of their service costs. Following Mettu and Plaxton, we study the incremental medians problem, where k is not known in advance, and the algorithm produces a nested sequence of facility sets where the kth set has size k. The algorithm is c-cost-competitive if the cost of each set is at most c times the cost of the optimum set of size k. We give improved incremental algorithms for the metric version: an 8-cost-competitive deterministic algorithm, a 2e ~ 5.44-cost-competitive randomized algorithm, a (24+epsilon)-cost-competitive, poly-time deterministic algorithm, and a (6e+epsilon ~ .31)-cost-competitive, poly-time randomized algorithm. The algorithm is s-size-competitive if the cost of the kth set is at most the minimum cost of any set of size k, and has size at most s k. The optimal size-competitive ratios for this problem are 4 (deterministic) and e (randomized). We present the first poly-time O(log m)-size-approximation algorithm for the offline problem and first poly-time O(log m)-size-competitive algorithm for the incremental problem. Our proofs reduce incremental medians to the following online bidding problem: faced with an unknown threshold T, an algorithm submits "bids" until it submits a bid that is at least the threshold. It pays the sum of all its bids. We prove that folklore algorithms for online bidding are optimally competitive.Comment: conference version appeared in LATIN 2006 as "Oblivious Medians via Online Bidding

Algebraic Comparison of Partial Lists in Bioinformatics

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset

Four Lessons in Versatility or How Query Languages Adapt to the Web

Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3C’s GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a “Web of Data”

Awareness Logic: A Kripke-based Rendition of the Heifetz-Meier-Schipper Model

Heifetz, Meier and Schipper (HMS) present a lattice model of awareness. The HMS model is syntax-free, which precludes the simple option to rely on formal language to induce lattices, and represents uncertainty and unawareness with one entangled construct, making it difficult to assess the properties of either. Here, we present a model based on a lattice of Kripke models, induced by atom subset inclusion, in which uncertainty and unawareness are separate. We show the models to be equivalent by defining transformations between them which preserve formula satisfaction, and obtain completeness through our and HMS' results.Comment: 18 pages, 2 figures, proceedings of DaLi conference 202

Expression of insulin-like growth factor I by activated hepatic stellate cells reduces fibrogenesis and enhances regeneration after liver injury

BACKGROUND/AIM: Hepatic stellate cells (HSCs) express alpha-smooth muscle actin (alphaSMA) and acquire a profibrogenic phenotype upon activation by noxious stimuli. Insulin-like growth I (IGF-I) has been shown to stimulate HSCs proliferation in vitro, but it has been reported to reduce liver damage and fibrogenesis when given to cirrhotic rats. METHODS: The authors used transgenic mice (SMP8-IGF-I) expressing IGF-I under control of alphaSMA promoter to study the influence of IGF-I synthesised by activated HSCs on the recovery from liver injury. RESULTS: The transgene was expressed by HSCs from SMP8-IGF-I mice upon activation in culture and in the livers of these animals after CCl4 challenge. Twenty four hours after administration of CCl4 both transgenic and wild type mice showed similar extensive necrosis and increased levels of serum transaminases. However at 72 hours SMP8-IGF-I mice exhibited lower serum transaminases, reduced hepatic expression of alphaSMA, and improved liver morphology compared with wild type littermates. Remarkably, at this time all eight CCl4 treated wild type mice manifested histological signs of liver necrosis that was severe in six of them, while six out of eight transgenic animals had virtually no necrosis. In SMP8-IGF-I mice robust DNA synthesis occurred earlier than in wild type animals and this was associated with enhanced production of HGF and lower TGFbeta1 mRNA expression in the SMP8-IGF-I group. Moreover, Colalpha1(I) mRNA abundance at 72 hours was reduced in SMP8-IGF-I mice compared with wild type controls. CONCLUSIONS: Targeted overexpression of IGF-I by activated HSCs restricts their activation, attenuates fibrogenesis, and accelerates liver regeneration. These effects appear to be mediated in part by upregulation of HGF and downregulation of TGFbeta1. The data indicate that IGF-I can modulate the cytokine response to liver injury facilitating regeneration and reducing fibrosis

Persuasive argumentation and epistemic attitudes

These slides present the main notions and results of a work under construction that was presented in the 2nd DaLí Workshop, Dynamic Logic: New Trends and Applications in Porto, 9 October, 2019 and later published in the Lectures Notes in Computer Science (vol 12005). The work develops a formal study of persuasive dialogues among individuals, taking into account the epistemic attitudes of the involved agents. Abstract argumentation and dynamic epistemic logic provide the necessary tools for such an analysis. The interested reader is referred to the paper for further detailsUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech