On the Zero-Outage Secrecy-Capacity of Dependent Fading Wiretap Channels

It is known that for a slow fading Gaussian wiretap channel without channel state information at the transmitter and with statistically independent fading channels, the outage probability of any given target secrecy rate is non-zero, in general. This implies that the so-called zero-outage secrecy capacity (ZOSC) is zero and we cannot transmit at any positive data rate reliably and confidentially. When the fading legitimate and eavesdropper channels are statistically dependent, this conclusion changes significantly. Our work shows that there exist dependency structures for which positive zero-outage secrecy rates (ZOSR) are achievable. In this paper, we are interested in the characterization of these dependency structures and we study the system parameters in terms of the number of observations at legitimate receiver and eavesdropper as well as average channel gains for which positive ZOSR are achieved. First, we consider the setting that there are two paths from the transmitter to the legitimate receiver and one path to the eavesdropper. We show that by introducing a proper dependence structure among the fading gains of the three paths, we can achieve a zero secrecy outage probability (SOP) for some positive secrecy rate. In this way, we can achieve a non-zero ZOSR. We conjecture that the proposed dependency structure achieves maximum ZOSR. To better understand the underlying dependence structure, we further consider the case where the channel gains are from finite alphabets and systematically and globally solve the ZOSC. In addition, we apply the rearrangement algorithm to solve the ZOSR for continuous channel gains. The results indicate that the legitimate link must have an advantage in terms of the number of antennas and average channel gains to obtain positive ZOSR. The results motivate further studies into the optimal dependency structures.

Analyzing Isolation in Mobile Systems

As a safety-critical issue in complex mobile systems, isolation requires two or more mobile objects not to appear in the same place simultaneously. To ensure such isolation, a scheduling policy is needed to control and coordinate the movement of mobile objects. Unfortunately, existing task scheduling theories fails in providing effective solutions, because it is hardly possible to decompose a complex mobile system into multiple independent tasks. To solve this problem, a more fine-grained event scheduling is proposed in this paper to generate scheduling policies which can ensure the isolation of mobile objects. After defining event scheduling based on event-based formal models called dependency structures, a new event scheduling theory for mobile systems is developed accordingly. Then an algorithm for generating an event scheduling policy is proposed to implement the required isolation. Simulation experiments are conducted to prove the result of our theoretical analysis and show the effectiveness and scalability of the approach.

How can regime characteristics of catchments help in training of local and regional LSTM-based runoff models?

In the field of Deep Learning, the long short-term memory (LSTM) networks lie in the category of recurrent neural network (RNN) architectures. The distinctive capability of the LSTM is learning non linear long term dependency structures. This makes the LSTM a good candidate for prediction tasks in non linear time dependent systems such as prediction of runoff in a catchment. In this study, we use a large sample of 740 gauged catchments with very diverse hydro-geo-climatic conditions across France. We present a regime classification based on three hydro-climatic indices to identify and classify catchments with similar hydrological behaviors. We do this because we aim to investigate how regime derived information can be used in training LSTM-based runoff models. The LSTM-based models that we investigate include local models trained on individual catchments as well as regional models trained on a group of catchments. In local training, for each regime, we identify the optimal lookback, i.e. the length of the sequence of past forcing data that the LSTM needs to work through. We then use this length in training regional models that differ in two aspects: 1) hydrological homogeneity of the catchments used in their training, 2) configuration of the static attributes used in their inputs. We examine how each of these aspects contributes to learning of the LSTM in regional training. At every step of this study, we benchmark performances of the LSTM against a conceptual model (GR4J) on both train and unseen data. We show that the optimal lookback is regime dependent and homogeneity of the train catchments in regional training has a more significant contribution to learning of the LSTM than the number of the train catchments.

Ordering and aging properties of systems with dependent components governed by the Archimedean copula

Copula is one of the widely used techniques to describe the dependency structure between components of a system. Among all existing copulas, the family of Archimedean copulas is the popular one due to its wide range of capturing the dependency structures. In this paper, we consider the systems that are formed by dependent and identically distributed components, where the dependency structures are described by Archimedean copulas. We study some stochastic comparisons results for series, parallel, and general $r$ -out-of- $n$ systems. Furthermore, we investigate whether a system of used components performs better than a used system with respect to different stochastic orders. Furthermore, some aging properties of these systems have been studied. Finally, some numerical examples are given to illustrate the proposed results.

Dependency length minimization and its limits: a possible role for a probabilistic version of the Final-Over-Final Condition (to appear in Language)

A prominent principle in explaining a range of word order regularities is dependency locality, i.e. a principle that minimizes the linear distances (dependency lengths) between the head and its dependents. However, it remains unclear to what extent language users in fact observe locality when producing sentences under diverse conditions of cross-categorical harmony (such as the placement of verbal and nominal heads on the same vs different sides of their dependents), dependency direction (head-final vs head-initial) and parallel vs. hierarchical dependency structures (e.g. multiple adjectives dependent on the same head vs nested genitive dependents). Using 45 dependency-annotated corpora of diverse languages, we find that after controlling for harmony and conditioning on dependency types, dependency length minimization (DLM) is inversely correlated with the overall presence of head-final dependencies. This anti-DLM effect in sentences with more head-final dependencies is specifically associated with an accumulation of dependents in parallel structures and with disharmonic orders in hierarchical structures. We propose a detailed interpretation of these results and tentatively suggest a role for a probabilistic principle that favors embedding head-initial (e.g. VO) structures inside equally head-initial and thereby length-minimizing structures (e.g. relative clauses after the head noun) while head-final (OV) structures have a less pronounced preference for harmony and DLM. This is in line with earlier findings in research on the Greenbergian word order universals and with a probabilistic version of what has been suggested as the Final-Over-Final Condition more recently.

A Structure Self-Aware Model for Discourse Parsing on Multi-Party Dialogues

Conversational discourse structures aim to describe how a dialogue is organized, thus they are helpful for dialogue understanding and response generation. This paper focuses on predicting discourse dependency structures for multi-party dialogues. Previous work adopts incremental methods that take the features from the already predicted discourse relations to help generate the next one. Although the inter-correlations among predictions considered, we find that the error propagation is also very serious and hurts the overall performance. To alleviate error propagation, we propose a Structure Self-Aware (SSA) model, which adopts a novel edge-centric Graph Neural Network (GNN) to update the information between each Elementary Discourse Unit (EDU) pair layer by layer, so that expressive representations can be learned without historical predictions. In addition, we take auxiliary training signals (e.g. structure distillation) for better representation learning. Our model achieves the new state-of-the-art performances on two conversational discourse parsing benchmarks, largely outperforming the previous methods.

Responsible Citizens against an Irresponsible State: The Case of Greece amidst the COVID-19 Pandemic

How is it possible for citizens to act responsibly if they live in an irresponsible state? This is the key question that this paper revolves around in the context of the COVID-19 pandemic in Greece. Individual responsibility is the dominant ‘mantra’ of post-modernity and is widely spread by the neoliberal dogma. The individual has to take care of him/herself in any possible way to avoid risks without depending so much on the benevolent state, which, in the developed world, takes the form of a welfare state. Thus, a new type of citizen appears, the “responsible citizen”. The oxymoron, however, in the Greek case is that the state and particularly the political elites maintain bad practices of the past without being able to overcome the country’s path-dependency structures by acting responsibly. The concept of “empathy” is undoubtedly the missing link in this intriguing puzzle of good governance. Will the Greek political elites be able to recognize and embrace empathy in practice?

Regression trees with splitting based on changes of dependencies among covariates

Regression trees are powerful tools in data mining for analyzing data sets. Observations are usually divided into homogeneous groups, and then statistical models for responses are derived in the terminal nodes. This paper proposes a new approach for regression trees that considers the dependency structures among covariates for splitting the observations. The mathematical properties of the proposed method are discussed in detail. To assess the accuracy of the proposed model, various criteria are defined. The performance of the new approach is assessed by conducting a Monte-Carlo simulation study. Two real data sets on classification and regression problems are analyzed by using the obtained results.

Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing

In this work, we present a phenomenon-oriented comparative analysis of the two dominant approaches in English Resource Semantic (ERS) parsing: classic, knowledge-intensive and neural, data-intensive models. To reflect state-of-the-art neural NLP technologies, a factorization-based parser is introduced that can produce Elementary Dependency Structures much more accurately than previous data-driven parsers. We conduct a suite of tests for different linguistic phenomena to analyze the grammatical competence of different parsers, where we show that, despite comparable performance overall, knowledge- and data-intensive models produce different types of errors, in a way that can be explained by their theoretical properties. This analysis is beneficial to in-depth evaluation of several representative parsing techniques and leads to new directions for parser development.