pinecone/core-2020-05-10-deduplication huggingface dataset:
Observing Supermassive Black Holes across cosmic time: from
  phenomenology to physics

In the last decade, a combination of high sensitivity, high spatial
resolution observations and of coordinated multi-wavelength surveys has
revolutionized our view of extra-galactic black hole (BH) astrophysics. We now
know that supermassive black holes reside in the nuclei of almost every galaxy,
grow over cosmological times by accreting matter, interact and merge with each
other, and in the process liberate enormous amounts of energy that influence
dramatically the evolution of the surrounding gas and stars, providing a
powerful self-regulatory mechanism for galaxy formation. The different
energetic phenomena associated to growing black holes and Active Galactic
Nuclei (AGN), their cosmological evolution and the observational techniques
used to unveil them, are the subject of this chapter. In particular, I will
focus my attention on the connection between the theory of high-energy
astrophysical processes giving rise to the observed emission in AGN, the
observable imprints they leave at different wavelengths, and the methods used
to uncover them in a statistically robust way. I will show how such a combined
effort of theorists and observers have led us to unveil most of the SMBH growth
over a large fraction of the age of the Universe, but that nagging
uncertainties remain, preventing us from fully understating the exact role of
black holes in the complex process of galaxy and large-scale structure
formation, assembly and evolution.Comment: 46 pages, 21 figures. This review article appears as a chapter in the
  book: "Astrophysical Black Holes", Haardt, F., Gorini, V., Moschella, U and
  Treves A. (Eds), 2015, Springer International Publishing AG, Cha
Star Formation and Dynamics in the Galactic Centre

The centre of our Galaxy is one of the most studied and yet enigmatic places
in the Universe. At a distance of about 8 kpc from our Sun, the Galactic centre
(GC) is the ideal environment to study the extreme processes that take place in
the vicinity of a supermassive black hole (SMBH). Despite the hostile
environment, several tens of early-type stars populate the central parsec of
our Galaxy. A fraction of them lie in a thin ring with mild eccentricity and
inner radius ~0.04 pc, while the S-stars, i.e. the ~30 stars closest to the
SMBH (<0.04 pc), have randomly oriented and highly eccentric orbits. The
formation of such early-type stars has been a puzzle for a long time: molecular
clouds should be tidally disrupted by the SMBH before they can fragment into
stars. We review the main scenarios proposed to explain the formation and the
dynamical evolution of the early-type stars in the GC. In particular, we
discuss the most popular in situ scenarios (accretion disc fragmentation and
molecular cloud disruption) and migration scenarios (star cluster inspiral and
Hills mechanism). We focus on the most pressing challenges that must be faced
to shed light on the process of star formation in the vicinity of a SMBH.Comment: 68 pages, 35 figures; invited review chapter, to be published in
  expanded form in Haardt, F., Gorini, V., Moschella, U. and Treves, A.,
  'Astrophysical Black Holes'. Lecture Notes in Physics. Springer 201
Data Suppression Algorithms for Surveillance Applications of Wireless
  Sensor and Actor Networks

This paper introduces algorithms for surveillance applications of wireless
sensor and actor networks (WSANs) that reduce communication cost by suppressing
unnecessary data transfers. The objective of the considered WSAN system is to
capture and eliminate distributed targets in the shortest possible time.
Computational experiments were performed to evaluate effectiveness of the
proposed algorithms. The experimental results show that a considerable
reduction of the communication costs together with a performance improvement of
the WSAN system can be obtained by using the communication algorithms that are
based on spatiotemporal and decision aware suppression methods
Multiple forms of applications and impacts of a design theory -ten years of industrial applications of C-K theory

International audienceC-K theory has been developed by Armand Hatchuel and Benoit Weil and then by other researchers since 1990s. In this paper we show that its very abstract nature and its high degree of universality actually supported a large variety of industrial applications. We distinguish three types of applications: 1) C-K theory provides a new language, that supports new analysis and descriptive capacity and new teachable individual models of thoughts; 2) C-K theory provides a very general framework to better characterize the validity domain and the performance conditions of existing methods, leading to potential improvement of these methods; 3) C-K theory is the conceptual model at the root of new design methods that are today largely used in the industry
Discrete Parameters in Petri Nets

International audienceWith the aim of significantly increasing the modeling capability of Petri nets, we suggest that models involve parameters to represent the weights of arcs, or the number of tokens in places. We consider the property of coverability of markings. Two general questions arise: " Is there a parameter value for which the property is satisfied? " and " Does the property hold for all possible values of the parameters? ". We show that these issues are undecidable in the general case. Therefore, we also define subclasses of parameterised networks, depending on whether the parameters are used on places, input or output arcs of transitions. For some subclasses, we prove that certain problems become decidable, making these subclasses more usable in practice
Nested-Unit Petri Nets: A Structural Means to Increase Efficiency and Scalability of Verification on Elementary Nets

International audiencePetri nets can express concurrency and nondeterminism but not hierarchy. This article presents an extension of Petri nets, in which places can be grouped into so-called " units " expressing sequential components. Units can be recursively nested to reflect the hierarchical nature of complex systems. This model called NUPN (Nested-Unit Petri Nets) was originally developed for translating process calculi to Petri nets, but later found also useful beyond this setting. It allows significant savings in the memory representation of markings for both explicit-state and symbolic verification. Six tools already implement the NUPN model, which is also part of the next edition of the Model Checking Contest
Process visualization techniques for multi-perspective process comparisons

Organizations executing similar business processes need to understand the differences and similarities in activities performed across work environments. Presently, research interest is directed towards the potential of visualization for the display of process models, to support users in their analysis tasks. Although recent literature in process mining and comparison provide several methods and algorithms to perform process and log comparison, few contributions explore novel visualization approaches. This paper analyses process comparison from a design perspective, providing some practical visualization techniques as anal- ysis solutions (/to support process analysis). The design of the visual comparison has been tackled through three different points of view: the general model, the projected model and the side-by-side comparison in order to support the needs of business analysts. A case study is presented showing the application of process mining and visualization techniques to patient treatment across two Australian hospitals
Creating value through crowdsourcing : the antecedent conditions

15th International Conference, GDN 2015, Warsaw, Poland, June 22-26, 2015, Proceedings.The benefits of crowdsourcing are becoming more widely understood and there is a methodological move towards organisations using “participatory models” to engage stakeholder communities and align decision making more closely to the needs of stakeholders. Many tasks can now be distributed to “the crowd” for action. Our research aims to understand the antecedent conditions that inform management decisions to adopt crowdsourcing techniques as a means of value creation. Our preliminary findings suggest that to be successful, three antecedent criteria must be met – the task being crowdsourced must be modular in nature, a community of interest must be engaged, and there needs to be a structural capability within the organisation to be able to facilitate the engagement of the crowd and utilise the output from the crowd in a manner that creates value
Formal Concept Analysis from the Standpoint of Possibility Theory

Formal concept analysis (FCA) and possibility theory (PoTh) have been developed independently. They address different concerns in information processing: while FCA exploits relations linking objects and properties, and has applications in data mining and clustering, PoTh deals with the modeling of (graded) epistemic uncertainty. However, making a formal parallel between FCA and PoTh is fruitful. The four set-functions at work in PoTh have meaningful counterparts in FCA; this leads to consider operators neglected in FCA, and thus new fixed point equations. One of these pairs of equations, paralleling the one defining formal concepts in FCA, defines independent sub-contexts of objects and properties that have nothing in common. The similarity of the structures underlying FCA and PoTh is still more striking, using a cube of opposition (a device extending the traditional square of opposition in logic). Beyond the parallel between FCA and PoTh, this invited contribution, which largely relies on several past publications by the authors, also addresses issues pertaining to the possible meanings, degree of satisfaction vs. degree of certainty, of graded object-property links, which calls for distinct manners of handling the degrees. Other lines of interest for further research are briefly mentioned
Revisiting Pattern Structure Projections

Formal concept analysis (FCA) is a well-founded method for data analysis and
has many applications in data mining. Pattern structures is an extension of FCA
for dealing with complex data such as sequences or graphs. However the
computational complexity of computing with pattern structures is high and
projections of pattern structures were introduced for simplifying computation.
In this paper we introduce o-projections of pattern structures, a
generalization of projections which defines a wider class of projections
preserving the properties of the original approach. Moreover, we show that
o-projections form a semilattice and we discuss the correspondence between
o-projections and the representation contexts of o-projected pattern
structures.
  KEYWORDS: formal concept analysis, pattern structures, representation
contexts, projectionsComment: 16 pages, 4 figures, ICFCA-2015 conferenc
Mining hierarchical pathology data using inductive logic programming

Considerable amounts of data are continuously generated by pathologists in the form of pathology reports. To date, there has been relatively little work exploring how to apply machine learning and data mining techniques to these data in order to extract novel clinical relationships.  From a learning perspective, these pathology data possess a number of challenging properties, in particular, the temporal and hierarchical structure that is present within the data. In this paper, we propose a methodology based on inductive logic programming to extract novel associations from pathology excerpts. We discuss the challenges posed by analyzing these data and discuss how we address them. As a case study, we apply our methodology to Dutch pathology data for discovering possible causes of two rare diseases: cholangitis and breast angiosarcomas.status: publishe
Supervised Machine Learning Techniques to Detect TimeML Events in French and English

International audienceIdentifying events from texts is an information extraction task necessary for many NLP applications. Through the TimeML specifications and TempEval challenges, it has received some attention in the last years; yet, no reference result is available for French. In this paper, we try to fill this gap by proposing several event extraction systems, combining for instance Conditional Random Fields, language modeling and k-nearest-neighbors. These systems are evaluated on French corpora and compared with state-of-the-art methods on English. The very good results obtained on both languages validate our whole approach
Tree-Structured Named Entities Extraction from Competing Speech Transcriptions

International audienceWhen real applications are working with automatic speech transcription, the first source of error does not originate from the incoher-ence in the analysis of the application but from the noise in the automatic transcriptions. This study presents a simple but effective method to generate a new transcription of better quality by combining utterances from competing transcriptions. We have extended a structured Named Entity (NE) recognizer submitted during the ETAPE Challenge. Working on French TV and Radio programs, our system revises the transcriptions provided by making use of the NEs it has detected. Our results suggest that combining the transcribed utterances which optimize the F-measures, rather than minimizing the WER scores, allows the generation of a better transcription for NE extraction. The results show a small but significant improvement of 0.9% SER against the baseline system on the ROVER transcription. These are the best performances reported to date on this corpus. Index Terms: speech transcription, structured named entities, multi-pass decoding. When real applications are working with automatic speech transcription, the first error does not originate from the incoherence in the analysis of the application , but from the noise of the automatic transcription outputs. With a rate often close to one in three words incorrect in the transcription, the quality of the preprocessing is low and, as a result, the output analysis of the application is often unexploitable. An explanation for this low performance of speech recog-nizers can be found in [8]. Little lexical and syntactic information is effectively used to enable the computation of the decoding of the acoustic output. More complex information are reintegrated in a second decoding pass where only the best sequences of words produced during the first pass are considered. The main contribution of this study is to present a simple but effective method to generate a new transcription of better quality by combining several competing transcriptions. Current Automatic Speech Recognition (ASR) systems rely on various strategies and/or resources to discover the original utterances pronounced. As a consequence, errors made by competing ASRs are different, which make the transcriptions complementary. The Rover method exploits such complementarity to recombine several transcriptions and output
From AADL Model to LNT Specification

The verification of distributed real-time systems designed by architectural languages such as AADL (Architecture Analysis and Design Language) is a research challenge. These systems are often used in safety- critical domains where one mistake can result in physical damages and even life loss. In such domains, formal methods are a suitable solution for rigorous analysis. This paper studies the formal verification of distributed real-time systems modelled with AADL. We transform AADL model to another specification formalism enabling the verification. We choose LNT language which is an input to CADP toolbox for formal analysis. Then, we illustrate our approach with the ”Flight Control System” case study
In whose best interest? An agent-based model of high frequency trading

We study a stylized model of High Frequency Trading in which traders equipped with private values and costs operate in a Continuous Double Auction. They can revise their orders with different frequencies and, hence, (only) some agents can repeatedly revise and resubmit orders in the same session, mimicking the behavior of high frequency traders. All agents attempt to maximize profits, learning which bid and ask is to be posted in a given configuration of the book. We analyze the efficiency of the resulting market and the way the surplus from trading is apportioned among agents as a function of the number and type of high frequency traders. We find that the presence of a small proportion of high frequency traders increases the overall efficiency of the market; secondly, the ones who have the chance to frequently revise the offers learn to extract a disproportionate fraction of the profits that ordinarily would belong to slow traders
Podcast distribution on Gwanda using PrivHab : a multiagent secure georouting protocol

Capítol de llibre d'actes del congres: 13th Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 15).We present PrivHab, a georouting protocol that improves multiagent systems itinerary decision-making. PrivHab uses the mobility habits of the nodes of the network to select an itinerary for each agent carrying a piece of data. PrivHab makes use of cryptographic techniques to make the decisions while preserving nodes’ privacy. PrivHab uses a waypoint-based georouting that achieves a high performance and low overhead in rugged terrain areas that are plenty of physical obstacles. The store-carry-and-forward approach used is based on mobile agents and is designed to operate in areas that lack network infrastructure. We have evaluated PrivHab under the scope of a realistic podcast distribution application in remote rural areas. The PrivHab protocol is compared with a set of well-known delay-tolerant routing algorithms and shown to outperform them
Assessing an ontology for the representation of clinical protocols in decision support systems

In order to assess the expressiveness of the CompGuide ontology for Clinical Practice Guidelines, a study was conducted with fourteen students of the Integrated Masters in Biomedical Engineering from the University of Minho in Portugal to whom it was proposed the representation of multiple guidelines according to the ontology. They were then asked to evaluate the ontology through a questionnaire and written reports. Although the results seem promising, there is the need for significant improvements mainly in: the representation of medication prescriptions, the tasks used to retrieve information from the patient, the diversity of actions offered by the ontology, the expressiveness of conditions regarding the state of a patient, and temporal constraints.FCT - Fundação para a Ciência e a Tecnologiainfo:eu-repo/semantics/publishedVersio
Using mouse dynamics to assess stress during online exams

"Lecture notes in computer science series", ISSN 0302-9743, vol. 9121Stress is a highly complex, subjective and multidimensional phenomenon. Nonetheless, it is also one of our strongest driving forces, pushing us forward and preparing our body and mind to tackle the daily challenges, independently of their nature. The duality of the effects of stress, that can have positive or negative effects, calls for approaches that can take the best out of this biological mechanism, providing means for people to cope effectively with stress. In this paper we propose an approach, based on mouse dynamics, to assess the level of stress of students during online exams. Results show that mouse dynamics change in a consistent manner as stress settles in, allowing for its estimation from the analysis of the mouse usage. This approach will allow to understand how each individual student is affected by stress, providing additional valuable information for educational institutions to efficiently adapt and improve their teaching processes.This work is part-funded by ERDF - European Regional
Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation
for Science and Technology) within project FCOMP-01-0124-FEDER-028980
(PTDC/EEI-SII/1386/2012) and project PEst-OE/EEI/UI0752/2014
Detecting Anomalies in Embedded Computing Systems via a Novel HMM-Based Machine Learning Approach

Computing systems are vulnerable to anomalies that might occur during execution of deployed software: e.g., faults, bugs or deadlocks. When occurring on embedded computing systems, these anomalies may severely hamper the corresponding devices; on the other hand, embedded systems are designed to perform autonomously, i.e., without any human intervention, and thus it is difficult to debug an application to manage the anomaly. Runtime anomaly detection techniques are the primary means of being aware of anomalous conditions. In this paper, we describe a novel approach to detect an anomaly during the execution of one or more applications. Our approach describes the behaviour of the applications using the sequences of memory references generated during runtime. The memory references are seen as signals: they are divided in overlapping frames, then parametrized and finally described with Hidden Markov Models (HMM) for detecting anomalies. The motivations of using such methodology for embedded systems are the following: first, the memory references could be extracted with very low overhead with software or architectural tools. Second, the device HMM analysis framework, while being very powerful in gathering high level information, has low computational complexity and thus is suitable to the rather low memory and computational capabilities of embedded systems. We experimentally evaluated our proposal on a ARM9, Linux based, embedded system using the SPEC 2006 CPU benchmark suite and found that it shows very low error rates for some artificially injected anomalies, namely a malware, an infinite loop and random errors during execution
Pattern backtracking algorithm for the workflow satisfiability problem with user-independent constraints

The workflow satisfiability problem (WSP) asks whether there exists an assignment of authorised users to the steps in a workflow specification, subject to certain constraints on the assignment. (Such an assignment is called valid.) The problem is NP-hard even when restricted to the large class of user-independent constraints. Since the number of steps k is relatively small in practice, it is natural to consider a parametrisation of the WSP by k. We propose a new fixed-parameter algorithm to solve the WSP with user-independent constraints. The assignments in our method are partitioned into equivalence classes such that the number of classes is exponential in k only. We show that one can decide, in polynomial time, whether there is a valid assignment in an equivalence class. By exploiting this property, our algorithm reduces the search space to the space of equivalence classes, which it browses within a backtracking framework, hence emerging as an efficient yet relatively simple-to-implement or generalise solution method. We empirically evaluate our algorithm against the state-of-the-art methods and show that it clearly wins the competition on the whole range of our test problems and significantly extends the domain of practically solvable instances of the WSP
The Directed Dominating Set Problem: Generalized Leaf Removal and Belief
  Propagation

A minimum dominating set for a digraph (directed graph) is a smallest set of
vertices such that each vertex either belongs to this set or has at least one
parent vertex in this set. We solve this hard combinatorial optimization
problem approximately by a local algorithm of generalized leaf removal and by a
message-passing algorithm of belief propagation. These algorithms can construct
near-optimal dominating sets or even exact minimum dominating sets for random
digraphs and also for real-world digraph instances. We further develop a core
percolation theory and a replica-symmetric spin glass theory for this problem.
Our algorithmic and theoretical results may facilitate applications of
dominating sets to various network problems involving directed interactions.Comment: 11 pages, 3 figures in EPS forma