Miracle-dz/UniFAR-FTUs huggingface dataset:
Horizon Visibility Graphs and Time Series Merge Trees are Dual

In this paper we introduce the horizon visibility graph, a simple extension to the popular horizontal visibility graph representation of a time series, and show that it possesses a rigorous mathematical foundation in computational algebraic topology. This fills a longstanding gap in the literature on the horizontal visibility approach to nonlinear time series analysis which, despite a suite of successful applications across multiple domains, lacks a formal setting in which to prove general properties and develop natural extensions. The main finding is that horizon visibility graphs are dual to merge trees arising naturally over a filtered complex associated to a time series, while horizontal visibility graphs are weak duals of these trees. Immediate consequences include availability of tree-based reconstruction theorems, connections to results on the statistics of self-similar trees, and relations between visibility graphs and the emerging field of applied persistent homology.
Accurate and Energy-Efficient Classification with Spiking Random Neural Network: Corrected and Expanded Version

Artificial Neural Network (ANN) based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption from all leading technology companies worldwide. One of the major obstacles that have historically delayed large scale adoption of ANNs is the huge computational and power costs associated with training and testing (deploying) them. In the mean-time, Neuromorphic Computing platforms have recently achieved remarkable performance running more bio-realistic Spiking Neural Networks at high throughput and very low power consumption making them a natural alternative to ANNs. Here, we propose using the Random Neural Network (RNN), a spiking neural network with both theoretical and practical appealing properties, as a general purpose classifier that can match the classification power of ANNs on a number of tasks while enjoying all the features of a spiking neural network. This is demonstrated on a number of real-world classification datasets.
Autonomous navigation of MAVs in unknown cluttered environments

Recently, there have been many advances in the algorithms required for autonomous navigation in unknown environments, such as mapping, collision avoidance, trajectory planning, and motion control. These components have been integrated into drones with high‐end computers and graphics processors. However, further development is required to enable compute‐constrained platforms with such autonomous navigation capabilities. To address this issue, in this paper, we present an autonomous navigation framework for reaching a goal in unknown three‐dimensional cluttered environments. The framework consists of three main components. The first component is a computationally efficient method for mapping the environment from the disparity measurements obtained from a depth sensor. The second component is a stochastic approach to generate a path to a given goal, taking into account the field of view constraints on the space that is assumed to be safe for navigation. The third method is a fast algorithm for the online generation of motion plans, taking into account the robot's dynamic constraints, model and environmental uncertainty, and disturbances. We provide a qualitative and quantitative comparison with existing reaching a goal and exploration methods, showing the superior performance of our approach. Additionally, we present indoors and outdoors experiments using a robotic platform based on the Intel Ready to Fly drone kit, which represents the implementation, in the most computational constrained platform, of autonomous navigation in unknown cluttered environments demonstrated to date. Open source code is available at: https://github.com/IntelLabs/autonomousmavs. The video of the experimental results can be found in https://youtu.be/79IFfQfvXLE.
A Signal Subspace Rotation Method for Localization of Multiple Wideband Sound Sources

In this paper, the problem of extending narrowband multichannel sound source localization algorithms to the wideband case is addressed. The DOA estimation of narrowband algorithms is based on the estimate of inter-channel phase differences (IPD) between microphones of the sound sources. A new method for wideband sound source DOA estimation based on signal subspace rotation is present. The proposed algorithm normalizes the narrowband signal statistics by rotating the estimated signal subspace to the wideband counterpart in the eigenvector domain. Then the wideband DOA estimate can be obtained by estimating the normalized IPD from these wideband signal statistics. In addition to requiring less computational complexity compared to repeating the narrowband algorithms for all relevant frequencies of wideband signals, the proposed method also does not require any additional prior knowledge. The experimental results demonstrate the efficacy and the robustness of the proposed method.
Radio spectral index distribution of SDSS-FIRST sources across optical diagnostic diagrams

Context. The empirical relations between supermassive black holes and their host spheroids point towards the crucial role of galactic nuclei in affecting the properties of their hosts. A detailed understanding of how the activity of a galactic nucleus regulates the growth of its host is still missing. Aims. To understand the activity and the types of accretion of supermassive black holes in different hosts, it is essential to study the radio-optical properties of a large sample of extragalactic sources. In particular, we aim to study the radio spectral index trends across the optical emission line diagnostic diagrams to search for potential (anti)correlations. Methods. To this goal, we combined flux densities from the radio FIRST survey at 1.4 GHz (with the flux density range 10 mJy ≤ F1.4 ≤ 100 mJy) for 396 SDSS sources at intermediate redshift (0.04 ≤ z ≤ 0.4) with the Effelsberg radiotelescope measurements at 4.85 GHz and 10.45 GHz. The information about the optical emission-line ratios is obtained from the SDSS-DR7 catalogue. Results. Using the Effelsberg data, we were able to infer the two-point radio spectral index distributions for star-forming galaxies, composite galaxies (with a combined contribution to the line emission from the star formation and AGN activity), Seyferts, and low ionization narrow emission region (LINER) galaxies. Conclusions. While studying the distribution of steep, flat, and inverted sources across optical diagnostic diagrams, we found three distinct classes of radio emitters for our sample: (i) sources with a steep radio index, high ionization ratio, and high radio loudness, (ii) sources with a flat radio index, lower ionization ratio, and intermediate radio loudness, (iii) sources with an inverted radio index, low ionization ratio, and low radio loudness. The classes (i), (ii), and (iii) cluster mainly along the transition from Seyfert to LINER sources in the optical diagnostic (Baldwin, Phillips & Telervich; BPT) diagram. We interpret these groups as a result of the recurrent nuclear-jet activity.
A modular mobile robot for multi-robot applications

This paper presents the process of designing and constructing a desktop-size mobile robot aimed at multi-robot applications. We present the mechanical structure, the electronics and the software, focusing on the modularity concept, which determines the educational values of the entire design. The decision process underlying the design is presented in details: choosing motors, sensors and crucial electronics components, implementing communication buses, and construction of the additional modules. Possible multi-robot applications of this design are outlined at the end.
Domination above r-independence: does sparseness help?

Inspired by the potential of improving tractability via gap- or above-guarantee parametrisations, we investigate the complexity of Dominating Set when given a suitable lower-bound witness. Concretely, we consider being provided with a maximal r-independent set X (a set in which all vertices have pairwise distance at least r + 1) along the input graph G which, for r >= 2, lower-bounds the minimum size of any dominating set of G. In the spirit of gap-parameters, we consider a parametrisation by the size of the 'residual' set R := V (G) \ N [X]. Our work aims to answer two questions: How does the constant r affect the tractability of the problem and does the restriction to sparse graph classes help here? For the base case r = 2, we find that the problem is paraNP -complete even in apex- and bounded-degree graphs. For r = 3, the problem is W[2]-hard for general graphs but in FPT for nowhere dense classes and it admits a linear kernel for bounded expansion classes. For r >= 4, the parametrisation becomes essentially equivalent to the natural parameter, the size of the dominating set.
Classical glasses, black holes, and strange quantum liquids

From the dynamics of a broad class of classical mean-field glass models one may obtain a quantum model with finite zero-temperature entropy, a quantum transition at zero temperature, and a time-reparametrization (quasi-)invariance in the dynamical equations for correlations. The low eigenvalue spectrum of the resulting quantum model is directly related to the structure and exploration of metastable states in the landscape of the original classical glass model. This mapping reveals deep connections between classical glasses and the properties of SYK-like models.
Disambiguierung deutschsprachiger Diskursmarker: Eine Pilot-Studie

Discourse markers such as German aber, wohl or obwohl can be regarded as valuable information for a wide range of text-linguistic applications, since they provide important cues for the interpretation of texts or text segments. Unfortunately, many of them are highly ambiguous. Thus, for their use in applications like automatic text summarizations a reliable disambiguation of discourse markers is needed. This should be done automatically, since manual disambiguation is feasible only for small amounts of data. The aim of this pilot study, therefore, was to investigate methodological requirements of automatic disambiguation of German discourse markers. Two different methods known from word-sense disambiguation, Naive-Bayes and decisionlists, were used for the highly ambiguous marker wenn. A statistical approach was taken to compare the two approaches and different feature combinations.
Integrated Lane Change Model with Relaxation and Synchronization

A proposed lane change model can be integrated with a car-following model to form a complete microscopic driver model. The model resembles traffic better at a macroscopic level, especially regarding the amount of traffic volume per lane, the traffic speeds in different lanes, and the onset of congestion. In a new approach, lane change incentives are combined for determining a lane change desire. Included incentives are to follow a route, to gain speed, and to keep right. Classification of lane changes is based on behavior that depends on the level of lane change desire. Integration with a car-following model is achieved by influencing car-following behavior for relaxation and synchronization, that is, following vehicles in adjacent lanes. Other improvements of the model are trade-offs between lane change incentives and the use of anticipation speed for the speed gain incentive. Although all these effects are captured, the lane change model has only seven parameters. Loop detector data were used to validate and calibrate the model, and an accurate representation of lane distribution and the onset of congestion was shown.
Managing Consumerization of Personal Cloud 
Storage: A New Zealand Perspective

Employees‟ behavior of using personal cloud storage at the workplace might put corporates at risk. This trend has been happening globally and known as consumerization of personal cloud storage. This research reports on the state of this trend in New Zealand corporates. A mixed methods approach was employed with data collection through online anonymous surveys and an online focus group. It was found that personal cloud storage prevails widely throughout the corporates involved in the study. In light of earlier studies in IT consumerization and recent market researches on personal cloud storage, this study identifies many drivers, benefits and challenges of IT consumerization which remain significant in personal cloud storage consumerization, and additionally divulges new factors not previously surfaced in prior literature and publications. The results reveals that its challenges far outweigh its benefits and that the driving forces born within organizations are as considerably significant as those in the external environment. Resulting corporate risks such as security, support, regulatory obligations, data governance and policies are top-critical amongst other challenges with which corporates have to confront. Regardless of those challenges, corporates might gain several benefits, namely employee engagement, productivity, business agility and cost benefits to some extent.
The acquisition of Basque ergative case: an experimental study

Basque children omit ergative case markers for about five months before the production of this case mark becomes adult-like. This has been considered a problem in the acquisition of Basque and has been related to the ergative character of the language. The aim of this work is to present the results of a picture selection task done by 24 Basque bilingual children which show that the comprehension of Basque case marking and, more precisely, of the ergative case precedes the production of it.
An efficient hidden node reduction technique for multilayer perceptrons

An efficient method which yields an appropriate number of hidden nodes by iteratively removing unnecessary hidden nodes is proposed. The criterion for selecting the removable nodes is derived from the analysis of information transformation of individual neurons and the network as a whole. A well-trained multilayer perception (MLP) that has a sufficient number of hidden nodes is used. A hidden node is selected with an output weight of the smallest magnitude, and it is removed from the hidden layer. After the removal, additional training is performed. This procedure is iterated until the network performance is not affected severely. The method has been applied to some problems, including handwritten digit recognition, resulting in a significant reduction of hidden nodes. <<ETX>>
Robot Calibration: Modeling Measurement and Applications

Most currently used industrial robots are still programmed by a teach pendant, especially in the automotive industry. However the importance of off-line programming in industry as an alternative to teach-in programming is steadily increasing. The main reason for this trend is the need to minimize machine downtime and thus to improve the rate of robot utilization. Nonetheless, for a successful accomplishment of off-line programming the robots need to be not only repeatable but also accurate. In addition to improving robot accuracy through software (rather than by changing the mechanical structure or design of the robot), calibration techniques can also minimize the risk of having to change application programs due to slight changes or drifts (wearing of parts, dimension drifts or tolerances, and component replacement effects) in the robot system. This is mostly important in applications that may involve a large number of task points. Theoretically, with the availability of robot calibration systems integrated with off-line programming systems, it would be possible to implement off-line programming in industry (currently they are not ready to be used in large scale), where multiple robots are used, by programming only one robot and copying the programs from one robot to the others. One of the most evident advantages of this type of programming is the reduction in maintenance expenditure, since the robot can return faster to service and easy reprogramming means the software becomes operational with little effort. Robot calibration is an integrated process of modeling, measurement, numeric identification of actual physical characteristics of a robot, and implementation of a new model. The calibration procedure first involves the development of a kinematic model whose parameters represent accurately the actual robot. Next, specifically selected robot characteristics are measured using measurement instruments with known accuracy. Then a parameter identification procedure is used to compute the set of parameter values which, when introduced in the robot nominal model, accurately represents the measured robot behavior. Finally, the model in the position control software is corrected. Many factors such as numerical problems, measurement system deficiencies, cumbersome setups and commercial interests have defied the confidence of the industry in such systems, especially if one realizes that a robot will not normally need full calibration more than once or twice a year.
The Prospects and Pitfalls of “Just-So” Storytelling in Evolutionary Accounts of Religion

AbstractI discuss problems importing evolutionary language into the study of religion. It is not impossible to do, but it is difficult to carry out properly in practice. I suggest five criteria for scholarship in the study of religion to amount to good science when incorporating such language. They are 1) avoiding just-so storytelling as much as possible 2) the requirement to add a compelling level of explanation beyond the historical narrative 3) clearly distinguishing between proximate and ultimate forms of causation and explanation, and favoring proximate causes where possible 4) addressing the specific content of religion directly as part of the narrative 5) being explicit about the genre of scholarship undertaken, whether science-writing, humanistic exploration, or some mix of the two. Wiebe and Martin’s arguments do not end up rising to the challenge that they themselves have instigated to have a truly scientific study of religion.
Why should I tell you? Applying a culturally attuned privacy theory to understand global customers' online disclosure behaviours in e-commerce transactions

This paper explores the effect of cultural values and information privacy concerns on the online disclosure behaviours of global customers engaged in e-commerce activities. Hence, the general research question is as follows: In what ways do cultural values and privacy induce or deter online disclosure behaviours? To understand online disclosure behaviours from the cultural and privacy perspectives, the paper draws on two theoretical frameworks. Hall's (1976) high-context/low-context cultural theory illuminates how cultural values induce or discourage disclosure behaviours and Petronio's (2002) Communication Privacy Management (CPM) theory is useful in explaining the psychological boundaries between disclosure and privacy. Integrating these two theories offers a culturally-attuned privacy theory to understand online disclosure behaviours. Finally, the paper provides perspectives on the implications for e-commerce in its efforts to attract global customers.
DIRAC pilot framework and the DIRAC Workload Management System

DIRAC, the LHCb community Grid solution, has pioneered the use of pilot jobs in the Grid. Pilot Jobs provide a homogeneous interface to an heterogeneous set of computing resources. At the same time, Pilot Jobs allow to delay the scheduling decision to the last moment, thus taking into account the precise running conditions at the resource and last moment requests to the system. The DIRAC Workload Management System provides one single scheduling mechanism for jobs with very different profiles. To achieve an overall optimisation, it organizes pending jobs in task queues, both for individual users and production activities. Task queues are created with jobs having similar requirements. Following the VO policy a priority is assigned to each task queue. Pilot submission and subsequent job matching are based on these priorities following a statistical approach.
State-of-the-art in auralization of concet hall models - What is still missing?

Auralization is the process in which a 3-D model of a space is made audible by convolving the impulse response of the space with anechoic stimulus signal [1, 2]. One obvious application area of auralization is the design of concert halls. Many years people have dreamed of the possibility to listen to the designed hall before the actual construction. However, auralization systems do not yet provide authentic auralization which the designer and acoustic consultants could trust. By authentic auralization we mean the creation of a virtual auditory environment that is indistinguishable from the real auditory environment [3, 4]. In this paper, we discuss the auralization of concert hall models by pointing out issues which still need more research as well as issues which have already been solved. Technically, auralization process is a convolution of anechoic stimulus signal and a spatial impulse response of a space. The spatial impulse response, which contains information about the incoming directions of direct sound and reflections, can be obtained with simulation of sound propagation in a 3-D model. Naturally, spatial impulse responses can also be measured in a real concert hall. In other words, the auralization process requires the simulation or reproduction of the following three issues as accurately as possible:
Quality improvement and copyright protection for digital cultural content

The application of information technology in the field of cultural heritage content, is now widely diffused, and many different aspects are subject to a deep investigation. Among these this paper in particular analyses the activities carried on over the years at the Image Processing and Communications Laboratory (Laboratorio Comunicazioni ed Immagini - LCI) of the Department of Electronics and Telecommunications of the University of Florence with reference to fine arts images quality improvement and copyright protection. An overview of the European level activities carried out by the Lab is finally given.
A Hybrid Routing Protocol and Performance Evaluation in a Wireless Mesh Network

The size of mesh networks depends upon the usage scenario so that the use of any single routing algorithm alone, either proactive or reactive, may not work efficiently. This letter proposes a hybrid routing algorithm, where a different algorithm is applied depending on the hop count between source and destination nodes. The algorithm allows using a proactive algorithm when a destination is nearby and a reactive algorithm when a destination is away from a source node. In this letter, we adopt the Destination Sequenced Distance Vector routing protocol (DSDV) for a nearby destination and the Ad hoc On-Demand Vector routing protocol (AODV) for a remote destination. A node maintains only a shared routing table for routing protocols, which helps reducing the size of the routing table at a node within a mesh network. Its routing performance is compared to those in DSDV and AODV in terms of normalized routing load, average packet transmission delay, and the average number of entries in the routing table.