A genetic algorithm for the design of a fuzzy controller for active queue management

Active queue management (AQM) policies are those policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control policy. This paper proposes the adoption of a fuzzy proportional integral (FPI) controller as an active queue manager for Internet routers. The analytical design of the proposed FPI controller is carried out in analogy with a proportional integral (PI) controller, which recently has been proposed for AQM. A genetic algorithm is proposed for tuning of the FPI controller parameters with respect to optimal disturbance rejection. In the paper the FPI controller design metodology is described and the results of the comparison with random early detection (RED), tail drop, and PI controller are presented

Prospects for intermediate mass black hole binary searches with advanced gravitational-wave detectors

We estimated the sensitivity of the upcoming advanced, ground-based gravitational-wave observatories (the upgraded LIGO and Virgo and the KAGRA interferometers) to coalescing intermediate mass black hole binaries (IMBHB). We added waveforms modeling the gravitational radiation emitted by IMBHBs to detectors' simulated data and searched for the injected signals with the coherent WaveBurst algorithm. The tested binary's parameter space covers non-spinning IMBHBs with source-frame total masses between 50 and 1050 $\text{M}_{\odot}$ and mass ratios between $1/6$ and 1$\,$. We found that advanced detectors could be sensitive to these systems up to a range of a few Gpc. A theoretical model was adopted to estimate the expected observation rates, yielding up to a few tens of events per year. Thus, our results indicate that advanced detectors will have a reasonable chance to collect the first direct evidence for intermediate mass black holes and open a new, intriguing channel for probing the Universe over cosmological scales.Comment: 9 pages, 4 figures, corrected the name of one author (previously misspelled

Network dynamics in the healthy and epileptic developing brain

Electroencephalography (EEG) allows recording of cortical activity at high temporal resolution. EEG recordings can be summarised along different dimensions using network-level quantitative measures, e.g. channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different time scales and can be tracked dynamically. Here we describe the dynamics of network-state transitions in EEG recordings of spontaneous brain activity in normally developing infants and infants with severe early infantile epileptic encephalopathies (n=8, age: 1-8 months). We describe differences in measures of EEG dynamics derived from band power, and correlation-based summaries of network-wide brain activity. We further show that EEGs from different patient groups and controls may be distinguishable based on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the sharpness of switching from one correlation pattern to another show the largest differences between groups. These findings reveal that the early epileptic encephalopathies are associated with characteristic dynamic features at the network level. Quantitative network-based analyses like the one presented here may in future inform the clinical use of quantitative EEG for diagnosis

Towards Adaptive Flow Programming for the IoT: The Fluidware Approach

The objective of this position paper is to present Fluidware, a proposal towards an innovative programming model for the IoT, conceived to ease the development of flexible and robust large-scale IoT services and applications. The key innovative idea of Fluidware is to abstract collectives of devices of the IoT fabric as sources, digesters, and targets of distributed 'flows' of contextualized events, carrying information about data produced and actuating commands. Accordingly, programming services and applications implies declaratively specifying 'funnel processes' to channel, elaborate, and re-direct such flows in a fully-distributed way, as a means to coordinate the activities of devices and realize services and applications. The potential applicability of Fluidware and its expected advantages are exemplified via example in the area of ambient assisted living

Active Surface Structure of SnO2 Catalysts for CO2 Reduction Revealed by Ab Initio Simulations

Tin oxide (SnO2) is an efficient catalyst for the CO2 reduction reaction (CO2RR) to formic acid; however, the understanding of the SnO2 surface structure under working electrocatalytic conditions and the nature of catalytically active sites is a current matter of debate. Here, we employ ab initio density functional theory calculations to investigate how the selectivity and reactivity of SnO2 surfaces toward the CO2RR change at varying surface stoichiometry (i.e., reduction degree). Our results show that SnO2(110) surfaces are not catalytically active for the CO2RR or hydrogen evolution reaction, but rather they reduce under an applied external bias, originating surface structures exposing few metal tin layers, which are responsible for formic acid selectivity

Assisted labeling for spam account detection on twitter

Online Social Networks (OSNs) have become increasingly popular both because of their ease of use and their availability through almost any smart device. Unfortunately, these characteristics make OSNs also target of users interested in performing malicious activities, such as spreading malware and performing phishing attacks. In this paper we address the problem of spam detection on Twitter providing a novel method to support the creation of large-scale annotated datasets. More specifically, URL inspection and tweet clustering are performed in order to detect some common behaviors of spammers and legitimate users. Finally, the manual annotation effort is further reduced by grouping similar users according to some characteristics. Experimental results show the effectiveness of the proposed approach

DEFAULT MODE NETWORK AND WORKING MEMORY NETWORK DURING AN FMRI WORKING MEMORY TASK: DIFFERENCES AND CORRELATIONS WITH BEHAVIORAL PERFORMANCE

INTRODUCTION Previous neuroimaging studies have shown that working memory load has marked effects on regional neural activation[1-5]. However, the mechanism through which working memory load modulates brain connectivity is still unclear. During a working memory task, two of the most involved networks are the default mode network (DMN) and the working memory network (WMN)[6-7]: the selective focus on these networks can be useful in better understanding the load effects. Spatial independent component analysis (ICA)[8] has becomes a reliable technique to investigate the networks involved during an fMRI task, as it extracts spatiotemporal patterns of neural activity maximizing spatial independence. A specific study, conducted with ICA, investigating on how the load and phase of a working memory task are related with the activation and response time, is nowadays lacking. The aim of this work is to use the time course of DMN and WMN, selected by means of ICA, for studying: a) how these networks are involved with the complexity of the task and the phase; b) how, in these networks, complexity and phase are correlated with reaction times. METHODS MR Data Acquisition and preprocessing Fifteen young adult healthy and right-handed were involved. The MR protocol consisted of one anatomical sequence 3D T1-weighted MP-RAGE (Voxel size: 1 x 1 x 1 mm) and three functional acquisitions of 15 minutes each performed with a T2*-weighted EPI sequence (TR/TE: 1500/30, In- plane resolution: 3.5x3.5 mm, Thickness: 3.5 mm, Nr of slices: 24, Field of view: 64 x 64 mm). All the images were collected with a Siemens Allegra 3T MR scanner (Siemens, Erlangen, Germany) and a standard head coil. During the fMRI acquisition the subjects performed a delayed spatial working memory paradigm presented with three levels of difficulty. The memory set consisted of one, three or five circles presented randomly in different locations and to the subjects were asked to judge whether or not a given target stimulus had been part of a previous memory stimulus set. Every experiment consisted of 90 working memory trials, 30 per load, divided in three runs. Data were analyzed with Brain Voyager QX. 2.4 (Brain Innovation, Maastricht, The Netherlands). FMRI preprocessing included: 3D head-motion correction, slice-scan time correction, spatial smoothing, temporal high pass filter and linear trend removal. Anatomic 3D data set was inhomogeneities corrected, filtered and transformed into Talairach coordinates and coregistered with the functional information. Independent Component Analysis This analysis was conducted using Brainvoyager QX 2.4. ICA analysis was performed on each subject\u2019s three functional acquisitions. A subsequent total ICA group analysis[9-10] was achieved by an inter- subject ICA group analysis of all the intra-subject ICA group analysis. From the obtained maps were selected two Independent Components (ICs) containing the WMN[1,2]: WMN1 defined by SPL and Precuneus, and WMN2 with DLPFC and IPS (Fig. 1b-c). Also one IC describing the DMN was considered, with PCC, IPL and MPFC (Fig. 1a)[11]. For each run of all the subjects the ICs time course was considered: three time windows of 3TR (4.5s) for each working memory task phase (encode, maintenance and retrieval) were selected taking into account the haemodynamic response by delaying the window of 5 volumes events from the start of every trial. The window time course was corrected for a baseline value. Mean values of the ICs where examined and a subsequent correlation between the mean values and the response time in every trial was estimated. A 3x3 two-way ANOVA on Fisher transformed correlation was conducted to test the variation of loads (load1=less complex, load3=more complex), phases and runs. Figure 1: Networks selected from ICA analysis (transversal view): (a) DMN, (b) WMN1 (c) WMN2. RESULTS Figure 2 exhibits window mean activities and correlations divided for phase and load. DMN mean activity is negative while WMN1-2 mean activities have opposite behaviors regarding the phase, but similar concerning with the complexity (Fig. 2a-c). DMN shows a reduction of the correlation from encode to retrieval, instead of WM1-2 where it grows (Fig. 2d-f). The ANOVA showed significant variation for the phases over all the subjects in WMN1-2, an interaction of the variation of phases and runs in WMN2 and a interaction of phases, runs and loads in DMN. DISCUSSION These findings suggest that working memory networks (WMNs), as isolated by means of IC A, display substantially opposed mean values related to a different areas specialization. WMN1 seems to be more involved in the first part of the mnemonic phase and the amount of this involvement is associated to the trial: the more complicated the task, the higher the activation with respect to baseline. On the other hand, WMN2 increases from the first to the last part of the trial and is probably more involved in the operation of retrieval. In Figure 2e-f it is also shown that in the retrieval there is a stronger correlation between WMN1-2 mean values and the response time probably because this phase is the more complex. DMN exhibits, over all the phases, smaller than zero mean values (due to the task inducted deactivation). In contrast, its correlation has a different trend and increases above zero during the maintenance, probably due to the free thought of this phase. The different behavior of load 3 is probably due to the fact that this type of complexity is totally different from the other two. In conclusion, this study shows that, by means of ICA, it is possible to isolate networks of connected regions and relate their time courses to task phases and behavioral performance. This is a promising approach to advance the understanding of connectivity modulations in several brain networks, including WMNs and DMN

A Smart Assistant for Visual Recognition of Painted Scenes

Nowadays, smart devices allow people to easily interact with the surrounding environment thanks to existing communication infrastructures, i.e., 3G/4G/5G or WiFi. In the context of a smart museum, data shared by visitors can be used to provide innovative services aimed to improve their cultural experience. In this paper, we consider as case study the painted wooden ceiling of the Sala Magna of Palazzo Chiaramonte in Palermo, Italy and we present an intelligent system that visitors can use to automatically get a description of the scenes they are interested in by simply pointing their smartphones to them. As compared to traditional applications, this system completely eliminates the need for indoor positioning technologies, which are unfeasible in many scenarios as they can only be employed when museum items are physically distinguishable. Experimental analysis aimed to evaluate the performance of the system in terms of accuracy of the recognition process, and the obtained results show its effectiveness in a real-world application scenario