Miracle-dz/UniFAR-FTUs huggingface dataset:
Embedded digital protection for IPMSM drives

This paper presents the development, real-time implementation, and experimental testing of a new embedded digital protection for three phase (3φ) interior permanent magnet synchronous motor (IPMSM) drives. The proposed digital protection is established through extracting the high frequency sub-band contents present in the d-q-axis components of the currents drawn by an IPMSM. the desired high frequency sub-band contents are extracted using the wavelet packet transform (WPT). These extracted high frequency sub-band contents contain signature information that allow detecting and classifying transient disturbances occurring in IPMSM drives. Such a structure of the d - q WPT-based digital protection simplifies its implementation, and facilitates its embedding within the controller of an IPMSM drive. Performances of the proposed digital protection are investigated experimentally on a laboratory 5-hp IPMSM drive. Experimental test results for various fault and non-fault transient disturbances demonstrate fast and accurate detection, classification, and responses. Performance features, along with simple implementation of the d - q WPT-based digital protection support its application in IPMSM drives.
Multiple embryonic origins of nitric oxide synthase-expressing GABAergic neurons of the neocortex

Cortical GABAergic interneurons in rodents originate in three subcortical regions: the medial ganglionic eminence (MGE), the lateral/caudal ganglionic eminence (LGE/CGE), and the preoptic area (POA). Each of these neuroepithelial precursor domains contributes different interneuron subtypes to the cortex. Neuronal NOS (nNOS)-expressing neurons represent a heterogenous population of cortical interneurons. We examined the development of these cells in the mouse embryonic cortex and their abundance and distribution in adult animals. Using genetic lineage tracing in transgenic mice we find that nNOS type I cells originate only in the MGE whereas type II cells have a triple origin in the MGE, LGE/CGE, and POA. The two populations are born at different times during development, occupy different layers in the adult cortex and have distinct neurochemical profiles. nNOS neurons are more numerous in the adult cortex than previously reported and constitute a significant proportion of the cortical interneuron population. Our data suggest that the heterogeneity of nNOS neurons in the cortex can be attributed to their multiple embryonic origins which likely impose distinct genetic specification programs.
Robust TTS duration modelling using DNNS

Accurate modelling and prediction of speech-sound durations is an important component in generating more natural synthetic speech. Deep neural networks (DNNs) offer a powerful modelling paradigm, and large, found corpora of natural and expressive speech are easy to acquire for training them. Unfortunately, found datasets are seldom subject to the quality-control that traditional synthesis methods expect. Common issues likely to affect duration modelling include transcription errors, reductions, filled pauses, and forced-alignment inaccuracies. To combat this, we propose to improve modelling and prediction of speech durations using methods from robust statistics, which are able to disregard ill-fitting points in the training material. We describe a robust fitting criterion based on the density power divergence (the ß-divergence) and a robust generation heuristic using mixture density networks (MDNs). Perceptual tests indicate that subjects prefer synthetic speech generated using robust models of duration over the baselines.
A CENSUS OF X-RAY NUCLEAR ACTIVITY IN NEARBY GALAXIES

We have studied the X-ray nuclear activity of 187 nearby (distance less than 15 Mpc) galaxies observed with Chandra/ACIS. We found that 86 of them have a pointlike X-ray core, consistent with an accreting black hole (BH). We argue that the majority of them are nuclear BHs, rather than X-ray binaries. The fraction of galaxies with an X-ray-detected nuclear BH is higher (≈60%) for ellipticals and early type spirals (E to Sb), and lower (≈30%) for late-type spirals (Sc to Sm). There is no preferential association of X-ray cores with the presence of a large-scale bar; in fact, strongly barred galaxies appear to have slightly lower detection fraction and luminosity for their nuclear X-ray sources, compared with nonbarred or weakly barred galaxies of similar Hubble types. The cumulative luminosity distribution of the nuclear sources in the 0.3–8 keV band is a power law with slope ≈ −0.5, from ≈2 × 1038 erg s−1 to ≈1042 erg s−1. The Eddington ratio is lower for ellipticals (LX/LEdd ∼ 10−8) and higher for late-type spirals (up to LX/LEdd ∼ 10−4), but in all cases, the accretion rate is low enough to be in the radiatively inefficient regime. The intrinsic absorbing column density is generally low, especially for the less luminous sources: there appear to be no Type 2 nuclear BHs at luminosities ≲1039 erg s−1. The lack of a dusty torus or of other sources of intrinsic absorption (e.g., an optically thick disk wind) may be directly related to the lack of a standard accretion disk around those faint nuclear BHs. The fraction of obscured sources increases with the nuclear BH luminosity: two-thirds of the sources with LX > 1040 erg s−1 have a fitted column density greater than 1022 cm−2. This is in contrast to the declining trend of the obscured fraction with increasing luminosities, observed in more luminous active galactic nuclei and quasars.
Quick Shaving

Arc-consistency plays such a key role in constraint programming for solving real life problems that it is almost the only algorithm used for reducing domains. There are a few specific problems for which a stronger form of propagation, often called shaving, is more efficient. Nevertheless, in many cases. shaving at each node of the search tree is not worth doing: arc-consistency filtering is much faster, and the additional domain reductions inferred by shaving do not pay off. In this paper, we propose a new kind of shaving called QuickShaving, which is guided by the search. As QuickShaving may infer some additional domain reductions compared with arc-consistency, it can improve the search for a solution by an exponential ratio. Moreover, the advantage of Quick Shaving is that in practice, unlike a standard form of shaving, the additional domain reductions deduced by QuickShaving come at a very low overhead compared with arc-consistency.
The Case for Ubiquitous Transport-Level Encryption

Today, Internet traffic is encrypted only when deemed necessary. Yet modern CPUs could feasibly encrypt most traffic. Moreover, the cost of doing so will only drop over time. Tcpcrypt is a TCP extension designed to make end-to-end encryption of TCP traffic the default, not the exception. To facilitate adoption tcpcrypt provides backwards compatibility with legacy TCP stacks and middle-boxes. Because it is implemented in the transport layer, it protects legacy applications. However, it also provides a hook for integration with application-layer authentication, largely obviating the need for applications to encrypt their own network traffic and minimizing the need for duplication of functionality. Finally, tcpcrypt minimizes the cost of key negotiation on servers; a server using tcpcrypt can accept connections at 36 times the rate achieved using SSL.
The Y2 Stellar Evolutionary Tracks

We present a database of the latest stellar models of the Y2 (Yonsei-Yale) collaboration. This database contains the stellar evolutionary tracks from the pre-main-sequence birthline to the helium core flash that were used to construct the Y2 isochrones. We also provide a simple interpolation routine that generates stellar tracks for given sets of parameters (metallicity, mass, and α-enhancement).
Anti‐inflammatory effect of interleukin‐10 on human neutrophil respiratory burst involves inhibition of GM‐CSF‐induced p47PHOX phosphorylation through a decrease in ERK1/2 activity

Interleukin‐10 (IL‐10) exerts its anti‐inflammatory properties by down‐regulating polymorphonuclear neutrophil (PMN) functions such as reactive oxygen species (ROS) production via NADPH oxidase. The molecular mechanisms underlying this process are unclear. Partial phosphorylation of the NADPH oxidase cytosolic component p47PHOX induced by proinflammatory cytokines, such as granulocyte‐macrophage colony stimulating factor (GM‐CSF) and tumor necrosis factor (TNF)‐α, is essential for priming ROS production by PMN. The aim of this study was to determine whether IL‐10 inhibits GM‐CSF‐ and TNFα‐induced p47PHOX phosphorylation and to investigate the molecular mechanisms involved in this effect. We found that IL‐10 selectively inhibited GM‐CSF‐ but not TNFα‐induced p47PHOX phosphorylation in a concentration‐dependent manner. As GM‐CSF‐induced p47PHOX phosphorylation is mediated by extracellular signalregulated kinase 1/2 (ERK1/2), we tested the effect of IL‐10 on this pathway. We found that IL‐10 inhibited GM‐CSF‐induced ERK1/2 activity in an immunocomplex kinase assay. This inhibitory effect was confirmed by analyzing the phosphorylation status of the endogenous substrate of ERK1/2, p90RSK, in intact PMN. Furthermore, IL‐10 decreased ROS production by adherent GM‐CSF‐treated PMN in keeping with the higher ROS production observed in whole blood from IL‐10 knockout mice compared to their wild‐type counterparts. Together, these results suggest that IL‐10 inhibits GM‐CSF‐induced priming of ROS production by inhibiting p47PHOX phosphorylation through a decrease in ERK1/2 activity. This IL‐10 effect could contribute to the tight regulation of NADPH oxidase activity at the inflammatory site. —Dang, P.M. , Elbim, C, Marie, J.‐C. , Chiandotto, M. , Gougerot‐Pocidalo, M. ‐A. , El‐Benna, J. Anti‐inflammatory effect of interleukin‐10 on human neutrophil respiratory burst involves inhibition of GM‐CSF‐induced p47PHOX phosphorylation through a decrease in ERK1/2 activity. FASEB J. 20, E698–E709 (2006)
On a multiplicative update dual optimization algorithm for constrained linear MPC

We discuss a multiplicative update quadratic programming algorithm with applications to model predictive control for constrained linear systems. The algorithm, named PQP, is very simple to implement and thus verify, does not require projection, offers a linear rate of convergence, and can be completely parallelized. The PQP algorithm is equipped with conditions that guarantee the desired bound on suboptimality and with an acceleration step based on projection-free line search. We also show how PQP can take advantage of the parametric structure of the MPC problem, thus moving offline several calculations and avoiding large input/output dataflows. The algorithm is evaluated on two benchmark problems, where it is shown to compete with, and possibly outperform, other open source and commercial packages.
A Functional Geometry of fMRI BOLD Signal Interactions

We propose a method that captures the global patterns of functional connectivity [1] in the brain from a set of blood oxygenation level dependent (BOLD) signals in functional magnetic resonance imaging (fMRI) data acquired during a certain condition or task. The method represents the connectivity as a diffusion process on all points in the brain. By embedding the corresponding BOLD signals, into a diffusion map, or a so-called functional geometry we capture all pairwise relations of the signal in a Euclidean geometry. In this geometry proximity signifies close functional connectivity. The embedding captures relations on multiple scales, which are parameterized by the diffusion time. It establishes a space, wherein the roles of individual brain regions, their interaction with others, and the change and dynamics due to different conditions can be studied. The approach provides an explorative tool for the analysis of global interaction patterns in the brain, their correspondence with specific tasks, and their descriptiveness with regard to clinically relevant questions. The work is in the same vein as [2] where diffusion maps were used to perform dimensionality reduction by parameterizing entire brain states, to represent relations between brains. In Shen et al. [3] they were used to segment activated regions. In [4] initial density measurements of pre-chosen regions in functional geometries were related to certain tasks. In this work we present complete results of a method that detects regions of interest and measures their mutual relations, and relates them to clinically relevant variables. In the following we outline the method, present results, that indicate that the functional geometry is able to repeatably capture differences between subject sets. We conclude with a discussion of the method and its relation to existing approaches.
Discrimination Aware Data Mining in Internet of Things (IoT)

IoT is a technology where objects around us will be able to connect to each other and communicate via Internet. Recently, IoT has become an important technology in the world of Internet. However, it comes across problem of big data and extracting knowledge from such data using data mining techniques. Recently, it is observed that data mining increases the risk of violation of fundamental human right, called non-discrimination. It is obvious that data mining tasks in IoT will also face the risk of discrimination. Discrimination Aware Data Mining is an area which deals with finding methods to discover and/or prevent discrimination. This paper is the first step towards finding discrimination related issues in IoT. This paper describes discrimination discovery and prevention issues faced by IoT. This paper also specifies the huge future research avenue related to discrimination aware data mining in IoT.
Optimal pruning of feedforward neural networks based upon the Schmidt procedure

A common way of designing feedforward networks is to obtain a large network and then to prune less useful hidden units. Here, two non-heuristic pruning algorithms are derived from the Schmidt procedure. In both, orthonormal systems of basis functions are found, ordered, pruned, and mapped back to the original network. In the first algorithm, the orthonormal basis functions are found and ordered one at a time. In optimal pruning, the best subset of orthonormal basis functions is found for each size network. Simulation results are shown.
Accelerating the transition to low-carbon cities : Strategies for scaling-up local initiatives

As centres of energy consumption and innovation, cities can play an important role in combatting climate change. In cities worldwide, low-carbon initiatives are realized by pioneers that demonstrate that climate mitigation can be reconciled with urban development needs. Examples incude the creation of eco-district or large-scale retrofitting of building blocks. In particular initiatives that focus on energy conservation in the built environment can greatly contribute to the decarbonization of cities, as the building stock is responsible for approximately 40% of energy consumption and corresponding GHG emissions. While scholars and practitioners reflect on the need to scale-up such initiatives in order to accomplish low-carbon societal change, it is unclear how such processes can be governed. This dissertation offers theoretical guidance and practical instructions to scholars and practitioners that can be used to promote the scaling-up of local initiatives, thereby accelerating the transition to low-carbon cities.
Predicting Prevalence of Influenza-Like Illness From Geo-Tagged Tweets

Modeling disease spread and distribution using social media data has become an increasingly popular research area. While Twitter data has recently been investigated for estimating disease spread, the extent to which it is representative of disease spread and distribution in a macro perspective is still an open question. In this paper, we focus on macro-scale modeling of influenza-like illnesses (ILI) using a large dataset containing 8,961,932 tweets from Australia collected in 2015. We first propose modifications of the state-of-the-art ILI-related tweet detection approaches to acquire a more refined dataset. We normalize the number of detected ILI-related tweets with Internet access and Twitter penetration rates in each state. Then, we establish a state-level linear regression model between the number of ILI-related tweets and the number of real influenza notifications. The Pearson correlation coefficient of the model is 0.93. Our results indicate that: 1) a strong positive linear correlation exists between the number of ILI-related tweets and the number of recorded influenza notifications at state scale; 2) Twitter data has promising ability in helping detect influenza outbreaks; 3) taking into account the population, Internet access and Twitter penetration rates in each state enhances the prevalence modeling analysis.
A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications.
Reducing the overhead of dynamic compilation

The execution model for mobile, dynamically‐linked, object‐oriented programs has evolved from fast interpretation to a mix of interpreted and dynamically compiled execution. The primary motivation for dynamic compilation is that compiled code executes significantly faster than interpreted code. However, dynamic compilation, which is performed while the application is running, introduces execution delay. In this paper we present two dynamic compilation techniques that enable high performance execution while reducing the effect of this compilation overhead. These techniques can be classified as (1) decreasing the amount of compilation performed, and (2) overlapping compilation with execution.
Sensor Swarm query filtering: Heightened attack resilience for broadcast on-demand services

In broadcast systems a central server collects client queries for a given set of data items and schedules the next broadcast accordingly. Since anonymity has been shown to promote user participation, the client queries do not contain a client ID. However, this choice leaves ample room for service misuse. This paper identifies a number of prospective attacks and quantifies their impact on the performance of the system. A novel query filtering mechanism named Sensor Swarm is introduced to tackle these attacks. Extensive simulations show that the proposed scheme can ensure smooth system operation even under heavy attacks. Thus, a broadcast system that employs the Sensor Swarm filtering scheme can sustain excessive client misbehavior before resolving to Turing tests or compromising user anonymity.
An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques

Reliably distinguishing patients with verbal impairment due to brain damage, e.g. aphasia, cognitive communication disorder (CCD), from healthy subjects is an important challenge in clinical practice. A widely-used method is the application of word generation tasks, using the number of correct responses as a performance measure. Though clinically well-established, its analytical and explanatory power is limited. In this paper, we explore whether additional features extracted from task performance can be used to distinguish healthy subjects from aphasics or CCD patients. We considered temporal, lexical, and sublexical features and used machine learning techniques to obtain a model that minimizes the empirical risk of classifying participants incorrectly. Depending on the type of word generation task considered, the exploitation of features with state-of-the-art machine learning techniques outperformed the predictive accuracy of the clinical standard method (number of correct responses). Our analyses confirmed that number of correct responses is an adequate measure for distinguishing aphasics from healthy subjects. However, our additional features outperformed the traditional clinical measure in distinguishing patients with CCD from healthy subjects: The best classification performance was achieved by excluding number of correct responses. Overall, our work contributes to the challenging goal of distinguishing patients with verbal impairments from healthy subjects.
An Automatic Digital Audio Authentication/Forensics System

With the continuous rise in ingenious forgery, a wide range of digital audio authentication applications are emerging as a preventive and detective control in real-world circumstances, such as forged evidence, breach of copyright protection, and unauthorized data access. To investigate and verify, this paper presents a novel automatic authentication system that differentiates between the forged and original audio. The design philosophy of the proposed system is primarily based on three psychoacoustic principles of hearing, which are implemented to simulate the human sound perception system. Moreover, the proposed system is able to classify between the audio of different environments recorded with the same microphone. To authenticate the audio and environment classification, the computed features based on the psychoacoustic principles of hearing are dangled to the Gaussian mixture model to make automatic decisions. It is worth mentioning that the proposed system authenticates an unknown speaker irrespective of the audio content i.e., independent of narrator and text. To evaluate the performance of the proposed system, audios in multi-environments are forged in such a way that a human cannot recognize them. Subjective evaluation by three human evaluators is performed to verify the quality of the generated forged audio. The proposed system provides a classification accuracy of 99.2% ± 2.6. Furthermore, the obtained accuracy for the other scenarios, such as text-dependent and text-independent audio authentication, is 100% by using the proposed system.