Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-term temperature evolution of neutron stars undergoing episodic accretion outbursts

Context. Transient neutron star low-mass X-ray binaries undergo episodes of accretion, alternated with quiescent periods. During an accretion outburst, the neutron star heats up due to exothermic accretion-induced processes taking place in the crust. Besides the long-known deep crustal heating of nuclear origin, a likely non-nuclear source of heat, dubbed “shallow heating”, is present at lower densities. Most of the accretion-induced heat slowly diffuses into the core on a timescale of years. Over many outburst cycles, a state of equilibrium is reached when the core temperature is high enough that the heating and cooling (photon and neutrino emission) processes are in balance. Aims. We investigate how stellar characteristics and outburst properties affect the long-term temperature evolution of a transiently accreting neutron star. For the first time the effects of crustal properties are considered, particularly that of shallow heating. Methods. Using our code NSCool, we tracked the thermal evolution of a neutron star undergoing outbursts over a period of 105 yr. The outburst sequence is based on the regular outbursts observed from the neutron star transient Aql X-1. For each model we calculated the timescale over which equilibrium was reached and we present these timescales along with the temperature and luminosity parameters of the equilibrium state. Results. We performed several simulations with scaled outburst accretion rates, to vary the amount of heating over the outburst cycles. The results of these models show that the equilibrium core temperature follows a logarithmic decay function with the equilibrium timescale. Secondly, we find that shallow heating significantly contributes to the equilibrium state. Increasing its strength raises the equilibrium core temperature. We find that if deep crustal heating is replaced by shallow heating alone, the core would still heat up, reaching only a 2% lower equilibrium core temperature. Deep crustal heating may therefore not be vital to the heating of the core. Additionally, shallow heating can increase the quiescent luminosity to values higher than previously expected. The thermal conductivity in the envelope and crust, including the potentially low-conductivity pasta layer at the bottom of the crust, is unable to significantly alter the long-term internal temperature evolution. Stellar compactness and nucleon pairing in the core change the specific heat and the total neutrino emission rate as a function of temperature, with the consequences for the properties of the equilibrium state depending on the exact details of the assumed pairing models. The presence of direct Urca emission leads to the lowest equilibrium core temperature and the shortest equilibrium timescale.
CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data

We propose a neural network architecture for detecting intrusions on the controller area network (CAN). The latter is the standard communication method between the electronic control units (ECUs) of automobiles. However, CAN lacks security mechanisms and it has recently been shown that it can be attacked remotely. Hence, it is desirable to monitor CAN traffic to detect intrusions. In order to find both, known and unknown intrusion scenarios, we consider a novel unsupervised learning approach which we call CANet. To our knowledge, this is the first deep learning based intrusion detection system (IDS) that naturally handles the data structure of the high dimensional CAN bus, where different message types are sent at different times. Our method is evaluated on real and synthetic CAN data. A comparison with previous machine learning based methods shows that CANet outperforms them by a significant margin. For reproducibility of the method, our synthetic data is publicly available.
Psychiatric recidivism in a psychosocial rehabilitation setting: a survival analysis.

Survival analysis, a methodology employed in studies of survival after diagnosis of a life-threatening physical illness, is used to examine psychiatric recidivism. The probability of survival in the community without rehospitalization was calculated for 400 consecutive admissions to Thresholds, a psychiatric rehabilitation center serving formerly hospitalized patients. It was found that the probability of a client's being rehospitalized at least once within 15 months of Thresholds intake was . 55 and that the probability of rehospitalization varied with sex, race, age, and number of prior hospitalizations. The authors note that early intervention may be indicated for clients at higher risk of rehospitalization. They suggest that survival analysis be used in studies of psychiatric recidivism because it permits direct statistical comparisons between studies.
Thermalization/Relaxation in integrable and free field theories: an Operator Thermalization Hypothesis

Free or integrable theories are usually considered to be too constrained to thermalize. For example, the retarded two-point function of a free field, even in a thermal state, does not decay to zero at long times. On the other hand, the magnetic susceptibility of the critical transverse field Ising is known to thermalize, even though that theory can be mapped by a Jordan-Wigner transformation to that of free fermions. We reconcile these two statements by clarifying under which conditions conserved charges can prevent relaxation at the level of linear response and how such obstruction can be overcome. In particular, we give a necessary condition for the decay of retarded Green's functions. We give explicit examples of composite operators in free theories that nevertheless satisfy that condition and therefore do thermalize. We call this phenomenon the Operator Thermalization Hypothesis as a converse to the Eigenstate Thermalization Hypothesis.
Generating Education in-Game Data: The Case of an Ancient Theatre Serious Game

Learning Analytics have become an indispensable element of education, as digital mediums are increasingly used within formal and informal education. Integrating specifications for learning analytics in non-traditional educational mediums, such as serious games, has not yet reached the level of development necessary to fulfil their potential. Though much research has been conducted on the issue of managing and extracting value from learning analytics, the importance of specifications, methods and decisions for the initial creation of such data has been somewhat overlooked. To this end, we have developed a custom library that implements the Experience API specification within the Unity 3D game engine. In this paper, we present this library, as well as a representative scenario illustrating the procedure of generating and recording data. Through this work we aim to expand the reach of learning analytics into serious games, facilitate the generation of such data in commercially popular development tools and identify significant events, with educational value, to be
U.S. federally funded television public service announcements (PSAs) to prevent HIV/AIDS: a content analysis.

This article reports a content analysis of 56 English-language public service announcements (PSAs) for HIV/AIDS prevention produced since 1987 by the U.S. federal government for television broadcast. These PSAs do not lead target audiences through a logical sequence from awareness to motivation, skill building, and maintenance. The PSAs underutilize a strategy of "strategic ambiguity" to craft individual PSAs that can address the needs of and appeal to multiple target audiences, thereby directing information not only to heterosexuals, the primary target of these PSAs, but also to homosexuals and bisexuals. The PSAs largely ignore issues related to injection drug use and needle sharing. What drug-related portrayals there are focus on African American street junkies, which perpetuates racial stereotypes and fails to address occasional injection drug use. The PSAs exploit fear of HIV/AIDS to discourage drug use but do not offer drug treatment or counseling information. PSAs produced by the Clinton administration to promote condom use do not fully address key reasons why people fail to use condoms: concern about sexual pleasure, embarrassment about obtaining condoms, and lack of skills to negotiate condom use with sexual partners. Implications of these conclusions for the future of U.S. HIV/AIDS prevention are discussed.
Regulation of T cell cytokine production by dendritic cells

Previous work has established that the dendritic cells (DC) of mouse spleen regulate the IL‐2 production, and hence the extent of proliferation, of the CD8 T cells they activate. It is now reported here that interaction of primary CD8 T cells with splenic CD8α DC induced much higher production of IL‐3, IFN‐γ and granulocyte‐macrophage colony‐stimulating factor (GM‐CSF), as well as IL‐2, than did interaction with CD8α+ splenic DC. Furthermore, the CD8α DC also induced higher levels of IL‐2, IL‐3 and IL‐10 production in primary CD4 T cells, compared with that induced by CD8α+ DC. These quantitative differences did not involve qualitative shifts in the type of cytokine produced. Interleukin‐4 production remained low in all the primary T cell cultures and restimulation experiments in secondary cultures did not reveal any bias in the cytokine production profile. When exogenous IL‐2 was added to the primary cultures to ensure equal proliferation in response to CD8α or CD8α+ DC, the higher level of production of IL‐3, IFN‐γ and GM‐CSF induced by CD8α DC was maintained. Thus, this general control of T cell cytokine production by splenic DC involves factors additional to those that govern activation of T cells into cell cycle.
Systematic Review of Bibliography on Social Interactions using the Meta-analytical Approach

The general objective of this systematic review was to evaluate the evolution of studies on social interactions up to 2018, in addition to proposing a diagnostic tool on social interactions, sensors used, among others. The methodology of the research was the bibliographic research of an exploratory nature, using the data collection, it was possible to see that there is a significant growth of articles on this subject. The analysis identified different countries that conducted research on this subject and the most cited articles with their authors. Different jobs were found, such as care for the elderly, interactions in vehicular networks, social interactions in public environments, among others.
Quantum Distributed Algorithm for the All-Pairs Shortest Path Problem in the CONGEST-CLIQUE Model

The All-Pairs Shortest Path problem (APSP) is one of the most central problems in distributed computation. In the CONGEST-CLIQUE model, in which n nodes communicate with each other over a fully connected network by exchanging messages of O(łog n) bits in synchronous rounds, the best known general algorithm for APSP uses Õ(n1/3) rounds. Breaking this barrier is a fundamental challenge in distributed graph algorithms. In this paper we investigate for the first time quantum distributed algorithms in the CONGEST-CLIQUE model, where nodes can exchange messages of O(log n) quantum bits, and show that this barrier can be broken: we construct a Õ(n1/4)-round quantum distributed algorithm for the APSP over directed graphs with polynomial weights in the CONGEST-CLIQUE model. This speedup in the quantum setting contrasts with the case of the standard CONGEST model, for which Elkin et al. (PODC 2014) showed that quantum communication does not offer significant advantages over classical communication. Our quantum algorithm is based on a relationship discovered by Vassilevska Williams and Williams (JACM 2018) between the APSP and the detection of negative triangles in a graph. The quantum part of our algorithm exploits the framework for quantum distributed search recently developed by Le Gall and Magniez (PODC 2018). Our main technical contribution is a method showing how to implement multiple quantum searches (one for each edge in the graph) in parallel without introducing congestions.
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation: An Application to Hate-Speech Detection

Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal texts can easily lead to (un)intentional privacy violations. We propose the first privacy-preserving solution for text classification that is provably secure. Our method, which is based on Secure Multiparty Computation (SMC), encompasses both feature extraction from texts, and subsequent classification with logistic regression and tree ensembles. We prove that when using our secure text classification method, the application does not learn anything about the text, and the author of the text does not learn anything about the text classification model used by the application beyond what is given by the classification result itself. We perform end-to-end experiments with an application for detecting hate speech against women and immigrants, demonstrating excellent runtime results without loss of accuracy.
An Extended Data Object-driven Approach to Data Quality Evaluation: Contextual Data Quality Analysis

This research is an extension of a data object-driven approach to data quality evaluation allowing to analyse data object quality in scope of multiple data objects. Previously presented approach was used to analyse one particular data object, mainly focusing on syntactic analysis. It means that the primary data object quality can be analysed against secondary data objects of unlimited number. This opportunity allows making more comprehensive, in-depth contextual data object analysis. The given analysis was applied to open data sets, making comparison between previously obtained results and results of application of the extended approach, underlying importance and benefits of the given extension.
The Navier--Stokes equations in exterior Lipschitz domains: $\mathrm{L}^p$-theory

We show that the Stokes operator defined on $\mathrm{L}^p_{\sigma} (\Omega)$ for an exterior Lipschitz domain $\Omega \subset \mathbb{R}^n$ $(n \geq 3)$ admits maximal regularity provided that $p$ satisfies $| 1/p - 1/2| 0$. In particular, we prove that the negative of the Stokes operator generates a bounded analytic semigroup on $\mathrm{L}^p_\sigma (\Omega)$ for such $p$. In addition, $\mathrm{L}^p$-$\mathrm{L}^q$-mapping properties of the Stokes semigroup and its gradient with optimal decay estimates are obtained. This enables us to prove the existence of mild solutions to the Navier--Stokes equations in the critical space $\mathrm{L}^{\infty} (0 , T ; \mathrm{L}^3_{\sigma} (\Omega))$ (locally in time and globally in time for small initial data).
Online Consumers' Opinions Analysis for Marketing Strategy Evaluation

With over two billion users having access to social media accounts, people increasingly choose to express themselves online. Electronic word of mouth generates large amounts of data, making it a valuable source for big data analytics. This provides organisations with key capabilities for improved decision-making through mining insights directly from online sources. In this work we gathered and analysed the sentiment of consumers’ tweets regarding the release of two smartphone products. Tweeter data was collected using a custom-made Android application. The research question addressed in this study focused on whether the marketing positioning strategy of the company under investigation was successful after the release of two of its new products. To evaluate this, we compared the product positioning strategy of the firm before and after the release of the product. Consumers’ opinions were analysed to identify possible discrepancies between planned consumers’ reactions and sentiments, as strategized by the company, and how these were altered with the release of the product.
ORF11 Protein Interacts with the ORF9 Essential Tegument Protein in Varicella-Zoster Virus Infection

ABSTRACT The tegument proteins encoded by ORF11 and ORF9 of varicella-zoster virus (VZV) are conserved among all alphaherpesvirus. We previously demonstrated that the ORF9 gene is essential, whereas ORF11 is dispensable in vitro but its deletion severely impairs VZV infection of skin xenografts in the SCID mouse model in vivo. Here we report that ORF11 protein interacts with ORF9 protein in infected cells as well as in the absence of other viral proteins, and we have mapped the ORF11 protein domain involved in their interaction. Although ORF11 is an RNA binding protein, the interaction between ORF11 and ORF9 proteins was not mediated by RNA or DNA bridging. VZV recombinants with mutations preventing ORF11 protein binding to ORF9 protein had no effect on 6-day growth kinetics based on plaque numbers, but plaque sizes were reduced in vitro. However, disruption of the ORF11 and ORF9 protein interaction was associated with failure to replicate in skin xenografts in vivo. Further, we demonstrate that in the absence of their interaction, the ORF9 protein displays an identical cellular localization, accumulating in the trans-Golgi region, whereas the ORF11 protein exhibits aberrant localization, dispersing throughout the cytoplasm. Overall, our observations suggest that while complete tegument assembly may not be necessary for VZV replication in vitro, the interaction between the ORF11 and ORF9 proteins appears to be critical for the proper localization of ORF11 protein to the assembly complex and for production of infectious virus during VZV pathogenesis in skin.
Well Detection in Satellite Images using Convolutional Neural Networks

The Government of India conducts a well census every five years. It is time-consuming, costly, and usually incomplete. By using transfer learning-based object detection algorithms, we have built a system for the automatic detection of wells in satellite images. We analyze the performance of three object detection algorithms - Convolutional Neural Network, HaarCascade, and Histogram of Oriented Gradients on the task of well detection and find that the Convolutional Neural Network based YOLOv2 performs best and forms the core of our system. Our current system has a precision value of 0.95 and a recall value of 0.91 on our dataset. The main contribution of our work is to create a novel open-source system for well detection in satellite images and create an associated dataset which will be put in the public domain. A related contribution is the development of a general purpose satellite image annotation system to annotate and validate objects in satellite images. While our focus is on well detection, the system is general purpose and can be used for detection of other objects as well.
Manipulating Triadic Concept Analysis Contexts through Binary Decision Diagrams

Formal Concept Analysis (FCA) is an approach based on the mathematization and hierarchy of formal concepts. Nowadays, with the increasing of social network for personal and professional usage, more and more applications of data analysis on environments with high dimensionality (Big Data) have been discussed in the literature. Through the Formal Concept Analysis and Triadic Concept Analysis, it is possible to extract database knowledge in a hierarchical and systematized representation. It is common that the data set transforms the extraction of this knowledge into a problem of high computational cost. Therefore, this paper has an objective to evaluate the behavior of the algorithm for extraction triadic concepts using TRIAS in high dimensional contexts. It was used a synthetic generator known as SCGaz (Synthetic Context Generator a-z). After the analysis, it was proposed a representation of triadic contexts using a structure known as Binary Decision
A Framework to Evaluate Architectural Solutions for Ubiquitous Patient Identification in Health Information Systems

The lack of accurate, reliable and consistent patient information is a major issue in healthcare, despite a relatively high Health Information System (HIS) adoption level worldwide. The main reason for this appears to be patient records lacking accurate particulars, including links to associated care programs, disease classification and treatment plans. The causes for this are multiple, including incompatibility of healthcare standards between version releases, inconsistent HIS implementation, lack of effective data input / validation, and the rapid evolution of and absence of a single ‘universal’ technological solution. Sustainable, stable and long-term architectural solutions are required. This research builds on previous work identifying major challenges and root causes of the problem and proposing essential non-functional requirements for HIS architectures. The paper elaborates on non-functional requirements and proposes an evaluation framework (based on a new international standard) that can be used to assess aspiring HIS architectures for long term stability and self-evolution and thus to support strategic decision making from within the evolving HIS.
Quantum beamstrahlung: Prospects for a photon-photon collider.

Le traitement de la radiation dans les collisions impulsion-impulsion appropriees aux collisionneurs d'electron-position de haute energie, est etendu aux processus multiphonique. Le spectre multiphonique resultant est dur. On debat de la possibilite pour un collisonneur photon-photon utilisant cette radiation. On calcule le flux prevu a environ 1 ou 2 ordres de grandeurs superieurs a celui de la source a deux photons virtuels conventionnelle, dependant des parametres de la machine. Les processus photon-electron sont aussi amplifies par cette source a photons durs additionnelle
Exploring Bot Pervasiveness in Global Cities using Publicly Available Volunteered Geographic Information

Effective crisis management and response heavily relies on up-to-date and trustworthy information. Near realtime, volunteered geographic information (VGI) has previously been shown to be instrumental during disaster response by helping direct resources, create communication channels between the affected, etc. Trustworthiness continues to be a challenge when leveraging crowd sourced data, as quality information directly impacts the effectiveness of response. Previous research has demonstrated cloud-based VGI collection, storage, presentation, and bot mitigation using open source technologies and freely available web services. Alas, the technology was deployed as a prototype for small urban areas in the United States. This research explores bot pervasiveness in several global cities that have previously suffered a catastrophic event and/or are at risk for a future crisis event. The existence of non-trustworthy information in social media data has always been a known issue, taking steps to quantify the presence of bots in Twitter data can allow an end-user to more holistically understand their dataset.
An Intelligent Tutoring System for Procedural Training with Natural Language Interaction

In this paper we present a proposal of an Intelligent Tutoring Systems equipped with dialogue in natural language to facilitate student interaction with the learning environment, provide hints and answer students’ questions. This system is designed to be integrated with a 2D/3D virtual environment for procedural training, where it can maintain a dialogue with students adapted to the context. Our notion of context comprises: the specific features of the student; his/her progress in the development of the task; and the virtual environment where it is performed. The dialogue will be controlled by a dialogue manager, built on Watson Assistant, which has been chosen for its versatility. Additionally, we present an application example that describes the operation of the modules that constitute the proposed approach. Then, we provide some indications on how it will be evaluated with students shortly.