Miracle-dz/UniFAR-FTUs huggingface dataset:
Housing and Support Narratives of People Experiencing Mental Health Issues: Making My Place, My Home

Background: Choice, control, privacy, and security are widely reported housing preferences of mental health consumers, are associated with improved well-being and greater housing satisfaction, and are important for recovery. This paper describes housing and neighborhood experiences from a larger qualitative study that sought to learn with people experiencing mental health issues about their everyday lives in an Australian urban community. Methods: A participatory approach to health research informed this study. A participatory reference group, including four people with consumer perspective knowledge and experience of mental health issues and four mental health practitioners with service provider and researcher perspectives, worked together to design and implement this study over a 4-year period. Thirty-nine participants were recruited, including 18 women and 21 men living in metropolitan Melbourne and receiving community mental health care for ongoing mental health issues related mainly to either psychotic or affective disorders. Participants each took part in one to three interviews or a focus group. The data were transcribed and analyzed using narrative and thematic analytic strategies, underpinned by reflective discussions with the participatory reference group. Findings: Participants’ experiences of their housing and neighborhoods emphasized qualities that either contributed to or challenged their sense of being “at home. ” Identifying with a place as home was transformative, especially when supported by friendly neighborhood interactions, safety, and accessibility of local amenities. Unsatisfactory housing situations and limited income worked against participants’ efforts to regain a sense of well-being and improve their situations. When being home was challenging, strategies used to counteract this included getting a pet and getting out as a means of resisting isolation at home. Differing views and ways of using the available support workers were described, suggesting tensions between seeking to be self-sufficient and valuing support. Conclusions: Social housing locations and housing-related support should explicitly attend to safety and security concerns. Collaborative care planning and outreach support should attend to supports for navigating issues with neighbors, housing, harnessing natural supports, and opportunities for being in others’ company, as well as recognizing the importance of pets in people’s lives. Understanding the strategies that mental health consumers find helpful in creating a sense of being at home, and the role of “place” in recovery merit further consideration in research and practice.
A Unique Benthic Microbial Community Underlying the Phaeocystis antarctica-Dominated Amundsen Sea Polynya, Antarctica: A Proxy for Assessing the Impact of Global Changes

Polynyas in the polar seas are regarded as windows through which ecosystem responses associated with global climate changes are to be noticed. However, little information is available on benthic microbial communities in the Amundsen Sea polynya (ASP), where environmental changes due to global warming are occurring rapidly, from which future climate change-induced ecosystem responses could be assessed. We performed high-throughput sequencing of 16S rRNA genes and quantitative PCR in combination with biogeochemical analyses and metabolic rate measurements to determine the composition, diversity and controls of major microbial communities in sediments of the ASP. A large fraction of the sequenced benthic microbial community (40% on average) in the polynya was uniquely affiliated with the phylum Planctomycetes, whereas Thaumarchaeota (51% on average) predominated in non-polynya areas. The relative abundance of Planctomycetes correlated significantly with organic carbon (Corg) content in the polynya sediment underlying the Phaeocystis-dominated water column. These results suggest that Planctomycetes comprise a major bacterial group utilizing relatively recalcitrant Corg produced primarily by Phaeocystis blooms. In contrast, the predominance of chemolithoautotrohic Thaumarchaeota in the sea-ice zone was attributed to low Corg supply due to low primary productivity in the ice-covered water column. The Planctomycetes-dominated microbial communities in the ASP is in stark contrast to that Proteobacteria (Delta- and Gamma-proteobacteria) occupy ecological niches as primary mineralizers of organic materials in most benthic systems in the Southern Ocean, where organic materials in the sediments mostly originate from diatom blooms. Given that microbial communities respond quickly to environmental changes, and that global climate change is proceeding rapidly in the ASP, our results suggest that any modifications in the Planctomycetes-dominated microbial communities will provide valuable insight into changes in organic matter transport to the seafloor that may result from shifts in planktonic communities from Phaeocystis to diatoms associated with climate change.
Biological Determinants of Chemo-Radiotherapy Response in HPV-Negative Head and Neck Cancer: A Multicentric External Validation

Purpose: Tumor markers that are related to hypoxia, proliferation, DNA damage repair and stem cell-ness, have a prognostic value in advanced stage HNSCC patients when assessed individually. Here we aimed to evaluate and validate this in a multifactorial context and assess interrelation and the combined role of these biological factors in determining chemo-radiotherapy response in HPV-negative advanced HNSCC. Methods: RNA sequencing data of pre-treatment biopsy material from 197 HPV-negative advanced stage HNSCC patients treated with definitive chemoradiotherapy was analyzed. Biological parameter scores were assigned to patient samples using previously generated and described gene expression signatures. Locoregional control rates were used to assess the role of these biological parameters in radiation response and compared to distant metastasis data. Biological factors were ranked according to their clinical impact using bootstrapping methods and multivariate Cox regression analyses that included clinical variables. Multivariate Cox regression analyses comprising all biological variables were used to define their relative role among all factors when combined. Results: Only few biomarker scores correlate with each other, underscoring their independence. The different biological factors do not correlate or cluster, except for the two stem cell markers CD44 and SLC3A2 (r = 0.4, p < 0.001) and acute hypoxia prediction scores which correlated with T-cell infiltration score, CD8+ T cell abundance and proliferation scores (r = 0.52, 0.56, and 0.6, respectively with p < 0.001). Locoregional control association analyses revealed that chronic (Hazard Ratio (HR) = 3.9) and acute hypoxia (HR = 1.9), followed by stem cell-ness (CD44/SLC3A2; HR = 2.2/2.3), were the strongest and most robust determinants of radiation response. Furthermore, multivariable analysis, considering other biological and clinical factors, reveal a significant role for EGFR expression (HR = 2.9, p < 0.05) and T-cell infiltration (CD8+T-cells: HR = 2.2, p < 0.05; CD8+T-cells/Treg: HR = 2.6, p < 0.01) signatures in locoregional control of chemoradiotherapy-treated HNSCC. Conclusion: Tumor acute and chronic hypoxia, stem cell-ness, and CD8+ T-cell parameters are relevant and largely independent biological factors that together contribute to locoregional control. The combined analyses illustrate the additive value of multifactorial analyses and support a role for EGFR expression analysis and immune cell markers in addition to previously validated biomarkers. This external validation underscores the relevance of biological factors in determining chemoradiotherapy outcome in HNSCC.
Robot-Supported Collaborative Learning (RSCL): Social Robots as Teaching Assistants for Higher Education Small Group Facilitation

Acknowledging the benefits of active learning and the importance of collaboration skills, the higher education system has started to transform toward utilization of group activities into lecture hall culture. In this study, a novel interaction has been introduced, wherein a social robot facilitated a small collaborative group activity of students in higher education. Thirty-six students completed a 3 h activity that covered the main content of a course in Human Computer Interaction. In this within-subject study, the students worked in groups of four on three activities, moving between three conditions: instructor facilitation of several groups using pen and paper for the activity; tablets facilitation, also used for the activity; and robot facilitation, using tablets for the activity. The robot facilitated the activity by introducing the different tasks, ensuring proper time management, and encouraging discussion among the students. This study examined the effects of facilitation type on attitudes toward the activity facilitation, the group activity, and the robot, using quantitative, and qualitative measures. Overall students perceived the robot positively, as friendly and responsive, even though the robot did not directly respond to the students' verbal communications. While most survey items did not convey significant differences between the robot, tablet, or instructor, we found significant correlations between perceptions of the robot, and attitudes toward the activity facilitation, and the group activity. Qualitative data revealed the drawbacks and benefits of the robot, as well as its relative perceived advantages over a human facilitator, such as better time management, objectivity, and efficiency. These results suggest that the robot's complementary characteristics enable a higher quality learning environment, that corresponds with students' requirements and that a Robot Supportive Collaborative Learning (RSCL) is a promising novel paradigm for higher education.
Character-Level Quantum Mechanical Approach for a Neural Language Model

This article proposes a character-level neural language model (NLM) that is based on quantum theory. The input of the model is the character-level coding represented by the quantum semantic space model. Our model integrates a convolutional neural network (CNN) that is based on network-in-network (NIN). We assessed the effectiveness of our model through extensive experiments based on the English-language Penn Treebank dataset. The experiments results confirm that the quantum semantic inputs work well for the language models. For example, the PPL of our model is 10%–30% less than the states of the arts, while it keeps the relatively smaller number of parameters (i.e., 6 m).
Reinforcement learning for Dialogue Systems optimization with user adaptation. (Apprentissage par Renforcement pour l'optimisation des systèmes de dialogue via l'adaptation à l'utilisateur)

The most powerful artificial intelligence systems are now based on learned statistical models. In order to build efficient models, these systems must collect a huge amount of data on their environment. Personal assistants, smart-homes, voice-servers and other dialogue applications are no exceptions to this statement. A specificity of those systems is that they are designed to interact with humans, and as a consequence, their training data has to be collected from interactions with these humans. As the number of interactions with a single person is often too scarce to train a proper model, the usual approach to maximise the amount of data consists in mixing data collected with different users into a single corpus. However, one limitation of this approach is that, by construction, the trained models are only efficient with an "average" human and do not include any sort of adaptation; this lack of adaptation makes the service unusable for some specific group of persons and leads to a restricted customers base and inclusiveness problems. This thesis proposes solutions to construct Dialogue Systems that are robust to this problem by combining Transfer Learning and Reinforcement Learning. It explores two main ideas: The first idea of this thesis consists in incorporating adaptation in the very first dialogues with a new user. To that extend, we use the knowledge gathered with previous users. But how to scale such systems with a growing database of user interactions? The first proposed approach involves clustering of Dialogue Systems (tailored for their respective user) based on their behaviours. We demonstrated through handcrafted and real user-models experiments how this method improves the dialogue quality for new and unknown users. The second approach extends the Deep Q-learning algorithm with a continuous transfer process. The second idea states that before using a dedicated Dialogue System, the first in- teractions with a user should be handled carefully by a safe Dialogue System common to all users. The underlying approach is divided in two steps. The first step consists in learning a safe strategy through Reinforcement Learning. To that extent, we introduced a budgeted Reinforcement Learning framework for continuous state space and the underlying extensions of classic Reinforcement Learning algorithms. In particular, the safe version of the Fitted-Q algorithm has been validated, in term of safety and efficiency, on a dialogue system tasks and an autonomous driving problem. The second step consists in using those safe strategies when facing new users; this method is an extension of the classic e-greedy algorithm.
Exact Solutions for a Class of Wick-Type Stochastic (3+1)-Dimensional Modified Benjamin-Bona-Mahony Equations

In this paper, we investigate the Wick-type stochastic (3+1)-dimensional modified Benjamin–Bona–Mahony (BBM) equations. We present a generalised version of the modified tanh–coth method. Using the generalised, modified tanh–coth method, white noise theory, and Hermite transform, we produce a new set of exact travelling wave solutions for the (3+1)-dimensional modified BBM equations. This set includes solutions of exponential, hyperbolic, and trigonometric types. With the help of inverse Hermite transform, we obtained stochastic travelling wave solutions for the Wick-type stochastic (3+1)-dimensional modified BBM equations. Eventually, by application example, we show how the stochastic solutions can be given as white noise functional solutions.
Using Social Group Trajectories for Potential Impersonation Detection on Smart Buildings Access Control

In many domains, applications use people trajectories' spatiotemporal data for impersonation fraud detection purposes. Anomaly-based approaches consist in constructing mobility profiles based on users' frequent paths and schedules and comparing new trajectories against these profiles: if some new trajectory is not consistent with the user profile, a potential impersonation is detected. Previous studies, however, do not include traveling companions in users' profiles, although performing activities in social groups is inherent to human behavior. Physical access control systems on smart buildings can provide activity companions information since social groups naturally emerge on organizations hosted in such buildings and these systems can capture group trajectories. This paper explores the feasibility of using spatiotemporal mobility profiles enriched with group trajectory pattern data as a novel framework for impersonation fraud detection in smart buildings. An empirical analysis is conducted, and results show that it is feasible to add companions activities information to mobility profiles in order to enhance anomaly-based impersonation attack detection.
Contribution à la décomposition de données multimodales avec des applications en apprentisage de dictionnaires et la décomposition de tenseurs de grande taille. (Contribution to multimodal data processing with applications to dictionary learning and large-scale decomposition)

Dans ce travail, on s'interesse a des outils mathematiques speciaux appeles tenseurs qui sont formellement definis comme des tableaux multidimensionnels definis sur le produit tensoriel d'espaces vectoriels (chaque espace vectoriel etant muni de son systeme de coordonnees), le nombre d'espaces vectoriels impliques dans ce produit etant l'ordre du tenseur. L'interet pour les tenseurs est motive par certains travaux experimentaux qui ont prouve, dans divers contextes, que traiter des donnees multidimensionnelles avec des tenseurs plutot que des matrices donne un meilleur resultat aussi bien pour des tâches de regression que de classification. Dans le cadre de la these, nous nous sommes focalises sur une decomposition dite de Tucker et avons mis en place une methode pour l'apprentissage de dictionnaires, une technique pour l'apprentissage en ligne de dictionnaires, une approche pour la decomposition d'un tenseur de grandes tailles et enfin une methodologie pour la decomposition d'un tenseur qui croit par rapport a tous les modes. De nouveaux resultats theoriques concernant la convergence et la vitesse de convergence sont etablis et l'efficacite des algorithmes proposes, reposant soit sur la minimisation alternee, soit sur la descente de gradients par coordonnees, est demontree sur des problemes reels
Recurrent Neural Network Based on Statistical Recurrent Unit for Remaining Useful Life Estimation

In industry, efficient predictive maintenance tools can reduce maintenance costs and increase the safety and reliability of the monitored equipment, since they can anticipate equipment failures. In particular, making the efficient Remaining Useful Life (RUL) estimation of machinery is important to lead to appropriate maintenance actions. Traditional RUL approaches depend on prior knowledge of the equipment degradation process to predict RUL. However, in most cases, the accurate physical or expert models are not available. Following that, this paper proposes a Recurrent Neural Network (RNN) model architecture based on Statistical Recurrent Unit (SRU) for RUL estimation. The proposed architecture is able to extract hidden partners from multivariate time series sensor data with multiple operation condition faults and degradation. SRU outperforms other complex deep learning methods, since it obtains a multitude of past viewpoints by linear combinations of few averages. The proposed model is compared to state-of-the-art RUL approaches. Experimental results, using a turbofan aero engine data set, reveal that the proposed architecture outperforms state-of-the-art RUL approaches in most tests.
A Statistical Approach to Provide Explainable Convolutional Neural Network Parameter Optimization

Algorithms based on convolutional neural networks (CNNs) have been great attention in image processing due to their ability to find patterns and recognize objects in a wide range of scientific and industrial applications. Finding the best network and optimizing its hyperparameters for a specific application are central challenges for CNNs. Most state-of-the-art CNNs are manually designed, while techniques for automatically finding the best architecture and hyperparameters are computationally intensive, and hence, there is a need to severely limit their search space. This paper proposes a fast statistical method for CNN parameter optimization, which can be applied in many CNN applications and provides more explainable results. The authors specifically applied Taguchi based experimental designs for network optimization in a basic network, a simplified Inception network and a simplified Resnet network, and conducted a comparison analysis to assess their respective performance and then to select the hyperparameters and networks that facilitate faster training and provide better accuracy. The results show that up to a 6% increase in classification accuracy can be achieved after parameter optimization.
CC2: Defending Hybrid Worm on Mobile Networks with Two-Dimensional Circulation Control

As the hybrid worm can propagate by both personal social interactions and wireless communications, it has been identified as one of the most severe threats to the mobile Internet. This problem is expected to become worse with the boom of social applications and mobile services. In this work, we study the propagation dynamics of hybrid worms and propose a systematic countermeasure. The system maintains a set of community structure which describes the high-speed infection zone of worms and contains worm propagation by distributing the worm signature to the guard nodes selected from the periphery of each community. For those nodes that are geographically close but located in different communities , we evaluate the communication security between them based on the observed infection history and limit communications between insecure ones to avoid the worm spreading across communities. We also design an efficient worm signature forwarding strategy that enables most nodes in the network to reach an immune state before being infected by the worm. Extensive real-trace driven simulations verify the feasibility and effectiveness of the proposed methods.
Resource management in computer clusters : algorithm design and performance analysis. (Gestion des ressources dans les grappes d'ordinateurs : conception d'algorithmes et analyse de performance)

The growing demand for cloud-based services encourages operators to maximize resource efficiency within computer clusters. This motivates the development of new technologies that make resource management more flexible. However, exploiting this flexibility to reduce the number of computers also requires efficient resource-management algorithms that have a predictable performance under stochastic demand. In this thesis, we design and analyze such algorithms using the framework of queueing theory.Our abstraction of the problem is a multi-server queue with several customer classes. Servers have heterogeneous capacities and the customers of each class enter the queue according to an independent Poisson process. Each customer can be processed in parallel by several servers, depending on compatibility constraints described by a bipartite graph between classes and servers, and each server applies first-come-first-served policy to its compatible customers. We first prove that, if the service requirements are independent and exponentially distributed with unit mean, this simple policy yields the same average performance as balanced fairness, an extension to processor-sharing known to be insensitive to the distribution of the service requirements. A more general form of this result, relating order-independent queues to Whittle networks, is also proved. Lastly, we derive new formulas to compute performance metrics.These theoretical results are then put into practice. We first propose a scheduling algorithm that extends the principle of round-robin to a cluster where each incoming job is assigned to a pool of computers by which it can subsequently be processed in parallel. Our second proposal is a load-balancing algorithm based on tokens for clusters where jobs have assignment constraints. Both algorithms are approximately insensitive to the job size distribution and adapt dynamically to demand. Their performance can be predicted by applying the formulas derived for the multi-server queue.