Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Control Over Emotion Networks Through Connectivity‐Based Neurofeedback

Abstract Most mental functions are associated with dynamic interactions within functional brain networks. Thus, training individuals to alter functional brain networks might provide novel and powerful means to improve cognitive performance and emotions. Using a novel connectivity‐neurofeedback approach based on functional magnetic resonance imaging (fMRI), we show for the first time that participants can learn to change functional brain networks. Specifically, we taught participants control over a key component of the emotion regulation network, in that they learned to increase top‐down connectivity from the dorsomedial prefrontal cortex, which is involved in cognitive control, onto the amygdala, which is involved in emotion processing. After training, participants successfully self‐regulated the top‐down connectivity between these brain areas even without neurofeedback, and this was associated with concomitant increases in subjective valence ratings of emotional stimuli of the participants. Connectivity‐based neurofeedback goes beyond previous neurofeedback approaches, which were limited to training localized activity within a brain region. It allows to noninvasively and nonpharmacologically change interconnected functional brain networks directly, thereby resulting in specific behavioral changes. Our results demonstrate that connectivity‐based neurofeedback training of emotion regulation networks enhances emotion regulation capabilities. This approach can potentially lead to powerful therapeutic emotion regulation protocols for neuropsychiatric disorders.
Families' experiences of raising concerns in health care services: An interpretative phenomenological analysis.

BACKGROUND This exploratory study aimed to increase understanding of the experiences of families of people with intellectual disabilities when noticing and raising concerns in services. A qualitative design was employed. METHODS Seven participants (all female) were recruited through local and national voluntary agencies; five were mothers of people with intellectual disabilities, one was the aunt and one the sister. Participants took part in semi-structured interviews centred on their experiences of noticing and raising concerns, these were recorded and transcribed. The data were analyzed using interpretative phenomenological analysis (IPA; Smith, 1996). RESULTS The data were grouped into three superordinate themes: the nature and importance of concerns, relationships between familes and staff and the process of raising concerns. A key and surprising finding was the importance of "the little things. " CONCLUSIONS This research highlights important implications for services such as the need to simplify the process of raising concerns, attend to the relationship with families and ensure advocacy services are identified for those without family.
Optimization Techniques for an Ergonomic Human-Robot Interaction. (Techniques d'Optimisation pour une Interaction Humain-Robot Ergonomique)

L’interaction Humain-Robot est un domaine de recherche en pleine expansion parmi la communaute robotique. De par sa nature il reunit des chercheurs venant de domaines varies, tels que psychologie, sociologie et, bien entendu, robotique. Ensemble, ils definissent et dessinent les robots avec lesquels nous interagirons dans notre quotidien.Comme humains et robots commencent a travailler en environnement partages, la diversite des tâches qu’ils peuvent accomplir augmente drastiquement. Cela cree de nombreux defis et questions qu’il nous faut adresser, en terme de securite et d’acceptation des systemes robotiques.L’etre humain a des besoins et attentes bien specifiques qui ne peuvent etre occultes lors de la conception des interactions robotiques. D’une certaine maniere, il existe un besoin fort pour l’emergence d’une veritable interaction humain-robot ergonomique.Au cours de cette these, nous avons mis en place des methodes pour inclure des criteres ergonomiques et humains dans les algorithmes de prise de decisions, afin d’automatiser le processus de generation d’une interaction ergonomique. Les solutions que nous proposons se basent sur l’utilisation de fonctions de cout encapsulant les besoins humains et permettent d’optimiser les mouvements du robot et le choix des actions. Nous avons ensuite applique cette methode a deux problemes courants d’interaction humain-robot.Dans un premier temps, nous avons propose une technique pour ameliorer la lisibilite des mouvements du robot afin d’arriver a une meilleure comprehension des ses intentions. Notre approche ne requiert pas de modeliser le concept de lisibilite de mouvements mais penalise les trajectoires qui amenent a une interpretation erronee ou tardive des intentions du robot durant l’accomplissement d’une tâche partagee. Au cours de plusieurs etudes utilisateurs nous avons observe un gain substantiel en terme de temps de prediction et une reduction des erreurs d’interpretation.Puis, nous nous sommes atteles au probleme du choix des actions et des mouvements qui vont maximiser l’ergonomie physique du partenaire humain. En utilisant une mesure d’ergonomie des postures humaines, nous simulons les actions et mouvements du robot et de l’humain pour accomplir une tâche donnee, tout en evitant les situations ou l’humain serait dans une posture de travail a risque. Les etudes utilisateurs menees montrent que notre methode conduit a des postures de travail plus sur et a une interaction percue comme etant meilleure.
A Deep Learning Framework for Automatic Diagnosis in Lung Cancer

We developed a deep learning framework that helps to automatically identify and segment lung cancer areas in patients' tissue specimens. The study was based on a cohort of lung cancer patients operated at the Uppsala University Hospital. The tissues were reviewed by lung pathologists and then the cores were compiled to tissue micro-arrays (TMAs). For experiments, hematoxylin-eosin stained slides from 712 patients were scanned and then manually annotated. Then these scans and annotations were used to train segmentation models of the developed framework. The performance of the developed deep learning framework was evaluated on fully annotated TMA cores from 178 patients reaching pixel-wise precision of 0.80 and recall of 0.86. Finally, publicly available Stanford TMA cores were used to demonstrate high performance of the framework qualitatively.
Mass deformed ABJM and PT symmetry

: We consider real mass and FI deformations of ABJM theory preserving supersymmetry in the large N limit, and compare with holographic results. On the (cid:12)eld theory side, the problems amounts to a spectral problem of a non-Hermitian Hamiltonian. For certain values of the deformation parameters this is invariant under an antiunitary operator (generalised PT symmetry), which ensures the partition function remains real and allows us to calculate the free energy using tools from statistical physics. The results obtained are compatible with previous work, the important new feature being that these are obtained directly from the real deformations, without analytic continuation.
Design exploration of 3D stacked non-volatile memory by conductive bridge based crossbar

Non-volatile memory (NVM) is one recent promising solution to build the next generation of memory system. Compared to other non-volatile devices such as flash, phase-change random-access-memory (PCRAM), memristor and etc., the emerging conductive-bridge random-access-memory (CBRAM) has shown advantages in accessing speed, power and endurance. In this paper, design of 3D-stacked NVM is explored with the use of CBRAM-crossbar. Specifically, accurate performance modeling of CBRAM-crossbar structure is studied within the corresponding design platform developed at device and system levels. Experiments show that, compared to PCRAM, the proposed CBRAM-crossbar based memory achieves 10x~100x faster accessing time, at least 100x less operation power, and 100x longer endurance.
Relationship between sports experience and executive function in 6-12-year-old children: independence from physical fitness and moderation by gender.

The purpose of this study was to evaluate the relationship between sports experience (i.e., tennis experience) and executive function in children while controlling for physical activity and physical fitness. Sixty-eight participants (6-12 years old, 34 males and 34 females) were enrolled in regular tennis lessons (mean = 2.4 years, range = 0.1-7.3 years) prior to the study. Executive functions, including inhibitory control (the Stroop Color-Word Test), working memory (the 2-back Task), and cognitive flexibility (the Local-global Task) were evaluated. Participants' levels of daily physical activity, ranging from moderate to vigorous, were evaluated using triaxial accelerometers. The total score for physical fitness was assessed using the Tennis Field Test. Hierarchical multiple regression analyses revealed interaction effects between gender and tennis experience on participants' reaction time (RT) on the switch cost of the Local-global Task after controlling for age, BMI, gender, physical activity, physical fitness, and tennis experience. Longer tennis experience was associated with shorter switch cost in males but not in females. Higher scores on physical fitness were positively associated with lower interference scores on the Stroop Color-Word Test, RT on the 2-back Task, and RT in the switching condition of the Local-global Task, after controlling for age, BMI, gender, and physical activity. In conclusion, all three foundational components of executive function (i.e., inhibitory control, working memory, and cognitive flexibility) were more strongly related to physical fitness than to physical activity in males and females, whereas greater cognitive flexibility was related to tennis experience only in the males.
ICT as the Facilitator of Entrepreneurial Activity: An Empirical Investigation

Internet and the related information and communications technologies (the ICTs) are profoundly shaping economic transactions of all kinds. They are also expected to open up entrepreneurial opportunities and reduce the competitive advantage of large firms, thus making room for small and young firms. The EU policy makers have already started to introduce policy initiatives for better use of ICT into the actions to support entrepreneurship, such as promoting digital entrepreneurship. At the moment, however, we have scarce empirical evidence about the impact of ICT on entrepreneurial activity. Our study provides such evidence, by investigating the relationship between ICT use and startup rates, using longitudinal industry level data for Slovenia. The results of the regression analysis show that entry rates are higher in industries that are characterized by higher ICT use, thus providing support to the government policy efforts.
The TURBO Diaries: Application-controlled Frequency Scaling Explained

Most multi-core architectures nowadays support dynamic voltage and frequency scaling (DVFS) to adapt their speed to the system's load and save energy. Some recent architectures additionally allow cores to operate at boosted speeds exceeding the nominal base frequency but within their thermal design power. In this paper, we propose a general-purpose library that allows selective control of DVFS from user space to accelerate multi-threaded applications and expose the potential of heterogeneous frequencies. We analyze the performance and energy trade-offs using different DVFS configuration strategies on several benchmarks and real-world workloads. With the focus on performance, we compare the latency of traditional strategies that halt or busy-wait on contended locks and show the power implications of boosting of the lock owner. We propose new strategies that assign heterogeneous and possibly boosted frequencies while all cores remain fully operational. This allows us to leverage performance gains at the application level while all threads continuously execute at different speeds. We also derive a model to help developers decide on the optimal DVFS configuration strategy, e.g, for lock implementations. Our indepth analysis and experimental evaluation of current hardware provides insightful guidelines for the design of future hardware power management and its operating system interface.
Rationale, Design and Methodology of the Computerized Registry of Patients with Venous Thromboembolism (RIETE)

Abstract Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a preventable cause of in-hospital death, and one of the most prevalent vascular diseases. There is a lack of knowledge with regards to contemporary presentation, management and outcomes of patients with VTE. Many clinically important subgroups (including the elderly, those with recent bleeding and pregnant patients) have been under-represented in clinical trials. Furthermore, design of clinical trials is challenging in some scenarios, such as in those with hemodynamically unstable PE. RIETE (Registro Informatizado Enfermedad TromboEmbolica) is a large prospective multinational ongoing registry, designed to address these unmet needs using representative data from multiple centres. Initiated in Spain in 2001, RIETE currently includes 179 centres in 24 countries and has enrolled more than 72,000 patients. RIETE has helped characterize the pattern of presentation and outcomes of VTE, including the aforementioned understudied subgroups. RIETE has recently expanded to collect long-term outcome data, and has broadened its inclusion criteria to enrol other forms of venous thrombosis (such as cerebral vein thrombosis and splanchnic vein thrombosis). The RIETE platform is also being used to conduct pragmatic comparative effectiveness studies, including randomized trials. Future steps would focus on collaboration with additional centres across the world, and efforts to ensure the quality and expansion of the registry. In conclusion, RIETE is a large ongoing registry of patients with VTE and other thrombotic conditions. Its results could be helpful for improving our understanding of the epidemiology, patterns of care and outcomes of patients with thrombotic disease.
Mechanism and consequence of abnormal calcium homeostasis in Rett syndrome astrocytes

Astrocytes play an important role in Rett syndrome (RTT) disease progression. Although the non-cell-autonomous effect of RTT astrocytes on neurons was documented, cell-autonomous phenotypes and mechanisms within RTT astrocytes are not well understood. We report that spontaneous calcium activity is abnormal in RTT astrocytes in vitro, in situ, and in vivo. Such abnormal calcium activity is mediated by calcium overload in the endoplasmic reticulum caused by abnormal store operated calcium entry, which is in part dependent on elevated expression of TRPC4. Furthermore, the abnormal calcium activity leads to excessive activation of extrasynaptic NMDA receptors (eNMDARs) on neighboring neurons and increased network excitability in Mecp2 knockout mice. Finally, both the abnormal astrocytic calcium activity and the excessive activation of eNMDARs are caused by Mecp2 deletion in astrocytes in vivo. Our findings provide evidence that abnormal calcium homeostasis is a key cell-autonomous phenotype in RTT astrocytes, and reveal its mechanism and consequence.
Improved SLM for PAPR Reduction in OFDM System

Selected mapping (SLM) is a promising peak-to-average power ratio (PAPR) reduction technique for orthogonal frequency division multiplexing (OFDM) system. In SLM, phase sequences are combined with the data for generating alternative signals, so as to reduce the PAPR. In this paper, a new criterion is developed to examine the effects of different phase sequence sets in SLM-OFDM based on the mathematical correlation analysis among the alternative signals. Furthermore, according to the proposed criteria, the chaotic phase sequence set is introduced for improving the PAPR reduction performance in SLM. Simulation results show that the improved SLM outperforms conventional methods such as proposed in [6].
In Vivo Multimodal Magnetic Resonance Imaging Changes After N-Methyl-d-Aspartate-Triggered Spasms in Infant Rats

Objective Despite the serious neurodevelopmental sequelae of epileptic encephalopathy during infancy, the pathomechanisms involved remain unclear. To find potential biomarkers that can reflect the pathogenesis of epileptic encephalopathy, we explored the neurometabolic and microstructural sequelae after infantile spasms using a rat model of infantile spasms and in vivo magnetic resonance imaging techniques. Methods Rats prenatally exposed to betamethasone were subjected to three rounds of intraperitoneal N-methyl-d-aspartate (NMDA) triggering of spasms or received saline injections (controls) on postnatal days (P) 12, 13, and 15. Chemical exchange saturation transfer imaging of glutamate (GluCEST) were performed at P15 and 22 and diffusion tensor imaging and additional spectroscopy (1H-MRI/MRS) of the cingulate cortex were serially done at P16, 23, and 30 and analyzed. Pathological analysis and western blotting were performed with rats sacrificed on P35. Results Within 24 h of the three rounds of NMDA-induced spasms, there was an acute increase in the GluCEST (%) in the cortex, hippocampus, and striatum. When focused on the cingulate cortex, mean diffusivity (MD) was significantly decreased during the acute period after multiple spasms with an increase in γ-aminobutyric acid (GABA), glutamate, and glutamine N-acetylaspartate-plus-N-acetylaspartylglutamate (tNAA), total choline, and total creatine. The juvenile rats also showed decreased MD on diffusion tensor imaging and significant decreases in taurine, tNAA, and macromolecules-plus-lipids in the cingulate cortex. Pathologically, there was a significant reduction in glial fibrillary acidic protein, myelin basic protein, and neuronal nuclei expression in the cingulate cortex of rats with NMDA-induced spasms. Significance These neurometabolic and microstructural alterations after NMDA-triggered spasms might be potential imaging biomarkers of epileptic encephalopathy.
Performance Analysis of FDD Massive MIMO Systems Under Channel Aging

In this paper, we study the effect of channel aging on the uplink and downlink performance of an FDD massive MIMO system, as the system dimension increases. Since the training duration scales linearly with the number of transmit dimensions, channel estimates become increasingly outdated in the communication phase, leading to performance degradation. To quantify this degradation, we first derive bounds on the mean squared channel estimation error. We use the bounds to derive deterministic equivalents of the receive SINRs, which yields a lower bound on the achievable uplink and downlink spectral efficiencies. For the uplink, we consider maximal ratio combining and MMSE detectors, while for the downlink, we consider matched filter and regularized zero forcing precoders. We show that the effect of channel aging can be mitigated by optimally choosing the frame duration. It is found that using all the base station antennas can lead to negligibly small achievable rates in high user mobility scenarios. Finally, numerical results are presented to validate the accuracy of our expressions and illustrate the dependence of the performance on the system dimension and channel aging parameters.
Aptamers-The Alternative To Antibodies Conjugated Gold Nanoparticles For Detection Of Staphylococcus Aureus

Staphylococcus Aureus (S. Aureus) pathogenic bacteria remain a challenge for the struggle against food safety. However, the detection become the overall tool extensively used in different bioanalytical applications. Aptamers are artificial functional DNA/RNA oligonucleotides selected in vitro from randomsequence nucleic acid libraries through an in vitro evolution process called systematic evolution of ligands by exponential enrichment (SELEX). Besides, Aptamers also known as chemical antibodies, and possess a number of advantages over antibodies. however, aptamers conjugated gold nanoparticles are nano systems well qualified for the development of detecting platforms for different analytical applications including nanomedicine as well as the diagnostic and therapeutic tool and even in the food stuffs for detecting different materials. Well, Gold nanoparticles with its properties, enhance aptasensor sensitivity as well as amplification process. Consequently, biosensors based aptamers is probably one of the most promising ways to solve some of the problems concerning the increasing need to develop highly sensitive, fast and economic methods of analysis of different targets molecules. Thus, this review aim to focus on aptamer biosensors as alternative over antibody modified gold nanoparticles for detection of S. Aureus in food stuffs. Keywords—aptamers, antibody, Gold nanoparticles, biosensors.
On Truly Block Eigensolvers via Riemannian Optimization

We study theoretical properties of block solvers for the eigenvalue problem. Despite a recent surge of interest in such eigensolver analysis, truly block solvers have received relatively less attention, in contrast to the majority of studies concentrating on vector versions and non-truly block versions that rely on the deflation strategy. In fact, truly block solvers are more widely deployed in practice by virtue of its simplicity without compromise on accuracy. However, the corresponding theoretical analysis remains inadequate for first-order solvers, as only local and kth gap-dependent rates of convergence have been established thus far. This paper is devoted to revealing significantly better or asyet-unknown theoretical properties of such solvers. We present a novel convergence analysis in a unified framework for three types of first-order Riemannian solvers, i.e., deterministic, vanilla stochastic, and stochastic with variance reduction, that are to find top-k eigenvectors of a real symmetric matrix, in full generality. In particular, the issue of zero gaps between eigenvalues, to the best of our knowledge for the first time, is explicitly considered for these solvers, which brings new understandings, e.g., the dependence of convergence on gaps other than the k-th one. We thus propose the concept of generalized k-th gap. Three types of solvers are proved to converge to a globally optimal solution at a global, generalized k-th gap-dependent, and linear or sub-linear rate. ∗Corresponding author. Proceedings of the 21 International Conference on Artificial Intelligence and Statistics (AISTATS) 2018, Lanzarote, Spain. PMLR: Volume 84. Copyright 2018 by the author(s).