Miracle-dz/UniFAR-FTUs huggingface dataset:
Mean kurtosis‐Curve (MK‐Curve) correction improves the test–retest reproducibility of diffusion kurtosis imaging at 3 T

Diffusion kurtosis imaging (DKI) is applied to gain insights into the microstructural organization of brain tissues. However, the reproducibility of DKI outside brain white matter, particularly in combination with advanced estimation to remedy its noise sensitivity, remains poorly characterized. Therefore, in this study, we investigated the variability and reliability of DKI metrics while correcting implausible values with a fit method called mean kurtosis (MK)‐Curve. A total of 10 volunteers (four women; age: 41.4 ± 9.6 years) were included and underwent two MRI examinations of the brain. The images were acquired on a clinical 3‐T scanner and included a T1‐weighted image and a diffusion sequence with multiple diffusion weightings suitable for DKI. Region of interest analysis of common kurtosis and tensor metrics derived with the MK‐Curve DKI fit was performed, including intraclass correlation (ICC) and Bland–Altman (BA) plot statistics. A p value of less than 0.05 was considered statistically significant. The analyses showed good to excellent agreement of both kurtosis tensor‐ and diffusion tensor‐derived MK‐Curve–corrected metrics (ICC values: 0.77–0.98 and 0.87–0.98, respectively), with the exception of two DKI‐derived metrics (axial kurtosis in the cortex: ICC = 0.68, and radial kurtosis in deep gray matter: ICC = 0.544). Non‐MK‐Curve–corrected kurtosis tensor‐derived metrics ranged from 0.01 to 0.52 and diffusion tensor‐derived metrics from 0.06 to 0.66, indicating poor to moderate reliability. No structural bias was observed in the BA plots for any of the diffusion metrics. In conclusion, MK‐Curve–corrected DKI metrics of the human brain can be reliably acquired in white and gray matter at 3 T and DKI metrics have good to excellent agreement in a test–retest setting.
Local martingale deflators for asset processes stopped at a default time $S^\tau$ or right before $S^{\tau-}$

Let $\mathbb{F}\subset \mathbb{G}$ be two filtrations and $S$ be a $\mathbb{F}$ semimartingale possessing a $\mathbb{F}$ local martingale deflator. Consider $\tau$ a $\mathbb{G}$ stopping time. We study the problem whether $S^{\tau-}$ or $S^{\tau}$ can have $\mathbb{G}$ local martingale deflators. A suitable theoretical framework is set up in this paper, within which necessary/sufficient conditions for the problem to be solved have been proved. Under these conditions, we will construct $\mathbb{G}$ local martingale deflators for $S^{\tau-}$ or for $S^{\tau}$. Among others, it is proved that $\mathbb{G}$ local martingale deflators are multiples of $\mathbb{F}$ local martingale deflators, with a multiplicator coming from the multiplicative decomposition of the Azema supermartingale of $\tau$. The proofs of the necessary/sufficient conditions require various results to be established about Azema supermartingale, about local martingale deflator, about filtration enlargement, which are interesting in themselves. Our study is based on a filtration enlargement setting. For applications, it is important to have a method to infer the existence of such setting from the knowledge of the market information. This question is discussed at the end of the paper.
Magnetism and activity of planet hosting stars

Abstract The magnetic activity levels of planet host stars may differ from that of stars not known to host planets in several ways. Hot Jupiters may induce activity in their hosts through magnetic interactions, or through tidal interactions by affecting their host's rotation or convection. Measurements of photospheric, chromospheric, or coronal activity might then be abnormally high or low compared to control stars that do not host hot Jupiters, or might be modulated at the planet's orbital period. Such detections are complicated by the small amplitude of the expected signal, by the fact that the signals may be transient, and by the difficulty of constructing control samples due to exoplanet detection biases and the uncertainty of field star ages. We review these issues, and discuss avenues for future progress in the field.
INDEPENDENT SUBSPACE ANALYSIS USING LOCALLY LINEAR EMBEDDING

While Independent Subspace Analysis provides a means of blindly separating sound sources from a single channel signal, it does have a number of problems. In particular the amount of information required for separation of sources varies with the signal. This is as a result of the variance-based nature of Principal Component Analysis, which is used for dimensional reduction in the Independent Subspace Analysis algorithm. In an attempt to overcome this problem the use of a non-variance based dimensional reduction method, Locally Linear Embedding, is proposed. Locally Linear Embedding is a geometry based dimensional reduction technique. The use of this approach is demonstrated by its application to single channel source separation, and its merits discussed.
Should I, can I, dare I? Patients’ view on stopping long-term antidepressant use, a qualitative study

ABSTRACT Background and aim The rise in long-term antidepressant use is concerning. Long-term antidepressant (AD) use, much longer than recommended by guidelines, can result in risk of adverse events and generate unnecessary costs. In order to mitigate these risks, patients views about their antidepressants and how to discontinue need to be taken into account. We aimed to explore patients’ experiences and views of discontinuing long-term AD, barriers and facilitators of discontinuation and required support. Methods Semi-structured face to face interviews were conducted with 14 patients with long-term AD use in primary care. Interviews were analysed thematically. Results Participants describe various perceptions about discontinuation. There is fear of returning to their depression, even in those who were ambivalent about the effectiveness and safety of AD continuation. Participants describe low confidence in their own coping resources, fear of stress, and previous negative experiences with stopping. This enhances their perception of AD dependence. Participants indicate the importance of the support of their GP and their social network to help them withdraw. Conclusion Discontinuation of long-term antidepressants is a complex issue for patients. More awareness of the lack of evidence and the potential risks of long-term AD continuation is required. By raising the issue and offering support during discontinuation GPs can help their patients stop AD. A greater focus on non-pharmacological approaches of depression in primary care is needed to reduce unnecessary AD use.
L5IN+: From an Analytical Platform to Optimization of Deep Inertial Odometry

Fifth generation of mobile communications (5G) and Deep Neural Networks (DNN) are two important technologies, which will enable new functions in the field of indoor positioning. This could be seen as the second major development after the innovation of smartphones, as a GNSS/INS alternative for indoor, location based applications. Optimization methods which work as a corrector, and as the uncertainty assessment for real life applications, guided us through the next level of challenges. In this paper, we have opened a novel interpretation of a deep network for inertial odometry which is robust to noisy labelled data that was detected from a 5G network. We also designed and developed analytical platform, which is considered a data collector and cellular positioning simulation. This platform was used to provide the input for the learning and optimization algorithms. The simulation website is implemented and available online under simulation2evaluation.herokuapp. com for researchers to generate ground truth trajectories and simulated cellular measurements with assigned quality and exact error values. We have proposed two approaches: (1) deep inertial odometry based on predicting velocity vector elements or relative positions and (2) Kalman Filtering to use, combine and test the absolute positions with the relative ones from the first approach. We finally provide numerical results of our experiments and a discussion of the effectiveness of our approaches.
The Representation of Energy, Energy Carriers and Fuels in the Open Energy Ontology

The Open Energy Ontology (OEO) is a BFO-based ontology for the domain of energy system analysis. Core of this domain is the concept of energy . Since the concept of energy is ambiguous and has not (yet) been introduced into BFO, we describe why the OEO classifies energy as a quality. We present how energy is differentiated from energy carriers, fuels and other related concepts. The process of energy conversion makes it possible to depict many different types of its generation and usage. Further, we describe the concept of origin of matter and energy. In this article, we define the core concepts of the OEO according to the BFO. We thus demonstrate how the OEO enables the consistent use of core concepts throughout the energy system analysis domain.
Calculation of Vibrational Frequencies by Hartree–Fock‐Based and Density Functional Theory

The sections in this article are Introduction Historical Overview Theoretical Overview HF Force Constants Density Functional Force Constants Basis Sets Calculation of Vibrational Frequencies by HF-Based Theory SQM Method SQM Force Fields for π Electron Systems Calculation of Vibrational Frequencies by DFT Performance of DFT Calculation Evaluation of Functionals and Basis Sets A New Approach to Vibrational Analysis by DFT WLS Method
Random Forest and XGBoost Based Fingerprinting Using MMSE: An Approach to Data-Centric AI to Enhance Indoor Wi-Fi Localization Systems

The indoor localization problem consists in identifying the Cartesian coordinates of an object or a personal asset in the buildings, malls, hospitals, campuses, factories, etc. To solve this problem, we consider a Wi-Fi-based localization method called fingerprinting, a two-step process, where a radio map of the monitored area is constructed by collecting signal strength from known locations. An unknown location is then predicted using this radio map as a reference. In this paper, we first propose an adapted Random Forest (RF) and Extreme Gradient Boosting (XGB) algorithms. This adaptation, combined with Minimum Mean Square Error (MMSE), improves the accuracy problem caused by the change of environment and extends the concept by adding a signal processing functionality as an edge cloud feature to address a dynamic cooperation clustering. By embedding the Wi-Fi Access Point (WAP) with multiple antennas, the signals sent by the Mobile User Equipment (MUE) can be processed to improve the accuracy of the bootstrap. Adding Minimum Mean Square Error (MMSE) is a kind of data-centric approach because it yields high-quality data as input. The noise inherent in the location data is reduced and thus the performance of the MMSE-aided RF and XGB improved. This enhancement is further extended by sharing data between WAPS. Thus, the MMSE processing and the sharing of such processed data between WAPS enhance the positioning model performance. The performance of these methods is evaluated through robust and extensive experiments in real-time indoor areas, with regular and reproducible scenarios. We found an interesting outcome that the proposed approach can offer better time-2-market compared to the traditional, non-Machine-Learning-based indoor positioning system approach
Detection of large-scale Ly α absorbers at large angles to the radio axis of high-redshift radio galaxies using SOAR

We present an investigation of the properties of the extended Ly$\alpha$ halo and the large-scale \ion{H}{I} absorbing structures associated with 5 high-redshift radio galaxies at z $>$ 2, using the Goodman long-slit spectrograph on the SOAR telescope, with the slit placed at large angles ($>$45$^{\circ}$) to the radio axis, to study regions that are unlikely to be illuminated by the active nucleus. Spatially extended Ly$\alpha$ emission is detected with large line widths (FWHM = 1000 -- 2500 km s$^{-1}$), which although impacted by resonant scattering, is suggestive of turbulent motion. We find a correlation between higher blueshifts and higher FWHM, which is an indication that radial motion dominates the bulk gas dynamics perpendicular to the radio axis, although we are unable to distinguish between outflow and infall scenarios due to the resonant nature of the Ly$\alpha$ line. Extended, blueshifted Ly$\alpha$ absorption is detected in the direction perpendicular to the radio axis in three radio galaxies with minimum spatial extents ranging from $\gtrsim$27 kpc to $\gtrsim$35 kpc, supporting the idea that the absorbing structure covers the entire Ly$\alpha$ halo, consistent with being part of a giant, expanding shell of gas enveloping the galaxy and its (detected) gaseous halo.
Ambient intelligence in assisted living environments

The paradigm of Ambient Intelligence (AmI) aims at supporting humans in achieving their everyday objectives by enriching physical environments with networks of distributed devices, such as sensors, actuators, and computational resources. AmI is not only the convergence of various technologies (i.e. sensor networks and industrial electronics) and related research fields (i.e. pervasive, distributed computing, and artificial intelligence), but it represents a major effort to integrate them and to make them really useful for everyday human life. In particular, the recognition of human activities, coupled with the knowledge of the user’s position in the indoor environment, represent two of the main pillars of the so-called “context-awareness”. A context-aware system is aware of what sensory data mean: it is able to associate meaning to observations, and to make the best use of sensory data once their meaning has been assessed. In this context, one of the most important research and development areas is represented by assisted living environments. The general goal of Ambient Assisted Living (AAL) solutions is to apply ambient intelligence technologies to enable people with specific demands, e.g. with disabilities or elderly, to live longer in their preferred environment. This thesis deals with two major problems that still prevent the spreading of AAL solutions in real environments: (i) the need for a common medium to transmit the sensory data and the information produced by algorithms and (ii) the unobtrusiveness of context-aware applications in terms of both placement of devices and period of observations (i.e. long-term care offering services or assistance on a daily basis over a long period of time for people who are not independent). In order to address these challenges, this thesis contributes to the Ambient Intelligence research field, applied to assisted living environments, by means of a holistic solution composed of a beyond the state-of-the-art middleware infrastructure, providing interoperability and service abstraction, and a suite of unobtrusive applications, built on top the proposed middleware, that allows the detection of the user’s context and behavioral deviations of his routine in indoor activities. The proposed solution has been thoroughly evaluated in the laboratory and in real testbeds offered by European FP7 projects, namely GiraffPlus and DOREMI, that showed its effectiveness in dealing with the requirements coming from the application of the AAL paradigm in the real world.
The Impact of Novel Computing Architectures on Large-Scale Distributed Web Information Retrieval Systems

Web search engines are the most popular mean of interaction with the Web. Realizing a search engine which scales even to such issues presents many challenges. Fast crawling technology is needed to gather the Web documents. Indexing has to process hundreds of gigabytes of data efficiently. Queries have to be handled quickly, at a rate of thousands per second. As a solution, within a datacenter, services are built up from clusters of common homogeneous PCs. However, Information Retrieval (IR) has to face issues raised by the growing amount of Web data, as well as the number of new users. In response to these issues, cost-effective specialized hardware is available nowadays. In our opinion, this hardware is ideal for migrating distributed IR systems to computer clusters comprising heterogeneous processors in order to respond their need of computing power. Toward this end, we introduce K-model, a computational model to properly evaluate algorithms designed for such hardware. We study the impact of K-model rules on algorithm design. To evaluate the benefits of using K-model in evaluating algorithms, we compare the complexity of a solution built using our properly designed techniques, and the existing ones. Although in theory competitors are more efficient than us, empirically, K-model is able to prove because our solutions have been shown to be faster than the state-of-the-art implementations.
BETAFIT: Stata module to fit a two-parameter beta distribution

betafit fits a two-parameter beta distribution, optionally as dependent on covariates. Stata 8 is required. Also included in this package is beta4, which also fits a two-parameter beta distribution, although to a single variable only. That may be used in Stata 4 through 7. This routine was previously circulated as beta.
New Combinatorial Properties and Algorithms for AVL Trees

In this thesis, new properties of AVL trees and a new partitioning of binary search trees named core partitioning scheme are discussed, this scheme is applied to three binary search trees namely AVL trees, weight-balanced trees, and plain binary search trees. We introduce the core partitioning scheme, which maintains a balanced search tree as a dynamic collection of complete balanced binary trees called cores. Using this technique we achieve the same theoretical efficiency of modern cache-oblivious data structures by using classic data structures such as weight-balanced trees or height balanced trees (e.g. AVL trees). We preserve the original topology and algorithms of the given balanced search tree using a simple post-processing with guaranteed performance to completely rebuild the changed cores (possibly all of them) after each update. Using our core partitioning scheme, we simultaneously achieve good memory allocation, space-efficient representation, and cache obliviousness. We also apply this scheme to arbitrary binary search trees which can be unbalanced and we produce a new data structure, called CacheOblivious General Balanced Tree (COG-tree). Using our scheme, searching a key requires O(logB n) block transfers and O(log n) comparisons in the external memory model and the cache-oblivious model. These complexities are theoretically efficient. Interestingly, the core partition for weight-balanced trees and COG-tree can be maintained with amortized O(logB n) block transfers per update whereas maintaining the core partition for AVL trees requires more than a poly-logarithmic amortized cost. Studying the properties of these trees also lead us to some other new properties of AVL trees and trees with bounded degree, namely, we present gap terminology and we prove Tarjan et al. ’s conjecture on the number of rotations in a sequence of deletions and insertions.
Direction-optimizing Label Propagation Framework for Structure Detection in Graphs: Design, Implementation, and Experimental Analysis

Label Propagation is not only a well-known machine learning algorithm for classification but also an effective method for discovering communities and connected components in networks. We propose a new Direction-optimizing Label Propagation Algorithm (DOLPA) framework that enhances the performance of the standard Label Propagation Algorithm (LPA), increases its scalability, and extends its versatility and application scope. As a central feature, the DOLPA framework relies on the use of frontiers and alternates between label push and label pull operations to attain high performance. It is formulated in such a way that the same basic algorithm can be used for finding communities or connected components in graphs by only changing the objective function used. Additionally, DOLPA has parameters for tuning the processing order of vertices in a graph to reduce the number of edges visited and improve the quality of solution obtained. We present the design and implementation of the enhanced algorithm as well as our shared-memory parallelization of it using OpenMP. We also present an extensive experimental evaluation of our implementations using the LFR benchmark and real-world networks drawn from various domains. Compared with an implementation of LPA for community detection available in a widely used network analysis software, we achieve at most five times the F-Score while maintaining similar runtime for graphs with overlapping communities. We also compare DOLPA against an implementation of the Louvain method for community detection using the same LFR-graphs and show that DOLPA achieves about three times the F-Score at just 10% of the runtime. For connected component decomposition, our algorithm achieves orders of magnitude speedups over the basic LP-based algorithm on large-diameter graphs, up to 13.2× speedup over the Shiloach-Vishkin algorithm, and up to 1.6× speedup over Afforest on an Intel Xeon processor using 40 threads.
Observation of Acoustic Valley Vortex States and Observation of Acoustic Valley Vortex States and Valley-Chirality Locked Beam Splitting

The Letter reports an experimental observation of the classical version of valley polarized states in a two-dimensional hexagonal sonic crystal, where the inversion-symmetry breaking of scatterers induces an omnidirectional frequency gap. The acoustic valley states, which carry specific linear momenta and orbital angular momenta, were selectively excited by external Gaussian beams and conveniently confirmed by the pressure distribution outside the crystal, according to the criterion of momentum conservation. The vortex nature of such intriguing crystal states was directly characterized by scanning the phase profile inside the crystal. In addition, we observed a peculiar beam splitting phenomenon, in which the separated beams are constructed by different valleys and locked to the opposite vortex chirality. The exceptional sound transport, encoded with valley-chirality locked information, may serve as the basis of designing conceptually novel acoustic devices with unconventional functions.
Coverage and performance analysis of 5G non-standalone deployments

Fifth Generation (5G) networks are becoming the norm in the global telecommunications industry. In this paper, we present the first large-scale measurement study on commercial 5G Non Standalone (NSA) deployments in a European country. We leverage the collected dataset, which covers two Mobile Network Operators (MNOs) in Rome, Italy, to first, study network deployment and radio coverage aspects, and second, explore the performance of two use cases related to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). Results show that 5G NSA can provide higher downlink throughput and slightly lower latency compared to Fourth Generation (4G) systems. However, performance is influenced by several factors, including propagation conditions, system configurations, and handover events, ultimately highlighting the need for further system optimization.