Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Massive MIMO Detection for M-QAM Symbols

Massive multiple-input multiple-output (MIMO) systems significantly outperform small-scale MIMO systems in terms of data rate, making them an enabling technology for next-generation wireless systems. However, the increased number of antennas increases the computational difficulty of data detection, necessitating more efficient detection techniques. This paper presents a detector based on joint deregularized and box-constrained dichotomous coordinate descent (BOXDCD) with iterations for rectangular m-ary quadrature amplitude modulation (M-QAM) symbols. Deregularization maximized the energy of the solution. With the box-constraint, the deregularization forces the solution to be close to the rectangular boundary set. The numerical results demonstrate that the proposed detector achieves a considerable performance gain compared to existing detection algorithms. The performance advantage increases with the system size and signal-to-noise ratio.
Preservation of Stability in Delay Equations under Delay Perturbations

We consider a class of linear delay equations with perturbed time lags and present conditions which guarantee that the asymptotic stability of the trivial solution of the equation at hand is preserved under these perturbations. As an example we show how our results can be used to obtain an estimate on the maximum allowable sampling interval in the stabilization of a hybrid system with feedback delays. We also present applications of our perturbation theorem to obtain stability conditions for delay equations with multiple delays.
DePTVM: Decentralized Pseudonym and Trust Value Management for Integrated Networks

Evaluating and sharing user equipment (UE) trust across multiple network domains can greatly support security and trust management of future integrated heterogeneous networks. But the dilemma between identity privacy preservation and trust evaluation efficacy causes a big challenge in pseudonym and trust value management. Most existing approaches either rely on a trusted third party (TTP) and non-collusive parties, or deploy trusted execution environments (TEEs). They cannot be applied directly into a trustless heterogeneous network environment, where network domains do not trust with each other and it is hard to setup a fully trusted party. In this article, we propose DePTVM, a decentralized pseudonym and trust value management scheme for integrated heterogeneous networks, where different network operators jointly maintain a list of <inline-formula><tex-math notation="LaTeX">$< $</tex-math><alternatives><mml:math><mml:mo><</mml:mo></mml:math><inline-graphic xlink:href="yan-ieq1-3246799.gif"/></alternatives></inline-formula>pseudonym, trust value<inline-formula><tex-math notation="LaTeX">$>$</tex-math><alternatives><mml:math><mml:mo>></mml:mo></mml:math><inline-graphic xlink:href="yan-ieq2-3246799.gif"/></alternatives></inline-formula> pairs by employing verifiable shuffling and trust obfuscation based on blockchain in order to support anonymous trust evaluation and ensure pseudonym unlinkability. We analyze DePTVM with respect to correctness, unforgeability, anonymity and unlinkability, and evaluate its performance through simulations. Experimental results show that trust synchronization can be achieved across domains within 9 seconds with our experimental settings and the time taken by the most complex operation (i.e., verifiable shuffling) of operator agent increases linearly with the scale of maintained list. Analysis and experimental results imply DePTVM's potential in practical applications.
Modelling interception and transpiration at monthly time steps: Introducing daily variability through Markov chains

The book presents improved equations for monthly water resources models, in particular for interception and transpiration. Most of the existing monthly models do not make a distinction between interception and transipiration, while this distinction is very important for management purposes. Interception is direct feedback to the atmosphere, important to sustain rainfall. Transpiration is a good indicator for plant growth and biomass production. This distinction also contributes to the estimation of recharge and therewith of runoff. The derivations are based on the Markov theory for the occurrence of rain-days. The methodology can be used on the basis of an analysis of a few time series of daily data, at a spatial scale of 300km and not necessarily of the same period as the monthly data. Zimbabwe served as the case study, but derived equations can be used worldwide as long as the relationship between the monthly rainfall and the mean number of rain-days can be established.
Incidence of Guillain–Barré syndrome in South Korea during the early COVID-19 pandemic

Objectives Guillain–Barré syndrome (GBS) is an immune-mediated polyradiculoneuropathy, often triggered by infection. We aimed to investigate how the incidence of GBS changed in the early stages of the coronavirus 2019 (COVID-19) pandemic when nationwide infections declined due to non-pharmaceutical interventions. Methods We conducted a nationwide population-based retrospective GBS cohort study using data from the Health Insurance Review and Assessment Service of Korea. Patients with new-onset GBS were defined as those who were first hospitalized between 1 January 2016 and 31 December 2020 with an International Classification of Disease, 10th Revision code, for GBS (G61.0) as a primary diagnosis. The incidence of GBS in the pre-pandemic years (2016–2019) was compared with that in the first pandemic year (2020). Nationwide epidemiological data for infections were collected from the national infectious disease surveillance system. A correlation analysis was performed to determine the incidence of GBS and nationwide trends of various infections. Results Overall, 3,637 new-onset GBS cases were identified. The age-standardized incidence of GBS in the first pandemic year was 1.10 (95% confidence interval, 1.01–1.19) per 100,000 persons. Compared to the first pandemic year, the incidence of GBS during the pre-pandemic years (1.33–1.68/100,000 persons/year) was significantly higher, with incidence rate ratios of 1.21–1.53 (P < 0.001). Nationwide cases of upper respiratory viral infections were significantly reduced in the first pandemic year; however, Campylobacter infections peaked in the summer of the pandemic. The nationwide epidemiology of parainfluenza virus, enterovirus, and Campylobacter infections correlated positively with GBS incidence. Conclusion The overall GBS incidence decreased in the early stages of the COVID-19 pandemic, which can be attributed to the dramatic reduction in viral illnesses due to public measures.
Revisiting a Natural Experiment: Do Legislators with Daughters Vote More Liberally on Women’s Issues?

The pioneering work of Washington in 2008 shows that legislators with daughters cast more liberal roll call votes on women’s issues. Costa and coauthors in 2019 find that this pattern subsides in more recent congresses and speculate that increasing party polarization might diminish the “daughter effect. ” We investigate patterns of change over time by looking at eight congresses prior to the four studied by Washington and eight subsequent congresses, including three not included by Costa and coauthors. Contrary to the party polarization hypothesis, we find no daughter effect prior to the period that Washington studied and no effect thereafter.
The uniqueness of the measure of maximal entropy for geodesic flows on rank $1$ manifolds

In this paper we prove a conjecture of A. Katok, stating that the geodesic flow on a compact rank 1 manifold admits a uniquely determined invariant measure of maximal entropy. This generalizes previous work of R. Bowen and G. Margulis. As an application we show that the exponential growth rate of the set of singular closed geodesics is strictly smaller than the topological entropy.
Mass and magnification maps for the Hubble Space Telescope Frontier Fields clusters: implications for high-redshift studies

Extending over three Hubble Space Telescope (HST) cycles, the Hubble Frontier Fields (HFF) initiative constitutes the largest commitment ever of HST time to the exploration of the distant Universe via gravitational lensing by massive galaxy clusters. Here, we present models of the mass distribution in the six HFF cluster lenses, derived from a joint strong- and weak-lensing analysis anchored by a total of 88 multiple-image systems identified in existing HST data. The resulting maps of the projected mass distribution and of the gravitational magnification effectively calibrate the HFF clusters as gravitational telescopes. Allowing the computation of search areas in the source plane, these maps are provided to the community to facilitate the exploitation of forthcoming HFF data for quantitative studies of the gravitationally lensed population of background galaxies. Our models of the gravitational magnification afforded by the HFF clusters allow us to quantify the lensing-induced boost in sensitivity over blank-field observations and predict that galaxies at z > 10 and as faint as m(AB) = 32 will be detectable, up to 2 mag fainter than the limit of the Hubble Ultra Deep Field.
Design and Evaluation of Using Head-Mounted Virtual Reality for Learning Clinical Procedures: Mixed Methods Study

Background The capacity of health care professionals to perform clinical procedures safely and competently is crucial as it will directly impact patients’ outcomes. Given the ability of head-mounted virtual reality to simulate the authentic clinical environment, this platform should be suitable for nurses to refine their clinical skills for knowledge and skills acquisition. However, research on head-mounted virtual reality in learning clinical procedures is limited. Objective The objectives of this study were (1) to describe the design of a head-mounted virtual reality system and evaluate it for education on clinical procedures for nursing students and (2) to explore the experience of nursing students using head-mounted virtual reality for learning clinical procedures and the usability of the system. Methods This usability study used a mixed method approach. The stages included developing 3D models of the necessary instruments and materials used in intravenous therapy and subcutaneous injection procedures performed by nurses, followed by developing the procedures using the Unreal Engine (Epic Games). Questionnaires on the perception of continuance intention and the System Usability Scale were used along with open-ended questions. Results Twenty-nine nursing students took part in this questionnaire study after experiencing the immersive virtual reality (IVR) intervention. Participants reported largely favorable game perception and learning experience. Mean perception scores ranged from 3.21 to 4.38 of a maximum score of 5, while the mean system usability score was 53.53 of 100. The majority found that the IVR experience was engaging, and they were immersed in the game. The challenges encountered included unfamiliarity with the new learning format; technological constraints, such as using hand controllers; and physical discomfort. Conclusions The conception of IVR for learning clinical procedures through deliberate practice to enhance nurses’ knowledge and skills is promising. However, refinement of the prototypes is required to improve user experience and learning. Future research can explore other ways to use IVR for better education and health care purposes.
Case-cohort and case-control analysis with Cox's model

Prentice (1986) proposed the case-cohort design and studied a pseudolikelihood estimator of regression parameters in Cox's model. We derive a class of estimating equations for case-cohort sampling, each depending on a different estimator of the population distribution, which lead naturally to simple estimators that improve on Prentice's pseudolikelihood estimator. We also discuss an equivalence between case-control and case-cohort sampling in terms of the estimation of regression parameters in Cox's model.
Efficacy of Artificial Oocyte Activation in Improving the Reproductive Outcome in Poor Responders: A Single Centre Cohort Study

Objective: Achieving pregnancy in poor ovarian response patients is a challenge. Failed fertilization after ICSI, despite normal semen parameters is due to defects in oocyte activation. In-vitro activation of oocytes using Ca+2 agents can be useful in increasing the fertilization rates in these patients. This study aimed to evaluate the efficacy of artificial oocyte activation by calcium ionophores in poor responders in improving fertilization, cleavage, implantation and clinical pregnancy rates. Materials and methods: This is a prospective, cohort study conducted on 120 patients having poor ovarian response, (POSEIDON criteria) undergoing in-vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment at Southend Fertility and IVF, New Delhi from 1st August 2019 to 31st March 2020. Exclusion criterion was patients with partners with abnormal semen parameters. After OPU patients were randomized into two groups, study group (n=50) underwent ICSI-AOA (ICSI followed by artificial oocyte activation) using calcium ionophore- GM508 Cult-Active Solution) while the controls (n=57) were subjected to ICSI only. Results: Comparison of ICSI-AOA and ICSI groups showed: (i) number of fertilized oocytes - 2.42 vs. 2.16, p = 0.049 (ii) No. of cleavage stage embryos 2.32 vs. 1.96, p = 0.008 (iii) No. of grade A embryos 1.52 vs. 1.04, p = 0.009 (iv) fertilization rate - 89.00% vs. 83.04%, p = 0.093 (v) cleavage rate - 96.33% vs. 92.55%, p = 0.165 (vi) implantation rate - 27.14% vs. 11.74%, p = 0.098 (vii) clinical pregnancy rate - 34.3% vs. 20.5%, p = 0.167. Conclusion: The number of fertilized oocytes, grade A embryos and cleavage stage embryos formed after ICSI-AOA were statistically significantly more than ICSI. ICSI-AOA has not shown improvement in fertilization, cleavage, implantation and clinical pregnancy rate. From the present study the conclusive evidence cannot be drawn due to small sample size hence further studies are needed on a larger population.
Extreme Ultraviolet Explorer spectra of the Io plasma torus: Improved spectral resolution and new results

Spectral images of the Io plasma torus obtained from ∼165 hours of observation by the Extreme Ultraviolet Explorer (EUVE) spacecraft during the 124 encounter of the Galileo spacecraft with the torus and at an earlier epoch in 1996 are analyzed by a spectral-resolution-enhancing deconvolution algorithm in order to estimate averaged torus properties. Because of our analysis's improved spectral resolution, we are better able to separate lines arising from different ion species as compared to earlier EUVE studies. We exploit this capability to estimate emission-weighted whole-torus averages of electron temperature and ion fraction. Comparing the 1996 results for the torus to those of 1999, we estimate that (1) the electrons were hotter (5.4 versus 4.6 eV), (2) the luminosity was higher (375 versus 245 GW in the 350–730 A region), (3) the mass was lower (by 10–15%), (4) the sulfur/oxygen ratio was higher (0.8 versus 0.5), and (5) the ionization level was greater (1.2 versus 0.8 for [S++]/[S+], 0.22 versus 0.15 for [O++]/[O+]) in the 1996 observation than during 124. Considering the constraints of formation, chemistry, transport, and mass loss, the torus has fewer degrees of freedom than observables, so these two snapshots give us clues as to how torus characteristics may covary. For example, our data are consistent with the hypothesis that electron temperature and density are anticorrelated. We also confirm that the Io-correlated enhancement of torus brightness discovered by Voyager still persists. However, we raise new doubts that the torus's dawn-dusk brightness asymmetry is the result of torus mass loss that is controlled by the total mass of the torus. We also estimate corrections to the electron impact collision strengths for many EUV multiplets of interest for understanding the torus and other sulfur and oxygen plasmas of astrophysical interest, finding, for example, that the O+/S++ 484 A feature is about an order of magnitude brighter than that predicted by available electron collision strength data.
Statistical shape analysis for bio-structures : local shape modelling, techniques and applications

A Statistical Shape Model (SSM) is a statistical representation of a shape obtained from data to study variation in shapes. Work on shape modelling is constrained by many unsolved problems, for instance, difficulties in modelling local versus global variation. SSM have been successfully applied in medical image applications such as the analysis of brain anatomy. Since brain structure is so complex and varies across subjects, methods to identify morphological variability can be useful for diagnosis and treatment. The main objective of this research is to generate and develop a statistical shape model to analyse local variation in shapes. Within this particular context, this work addresses the question of what are the local elements that need to be identified for effective shape analysis. Here, the proposed method is based on a Point Distribution Model and uses a combination of other well known techniques: Fractal analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space representation for the problem of contour localisation. Similarly, Diffusion Maps are employed as a spectral shape clustering tool to identify sets of local partitions useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis method based on the Gaussian and Laplacian pyramids is explained and used to compare the featured Local Shape Model. Experimental results on a number of real contours such as animal, leaf and brain white matter outlines have been shown to demonstrate the effectiveness of the proposed model. These results show that local shape models are efficient in modelling the statistical variation of shape of biological structures. Particularly, the development of this model provides an approach to the analysis of brain images and brain morphometrics. Likewise, the model can be adapted to the problem of content based image retrieval, where global and local shape similarity needs to be measured.
Relationship-based clustering and cluster ensembles for high-dimensional data mining

This dissertation takes a relationship-based approach to cluster analysis of high (1000 and more) dimensional data that side-steps the ‘curse of dimensionality’ issue by working in a suitable similarity space instead of the original feature space. We propose two frameworks that leverage graph algorithms to achieve relationship-based clustering and visualization, respectively. In the visualization framework, the output from the clustering algorithm is used to reorder the data points so that the resulting permuted similarity matrix can be readily visualized in 2 dimensions, with clusters showing up as bands. Results on retail transaction, document (bag-of-words), and web-log data show that our approach can yield superior results while also taking additional balance constraints into account. The choice of similarity is a critical step in relationship-based clustering and this motivates our systematic comparative study of the impact of similarity measures on the quality of document clusters . The key findings of our experimental study are: (i) Cosine, correlation, and extended Jaccard similarities perform comparably; (ii) Euclidean distances do not work well; (iii) graph partitioning tends to be superior to k-means and SOMs especially when balanced clusters are desired; and (iv) performance curves generally do not cross. We also propose a cluster quality evaluation measure based on normalized mutual information and find an analytical relation between similarity measures. It is widely recognized that combining multiple classification or regression models typically provides superior results compared to using a single, well-tuned model. However, there are no well known approaches to combining multiple clusterings. The idea of combining cluster labelings without accessing the original features leads to a general knowledge reuse framework that we call cluster ensembles. We propose a formal definition of the cluster ensemble as an optimization problem. Taking a relationship-based approach we propose three effective and efficient combining algorithms for solving it heuristically based on a hypergraph model. Results on synthetic as well as real data-sets show that cluster ensembles can (i) improve quality and robustness, and (ii) enable distributed clustering, and (iii) speed up processing significantly with little loss in quality.
Stability criteria for large-scale systems

Recent research into large-scale system stability has proceeded via two apparently unrelated approaches. For Lyapunov stability, it is assumed that the system can be broken down into a number of subsystems, and that for each subsystem one can find a Lyapunov function (or something akin to a Lyapunov function). The alternative approach is an input-output approach; stability criteria are derived by assuming that each subsystem has finite gain. The input-output method has also been applied to interconnections of passive and of conic subsystems. This paper attempts to unify many of the previous results, by studying linear interconnections of so-called "dissipative" subsystems. A single matrix condition is given which ensures both input-output stability and Lyapunov stability. The result is then specialized to cover interconnections of some special types of dissipative systems, namely finite gain systems, passive systems, and conic systems.
Quasi-phase-matched probe-energy electro-optic sampling as a method of narrowband terahertz detection

Implementation of free-space quasi-phase-matched electro-optic detection is reported, based on measuring the energy variation of a femtosecond laser pulse induced by a terahertz field. Narrowband probe-energy type of detection is demonstrated using wide-aperture periodically poled Mg:Y:LiNbO3 crystals with as-grown domain gratings. Spectra of air plasma generation measured by this method are compared with the spectrum measured by the conventional ZnTe-based probe-phase ellipsometry scheme. Besides, the conventional method was performed for both types of sources, for the air plasma generation, and for the quasi-phase-matched optical rectification in the same periodically poled Mg:Y:LiNbO3 crystals.
The development of discrete active auroral forms

Using analytical and numerical methods, it is shown that all the basic forms of the active arcs can be described within the framework of a nonlinear Alfven wave model. The auroral forms are then considered as nonlinear two-dimensional Alfven vortex structures that are uniform along the ambient magnetic field. They can exist in a cold inhomogeneous plasma, e.g., the Earth's ionosphere. Large-scale spirals and folds can recover the initial form of a typical arc. In the case of a small solitary disturbance of the current density or a sharp bending of the current sheets, vortex chains are formed in the spirals and the folds. >