Miracle-dz/UniFAR-FTUs huggingface dataset:
PHOTONIC LOCALIZATION IN ONE-DIMENSIONAL K-COMPONENT FIBONACCI STRUCTURES

We studied the photonic localization of one-dimensional k-component Fibonacci structures ~KCFS’s! , in which k different intervals are ordered according to a substitution rule. By using a transfer-matrix method, the optical transmission through KCFS’s is obtained. It is demonstrated that the transmission coefficient has a rich structure, which depends on the wavelength of light and the number of different incommensurate intervals k. For the KCFS’s with an identical k, by increasing the layer number of the sequences, more and more transmission dips develop and some of them approach zero transmission, which may finally make a onedimensional photonic band gap. For a series of finite KCFS’s, by increasing the number of different incommensurate intervals k, the total transmission over the spectral region of interest decreases gradually and the width of photonic band gap becomes larger. This property may be useful in the design of the high-performance optical and electronic devices. As for the infinite KCFS’s, the transmission coefficient is singularly continuous and multifractal analysis is employed to characterize the transmission spectra. A dimensional spectrum of singularities associated with the transmission spectrum f (a) demonstrates that the light propagation in the KCFS’s presents scaling properties and hence shows a genuine multifractality. @S0163-1829 ~98! 10103-0#
On the highest non-breaking wave in a group: fully nonlinear water wave breathers versus weakly nonlinear theory

Abstract In nature, water waves usually propagate in groups and the open question about the characteristics of the highest possible wave in a group is of significant theoretical and practical interest. We examine the problem of the highest non-breaking wave in a wave group by direct numerical simulations of the exact Euler equations. The main aim of the study is twofold: (i) to describe the highest wave in a group in fully nonlinear setting and find its dependence on parameters; (ii) to examine correspondence between the exact breather solutions of weakly nonlinear analytic theory based on the integrable nonlinear Schrödinger (NLS) equation and their strongly nonlinear analogues. In contrast to weakly nonlinear models the very notion of the highest wave is ill-defined: the maximal crest elevation, the maximal trough-to-crest height and the deepest trough all occur at close but different moments; correspondingly, we have to speak about distinctively different extreme waves. In the simulations small initial perturbation of a uniform wave train were prescribed in a way ensuring that the initial perturbation excites a single breather-type modulation. The ensuing growth results in higher wave magnitudes and takes longer time to develop compared with the NLS theory. The maxima of crest elevation noticeably exceed their weakly nonlinear analogues. The wave with the highest crest differs significantly from the unmodulated wave: the local wavelength contracts considerably, the crest becomes noticeably higher; the vicinity of the crest of such an extreme wave is close to that of the limiting Stokes periodic wave. Thus, the shape of the maximal crest wave is almost universal, i.e. it practically does not depend on the way the wave group evolved, or even whether there was initially more than one group. The evolution of a single NLS breather has been shown to have a qualitatively similar but quantitatively quite different analogue in the fully nonlinear setting. The one-to-one mapping of the NLS breather solutions onto fully nonlinear ones has been constructed. The fully nonlinear breathers are found to be robust, which provides grounds for applying the results for developing short-term deterministic forecasting of rogue waves.
Deep Learning-Based Network-Wide Energy Efficiency Optimization in Ultra-Dense Small Cell Networks

In ultra-dense small cell networks (UDSCNs), where a significant number of small cell base stations (SBSs) coexist, the amount of power consumed at the SBSs can be extremely high, rendering the efficient management of power consumption for the SBSs particularly important. Herein, we propose a deep-learning-based resource allocation strategy to maximize network-wide energy efficiency in the UDSCN by optimally controlling the transmit power and user association. In this regard, a novel deep neural network (DNN) structure comprising three separate DNN units, each of which determines the activation of the SBSs, user association, and transmit power, as well as an unsupervised-learning-based training methodology are designed. Simulation results verify that the proposed scheme achieves a near-optimal performance while requiring a short computation time.
Why probabilistic support is not inductive

This paper attempts to provide both an elaboration and a strengthening of the thesis of Popper & Miller (Nature, Lond. 302, 687f. (1983)) that probabilistic support is not inductive support. Although evidence may raise the probability of a hypothesis above the value it achieves on background knowledge alone, every such increase in probability has to be attributed entirely to the deductive connections that exist between the hypothesis and the evidence. We shall also do our best to answer all the criticisms of our thesis that are known to us. In 1878 Peirce drew a sharp distinction between 'explicative, analytic, or deductive’ and ‘amplifiative, synthetic, or (loosely speaking) inductive’ reasoning. He characterized the latter as reasoning in which ‘ the facts summed up in the conclusion are not among those stated in the premisses’. The Oxford English Dictionary records that the word ‘ampliative ’ was used in the same sense as early as 1842, and that in 1852 Hamilton wrote: ‘Philosophy is a transition from absolute ignorance to science, and its procedure is therefore ampliative. ’ This was the background to our letter to Nature on 21 April 1983. It was there shown that, relative to evidence e, the content of any hypothesis h may be split into two parts, the disjunction h V e (read here h or e) and the material conditional h ← e (read h if e) and the 'ampliative' part of h relative to e was identified with this conditional h ← e; that is, with the deductively weakest proposition that is sufficient, in the presence of e, to yield h. We then proved quite generally that if p(h,eb) ≠ 1 ≠ p (e, b) then s(h←e,e,b) = ct(h,eb) ct(e,b) < 0. (0.1) Here s(x,y,z) = p(x,yz) - p(x,z) is a measure of the support that y gives to x in the presence of z, and ct(x,z) = 1-p(x,z) is a measure of the content of x relative to z. Relative to b, both h's and its support by e may be added over the two factors h V e and h ←e: ct(h,b) = ct(h V e,b) + ct(h←e,b), (0.2) s(h,e,b) = s(h V e,e,b) + s(h←e,e,b) (0.3) What (0.1) establishes is that the ('ampliative') part of the hypothesis h that goes beyond the evidence e is invariably countersupported by the evidence. In other words, probabilistic support is not inductive support.
Rectus Femoris Muscle and Phase Angle as Prognostic Factor for 12-Month Mortality in a Longitudinal Cohort of Patients with Cancer (AnyVida Trial)

Background: Cancer-related malnutrition is still unrecognized and undertreated in clinical practice. The morphofunctional assessment of disease-related malnutrition (DRM) is a new approach that focuses on evaluating changes in body composition and function. The aim of this study is to evaluate the prognostic value of classic and emerging assessment of malnutrition at 12-months survival in cancer patients. Methods. We conducted a prospective study on cancer outpatients. Bioelectrical impedance with phase angle (PhA), nutritional ultrasound by rectus femoris cross-sectional area (RFCSA), hand grip strength, and “Timed Up and Go Test” (TUG) were evaluated as predictors of mortality. Results. Fifty-seven patients were included. The non-survivors had lower PhA values than the survivors (4.7° vs. 5.4°; p < 0.001), and we had the same results with RFCSA 2.98 cm2/m2 vs. 4.27 cm2/m2 (p = 0.03). Cut-off points were identified using the ROC (receiver operating characteristic) curves for PhA (≤5.6° cancer patients, ≤5.9° men, ≤5.3° women), RFCSA (≤4.47 cm2/m2 cancer patients, ≤4.47° men, ≤2.73° women) and rectus femoris-Y-axis (RF-Y-axis; ≤1.3 cm cancer patients, ≤1.06 men, ≤1 women). In multivariate logistic regression analysis, we found that high PhA was significantly associated with a lower mortality hazard ratio (HR: 0.42 95% CI: 0.21–0.84, p = 0.014). Likewise, high RFCSA was associated with a decrease in mortality risk in the crude model (HR: 0.61 95% CI: 0.39–0.96, p = 0.031). This trend was also maintained in the adjusted models by the confounding variables. Conclusions. Low PhA and RFCSA values are significant independent predictors of mortality in cancer patients. These cut-off points are clinical data that can be used for nutritional assessment and the prediction of clinical outcomes.
A cloud-based pipeline for analysis of FHIR and long-read data

Abstract Motivation As genome sequencing becomes cheaper and more accurate, it is becoming increasingly viable to merge this data with electronic health information to inform clinical decisions. Results In this work, we demonstrate a full pipeline for working with both PacBio sequencing data and clinical FHIR® data, from initial data to tertiary analysis. The electronic health records are stored in FHIR® (Fast Healthcare Interoperability Resource) format, the current leading standard for healthcare data exchange. For the genomic data, we perform variant calling on long-read PacBio HiFi data using Cromwell on Azure. Both data formats are parsed, processed and merged in a single scalable pipeline which securely performs tertiary analyses using cloud-based Jupyter notebooks. We include three example applications: exporting patient information to a database, clustering patients and performing a simple pharmacogenomic study. Availability and implementation https://github.com/microsoft/genomicsnotebook/tree/main/fhirgenomics Supplementary information Supplementary data are available at Bioinformatics Advances online.
Pendellösung Fringes in Distorted Crystals II. Application to Two-Beam Cases

Wave-optical principles of electron and X-ray diffraction in distorted crystals developed in Part I are applied to Laue cases where only one diffracted wave is excited appreciably. Using the two-beam approximations, it can be shown that the trajectory of a “modified Bloch-wave” is determined by a differential equation which has an analogous form to a special-relativistic equation of motion for a charged particle in an electric field. The apparent force which changes the direction of motion is caused by lattice distortions. The phase change along the trajectory is obtainable by the phase integral given in Part I. The amplitudes of the direct and the diffracted waves are given by considering the compatibility of crystal and vacuum waves as to the energy flows on the crystal boundaries. The boundary conditions are discussed for electron and X-ray cases separately. Pendellosung fringes are expected for both cases from an interference between two modified Bloch-waves corresponding to the different branches of .
Confidence Bands for Isotonic Median Curves Using Sign Tests

Suppose that one observes independent random variables (X1, Y1), (X2, Y2), …, (Xn, Yn) in R2 with unknown distributions, except that Median(Yi | Xi = M(x) for some unknown isotonic function M. We describe an explicit algorithm for the computation of confidence bands for the median function M whose running time is of order O(n2). The bands rely on multiscale sign tests and are shown to have desirable asymptotic properties.
Surface rippling induced by surface‐tension gradients during laser surface melting and alloying

During laser surface melting and alloying, temperature gradients on the melt surface between the laser‐beam impact point and the intersection line of the solid‐liquid interface with the surface generate surface‐tension gradients that sweep liquid away from beam impact point. The resulting flow of liquid creates a depression of the liquid surface beneath the beam and ridging of the liquid surface elsewhere. As the beam passes to other areas of the surface, this distortion of the liquid surface is frozen in, creating a roughened rippled surface. If the laser‐beam sweep velocity exceeds a critical velocity, the liquid does not have sufficient time to form ripples, and rippling from surface‐tension gradients can be avoided.
Comparative between Double Inversion Recovery and Fluid-Attenuated Inversion Recovery Sequences for Detection of Brain Multiple Sclerosis

Multiple sclerosis (MS) is the most common chronic inflammatory demyelinating disease of the central nervous system (CNS). MS diagnosis and disease monitoring are mostly based on Magnetic Resonance Imaging (MRI). This study aims to compare a double inversion recovery MRI sequence with a fluid-attenuated inversion recovery sequence in Multiple Sclerosis (MS) in the brain. A study was conducted on 75 patients whose aged between 10 and 70 years who were having MS in the brain to compare a double inversion recovery DIR sequence with a fluid-attenuated inversion recovery FLAIR sequence at King Salman Specialist Hospital. in this study the age group (30-49) and female patient represented the highest percentage (69%) and (60%) respectively, A correlation between DIR and FLAIR in terms of the number of MS lesions and mean ±SD was found. DIR detected a higher total number of MS lesions in 75 patients tested (a total of 1886 MS lesions were detected in DIR compared to 1723 MS lesions in FLAIR). FLAIR imaging showed higher rates of periventricular lesions (a total of 508 lesions, the Mean ±SD was 9.5± 9.5) than DIR imaging (a total of 423 lesions, the Mean ± SD was 9.0± 9.0), statistically significant (p = 0.007). The DIR sequence detected a higher total number of MS lesions than the FLAIR sequence, while the FLAIR sequence is better at detecting periventricular lesions. There was no statistically significant relationship between the patient’s age groups and the number of lesions in DIR and FLAIR P-value > 0.05.
Comparison of Objective Supercell Identification Techniques Using an Idealized Cloud Model

AbstractThe accuracy, reliability, and skill of several objective supercell identification methods are evaluated using 113 simulations from an idealized cloud model with 1-km horizontal grid spacing. Horizontal cross sections of vorticity and radar reflectivity at both mid- and low levels were analyzed for the presence of a supercell, every 5 min of simulation time, to develop a “truth” database. Supercells were identified using well-known characteristics such as hook echoes, inflow notches, bounded weak-echo regions (BWERs), and the presence of significant vertical vorticity.The three objective supercell identification techniques compared were the Pearson correlation (PC) using an analysis window centered on the midlevel storm updraft; a modified Pearson correlation (MPC), which calculates the PC at every point in the horizontal using a small 3 km × 3 km analysis window; and updraft helicity (UH). Results show that the UH method integrated from 2 to 5 km AGL, and using a threshold value of 180 m2 s−2, wa.
Point Cloud Classification by Domain Adaptation Using Recycling Max Pooling and Cutting Plane Identification

Deep models have been studied in point cloud classification for the applications of autonomous driving and robotics. One challenging issue is that the point cloud of the same object could be discrepantly captured depending on sensors. Such a difference is the main cause of the domain gap. The deep models trained with one domain of point clouds may not work well with other domains because of such a domain gap. A technique to reduce domain inconsistency is domain adaptation. In this paper, we propose an unsupervised domain adaptation with two novel schemes. First, to improve unreliable pseudo-label assignment, we introduce a voting-based procedure based on the recycling max pooling module, which involves self-paced learning. It helps to increase the training stability of the models. Second, to learn the geometrical characteristics of point clouds in unfamiliar settings, we propose a training method of cutting plane identification, which works in an unsupervised way. Testing with the popular point cloud dataset of PointDA-10 and Sim-to-Real, experiments show that our method increase classification accuracy by 6.5%-points on average, ModelNet and ShapeNet as the source domain and ScanNet, and ScanObjectNN as the target domain. From an ablation study, it was observed that each method contributes to improving the robustness of domain adaptation.
Quantum phases of a dipolar Bose-Einstein condensate in an optical lattice with three-body interaction

We investigate the quantum phases of a dipolar Bose-Einstein condensate (BEC) in an optical lattice based on the extended Bose-Hubbard model taking into account the three-body scattering. Accordingly, the phase diagrams from the superfluid state to the Mott-insulator state for such BEC systems are obtained and analyzed, employing both the mean-field approach and the functional-integral method. In particular, we explore the combined effects of three-body interaction and dipole-dipole interaction on the insulating lobes in detail. The experimental scenario is also discussed.
Narrative review: blood and tumor biomarker testing in non-small cell lung cancer without an oncogenic driver

Background and Objective For patients with metastatic non-small cell lung cancer (NSCLC) without an oncogenic driver, systemic therapy with immune checkpoint inhibitors (ICIs) alone or in combination with chemotherapy have significantly improved the outcomes. However, the majority of patients do not have a durable response, and there is a need for additional predictive biomarkers. The objective of this narrative review is to describe potential biomarkers for immunotherapy. Methods Narrative overview of the literature synthesizing the findings of literature reporting retrospective, prospective, and subset analyses of studies investigating potential predictive biomarkers for ICI. Key Content and Findings Tumor expression of programmed death ligand-1 (PD-L1) is the only clinically available biomarker for patients receiving ICI-based therapy. However, PD-L1 has significant limitations and studies have investigated the predictive value of higher PD-L1 expression levels. There has been interest in tumor mutation burden (TMB) based on the premise that a higher TMB would be associated with a more neoantigens, which would increase the likelihood of an immune response. The studies to date have not revealed a consistent association with TMB level and survival benefit. Kelch-like ECH Associated Protein 1 (KEAP1) and serine/threonine kinase 11 (STK11) mutations have been associated with worse outcomes with ICI but these mutations appear to be associated with a worse prognosis, and not predictive for ICI. Tumor infiltrating lymphocytes (TIL’s) are the mechanism of immune response, and there is interest in further investigating the presence, type and distribution of TIL’s to predict immune benefit. Circulating tumor deoxyribonucleic acid (ctDNA) levels, at baseline and on treatment samples, are being investigated to assess response to therapy and long-term benefit of ICI. Conclusions None of the current biomarkers in development are validated for use in routine clinical care. Given the complexity of NSCLC biology and immune response to ICI most likely a composite biomarker using multiple biomarkers will need to be develop.
Ekman Pumping and the Energetics of the Southern Hemisphere Eddy Life Cycle

AbstractIn the framework of the Lorenz energy cycle, the climatological and eddy life cycle characteristics of the generation of eddy available potential energy through Ekman pumping (EEPE) are evaluated using Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) data (1979–2011). EEPE exhibits an annual cycle that is maximized during a given hemisphere’s winter, with maximum values in the midtroposphere of the midlatitudes.Spectral analysis of the Southern Hemisphere storm track reveals that positive EEPE is associated with an anomalously small vertical phase tilt. A composite analysis of the Southern Hemisphere eddy life cycle reveals a maximum in EEPE that occurs after the peak eddy amplitude. Eddy life cycles during winter with large values of EEPE have higher values of eddy available potential energy and eddy kinetic energy than life cycles with small EEPE. However, baroclinic energy conversion remains unenhanced in life cycles with large values of EEPE. The lac.
Elderly Patients and Management in Intensive Care Units (ICU): Clinical Challenges

Abstract There is a growing population of older adults requiring admission to the intensive care unit (ICU). This population outpaces the ability of clinicians with geriatric training to assist in their management. Specific training and education for intensivists in the care of older patients is valuable to help understand and inform clinical care, as physiologic changes of aging affect each organ system. This review highlights some of these aging processes and discusses clinical implications in the vulnerable older population. Other considerations when caring for these older patients in the ICU include functional outcomes and morbidity, as opposed to merely a focus on mortality. An overall holistic approach incorporating physiology of aging, applying current evidence, and including the patient and their family in care should be used when caring for older adults in the ICU.
CO2 Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations

Most of China's carbon sink inversion research uses global atmospheric transport models to assimilate natural fluxes, which quantifies the biosphere and ocean carbon budget with a relative coarse spatial resolution and long timescale from a weekly or monthly perspective. Toward high‐resolution inversion of CO2 fluxes, a novel carbon flux forecast model was developed in this study, which was then further used to carry out carbon assimilation based on a regional chemical transport model (CMAQ) at higher spatial (64 × 64 km2) and temporal (1 hr) resolutions. An Ensemble Kalman Smoother was applied as the assimilation algorithm and further extended to assimilate surface CO2 observations. Concentrations and fluxes were simultaneously assimilated as state variables to help reduce the uncertainty in the initial CO2 fields with the joint data assimilation framework (JDAS). In general, the posterior fluxes reproduced the seasonal, daily and hourly variation effectively, demonstrating the ability to fully absorb and utilize observations. Moreover, the influence of the choice of assimilation window on the carbon flux inversion was assessed via sensitivity experiments, revealing 36 hr to be the optimum length. Evaluation of the prior and posterior flux simulations also indicated that JDAS offers reasonable improvements, making it suitable for fine‐scale flux optimization and estimation. In addition, the posterior biosphere estimation in mainland China (−682 TgC yr−1) tends to be the optimal mathematical solution under current sparse observation coverage with daytime photosynthetic uptake, which likely leads to the overestimation of the optimized CO2 sink. This study serves as a basis for future regional and urban assessment.