Miracle-dz/UniFAR-FTUs huggingface dataset:
Scanning Disk Rings and Winds in CO at 0.01–10 au: A High-resolution M-band Spectroscopy Survey with IRTF-iSHELL

We present an overview and first results from a M-band spectroscopic survey of planet-forming disks performed with iSHELL on the Infrared Telescope Facility, using two slits that provide resolving power R ≈ 60,000–92,000 (5–3.3 km s−1). iSHELL provides a nearly complete coverage at 4.52–5.24 μm in one shot, covering >50 lines from the R and P branches of 12CO and 13CO for each of multiple vibrational levels, and providing unprecedented information on the excitation of multiple emission and absorption components. Some of the most notable new findings of this survey are: (1) the detection of two CO Keplerian rings at <2 au (in HD 259431), (2) the detection of H2O rovibrational lines at 5 μm (in AS 205 N), and (3) the common kinematic variability of CO lines over timescales of 1–14 yr. By homogeneously analyzing this survey together with a previous survey of cooler stars, we discuss a unified view of CO spectra where emission and absorption components scan the disk surface across radii from a dust-free region within dust sublimation out to ≈10 au. We classify two fundamental types of CO line shapes interpreted as emission from Keplerian rings (double-peak lines) and a disk surface plus a low-velocity part of a wind (triangular lines), where CO excitation reflects different emitting regions (and their gas-to-dust ratio) rather than just the irradiation spectrum. A disk+wind interpretation for the triangular lines naturally explains several properties observed in CO spectra, including the line blueshifts, line shapes that turn into narrow absorption at high inclinations, and the frequency of disk winds as a function of the stellar type.
Nonuniform Observability for Moving Horizon Estimation and Stability with Respect to Additive Perturbation

This paper formalises the concepts of weakly and weakly regularly persistent input trajectory as well as their link to the Observability Grammian and the existence and uniqueness of solutions of Moving Horizon Estimation (MHE) problems. Additionally, thanks to a new time-uniform Implicit Function Theorem, these notions are proved to imply the stability of MHE solutions with respect to small additive perturbation in the measurements and in the dynamics, both uniformly and non-uniformly in time. Finally, examples and counter-examples of weakly persistent and weakly regularly persistent input trajectories are given in the case of 2D bearing-only navigation.
A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks

Kernel regression is an important nonparametric learning algorithm with an equivalence to neural networks in the inﬁnite-width limit. Understanding its generalization behavior is thus an important task for machine learning theory. In this work, we provide a theory of the inductive bias and generalization of kernel regression using a new measure characterizing the “learnability” of a given target function. We prove that a ker-nel’s inductive bias can be characterized as a ﬁxed budget of learnability, allocated to its eigenmodes, that can only be increased with the addition of more training data. We then use this rule to derive expressions for the mean and covariance of the predicted function and gain insight into the overﬁtting and adversarial robustness of kernel regression and the hardness of the classic parity problem. We show agreement between our theoretical results and both kernel regression and wide ﬁnite networks on real and synthetic learning tasks.
Quantifying Local Specialization in Deep Neural Networks

A neural network is locally specialized to the extent that parts of its computational graph (i.e. structure) can be abstractly represented as performing some comprehensible sub-task relevant to the overall task (i.e. functionality). Are modern deep neural networks locally specialized? How can this be quantified? In this paper, we consider the problem of taking a neural network whose neurons are partitioned into clusters, and quantifying how functionally specialized the clusters are. We propose two proxies for this: importance, which reflects how crucial sets of neurons are to network performance; and coherence, which reflects how consistently their neurons associate with features of the inputs. To measure these proxies, we develop a set of statistical methods based on techniques conventionally used to interpret individual neurons. We apply the proxies to partitionings generated by spectrally clustering a graph representation of the network's neurons with edges determined either by network weights or correlations of activations. We show that these partitionings, even ones based only on weights (i.e. strictly from non-runtime analysis), reveal groups of neurons that are important and coherent. These results suggest that graph-based partitioning can reveal local specialization and that statistical methods can be used to automatedly screen for sets of neurons that can be understood abstractly.
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces

We develop a minimax rate analysis to describe the reason that deep neural networks (DNNs) perform better than other standard methods. For nonparametric regression problems, it is well known that many standard methods attain the minimax optimal rate of estimation errors for smooth functions, and thus, it is not straightforward to identify the theoretical advantages of DNNs. This study tries to fill this gap by considering the estimation for a class of non-smooth functions that have singularities on hypersurfaces. Our findings are as follows: (i) We derive the generalization error of a DNN estimator and prove that its convergence rate is almost optimal. (ii) We elucidate a phase diagram of estimation problems, which describes the situations where the DNNs outperform a general class of estimators, including kernel methods, Gaussian process methods, and others. We additionally show that DNNs outperform harmonic analysis based estimators. This advantage of DNNs comes from the fact that a shape of singularity can be successfully handled by their multi-layered structure.
SARS-CoV-2 seroprevalence in three Kenyan health and demographic surveillance sites, December 2020-May 2021

Importance Most of the studies that have informed the public health response to the COVID-19 pandemic in Kenya have relied on samples that are not representative of the general population. Objective To determine the cumulative incidence of infection with SARS-CoV-2, from a randomly selected sample of individuals normally resident at three Health and Demographic Surveillance Systems (HDSSs) in Kenya. Design This was a cross-sectional population-based serosurvey conducted at Kilifi HDSS, Nairobi Urban HDSS, and Manyatta HDSS in Kenya. We selected age-stratified samples at HDSSs in Kilifi, Kisumu and Nairobi, in Kenya. Blood samples were collected from participants between 01 Dec 2020 and 27 May 2021. Setting Kilifi HDSS comprises a predominantly rural population, Manyatta HDSS comprises a predominantly semi-urban population, while Nairobi Urban HDSS comprises an urban population. The total population under regular surveillance at the three sites is ~470,000. Exposure We tested for IgG antibodies to SARS-CoV-2 spike protein using ELISA. Locally validated assay sensitivity and specificity were 93% (95% CI 88-96%) and 99% (95% CI 98-99.5%), respectively. Main Outcome and Measures The primary outcome measure was cumulative incidence of infection with SARS-COV-2 virus as evidenced by seropositivity to SARS-CoV-2 whole spike protein. We adjusted our estimates using classical methods and Bayesian modelling to account for assay performance. We performed multivariable logistic regression to test associations between seropositivity and age category, time period and sex. Results We recruited 2,559 individuals from the three HDSS sites, median age (IQR) 27years (10-78) and 52% were female. Seroprevalence at all three sites rose steadily during the study period. In Kilifi, Kisumu and Nairobi, seroprevalences at the beginning of the study were 14.5 % (9.1-21), 36.0 (28.2-44.4) and 32.4 % (23.1-42.4) respectively; at the end they were 27.6 % (21.4-33.9), 42.0 % (34.7-50.0) and 50.2 % (39.7-61.1), respectively. In multivariable logistic regression models that adjusted for sex and period of sample collections, age category was strongly associated with seroprevalence (p<0.001), with the highest seroprevalences being observed in the 35-44 and [≥]65 year age categories. Conclusion There has been substantial unobserved transmission of SARS-CoV-2 in the general population in Kenya. There is wide variation in cumulative incidence by location and age category.
Improving breast cancer diagnostics with artificial intelligence for MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a very high sensitivity in detecting breast cancer, but it often leads to unnecessary biopsies and patient workup. In this paper, we used an artificial intelligence (AI) system to improve the overall accuracy of breast cancer diagnosis and personalize management of patients undergoing DCE-MRI. On the internal test set (N=3,936 exams), our system achieved an area under the receiver operating characteristic curve (AUROC) of 0.92 (95% CI: 0.92-0.93). In a retrospective reader study, there was no statistically significant difference between 5 board-certified breast radiologists and the AI system (mean {Delta}AUROC +0.04 in favor of the AI system). Radiologists' performance improved when their predictions were averaged with AI's predictions (mean {Delta}AUPRC [area under the precision-recall curve] +0.07). Those hybrid predictions also increase interreader agreement (Fleiss' kappa {Delta} +0.21 (0.16-0.26)). We demonstrated the generalizability of the AI system using multiple data sets from Poland and the US. In subgroup analysis, we observed consistent results across different cancer subtypes and patient demographics. Using the decision curve analysis, we showed that the AI system can reduce unnecessary biopsies in the range of clinically relevant risk thresholds. This would lead to avoiding benign biopsies in up to 20% of all BI-RADS category 4 patients. Finally, we performed an error analysis, investigating situations where AI predictions were mostly incorrect. This exploratory work creates a foundation for deployment and prospective analysis of AI-based models for breast MRI.
Wild epigenetics: insights from epigenetic studies on natural populations

Epigenetic mechanisms such as DNA methylation, histone modifications and non-coding RNAs are increasingly targeted in studies of natural populations. Here, I review some of the insights gained from this research, examine some of the methods currently in use and discuss some of the challenges that researchers working on natural populations are likely to face when probing epigenetic mechanisms. While studies supporting the involvement of epigenetic mechanisms in generating phenotypic variation in natural populations are amassing, many of these studies are currently correlative in nature. Thus, while empirical data point to widespread contributions of epigenetic mechanisms in generating phenotypic variation, there are still concerns as to whether epigenetic variation is instead ultimately controlled by genetic variation. Disentangling these two sources of variation will be a key to resolving the debate about the importance of epigenetic mechanisms, and studies on natural populations that partition the relative contribution of genetic and epigenetic factors to phenotypic variation can play an important role in this debate.
A predictive flight-altitude model for avoiding future conflicts between an emblematic raptor and wind energy development in the Swiss Alps

Deployment of wind energy is proposed as a mechanism to reduce greenhouse gas emissions. Yet, wind energy and large birds, notably soaring raptors, both depend on suitable wind conditions. Conflicts in airspace use may thus arise due to the risks of collisions of birds with the blades of wind turbines. Using locations of GPS-tagged bearded vultures, a rare scavenging raptor reintroduced into the Alps, we built a spatially explicit model to predict potential areas of conflict with future wind turbine deployments in the Swiss Alps. We modelled the probability of bearded vultures flying within or below the rotor-swept zone of wind turbines as a function of wind and environmental conditions, including food supply. Seventy-four per cent of the GPS positions were collected below 200 m above ground level, i.e. where collisions could occur if wind turbines were present. Flight activity at potential risk of collision is concentrated on south-exposed mountainsides, especially in areas where ibex carcasses have a high occurrence probability, with critical areas covering vast expanses throughout the Swiss Alps. Our model provides a spatially explicit decision tool that will guide authorities and energy companies for planning the deployment of wind farms in a proactive manner to reduce risk to emblematic Alpine wildlife.
Fluctuations in T cell receptor and pMHC interactions regulate T cell activation

Adaptive immune responses depend on interactions between T cell receptors (TCRs) and peptide major histocompatibility complex (pMHC) ligands located on the surface of T cells and antigen presenting cells (APCs), respectively. As TCRs and pMHCs are often only present at low copy numbers their interactions are inherently stochastic, yet the role of stochastic fluctuations on T cell function is unclear. Here, we introduce a minimal stochastic model of T cell activation that accounts for serial TCR-pMHC engagement, reversible TCR conformational change and TCR aggregation. Analysis of this model indicates that it is not the strength of binding between the T cell and the APC cell per se that elicits an immune response, but rather the information imparted to the T cell from the encounter, as assessed by the entropy rate of the TCR-pMHC binding dynamics. This view provides an information-theoretic interpretation of T cell activation that explains a range of experimental observations. Based on this analysis, we propose that effective T cell therapeutics may be enhanced by optimizing the inherent stochasticity of TCR-pMHC binding dynamics.
Adaptive evolution of major histocompatibility complex class I immune genes and disease associations in coastal juvenile sea turtles

Characterizing polymorphism at the major histocompatibility complex (MHC) genes is key to understanding the vertebrate immune response to disease. Despite being globally afflicted by the infectious tumour disease fibropapillomatosis (FP), immunogenetic variation in sea turtles is minimally explored. We sequenced the α1 peptide-binding region of MHC class I genes (162 bp) from 268 juvenile green (Chelonia mydas) and 88 loggerhead (Caretta caretta) sea turtles in Florida, USA. We recovered extensive variation (116 alleles) and trans-species polymorphism. Supertyping analysis uncovered three functional MHC supertypes corresponding to the three well-supported clades in the phylogeny. We found significant evidence of positive selection at seven amino acid sites in the class I exon. Random forest modelling and risk ratio analysis of Ch. mydas alleles uncovered one allele weakly associated with smooth FP tumour texture, which may be associated with disease outcome. Our study represents the first characterization of MHC class I diversity in Ch. mydas and the largest sample of sea turtles used to date in any study of adaptive genetic variation, revealing tremendous genetic variation and high adaptive potential to viral pathogen threats. The novel associations we identified between MHC diversity and FP outcomes in sea turtles further highlight the importance of evaluating genetic predictors of disease, including MHC and other functional markers.
New results in the theory of plane strain flexure of incompressible isotropic hyperelastic materials

New results on the classical problem of bending by end moments for incompressible isotropic hyperelastic materials within the framework of nonlinear elasticity are investigated and presented in this paper. The particular results of concern here include (i) the adaptation of Rivlin's standard analysis to the case where one end of the beam is fixed and the other end is subjected to a bending moment; and (ii) results on the finite bending of (infinitesimally) thin isotropic hyperelastic plates which are valid for large deformations, extending the classical results from the linear elasticity theory which are restricted to small deformations. An interesting feature observed in this context is that a flexed thin plate develops an oscillatory surface along the circular arc near the free end, due to local (small) deviations of the radius of curvature. A potential application to the bending of a biological soft tissue, namely the aortic valve leaflet, is briefly described by way of an example. Finally, some new results are obtained for finite bending of hyperelastic materials that exhibit limiting chain extensibility at the molecular level and involve constraints on the deformation. The amount of bending that such materials can sustain is limited by the constraint. On using a limiting chain extensibility model, closed-form solutions for the Cauchy stress components, the bending moment and the normal out-of-plane force required to sustain the bending deformation are derived.
Supranational authorities and private actors as drivers of single market integration? The state of the Union in electricity and banking

ABSTRACT The creation of a European Banking Union and a European Energy Union have been two high-profile integration projects in a decade characterised by multiple crises. While recent research has focused on the direct effects of crisis on European integration dynamics, less attention has been paid to policy outcomes of market integration in sectors such as banking and energy. To address this gap, the study examines how distinct paths of integration, and the types of institutional change they involve, translate into patterns of sectoral governance. Further, it analyses the consequences of diverging patterns of sectoral governance for policy outcomes, examining whether the existence of supranational coordination bodies and public–private interaction is conducive to market integration. The findings from the analysis of the banking and electricity sectors suggest that while we see resilience and renewed integration in institutional terms, the change affecting policy outcomes appears to be more limited.
Beyond Meroni. What does it take to achieve a single market for network industries?

ABSTRACT The EU single market for network industries has not been accomplished after more than three decades. The clearest shortcoming of this incipient EU internal market is the lack of EU independent regulators. This design failure reflects the uneasy equilibrium between the political (un)willingness of the Member States and EU institutions, legal constraints emerging from EU case-law and economic concerns. After providing economic evidence of a failing single market in network industries and of the awareness of this failure in EU institutions, we discuss the problematic EU governance in these sectors, followed by the Meroni doctrine (hindering the single market) and its recent revision by the Court of Justice. The adaptation of NRAs in EU-wide networks together with the emergence of weak EU agencies has helped to make NRAs more EU-oriented, but this is insufficient to arrive at a single market for network industries.
Strong Response to SARS-CoV-2 Vaccine Additional Doses Among Patients With Inflammatory Bowel Diseases

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has disrupted health care and has resulted in high mortality rates.1 Vaccination is an international priority to mitigate the risks of SARS-CoV-2. The initial trials for development of SARS-CoV-2 vaccines excluded individuals with immunocompromising conditions.2
Stochastic Assessment of Voltage Dips Caused by Faults in Large Transmission System

This thesis deals with voltage dip characterization of transmission systems by using stochastic methods. Voltage dips are short duration reductions in the rms voltage. Motor starting and transformer energising cause voltage dips that may be locally treated. Most severe dips are due to short-circuit and earth faults. Faults at transmission levels cause dips that can be observed far away from the fault location. Statistical dip characterization of individual sites of a power network is essential to decide about mitigation methods. Statistical characterization of an entire transmission network is needed for regulatory purposes. Statistics on voltage dip events may be obtained by using monitoring of the power supply. Monitoring presents limitations when used for individual site characterization. The alternative is stochastic assessment of voltage dips. Stochastic assessment of voltage dips combines stochastic data concerning the fault likelihood with deterministic data regarding the remaining voltages during the fault. In this thesis, stochastic assessment of voltage dips is used to characterize the dip performance of an existing large transmission system. The theoretical background for calculating the remaining voltages during a fault is developed. Balanced and unbalanced dips are considered. The effect of power transformers between the fault point and the observation bus is taken into account. A matrix notation is introduced in order to describe the dip magnitudes for all buses due to all possible faults. The resulting dip-matrix is then combined with the fault occurrence rates corresponding to the fault positions. This results in dip frequencies for each bus. Site indices are calculated for each bus and system indices are calculated for the system as a whole. Results are presented in tables, graphs and diagrams. An integer optimisation model is introduced in order to determine the optimal locations for a limited number of monitors. The monitor reach area is introduced to describe the area that can be observed from a given monitor position. The matrix notation introduced for stochastic assessment of dips, is used to determine the monitor reach areas for all potential locations. A number of cases are solved considering different monitor thresholds. It is shown that the average number of dips of a given magnitude can be well identified for the optimal locations chosen.
Bisphosphonate therapy in otosclerosis: A scoping review

Otosclerosis, a leading cause of deafness in adults, results from defective bone remodeling of the otic capsule. Bisphosphonates have been used to decrease bone remolding in many diseases, including otosclerosis. This study analyzes whether current literature supports bisphosphonate therapy as an effective treatment for otosclerosis.