Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlations at higher-order exceptional points in non-Hermitian models

We investigate the decay of spatial correlations of $\mathcal{PT}$-symmetric non-Hermitian one-dimensional models that host higher-order exceptional points. Beyond a certain correlation length, they develop anomalous power-law behavior that indicates strong suppression of correlations in the non-Hermitian setups as compared to the Hermitian ones. The correlation length is also reflected in the entanglement entropy where it marks a change from logarithmic growth at short distance to a constant value at large distance, characteristic of an insulator, despite the spectrum being gapless. Two different families of models are investigated, both having a similar spectrum constrained by particle-hole symmetry. The first model offers an experimentally attractive way to generate arbitrary higher-order exceptional points and represents a non-Hermitian extension of the Dirac Hamiltonian for general spin. At the critical point it displays a decay of the correlations $\sim 1/x^2$ and $1/x^3$ irrespective of the order of the exceptional point. The second model is constructed using unidirectional hopping and displays enhanced suppression of correlations $\sim 1/x^a$, $a\ge 2$ with a power law that depends on the order of the exceptional point.
Topological Guided Actor-Critic Modular Learning of Continuous Systems with Temporal Objectives

This work investigates the formal policy synthesis of continuous-state stochastic dynamic systems given high-level specifications in linear temporal logic. To learn an optimal policy that maximizes the satisfaction probability, we take a product between a dynamic system and the translated automaton to construct a product system on which we solve an optimal planning problem. Since this product system has a hybrid product state space that results in reward sparsity, we introduce a generalized optimal backup order, in reverse to the topological order, to guide the value backups and accelerate the learning process. We provide the optimality proof for using the generalized optimal backup order in this optimal planning problem. Further, this paper presents an actor-critic reinforcement learning algorithm when topological order applies. This algorithm leverages advanced mathematical techniques and enjoys the property of hyperparameter self-tuning. We provide proof of the optimality and convergence of our proposed reinforcement learning algorithm. We use neural networks to approximate the value function and policy function for hybrid product state space. Furthermore, we observe that assigning integer numbers to automaton states can rank the value or policy function approximated by neural networks. To break the ordinal relationship, we use an individual neural network for each automaton state's value (policy) function, termed modular learning. We conduct two experiments. First, to show the efficacy of our reinforcement learning algorithm, we compare it with baselines on a classic control task, CartPole. Second, we demonstrate the empirical performance of our formal policy synthesis framework on motion planning of a Dubins car with a temporal specification.
WD J004917.14−252556.81, the most massive pulsating white dwarf

We present APO and Gemini time-series photometry of WD J004917.14−252556.81, an ultramassive DA white dwarf with Teff = 13 020 K and log g = 9.34. We detect variability at two significant frequencies, making J0049−2525 the most massive pulsating white dwarf currently known with M⋆ = 1.31 M⊙ (for a CO core) or 1.26 M⊙ (for an ONe core). J0049−2525 does not display any of the signatures of binary mergers, there is no evidence of magnetism, large tangential velocity, or rapid rotation. Hence, it likely formed through single star evolution and is likely to have an ONe core. Evolutionary models indicate that its interior is ≳ 99 per cent crystallized. Asteroseismology offers an unprecedented opportunity to probe its interior structure. However, the relatively few pulsation modes detected limit our ability to obtain robust seismic solutions. Instead, we provide several representative solutions that could explain the observed properties of this star. Extensive follow-up time-series photometry of this unique target has the potential to discover a significant number of additional pulsation modes that would help overcome the degeneracies in the asteroseismic fits, and enable us to probe the interior of an ≈1.3 M⊙ crystallized white dwarf.
Learning Symbols for Hierarchical Control from Interaction: Controllability Control

One of the most challenging tasks in robotics is the perception and compact representation of situations in unstructured, natural environments. The severity of this task lies also in the high dimensionality of these highly complex environments. The representation should for instance classify situations in a way that is relevant for action selection and allows for hierarchical decision making. The downscaling of high dimensional states to lower dimensional, potentially symbolic representations is an important step to solve planning and controlling problems within these environments. This thesis uses the concept of controllability states as symbolic representations for the states of an environment. A controllability state specifies, which degrees of freedom are controllable in a given state. The core contribution of this thesis is to show how such symbolic representations of controllability can be extracted based on the data an agent collects during the interaction with a continuous environment. The developed method is demonstrated in a 2-dimensional environment. Based on the learned symbolic representations and dynamic models associated to each symbol I derive the Controllability Control method: a controller that is able to provide a sequence of control instructions, that finally lead to a desired controllability state and hence to the controllability of one or more specific degrees of freedom. The Controllability Control model has successfully been tested for the task of moving an object, that is initially not controllable to a desired position.
Flexible K Nearest Neighbors Classifier: Derivation and Application for Ion-mobility Spectrometry-based Indoor Localization

The K Nearest Neighbors (KNN) classifier is widely used in many fields such as fingerprint-based localization or medicine. It determines the class membership of an unlabelled sample based on the class memberships of the K labelled samples, the so-called nearest neighbors, that are closest to the unlabelled sample. The choice of K has been the topic of various studies and proposed KNN-variants. Yet no variant has been proven to outperform all other variants. In this paper a new KNN-variant is proposed, which ensures that the K nearest neighbors are indeed close to the unlabelled sample and finds K along the way. The proposed algorithm is tested and compared to the standard KNN in theoretical scenarios and for indoor localization based on ion-mobility spectrometry fingerprints. It achieves a higher classification accuracy than the KNN in the tests, while having the same computational demand.
Computational Power of Particle Methods

The computational power of a compute model determines the class of problems it can solve. Automata theory allows describing the computational power of abstract machines (automata) and the problems they can solve. At the top of the Chomsky hierarchy of formal languages and grammars are Turing machines, the most powerful automata, which resemble the concept on which most modern computers are built. Here, we investigate the computational power of particle methods, a well-established class of algorithms with applications in scientific computing and computer simulation. Although particle methods can be interpreted as automata based on their formal definition, their computational power has so far not been studied. We address this by analyzing Turing completeness of particle methods. In particular, we prove two sets of restrictions under which a particle method is still Turing complete, and we show when it loses Turing completeness. This contributes to understanding the theoretical foundations of particle methods and provides insight into the powerfulness of computer simulations.
High-Performance and Flexible Parallel Algorithms for Semisort and Related Problems

Semisort is a fundamental algorithmic primitive widely used in the design and analysis of efficient parallel algorithms. It takes input as an array of records and a function extracting a key per record, and reorders them so that records with equal keys are contiguous. Since many applications only require collecting equal values, but not fully sorting the input, semisort is broadly applicable, e.g., in string algorithms, graph analytics, and geometry processing, among many other domains. However, despite dozens of recent papers that use semisort in their theoretical analysis and the existence of an asymptotically optimal parallel semisort algorithm, most implementations of these parallel algorithms choose to implement semisort by using comparison or integer sorting in practice, due to potential performance issues in existing semisort implementations. In this paper, we revisit the semisort problem, with the goal of achieving a high-performance parallel semisort implementation with a flexible interface. Our approach can easily be extended to two related problems, histogram and collect-reduce. Our algorithms achieve strong speedups in practice, and importantly, outperform state-of-the-art parallel sorting and semisorting methods for almost all settings we tested, with varying input sizes, distribution, and key types. On average (geometric means), our semisort implementation is at least 1.27x faster the best of the tested baselines. We also test two important applications with real-world data, and show that our algorithms improve the performance (up to 2.13x) over existing approaches. We believe that many other parallel algorithm implementations can be accelerated using our results.
Quantum geometry of expectation values

We propose a novel framework for the quantum geometry of expectation values over arbitrary sets of operators and establish a link between this geometry and the eigenstates of Hamiltonian families generated by these operators. We show that the boundary of expectation value space corresponds to the ground state, which presents a natural bound that generalizes Heisenberg's uncertainty principle. To demonstrate the versatility of our framework, we present several practical applications, including providing a stronger nonlinear quantum bound that violates the Bell inequality and an explicit construction of the density functional. Our approach provides an alternative time-independent quantum formulation that transforms the linear problem in a high-dimensional Hilbert space into a nonlinear algebro-geometric problem in a low dimension, enabling us to gain new insights into quantum systems.
Análisis de la efectividad de la rehabilitación cardíaca en España: una revisión sistemática exploratoria

Resumen Se sabe poco sobre las características de los programas de rehabilitación cardíaca implantados en la actualidad en España, por lo que el objetivo de este estudio fue conocer la efectividad de la rehabilitación cardíaca en pacientes con cardiopatía isquémica en España, mediante una revisión sistemática exploratoria de la literatura científica en distintas bases de datos desde 2010 hasta 2018. Se incluyeron 35 artículos cuyo objetivo era medir la efectividad de los programas de rehabilitación cardíaca desde distintas perspectivas. Participaron más del doble de hombres que de mujeres y solo el 8,6% fueron ensayos clínicos. Se observaron mejoras en relación con la mortalidad y las variables fisiológicas y de calidad de vida de los pacientes, pero con resultados dispares en morbilidad, estados de ánimo, mantenimiento de hábitos saludables a largo plazo y rentabilidad de los programas.
Game theory and evolution

Maynard Smith and Price (1973) have shown that the methods and terminology of Game Theory can be used to study the evolution of various strategies in animal species. They write a ij as the mean ‘value’ to the user of strategy i when his opponent uses strategy j; these values a ij are related to the fitness of the user of i, in the sense that, if a ij is large, it is advantageous to use strategy i when one's opponent uses strategy j, and this advantage will be reflected in an increase in one's reproductive potential. For example, these may be a contest for territory, or dominance rights.
Invited Perspective: Toxic Metals and Hypertensive Disorders of Pregnancy

Lauren A. Eaves1,2 and Rebecca C. Fry1,2,3,4 Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill (UNC-Chapel Hill), Chapel Hill, North Carolina, USA Institute for Environmental Health Solutions, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, North Carolina, USA Curriculum in Toxicology and Environmental Medicine, UNC-Chapel Hill, Chapel Hill, North Carolina, USA Department of Pediatrics, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
Non‐response for health‐related quality of life outcomes in ICU patients: A systematic review of the reporting in randomised trials

Health‐related quality of life (HRQoL) is frequently assessed in randomised clinical trials (RCTs) in the intensive care unit (ICU), but data are limited regarding the proportions of patients without responses or not surviving to HRQoL follow‐up and the handling of this. We aimed to describe the extent and pattern of missing HRQoL data in intensive care trials and describe how these data and deaths were handled statistically.
Epigenetic Control of Translation Checkpoint and Tumor Progression via RUVBL1‐EEF1A1 Axis

Epigenetic dysregulation is reported in multiple cancers including Ewing sarcoma (EwS). However, the epigenetic networks underlying the maintenance of oncogenic signaling and therapeutic response remain unclear. Using a series of epigenetics‐ and complex‐focused CRISPR screens, RUVBL1, the ATPase component of NuA4 histone acetyltransferase complex, is identified to be essential for EwS tumor progression. Suppression of RUVBL1 leads to attenuated tumor growth, loss of histone H4 acetylation, and ablated MYC signaling. Mechanistically, RUVBL1 controls MYC chromatin binding and modulates the MYC‐driven EEF1A1 expression and thus protein synthesis. High‐density CRISPR gene body scan pinpoints the critical MYC interacting residue in RUVBL1. Finally, this study reveals the synergism between RUVBL1 suppression and pharmacological inhibition of MYC in EwS xenografts and patient‐derived samples. These results indicate that the dynamic interplay between chromatin remodelers, oncogenic transcription factors, and protein translation machinery can provide novel opportunities for combination cancer therapy.
A comparison of anatomic and cellular transcriptome structures across 40 human brain diseases

Genes associated with risk for brain disease exhibit characteristic expression patterns that reflect both anatomical and cell type relationships. Brain-wide transcriptomic patterns of disease risk genes provide a molecular-based signature, based on differential co-expression, that is often unique to that disease. Brain diseases can be compared and aggregated based on the similarity of their signatures which often associates diseases from diverse phenotypic classes. Analysis of 40 common human brain diseases identifies 5 major transcriptional patterns, representing tumor-related, neurodegenerative, psychiatric and substance abuse, and 2 mixed groups of diseases affecting basal ganglia and hypothalamus. Further, for diseases with enriched expression in cortex, single-nucleus data in the middle temporal gyrus (MTG) exhibits a cell type expression gradient separating neurodegenerative, psychiatric, and substance abuse diseases, with unique excitatory cell type expression differentiating psychiatric diseases. Through mapping of homologous cell types between mouse and human, most disease risk genes are found to act in common cell types, while having species-specific expression in those types and preserving similar phenotypic classification within species. These results describe structural and cellular transcriptomic relationships of disease risk genes in the adult brain and provide a molecular-based strategy for classifying and comparing diseases, potentially identifying novel disease relationships.
Penta- and hexavalent vaccination of extremely and very-to-moderate preterm infants born at less than 34 weeks and/or under 1500 g: A systematic literature review

ABSTRACT Debate regarding vaccinating high-risk infants with penta- and hexavalent vaccines persists, despite their good immunogenicity and acceptable safety profile in healthy full-term infants. We report the findings of a systematic literature search that aimed to present data on the immunogenicity, efficacy, effectiveness, safety, impact, compliance and completion of penta- and hexavalent vaccination in high-risk infants, including premature newborns. Data from the 14 studies included in the review showed that the immunogenicity and the safety profile of penta- and hexavalent vaccines in preterm infants was generally similar to those seen in full-term infants, with the exception of an increase in cardiorespiratory adverse events such as apnea, bradycardia and desaturation following vaccination in preterm infants. Despite recommendations of vaccinating preterm infants according to their actual age, and the relatively high completion rate of the primary immunization schedule, vaccination was often delayed, increasing the vulnerability of this high-risk population to vaccine-preventable diseases. Plain Language Summary Combined vaccines such as penta- and hexavalent vaccines against multiple childhood diseases are widely used in healthy babies born at term. However, it is still debated whether these vaccines act the same way in babies considered to be high-risk: babies born prematurely at <34 weeks of pregnancy, those with a birthweight of <1500 g or babies with chronic diseases. We did a systematic literature search to find studies on such high-risk babies vaccinated with penta- or hexavalent vaccines; we focused on their antibody levels following vaccination, side effects, and protection from the diseases against which they were vaccinated. We also analyzed whether they were vaccinated on time and with all the doses recommended for healthy full-term babies. We found 14 studies that included premature babies. The results of these studies suggest that premature babies’ immune systems respond to penta- and hexavalent vaccines in largely the same way as those of full-term babies; side effects of penta- and hexavalent vaccines are also mostly similar to those seen in full-term babies. However, side effects like pauses in breathing, slow heart rate or low blood oxygen levels seem to be more common in preterm babies; for safety, these babies should be monitored closely after vaccination. Preterm babies are often vaccinated with a delay compared to the recommended schedule. No studies reported data on protection from the diseases covered by penta- and hexavalent vaccinations in preterm babies. More research is needed on penta- and hexavalent vaccination of other high-risk babies besides those born prematurely.
Imaging Radial Distribution Functions of Complex Particles by Relayed Dynamic Nuclear Polarization

The physical properties of many modern multi-component materials are determined by their internal microstructure. Tools capable of characterizing complex nanoscale architectures in composite materials are, therefore, essential to design materials with targeted properties. Depending on the morphology and the composition, structures may be measured by laser diffraction, scattering methods, or by electron microscopy. However, it can be difficult to obtain contrast in materials where all the components are organic, which is typically the case for formulated pharmaceuticals, or multi-domain polymers. In nuclear magnetic resonance (NMR) spectroscopy, chemical shifts allow a clear distinction between organic components and can in principle provide the required chemical contrast. Here, we introduce a method to obtain radial images of the internal structure of multi-component particles from NMR measurements of the relay of nuclear hyperpolarization obtained from dynamic nuclear polarization. The method is demonstrated on two samples of hybrid core–shell particles composed of a core of polystyrene with a shell of mesostructured silica filled with the templating agent CTAB and is shown to yield accurate images of the core–shell structures with a nanometer resolution.
GENETIC ALGORITHM BASED OPTIMAL DESIGN FOR CMAC CONTROLLER

Using genetic algorithm(GA),the optimal design for cerebellar model articulation controller(CMAC) is first carried out with the limited overshoot. In addition,the proposed method is proved to be effective by the simulation of the wire tension CMAC controller optimal design.The simulation is also compared to the wire optimal PID controller in this paper.This method not only can overcome the too much times by traditional trialanderror design method, but also is able to obtain optimal control system design.The simulation results show that the proposed design method is more stable and easier than optimal PID. 
Efficacy of rice husk and halophilic bacterial biofilms in the treatment of saline water

Water salinity causes less production of agricultural productivity, low economic returns, soil destructions, less sustainability, and reduction in the germination rate. The current study was aimed to understand the combined potential of halophilic bacteria and rice husk in treating water salinity. In total, 10 halophilic bacterial isolates were isolated from Khewra Mines, Pakistan. Bacterial isolates were characterized by biochemical tests. 16S rRNA gene sequencing identified the isolate SO 1 as Bacillus safensis (accession number ON203008) being the promising halophilic bacteria tolerating upto 3 M NaCl concentration. Next, rice husk was used as carbon source for bacterial biofilm formation, growth and propagation. For saline water treatment, the experimental setting comprising glass wool, rice husk and artificial sea water (3 M) was set. B. safensis biofilm was developed in test samples to desaline the saline water containing 3 M NaCl concentration. Following NaCl decline, flame photometric analysis was used to check the desalination extent of treated saline water. Results showed decreased sodium level in sea water in the presence of rice husk and glass wool. The eluted water used for the germination of Zea mays seeds showed improved growth performance. Also, decreased photosynthetic pigments (chlorophyll “a” = 18.99, and chlorophyll “b” = 10.65), sugar contents (0.7593), and increased carotenoid (1526.91), protein contents (0.4521) were noted compared to control. This eco‐friendly approach for bioremediation of salt‐affected soils to optimize crop yields under stress through halophilic bacteria and rice husk may overcome the problem of the reduced yield of cash crops/agriculture and water shortage by salinity.
A Fast Gaussian Filtering Algorithm for Surface Roughness Measurements

The Gaussian filters for surface roughness measurements are designed by using both the Gaussian function's approximation method and the impulse invariant transformation. The zero-phase-shift recursive algorithms of the Gaussian approximation filters are so simple that they are completed easily. There are a small number of computation operations in these recursive algorithms, the filtering algorithms are therefore efficient. The Gaussian approximation filters, consisting of more cascaded basic filters, result in higher accuracy of the amplitude transmission characteristics without increasing a large number of computation operations. Two filtering algorithms, with less then 2% and 1% of the transmission characteristic deviations and less then 1 second needed for a 2D roughness filtering using the currently common computers, are given.