Miracle-dz/UniFAR-FTUs huggingface dataset:
The German Lipoprotein Apheresis Registry—Summary of the ninth annual report

During 2012–2020, 89 German apheresis centers collected retrospective and prospective observational data of 2028 patients undergoing regular lipoprotein apheresis (LA) for the German Lipoprotein Apheresis Registry (GLAR). More than 47 500 LA sessions are documented in GLAR. In 2020, all patients treated with LA showed a high immediate median reduction rate of LDL‐C (68.2%, n = 1055) and Lp(a) (72.4%, n = 994). Patient data were analyzed for the incidence rate of major coronary events (MACE) 1 and 2 years before the beginning of LA treatment (y‐2 and y‐1) and prospectively up to 7 years on LA (y + 1 to y + 7). During the first 2 years of LA (y + 1 and y + 2), a MACE reduction of 78% was observed. Current analysis of GLAR data shows very low incidence rates of cardiovascular events in patients with high LDL‐C and/or high Lp(a) levels, progressive ASCVD, and maximally tolerated lipid lowering medication regular by LA results.
Observations of the 14.5-GHz transition of interstellar H2CO towards continuum sources and dark clouds

On a observe la transition 2 11 -2 12 de l'ortho H 2 CO a 14,5 GHz avec le radiotelescope de Parkes en direction d'environ 100 sources de continuum et nuages sombres et on l'a comparee aux observations de la transition 1 10 -1 11 . Les observations suggerent des densites H 2 de 10 5 cm −3 pour les nuages proches de regions HII et 10 4 cm −3 pour les autres. L'abondance correspondante de H 2 CO par rapport a H 2 est de l'ordre de 5×10 −11 dans les deux cas
Dielectric properties of hydrogen-terminated Si(111) ultrathin films

Dielectric properties of Si(111) ultrathin films have been investigated using first-principles ground-states calculations in external electrostatic fields. With increasing thickness of Si(111) ultrathin films, the optical dielectric constant evaluated at the center of the slab converges to the experimental bulk dielectric constant at a thickness of only eight bilayers, while the energy gap of the slab is still larger than that of bulk Si. The converged theoretical dielectric constant for bulk Si is only 6.2% higher than the experimental one. Furthermore, spatial variations of the dielectric constant have also been evaluated using the position-dependent macroscopic field given by a clear-cut definition. The results show that the dielectric constant is reduced distinctly at the first few bilayers from the surface, which stems from the penetration of depolarized charges induced at the surface. Such an effective reduction of the depolarization field near the surface is one of the reasons for the decrease in o.
Learning to See Through With Events

Although synthetic aperture imaging (SAI) can achieve the seeing-through effect by blurring out off-focus foreground occlusions while recovering in-focus occluded scenes from multi-view images, its performance is often deteriorated by dense occlusions and extreme lighting conditions. To address the problem, this paper presents an Event-based SAI (E-SAI) method by relying on the asynchronous events with extremely low latency and high dynamic range acquired by an event camera. Specifically, the collected events are first refocused by a Refocus-Net module to align in-focus events while scattering out off-focus ones. Following that, a hybrid network composed of spiking neural networks (SNNs) and convolutional neural networks (CNNs) is proposed to encode the spatio-temporal information from the refocused events and reconstruct a visual image of the occluded targets. Extensive experiments demonstrate that our proposed E-SAI method can achieve remarkable performance in dealing with very dense occlusions and extreme lighting conditions and produce high-quality images from pure events. Codes and datasets are available at https://dvs-whu.cn/projects/esai/.
Bayesian survival analysis with INLA

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article"Bayesian survival analysis with BUGS"(Alvares et al., 2021). In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach.
Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance

Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high peaks of electricity demand, grid instability exacerbated by the intermittency of renewable generation, and climate change on a global scale amplified by increasing carbon emissions. While current practices are growingly inadequate, the pathway of artificial intelligence (AI)-based methods to widespread adoption is hindered by missing aspects of trustworthiness. The CityLearn Challenge is an exemplary opportunity for researchers from multi-disciplinary fields to investigate the potential of AI to tackle these pressing issues within the energy domain, collectively modeled as a reinforcement learning (RL) task. Multiple real-world challenges faced by contemporary RL techniques are embodied in the problem formulation. In this paper, we present a novel method using the solution function of optimization as policies to compute the actions for sequential decision-making, while notably adapting the parameters of the optimization model from online observations. Algorithmically, this is achieved by an evolutionary algorithm under a novel trajectory-based guidance scheme. Formally, the global convergence property is established. Our agent ranked first in the latest 2021 CityLearn Challenge, being able to achieve superior performance in almost all metrics while maintaining some key aspects of interpretability.
An Analytical Model of Turbulence in Parker Spiral Geometry and Associated Magnetic Field Line Lengths

Understanding the magnetic connections from the Sun to interplanetary space is crucial for linking in situ particle observations with the solar source regions of the particles. A simple connection along the large-scale Parker spiral magnetic field is made complex by the turbulent random walk of field lines. In this paper, we present the first analytical model of heliospheric magnetic fields where the dominant 2D component of the turbulence is transverse to the Parker spiral. The 2D wave field is supplemented with a minor wave field component that has asymptotic slab geometry at small and large heliocentric distances. We show that turbulence spreads field lines from a small source region at the Sun to a 60° heliolongitudinal and heliolatitudinal range at 1 au, with a standard deviation of the angular spread of the field lines of 14°. Small source regions map to an intermittent range of longitudes and latitudes at 1 au, consistent with dropouts in solar energetic particle intensities. The lengths of the field lines are significantly extended from the nominal Parker spiral length of 1.17 au up to 1.6 au, with field lines from sources at and behind the west limb considerably longer than those closer to the solar disk center. We discuss the implications of our findings for understanding charged particle propagation and the importance of understanding the turbulence properties close to the Sun.
Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories

This paper proposes a novel model-based policy gradient algorithm for tracking dynamic targets using a mobile robot, equipped with an onboard sensor with limited field of view. The task is to obtain a continuous control policy for the mobile robot to collect sensor measurements that reduce uncertainty in the target states, measured by the target distribution entropy. We design a neural network control policy with the robot $SE(3)$ pose and the mean vector and information matrix of the joint target distribution as inputs and attention layers to handle variable numbers of targets. We also derive the gradient of the target entropy with respect to the network parameters explicitly, allowing efficient model-based policy gradient optimization.
Improved Convergence Guarantees for Shallow Neural Networks

We continue a long line of research aimed at proving convergence of depth 2 neural networks, trained via gradient descent, to a global minimum. Like in many previous works, our model has the following features: regression with quadratic loss function, fully connected feedforward architecture, RelU activations, Gaussian data instances and network initialization, adversarial labels. It is more general in the sense that we allow both layers to be trained simultaneously and at {\em different} rates. Our results improve on state-of-the-art [Oymak Soltanolkotabi 20] (training the first layer only) and [Nguyen 21, Section 3.2] (training both layers with Le Cun's initialization). We also report several simple experiments with synthetic data. They strongly suggest that, at least in our model, the convergence phenomenon extends well beyond the ``NTK regime''.
Searching for Intelligent Life in Gravitational Wave Signals Part I: Present Capabilities and Future Horizons

We show that the Laser Interferometer Gravitational Wave Observatory (LIGO) is a powerful instrument in the Search for Extraterrestrial Intelligence (SETI). LIGO’s ability to detect gravitational waves (GWs) from astrophysical sources, such as binary black hole mergers, also provides the potential to detect extraterrestrial mega-technology, such as R apid A nd/or M assive A ccelerating spacecraft (RAMAcraft). We show that LIGO is sensitive to RAMAcraft of 1 Jupiter mass accelerating to a fraction of the speed of light (e.g. 30%) from 10 − 100 kpc or a Moon mass from 1 − 10 pc. While existing SETI searches can probe on the order of ten-thousand stars for human-scale technology (e.g. radio waves), LIGO can probe all 10 11 stars in the Milky Way for RAMAcraft. Moreover, thanks to the 𝑓 − 1 scaling of RAMAcraft signals, our sensitivity to these objects will increase as low-frequency detectors are developed and improved, allowing for the detection of smaller masses further from Earth. In particular, we ﬁnd that DECIGO and the Big Bang Observer (BBO) will be about 100 times more sensitive than LIGO, increasing the search volume by 10 6 , while LISA and Pulsar Timing Arrays (PTAs) may improve sensitivities to objects with long acceleration periods. In this paper, we calculate the waveforms for linearly-accelerating RAMAcraft in a form suitable for LIGO, Virgo, and KAGRA searches and provide the range for a variety of masses and accelerations. We expect that the current and upcoming GW detectors will soon become an excellent complement to the existing SETI eﬀorts.
Smoothing Policy Iteration for Zero-sum Markov Games

—Zero-sum Markov Games (MGs) has been an ef- ﬁcient framework for multi-agent systems and robust control, wherein a minimax problem is constructed to solve the equilib- rium policies. At present, this formulation is well studied under tabular settings wherein the maximum operator is primarily and exactly solved to calculate the worst-case value function. However, it is non-trivial to extend such methods to handle complex tasks, as ﬁnding the maximum over large-scale action spaces is usually cumbersome. In this paper, we propose the smoothing policy iteration (SPI) algorithm to solve the zero-sum MGs approximately, where the maximum operator is replaced by the weighted LogSumExp (WLSE) function to obtain the nearly optimal equilibrium policies. Specially, the adversarial policy is served as the weight function to enable an efﬁcient sampling over action spaces. We also prove the convergence of SPI and analyze its approximation error in ∞− norm based on the contraction mapping theorem. Besides, we propose a model-based algorithm called Smooth adversarial Actor-critic (SaAC) by extending SPI with the function approximations. The target value related to WLSE function is evaluated by the sampled trajectories and then mean square error is constructed to optimize the value function, and the gradient-ascent-descent methods are adopted to optimize the protagonist and adversarial policies jointly. In addition, we incorporate the reparameterization technique in model-based gradient back-propagation to prevent the gradient vanishing due to sampling from the stochastic policies. We verify our algorithm in both tabular and function approximation settings. Results show that SPI can approximate the worst-case value function with a high accuracy and SaAC can stabilize the training process and improve the adversarial robustness in a large margin.
Inverse scattering method for nonlinear Klein--Gordon equation coupled with a scalar field

In the present work, a novel class of negative order Ablowitz-Kaup- Newell-Segur (AKNS) nonlinear evolution equations are obtained by applying the Lax hierarchy of the generalized Zakharov-Shabat (ZS) system. The inverse scattering problem on the whole axis is examined in the case where the ZS system consists of two equations and admits a real symmetric potential. Referring to these results, the N-soliton solutions for the integro-diﬀerential version of the nonlinear Klein–Gordon equation coupled with a scalar ﬁeld are obtained by using the inverse scattering method.
Hodge Decomposition of the Remittance Network on the XRP ledger in the Price Hike of January 2018

This study analyzes the remittance transaction recorded on the XRP ledger for ETH and USD from July 2017 to Jun 2018, including the bubble period in early 2018. Using the Hodge decomposition, we estimate the ``loop flow'' in the international remittance of cryptoassets during the bubble period. We found characteristic differences between those fiat currencies and cryptoassets during the bubble period. For ETH, there was a significant increase in the loop flow during the cryptoasset price peak. This might be related to money laundering or arbitrage transaction. There was a slight increase in the loop flow for USD during the cryptoasset price peak.
Applications of AI in Astronomy

: We provide a brief, and inevitably incomplete overview of the use of Machine Learning (ML) and other AI methods in astronomy, astrophysics, and cosmology. Astronomy entered the big data era with the first digital sky surveys in the early 1990s and the resulting Terascale data sets, which required automating of many data processing and analysis tasks, for example the star-galaxy separation, with billions of feature vectors in hundreds of dimensions. The exponential data growth continued, with the rise of synoptic sky surveys and the Time Domain Astronomy, with the resulting Petascale data streams and the need for a real-time processing, classification, and decision making. A broad variety of classification and clustering methods have been applied for these tasks, and this remains a very active area of research. Over the past decade we have seen an exponential growth of the astronomical literature involving a variety of ML/AI applications of an ever increasing complexity and sophistication. ML and AI are now a standard part of the astronomical toolkit. As the data complexity continues to increase, we anticipate further advances leading towards a collaborative human-AI discovery.
Is the remnant of SN1006 crab-like?

The solid-state spectrometer on the Einstein Observatory and the GSFC cosmic X-ray spectrometer on OSO 8 have observed the X-ray spectrum of SN 1006. The data can be well represented by a power-law model with alpha = 1.2, similar to the spectrum of the Crab Nebula. This is in contrast to the radio and X-ray maps of SN 1006 which show a shell structure more typical of SNR with thermal X-ray emission. The X-ray spectrum is suggestive of nonthermal synchrotron emission, raising the possibility that the remnant of SN 1006 contains a source of relativistic electrons.
Sums of Independent Circular Random Variables and Maximum Likelihood Circular Uniformity Tests Based on Nonnegative Trigonometric Sums Distributions

The circular uniform distribution on the unit circle is closed under summation, that is, the sum of independent circular uniformly distributed random variables is also circular uniformly distributed. In this study, it is shown that a family of circular distributions based on nonnegative trigonometric sums (NNTS) is also closed under summation. Given the flexibility of NNTS circular distributions to model multimodality and skewness, these are good candidates for use as alternative models to test for circular uniformity to detect different deviations from the null hypothesis of circular uniformity. The circular uniform distribution is a member of the NNTS family, but in the NNTS parameter space, it corresponds to a point on the boundary of the parameter space, implying that the regularity conditions are not satisfied when the parameters are estimated by using the maximum likelihood method. Two NNTS tests for circular uniformity were developed by considering the standardised maximum likelihood estimator and the generalised likelihood ratio. Given the nonregularity condition, the critical values of the proposed NNTS circular uniformity tests were obtained via simulation and interpolated for any sample size by the fitting of regression models. The validity of the proposed NNTS circular uniformity tests was evaluated by generating NNTS models close to the circular uniformity null hypothesis.
Social stratification in networks: insights from co-authorship networks

It has been observed that real-world social networks often exhibit stratification along economic or other lines, with consequences for class mobility and access to opportunities. With the rise in human interaction data and extensive use of online social networks, the structure of social networks (representing connections between individuals) can be used for measuring stratification. However, although stratification has been studied extensively in the social sciences, there is no single, generally applicable metric for measuring the level of stratification in a network. In this work, we first propose the novel Stratification Assortativity (StA) metric, which measures the extent to which a network is stratified into different tiers. Then, we use the StA metric to perform an in-depth analysis of the stratification of five co-authorship networks. We examine the evolution of these networks over 50 years and show that these fields demonstrate an increasing level of stratification over time, and, correspondingly, the trajectory of a researcher’s career is increasingly correlated with her entry point into the network.
Constraining interacting dark energy models with the halo concentration–mass relation

The interacting dark energy (IDE) model is a promising alternative cosmological model that has the potential to solve the fine-tuning and coincidence problems by considering the interaction between dark matter and dark energy. Previous studies have shown that the energy exchange between the dark sector components in this model can significantly affect the dark matter halo properties. In this study, by utilizing a large set of cosmological N-body simulations, we analyse the redshift evolution of the halo concentration–mass (c–M) relation in the IDE model, and show that the c–M relation is a sensitive proxy of the interaction strength parameter ξ2, especially at lower redshifts. Furthermore, we construct parametrized formulae to quantify the dependence of the c–M relation on ξ2 at redshifts ranging from z = 0 to z = 0.6. Our parametrized formulae provide a useful theoretical tool in constraining ξ2 with the observational c–M relation. As a first attempt, we use the data from X-ray, gravitational lensing, and galaxy rotational curve observations and obtain a tight constraint on ξ2, i.e. ξ2 = 0.071 ± 0.034. Our work demonstrates that the halo c–M relation, which reflects the halo assembly history, is a powerful probe to constrain the IDE model.
Early-forming Massive Stars Suppress Star Formation and Hierarchical Cluster Assembly

Feedback from massive stars plays an important role in the formation of star clusters. Whether a very massive star is born early or late in the cluster formation timeline has profound implications for the star cluster formation and assembly processes. We carry out a controlled experiment to characterize the effects of early-forming massive stars on star cluster formation. We use the star formation software suite Torch, combining self-gravitating magnetohydrodynamics, ray-tracing radiative transfer, N-body dynamics, and stellar feedback, to model four initially identical 104 M ⊙ giant molecular clouds with a Gaussian density profile peaking at 521.5 cm−3. Using the Torch software suite through the AMUSE framework, we modify three of the models, to ensure that the first star that forms is very massive (50, 70, and 100 M ⊙). Early-forming massive stars disrupt the natal gas structure, resulting in fast evacuation of the gas from the star-forming region. The star formation rate is suppressed, reducing the total mass of the stars formed. Our fiducial control model, without an early massive star, has a larger star formation rate and total efficiency by up to a factor of 3, and a higher average star formation efficiency per freefall time by up to a factor of 7. Early-forming massive stars promote the buildup of spatially separate and gravitationally unbound subclusters, while the control model forms a single massive cluster.
Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain

Multilingual Neural Machine Translation (MNMT) models leverage many language pairs during training to improve translation quality for low-resource languages by transferring knowledge from high-resource languages. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation which includes terminology-speciﬁc error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer terminology errors.
Review of medical data analysis based on spiking neural networks

Medical data mainly includes various types of biomedical signals and medical images, which can be used by professional doctors to make judgments on patients' health conditions. However, the interpretation of medical data requires a lot of human cost and there may be misjudgments, so many scholars use neural networks and deep learning to classify and study medical data, which can improve the efficiency and accuracy of doctors and detect diseases early for early diagnosis, etc. Therefore, it has a wide range of application prospects. However, traditional neural networks have disadvantages such as high energy consumption and high latency (slow computation speed). This paper presents recent research on signal classification and disease diagnosis based on a third-generation neural network, the spiking neuron network, using medical data including EEG signals, ECG signals, EMG signals and MRI images. The advantages and disadvantages of pulsed neural networks compared with traditional networks are summarized and its development orientation in the future is prospected.