nils-herrmann/clean_scirepeval huggingface dataset:
Standardizing kilonovae and their use as standard candles to measure the Hubble constant

The detection of GW170817 is revolutionizing many areas of astrophysics with the joint observation of gravitational waves and electromagnetic emissions. These multi-messenger events provide a new approach to determine the Hubble constant, thus, they are a promising candidate for mitigating the tension between measurements of Type Ia supernovae via the local distance ladder and the Cosmic Microwave Background. In addition to the "standard siren" provided by the gravitational-wave measurement, the kilonova itself has characteristics that allow to improve existing measurements or to perform yet another, independent measurement of the Hubble constant without gravitational-wave information. Here, we employ standardization techniques borrowed from the type-Ia community and apply them to kilonovae, not using any information from the gravitational-wave signal. We use two versions of this technique, one derived from direct observables measured from the lightcurve, and the other based on inferred ejecta parameters, e.g., mass, velocity, and composition. According to these techniques, we obtain constraints of $D = 31^{+17}_{-11}$ Mpc or $D = 36^{+9}_{-7}$ Mpc (median and symmetric 68% credible interval) for the distance of GW170817 for the measured and inferred analysis respectively. These lead to Hubble Constant measurements of $H_0 = 109^{+49}_{-35}$ km $\mathrm{s}^{-1}$ $\mathrm{Mpc}^{-1}$ and $H_0 = 85^{+21}_{-16}$ km $\mathrm{s}^{-1}$ $\mathrm{Mpc}^{-1}$. We show in this way that the kilonova measurement is competitive with the gravitational-wave measurements as an independent constraint on $H_0$.
Identification of gatekeeper diseases on the way to cardiovascular mortality

Multimorbidity, the co-occurrence of two or more chronic diseases such as diabetes, obesity or cardiovascular diseases in one patient, is a frequent phenomenon. To make care more efficient, it is of relevance to understand how different diseases condition each other over the life time of a patient. However, most of our current knowledge on such patient careers is either confined to narrow time spans or specific (sets of) diseases. Here, we present a population-wide analysis of long-term patient trajectories by clustering them according to their disease history observed over 17 years. When patients acquire new diseases, their cluster assignment might change. A health trajectory can then be described by a temporal sequence of disease clusters. From the transitions between clusters we construct an age-dependent multilayer network of disease clusters. Random walks on this multilayer network provide a more precise model for the time evolution of multimorbid health states when compared to models that cluster patients based on single diseases. Our results can be used to identify decisive events that potentially determine the future disease trajectory of a patient. We find that for elderly patients the cluster network consists of regions of low, medium and high in-hospital mortality. Diagnoses of diabetes and hypertension are found to strongly increase the likelihood for patients to subsequently move into the high-mortality region later in life.
Could short-lasting non-specific immunity explain seasonal spacing of epidemic diseases?

Common respiratory viruses cause seasonal epidemics in sequential patterns. The underlying mechanisms for this pattern have been debated for some time. For influenza, contenders include temperature, humidity, and vitamin D levels. While such seasonal drivers may be sufficient to explain midwinter peaking it is unclear if other respiratory viruses peaking in spring, summer, or fall have individual environmental drivers. Here we present a dynamic model of interacting diseases, in an effort to explain observed seasonal patterns without requiring multiple seasonal drivers. Our model extends the classical SIRS-model to include multiple diseases that interact via a short-lasting, non-specific immunity component. This temporal protection is triggered by exposure to any epidemic disease and lasts only a couple of weeks. We show that the inhibiting disease-interaction allows recurrent epidemic behaviour without any seasonal driving. In the presence of a single seasonal driver our model parsimoniously predicts epidemic patterns such as sequentially occurring epidemic diseases. We also present a two-disease simulation reproducing multiple features observed in time series of parainfluenza strains PIV-3 and biennial PIV-1 epidemic patterns. Complex epidemic patterns of seasonal diseases may be explained by non-specific innate or cloub cell (T-cell) mediated immune responses that result in interaction between unrelated respiratory epidemic diseases.
Agglomerative likelihood clustering

We consider the problem of fast time-series data clustering. Building on previous work modeling, the correlation-based Hamiltonian of spin variables we present an updated fast non-expensive agglomerative likelihood clustering algorithm (ALC). The method replaces the optimized genetic algorithm based approach (f-SPC) with an agglomerative recursive merging framework inspired by previous work in econophysics and community detection. The method is tested on noisy synthetic correlated time-series datasets with a built-in cluster structure to demonstrate that the algorithm produces meaningful non-trivial results. We apply it to time-series datasets as large as 20 000 assets and we argue that ALC can reduce computation time costs and resource usage costs for large scale clustering for time-series applications while being serialized, and hence has no obvious parallelization requirement. The algorithm can be an effective choice for state-detection for online learning in a fast non-linear data environment, because the algorithm requires no prior information about the number of clusters.
Y-Net: A Hybrid Deep Learning Reconstruction Framework for Photoacoustic Imaging in vivo

Photoacoustic imaging (PAI) is an emerging non-invasive imaging modality combining the advantages of deep ultrasound penetration and high optical contrast. Image reconstruction is an essential topic in PAI, which is unfortunately an ill-posed problem due to the complex and unknown optical/acoustic parameters in tissue. Conventional algorithms used in PAI (e.g., delay-and-sum) provide a fast solution while many artifacts remain, especially for linear array probe with limited-view issue. Convolutional neural network (CNN) has shown state-of-the-art results in computer vision, and more and more work based on CNN has been studied in medical image processing recently. In this paper, we present a non-iterative scheme filling the gap between existing direct-processing and post-processing methods, and propose a new framework Y-Net: a CNN architecture to reconstruct the PA image by optimizing both raw data and beamformed images once. The network connected two encoders with one decoder path, which optimally utilizes more information from raw data and beamformed image. The results of the test set showed good performance compared with conventional reconstruction algorithms and other deep learning methods. Our method is also validated with experiments both in-vitro and in vivo, which still performs better than other existing methods. The proposed Y-Net architecture also has high potential in medical image reconstruction for other imaging modalities beyond PAI.
Topology of the Nodal Set of Random Equivariant Spherical Harmonics on 𝕊3

We show that real and imaginary parts of equivariant spherical harmonics on ${{\mathbb{S}}}^3$ have almost surely a single nodal component. Moreover, if the degree of the spherical harmonic is $N$ and the equivariance degree is $m$, then the expected genus is proportional to $m \left (\frac{N^2 - m^2}{2} + N\right ) $. Hence, if $\frac{m}{N}= c $ for fixed $0 < c < 1$, then the genus has order $N^3$.
Situation-Aware Left-Turning Connected and Automated Vehicle Operation at Signalized Intersections

One challenging aspect of the connected and automated vehicle (CAV) operation in mixed traffic is the development of a situation-awareness module for CAVs. While operating on public roads, CAVs need to assess their surroundings, especially the intentions of non-CAVs. Generally, CAVs demonstrate a defensive driving behavior, and CAVs expect other nonautonomous entities on the road will follow the traffic rules or common driving behavior. However, the presence of aggressive human drivers in the surrounding environment, who may not follow traffic rules and behave abruptly, can lead to serious safety consequences. In this article, we have addressed the CAV and non-CAV interaction by evaluating a situation-awareness module for left-turning CAV operations in an urban area. Existing literature does not consider the intent of the following vehicle for a CAV’s left-turning movement, and existing CAV controllers do not assess the following non-CAV’s intents. Based on our simulation study, the situation-aware CAV controller module reduces up to 47% of the abrupt braking of the following non-CAVs for scenarios with different opposing through movement compared to the base scenario with the autonomous vehicle, without considering the following vehicle’s intent. The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800, and 1000 vehicle/hour/lane are about 58%, 52%, and 62%, respectively, considering both scenarios for the aggressive human driver following the CAV if the following vehicle’s intent is considered by a CAV in making a left-turn at an intersection.
calculation worst-case Value-at-Risk prediction using empirical data under model uncertainty

Quantification of risk positions under model uncertainty is of crucial importance from both viewpoints of external regulation and internal management. The concept of model uncertainty, sometimes also referred to as model ambiguity. Although we know the family of models, we cannot precisely decide which one to use. Given the set $\mathcal{P}$, the value of the risk measure $\rho$ varies in a range over the set of all possible models. The largest value in such a range is referred to as a worst-case value, and the corresponding model is called a worst scenario. Value-at-Risk(VaR) has become a very popular risk-measurement tool since it was first proposed. Naturally, WVaR(worst-case Value-at-Risk) attracts the attention of many researchers. Although many literatures investigated WVaR, the implications for empirical data analysis remain rare. In this paper, we proposed a special model uncertainty market model to simply the $\mathcal{P}$ to a set contain finite number of probability distributions. The model has the structure of the two-layer mixed distribution model. We used change point detection method to divide the returns series and then used EM algorithm to estimate the parameters. Finally, we calculated VaR, WVaR(worst-case Value-at-Risk) and BVaR(best-case Value-at-Risk) for four financial markets and then analyzed their different performance.
Formation and evolution of globular clusters in cosmological simulations

In a series of three papers, we introduced a novel cluster formation model that describes the formation, growth, and disruption of star clusters in high-resolution cosmological simulations. We tested this model on a Milky Way-sized galaxy and found that various properties of young massive clusters, such as the mass function and formation efficiency, are consistent with observations in the local universe. Interestingly, most massive clusters – globular cluster candidates – are preferentially formed during major merger events. We follow the dynamical evolution of clusters in the galactic tidal field. Due to tidal disruption, the cluster mass function evolves from initial power law to a peaked shape. The surviving clusters at z = 0 show a broad range of metallicity [Fe/H] from -3 to -0.5. A robust prediction of the model is the age–metallicity relation, in which metal-rich clusters are systematically younger than metal-poor clusters by up to 3 Gyr.
Electromagnetic transients and gravitational waves from white dwarf disruptions by stellar black holes in triple systems

Mergers of binaries comprising compact objects can give rise to explosive transient events, heralding the birth of exotic objects that cannot be formed through single-star evolution. Using a large number of direct N-body simulations, we explore the possibility that a white dwarf (WD) is dynamically driven to tidal disruption by a stellar-mass black hole (BH) as a consequence of the joint effects of gravitational wave (GW) emission and Lidov–Kozai oscillations imposed by the tidal field of an outer tertiary companion orbiting the inner BH–WD binary. We explore the sensitivity of our results to the distributions of natal kick velocities imparted to the BH and WD upon formation, adiabatic mass loss, semimajor axes and eccentricities of the triples, and stellar-mass ratios. We find rates of WD–tidal disruption events (TDEs) in the range 1.2 × 10−3 − 1.4 Gpc−3 yr−1 for z ≤ 0.1, rarer than stellar TDEs in triples by a factor of ∼3–30. The uncertainty in the TDE rates may be greatly reduced in the future using GW observations of Galactic binaries and triples with LISA. WD–TDEs may give rise to high-energy X-ray or gamma-ray transients of duration similar to long gamma-ray bursts but lacking the signatures of a core-collapse supernova, while being accompanied by a supernova-like optical transient that lasts for only days. WD–BH and WD–NS binaries will also emit GWs in the LISA band before the TDE. The discovery and identification of triple-induced WD–TDE events by future time domain surveys and/or GWs could enable the study of the demographics of BHs in nearby galaxies.
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

This paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph-level representations are critical in a variety of real-world applications such as predicting the properties of molecules and community analysis in social networks. Traditional graph kernel based methods are simple, yet effective for obtaining fixed-length representations for graphs but they suffer from poor generalization due to hand-crafted designs. There are also some recent methods based on language models (e.g. graph2vec) but they tend to only consider certain substructures (e.g. subtrees) as graph representatives. Inspired by recent progress of unsupervised representation learning, in this paper we proposed a novel method called InfoGraph for learning graph-level representations. We maximize the mutual information between the graph-level representation and the representations of substructures of different scales (e.g., nodes, edges, triangles). By doing so, the graph-level representations encode aspects of the data that are shared across different scales of substructures. Furthermore, we further propose InfoGraph*, an extension of InfoGraph for semi-supervised scenarios. InfoGraph* maximizes the mutual information between unsupervised graph representations learned by InfoGraph and the representations learned by existing supervised methods. As a result, the supervised encoder learns from unlabeled data while preserving the latent semantic space favored by the current supervised task. Experimental results on the tasks of graph classification and molecular property prediction show that InfoGraph is superior to state-of-the-art baselines and InfoGraph* can achieve performance competitive with state-of-the-art semi-supervised models.
Improving Deep Reinforcement Learning in Minecraft with Action Advice

Training deep reinforcement learning agents complex behaviors in 3D virtual environments requires significant computational resources. This is especially true in environments with high degrees of aliasing, where many states share nearly identical visual features. Minecraft is an exemplar of such an environment. We hypothesize that interactive machine learning IML, wherein human teachers play a direct role in training through demonstrations, critique, or action advice, may alleviate agent susceptibility to aliasing. However, interactive machine learning is only practical when the number of human interactions is limited, requiring a balance between human teacher effort and agent performance. We conduct experiments with two reinforcement learning algorithms which enable human teachers to give action advice, Feedback Arbitration and Newtonian Action Advice, under visual aliasing conditions. To assess potential cognitive load per advice type, we vary the accuracy and frequency of various human action advice techniques. Training efficiency, robustness against infrequent and inaccurate advisor input, and sensitivity to aliasing are examined.
Probabilistic Residual Learning for Aleatoric Uncertainty in Image Restoration

Aleatoric uncertainty is an intrinsic property of ill-posed inverse and imaging problems. Its quantification is vital for assessing the reliability of relevant point estimates. In this paper, we propose an efficient framework for quantifying aleatoric uncertainty for deep residual learning and showcase its significant potential on image restoration. In the framework, we divide the conditional probability modeling for the residual variable into a deterministic homo-dimensional level, a stochastic low-dimensional level and a merging level. The low-dimensionality is especially suitable for sparse correlation between image pixels, enables efficient sampling for high dimensional problems and acts as a regularizer for the distribution. Preliminary numerical experiments show that the proposed method can give not only state-of-the-art point estimates of image restoration but also useful associated uncertainty information.
Health-Informed Policy Gradients for Multi-Agent Reinforcement Learning

This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then extended to a multi-agent variant of the proximal policy optimization algorithm and demonstrated on simple particle environments that have characteristics such as system health, risk-taking, semi-expendable agents, continuous action spaces, and partial observability. We show significant improvement in learning performance compared to policy gradient methods that do not perform multi-agent credit assignment.
What is the Point of Fairness? Disability, AI and The Complexity of Justice

Work integrating conversations around AI and Disability is vital and valued, particularly when done through a lens of fairness. Yet at the same time, analyzing the ethical implications of AI for disabled people solely through the lens of a singular idea of "fairness" risks reinforcing existing power dynamics, either through reinforcing the position of existing medical gatekeepers, or promoting tools and techniques that benefit otherwise-privileged disabled people while harming those who are rendered outliers in multiple ways. In this paper we present two case studies from within computer vision - a subdiscipline of AI focused on training algorithms that can "see" - of technologies putatively intended to help disabled people but, through failures to consider structural injustices in their design, are likely to result in harms not addressed by a "fairness" framing of ethics. Drawing on disability studies and critical data science, we call on researchers into AI ethics and disability to move beyond simplistic notions of fairness, and towards notions of justice.
An Efficient Evolutionary Algorithm for Minimum Cost Submodular Cover

In this paper, the Minimum Cost Submodular Cover problem is studied, which is to minimize a modular cost function such that the monotone submodular benefit function is above a threshold. For this problem, an evolutionary algorithm EASC is introduced that achieves a constant, bicriteria approximation in expected polynomial time; this is the first polynomial-time evolutionary approximation algorithm for Minimum Cost Submodular Cover. To achieve this running time, ideas motivated by submodularity and monotonicity are incorporated into the evolutionary process, which likely will extend to other submodular optimization problems. In a practical application, EASC is demonstrated to outperform the greedy algorithm and converge faster than competing evolutionary algorithms for this problem.
Linear Dynamics: Clustering without identification

Clustering time series is a delicate task; varying lengths and temporal offsets obscure direct comparisons. A natural strategy is to learn a parametric model foreach time series and to cluster the model parameters rather than the sequences themselves. Linear dynamical systems are a fundamental and powerful parametric model class. However, identifying the parameters of a linear dynamical systems is a venerable task, permitting provably efficient solutions only in special cases. In this work, we show that clustering the parameters of unknown linear dynamical systems is, in fact, easier than identifying them. We analyze a computationally efficient clustering algorithm that enjoys provable convergence guarantees under a natural separation assumption. Although easy to implement, our algorithm is general, handling multi-dimensional data with time offsets and partial sequences. Evaluating our algorithm on both synthetic data and real electrocardiogram (ECG) signals, we see significant improvements in clustering quality over existing baselines.
Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validatio

Determining possible failure scenarios is a critical step in the evaluation of autonomous vehicle systems. Real world vehicle testing is commonly employed for autonomous vehicle validation, but the costs and time requirements are high. Consequently, simulation driven methods such as Adaptive Stress Testing (AST) have been proposed to aid in validation. AST formulates the problem of finding the most likely failure scenarios as a Markov decision process, which can be solved using reinforcement learning. In practice, AST tends to find scenarios where failure is unavoidable and tends to repeatedly discover the same types of failures of a system. This work addresses these issues by encoding domain relevant information into the search procedure. With this modification, the AST method discovers a larger and more expressive subset of the failure space when compared to the original AST formulation. We show that our approach is able to identify useful failure scenarios of an autonomous vehicle policy.
Falls Prediction in eldery people using Gated Recurrent Units

Falls prevention, especially in older people, becomes an increasingly important topic in the times of aging societies. In this work, we present Gated Recurrent Unit-based neural networks models designed for predicting falls (syncope). The cardiovascular systems signals used in the study come from Gravitational Physiology, Aging and Medicine Research Unit, Institute of Physiology, Medical University of Graz. We used two of the collected signals, heart rate, and mean blood pressure. By using bidirectional GRU model, it was possible to predict the syncope occurrence approximately ten minutes before the manual marker.