Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning the galaxy-environment connection with graph neural networks

Galaxies co-evolve with their host dark matter halos. Models of the galaxy-halo connection, calibrated using cosmological hydrodynamic simulations, can be used to populate dark matter halo catalogs with galaxies. We present a new method for inferring baryonic properties from dark matter subhalo properties using message-passing graph neural networks (GNNs). After training on subhalo catalog data from the Illustris TNG300-1 hydrodynamic simulation, our GNN can infer stellar mass from the host and neighboring subhalo positions, kinematics, masses, and maximum circular velocities. We find that GNNs can also robustly estimate stellar mass from subhalo properties in 2d projection. While other methods typically model the galaxy-halo connection in isolation, our GNN incorporates information from galaxy environments, leading to more accurate stellar mass inference.
Tracking the multifield dynamics with cosmological data: a Monte Carlo approach

We introduce a numerical method specifically designed for investigating generic multifield models of inflation where a number of scalar fields ϕ K are minimally coupled to gravity and live in a field space with a non-trivial metric 𝒢> IJ (ϕ K ). Our algorithm consists of three main parts. Firstly, we solve the field equations through the entire inflationary period, deriving predictions for observable quantities such as the spectrum of scalar perturbations, primordial gravitational waves, and isocurvature modes. We also incorporate the transfer matrix formalism to track the behavior of adiabatic and isocurvature modes on super-horizon scales and the transfer of entropy to scalar modes after the horizon crossing. Secondly, we interface our algorithm with Boltzmann integrator codes to compute the subsequent full cosmology, including the cosmic microwave background anisotropies and polarization angular power spectra. Finally, we develop a novel sampling algorithm able to efficiently explore a large volume of the parameter space and identify a sub-region where theoretical predictions agree with observations. In this way, sampling over the initial conditions of the fields and the free parameters of the models, we enable Monte Carlo analysis of multifield scenarios. We test all the features of our approach by analyzing a specific model and deriving constraints on its free parameters. Our methodology provides a robust framework for studying multifield inflation, opening new avenues for future research in the field.
Quantum Parallelized Variational Quantum Eigensolvers for Excited States

Calculating excited-state properties of molecules and solids is one of the main computational challenges of modern electronic structure theory. By combining and advancing recent ideas from the field of quantum computing we propose a more effective variational quantum eigensolver based on quantum parallelism: Initial ans\"atze for various excited states are prepared into a single pure state through a minimal number of ancilla qubits. Then a global rotation in the targeted subspace is optimized. Our approach thus avoids the progressive accumulation of errors prone to schemes that calculate excited states successively. Energy gaps and transition amplitudes between eigenstates can immediately be extracted. Moreover, the use of variable auxiliary weights makes the algorithm more resilient to noise and greatly simplifies the optimization procedure. We showcase our algorithm and illustrate its effectiveness for different molecular systems. The interaction effects are treated through generalized unitary coupled cluster ans\"atze and, accordingly, the common unfavorable and artificial extension to the entire Fock space is circumvented.
SIFTER: A Task-specific Alignment Strategy for Enhancing Sentence Embeddings

The paradigm of pre-training followed by fine-tuning on downstream tasks has become the mainstream method in natural language processing tasks. Although pre-trained models have the advantage of generalization, their performance may still vary significantly across different domain tasks. This is because the data distribution in different domains varies. For example, the different parts of the sentence 'He married Smt. Dipali Ghosh in 1947 and led a very happy married life' may have different impact for downstream tasks. For similarity calculations, words such as 'led' and 'life' are more important. On the other hand, for sentiment analysis, the word 'happy' is crucial. This indicates that different downstream tasks have different levels of sensitivity to sentence components. Our starting point is to scale information of the model and data according to the specifics of downstream tasks, enhancing domain information of relevant parts for these tasks and reducing irrelevant elements for different domain tasks, called SIFTER. In the experimental part, we use the SIFTER to improve SimCSE by constructing positive sample pairs based on enhancing the sentence stem and reducing the unimportant components in the sentence, and maximize the similarity between three sentences. Similarly, SIFTER can improve the gate mechanism of the LSTM model by short-circuiting the input gate of important words so that the LSTM model remembers the important parts of the sentence. Our experiments demonstrate that SIFTER outperforms the SimCSE and LSTM baselines.
Sample Attackability in Natural Language Adversarial Attacks

Adversarial attack research in natural language processing (NLP) has made significant progress in designing powerful attack methods and defence approaches. However, few efforts have sought to identify which source samples are the most attackable or robust, i.e. can we determine for an unseen target model, which samples are the most vulnerable to an adversarial attack. This work formally extends the definition of sample attackability/robustness for NLP attacks. Experiments on two popular NLP datasets, four state of the art models and four different NLP adversarial attack methods, demonstrate that sample uncertainty is insufficient for describing characteristics of attackable/robust samples and hence a deep learning based detector can perform much better at identifying the most attackable and robust samples for an unseen target model. Nevertheless, further analysis finds that there is little agreement in which samples are considered the most attackable/robust across different NLP attack methods, explaining a lack of portability of attackability detection methods across attack methods.
A Hurewicz-type Theorem for the Dynamic Asymptotic Dimension with Applications to Coarse Geometry and Dynamics

We prove a Hurewicz-type theorem for the dynamic asymptotic dimension originally introduced by Guentner, Willett, and Yu. Calculations of (or simply upper bounds on) this dimension are known to have implications related to cohomology of group actions and the K-theory of their transformation group C*-algebras. Moreover, these implications are relevant to the current classification program for C*-algebras. As a corollary of our main theorem, we show the dynamic asymptotic dimension of actions by groups on profinite completions along sequential filtrations by normal subgroups is subadditive over extensions of groups, which shows that many such actions by elementary amenable groups are finite dimensional. We combine this with other novel results relating the dynamic asymptotic dimension of such an action to the asymptotic dimension of a corresponding box space. This allows us to give upper bounds on the asymptotic dimension of many box spaces (including examples from infinitely-many groups with exponential growth) using a generalization of the Hirsch length for elementary amenable groups. For some of these examples, we can also find lower bounds by utilizing the theory of ends of groups.
Calibrated measurements of the radio emission of cosmic ray air showers

This work investigates the properties of the radio emission of cosmic ray air showers, in particular by reconstructing the lateral distribution of the emission on an event-by-event basis. The LOPES experiment is measuring the radio emission in the frequency range 40-80 MHz in coincidence with the air shower experiment KASCADE-Grande. For the present analysis a set of data is used which was measured with east-west oriented antennas. By selecting the high energy air showers the occurring short radio pulses can be observed and the lateral distribution of the radio signal for individual showers reconstructed. Based on a performed amplitude calibration of the radio signals for each antenna the absolute value of the field strength is known. Therefore the scale parameter R 0 and the field strength 1 ∈0 of the lateral behavior can be determined. The obtained most probable value of the scale parameter results in R 0 ≈ 125 m, and no significant dependence of this values on general shower parameters, like angle-of-incidence or primary energy could be found. A comparison of the field strength ∈ 0 with the value obtained by a parameterization of earlier LOPES measurements yields significant differences, whereby the field strengths determined from lateral distributions are systematically above the values of the parameterization. Using Monte Carlo simulations of the radio emission of extensive air showers, adjusted to the individual measured events by using the known individual shower parameters, direct comparisons with the measurements are performed. As a result of this, a systematically flatter lateral distribution was obtained for the measurements. In particular, measured events with large scale parameter (R 0 > 200 m) could not be reproduced by the simulations. The comparisons of the field strength at different distances with the simulations show a significant discrepancy close to the shower core. This arises from the larger scale parameters at the measurements. However, an important conclusion from the comparison of measurements with simulations results in a deviation of only ≈ 10% of the absolute values of the field strength at a distance of 75 m.
Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture

Downsampling layers, including pooling and strided convolutions, are crucial components of the convolutional neural network architecture that determine both the granularity/scale of image feature analysis as well as the receptive field size of a given layer. To fully understand this problem, we analyse the performance of models independently trained with each pooling configurations on CIFAR10, using a ResNet20 network, and show that the position of the downsampling layers can highly influence the performance of a network and predefined downsampling configurations are not optimal. Network Architecture Search (NAS) might be used to optimize downsampling configurations as an hyperparameter. However, we find that common one-shot NAS based on a single SuperNet does not work for this problem. We argue that this is because a SuperNet trained for finding the optimal pooling configuration fully shares its parameters among all pooling configurations. This makes its training hard, because learning some configurations can harm the performance of others. Therefore, we propose a balanced mixture of SuperNets that automatically associates pooling configurations to different weight models and helps to reduce the weight-sharing and inter-influence of pooling configurations on the SuperNet parameters. We evaluate our proposed approach on CIFAR10, CIFAR100, as well as Food101 and show that in all cases, our model outperforms other approaches and improves over the default pooling configurations.
Structural Inequities in the Kin Safety Net: Mapping the Three-Generational Network throughout Early Adulthood

Research in the intergenerational transmission of socioeconomic status (SES) consistently shows that the SES of one generation benefits the next. Demographic processes shape the kin structures that serve as conduits for the transmission of SES. Few studies have examined these trends together to describe experiences in evolving kin structures throughout the life course and across generations. This article applies demographic techniques to fertility, marital, and mortality data from three generations in the Panel Survey of Income Dynamics to simulate the amount of time young adults would spend within consequential kin structures. High-SES adults spend more years of their young adulthood in advantageous kin structures with greater potential for kin support and capital accumulation, while low-SES adults spend a larger portion of their young adulthoods as single parents, sandwiched between widowed parents and children, and as adult orphans. The kin network inequities have grown since the 1980s, driven by lagging mortality improvements and increasing single parenthood among low-SES families.
Shared sorrow, shared costs: cost-effectiveness analysis of the Empowerment group therapy approach to treat affective disorders in refugee populations

Background Refugees and asylum seekers (RAS) in Germany need tailored and resource-oriented mental healthcare interventions. Aims To evaluate the cost-effectiveness of group psychotherapy for RAS with moderate depressive symptoms. Method This is a post hoc cost-effectiveness analysis of Empowerment group psychotherapy that was embedded in a stratified stepped and collaborative care model (SCCM) from the multicentre randomised controlled MEHIRA trial. One hundred and forty-nine participants were randomly assigned to SCCM or treatment as usual (TAU) and underwent Empowerment (i.e. level 3 of the SCCM for adults) or TAU. Effects were measured with the nine-item Patient Health Questionnaire (PHQ-9) and quality adjusted life-years (QALY) post-intervention. Health service and intervention costs were measured. Incremental cost-effectiveness ratios (ICER) were estimated and net monetary benefit (NMB) regressions with 95% confidence intervals were performed. Cost-effectiveness was ascertained for different values of willingness to pay (WTP) using cost-effectiveness acceptability curves for probable scenarios. Trial registration number: NCT03109028 on ClinicalTrials.gov. Results Health service use costs were significantly lower for Empowerment than TAU after 1 year. Intervention costs were on average €409.6. Empowerment led to a significant change in PHQ-9 scores but not QALY. Bootstrapped mean ICER indicated cost-effectiveness according to PHQ-9 and varied considerably for QALY in the base case. NMB for a unit reduction in PHQ-9 score at WTP of €0 was €354.3 (€978.5 to −€269.9). Results were confirmed for different scenarios and varying WTP thresholds. Conclusions The Empowerment intervention was cost-effective in refugees with moderate depressive symptoms regarding the clinical outcome and led to a reduction in direct healthcare consumption. Concerning QALYs, there was a lack of confidence that Empowerment differed from TAU.
Postmortem Evaluation of Cardiac Valvular Disease in Bald Eagles (Haliaeetus leucocephalus) and a Golden Eagle (Aquila chrysaetos)

Abstract: Limited data are available regarding cardiac diseases in birds of prey despite their prevalence in these avian species. Literature regarding valvular lesions in birds of prey is scarce and includes single reports of left atrioventricular valvular endocarditis in an adult, free-ranging, male bald eagle (Haliaeetus leucocephalus) and aortic valvular endocarditis in an adult, free-ranging, female red-tailed hawk (Buteo jamaicensis). The purpose of this study was to evaluate the prevalence, signalment, gross necropsy findings, and histologic lesions of valvular lesions in eagles. In this retrospective study, necropsy reports for 24 free-ranging and captive eagles were evaluated over a 15-year period (July 3, 2006–February 28, 2021). Six (25%; 95% confidence interval: 8.9–58.9) birds, 5 bald eagles and 1 golden eagle (Aquila chrysaetos), met the inclusion criteria. Five (83.3%) of the 6 birds had valvular degeneration, 2 (33.3%) had endocarditis, and Staphylococcus aureus was cultured from 1 (16.7%) of the endocarditis cases. The 6 eagles with valvular lesions were all captive adults. Four of the birds were female (66.7%), and the aortic and left atrioventricular valves were equally affected. Acute or chronic cerebral infarcts were present in all 6 birds. Valvular cardiac disease should be considered as a differential diagnosis in eagles exhibiting respiratory distress, neurologic signs, syncope, or in cases of sudden death.
Splenic Small Cell Lymphoma with Presumed Secondary Immune-Mediated Hemolytic Anemia in an African Penguin (Spheniscus demersus)

Abstract: A 2-year-old male African penguin (Spheniscus demersus) was presented to a veterinary teaching hospital for evaluation of a previously diagnosed subclinical, marked regenerative anemia. Physical examination at the zoological institution demonstrated biliverdinuria and pale oral mucous membranes. Diagnostic tests performed on the penguin since the diagnosis and prior to presentation to the veterinary teaching hospital included serial complete blood counts, plasma biochemistry panels, radiographic imaging, blood and plasma heavy metal testing, and infectious disease testing. The abnormal diagnostic test results were consistent with marked regenerative anemia and splenomegaly. At the veterinary teaching hospital, further diagnostic testing was ordered in an attempt to determine the cause of the biliverdinuria and pale oral mucous membranes. The diagnostic tests performed included a full-body contrast computed tomographic scan, upper gastrointestinal endoscopic procedure, bone marrow aspiration and evaluation, saline agglutination testing, blood Plasmodium species polymerase chain reaction screening, a vitamin profile panel, and repeat blood heavy metal testing. The complete blood count demonstrated a marked, regenerative anemia with the presence of dysplastic erythrocytes, and splenomegaly was found on the computed tomographic images without identifying a definitive cause. Primary disease differentials for the diagnosed regenerative anemia included a myelodysplastic syndrome and primary or secondary immune-mediated hemolytic anemia. The penguin was treated with oral prednisolone as an immunomodulatory agent; however, it did not result in a positive treatment response. The patient developed hyporexia, weight loss, and lethargy 2 months post presentation to the veterinary teaching hospital. Additional therapy with cyclophosphamide was initiated, and the penguin improved clinically, but then declined. The patient was euthanized due to a poor quality of life and prognosis 4 months after initial presentation and 1.5 years after the first complete blood count revealed the penguin to be anemic. Microscopic review of submitted postmortem tissue samples demonstrated a monomorphic population of neoplastic small lymphocytes infiltrating the spleen, consistent with splenic small cell lymphoma. The neoplastic cells did not label with the T-cell marker CD3 or B-cell markers CD20, CD79a, and Pax-5.
Catenae: Introducing a Novel Unit of Syntactic Analysis

This paper introduces a novel unit of syntactic analysis, the catena (Latin for chain; plural catenae). The catena is defined in a dependency-based grammar as a word or a combination of words that is continuous with respect to dominance. According to this definition, any dependency tree or any subtree (complete or partial) of a dependency tree qualifies as a catena. The paper demonstrates that idioms are stored as catenae and that the elided material of ellipsis mechanisms (e.g., answer fragments, gapping, stripping, VP ellipsis, pseudogapping, sluicing, and comparative deletion) is a catena. Constituents are always catenae, but many catenae are not constituents. Based on the flexibility and utility of the catena concept, the claim is put forth and defended that the catena is the fundamental unit of syntax, not the constituent.
Understanding Economic Sanctions on Iran: A Survey

Abstract This paper surveys the literature on the economic effects of sanctions on Iran, with a principal focus on those imposed after 2006. We discuss the challenges of isolating the impacts of sanctions and evaluate recent methodological approaches used to measure causal effects. Specifically, we focus on the application of the synthetic control method to disaggregate the exogenous and endogenous factors responsible for economic underperformance in Iran. We review studies on the macroeconomic impacts of Iran sanctions and examine those that focus on the sectoral effects of sanctions. Our survey reveals a large and growing body of literature that remains incomplete. There are significant gaps in the existing research on the impact of sanctions. Iran will likely remain the most important case study as researchers seek to fill those gaps.
Genetic structure and first genome‐wide insights into the adaptation of a wild relative of grapevine, Vitis berlandieri

In grafted plants, such as grapevine, increasing the diversity of rootstocks available to growers is an ideal strategy for helping plants to adapt to climate change. The rootstocks used for grapevine are hybrids of various American Vitis, including V. berlandieri. The rootstocks currently use in vineyards are derived from breeding programs involving very small numbers of parental individuals. We investigated the structure of a natural population of V. berlandieri and the association of genetic diversity with environmental variables. In this study, we collected seeds from 78 wild V. berlandieri plants in Texas after open fertilization. We genotyped 286 individuals to describe the structure of the population, and environmental information collected at the sampling site made it possible to perform genome–environment association analysis (GEA). De novo long‐read whole‐genome sequencing was performed on V. berlandieri and a STRUCTURE analysis was performed. We identified and filtered 104,378 SNPs. We found that there were two subpopulations associated with differences in elevation, temperature, and rainfall between sampling sites. GEA identified three QTL for elevation and 15 QTL for PCA coordinates based on environmental parameter variability. This original study is the first GEA study to be performed on a population of grapevines sampled in natural conditions. Our results shed new light on rootstock genetics and could open up possibilities for introducing greater diversity into genetic improvement programs for grapevine rootstocks.