Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond the Standard Model with BlackHawk v2.0

We present the new version of BlackHawk v2.0. BlackHawk is a public code designed to compute the Hawking radiation spectra of (primordial) black holes. In the version 2.0, we have added several non-standard BH metrics: charged, higher dimensional and polymerized black holes, in addition to the usual rotating (Kerr) BHs. BlackHawk also embeds some additional scripts and numerical tables that can prove useful in e.g. dark matter studies. We describe these new features and provide some examples of the capabilities of the code. A tutorial for BlackHawk is available on the TOOLS2021 website: https://indico.cern. ch/event/1076291/contributions/4609967/
Linear quadratic mean field social optimization: Asymptotic solvability

This paper studies asymptotic solvability of a linear quadratic (LQ) mean field social optimization problem, featured with control dependent individual noises as well as a common noise dependent on a mean field control term. We employ a rescaling approach to derive a low dimensional Riccati ordinary differential equation (ODE) system. In the case that the social optimization problem is asymptotically solvable, the solution is shown to converge to a mean field limit. Due to the presence of controls into the noise terms, the control weighting matrix in the cost functional can be indefinite in order to obtain finite costs. This feature also leads to high non-linearity of the associated Riccati ODE system, and extra work is involved in deriving the limiting low-dimensional ODEs. We obtain a necessary and sufficient condition for the asymptotic solvability of the social optimization problem.
Electronic health record-based readmission risk model performance for patients undergoing outpatient parenteral antibiotic therapy (OPAT)

Background Outpatient Parenteral Antibiotic Therapy (OPAT) provides coordinated services to deliver parenteral antibiotics outside of the acute care setting. However, the reduction in monitoring and supervision may impact the risks of readmission to the hospital. While identifying those at greatest risk of hospital readmission through use of computer decision support systems could aid in its prevention, validation of such tools in this patient population is lacking. Objective The primary aim of this study is to determine the ability of the electronic health record-embedded EPIC Unplanned Readmission Model 1 to predict all-cause 30-day hospital unplanned readmissions in discharged patients receiving OPAT through the Duke University Heath System (DUHS) OPAT program. We then explored the impact of OPAT-specific variables on model performance. Methods This retrospective cohort study included patients ≥ 18 years of age discharged to home or skilled nursing facility between July 1, 2019 –February 1, 2020 with OPAT care initiated inpatient and coordinated by the DUHS OPAT program and with at least one Epic readmission score during the index hospitalization. Those with a planned duration of OPAT < 7 days, receiving OPAT administered in a long-term acute care facility (LTAC), or ongoing renal replacement therapy were excluded. The relationship between the primary outcome (unplanned readmission during 30-day post-index discharge) and Epic readmission scores during the index admission (discharge and maximum) was examined using multivariable logistic regression models adjusted for additional predictors. The performance of the models was assessed with the scaled Brier score for overall model performance, the area under the receiver operating characteristics curve (C-index) for discrimination ability, calibration plot for calibration, and Hosmer-Lemeshow goodness-of-fit test for model fit. Results The models incorporating maximum or discharge Epic readmission scores showed poor discrimination ability (C-index 0.51, 95% CI 0.45 to 0.58 for both models) in predicting 30-day unplanned readmission in the Duke OPAT cohort. Incorporating additional OPAT-specific variables did not improve the discrimination ability (C-index 0.55, 95% CI 0.49 to 0.62 for the max score; 0.56, 95% CI 0.49 to 0.62 for the discharge score). Although models for predicting 30-day unplanned OPAT-related readmission performed slightly better, discrimination ability was still poor (C-index 0.54, 95% CI 0.45 to 0.62 for both models). Conclusion EPIC Unplanned Readmission Model 1 scores were not useful in predicting either all-cause or OPAT-related 30-day unplanned readmission in the DUHS OPAT cohort. Further research is required to assess other predictors that can distinguish patients with higher risks of 30-day unplanned readmission in the DUHS OPAT patients.
Adapt or Perish : Evidence of CEO Adaptability to Industry Shocks

Prior turnover literature documents that poor performance leads a board of directors to terminate the CEO, but does not explore the underlying causes of the CEO’s poor performance. Recognizing that terminated CEOs have often been successful earlier in their tenure, we conjecture that changes in a firm’s business environment can cause the board to decide that the existing CEO’s skills do not fit with the firm’s current leadership needs. Our results suggest that CEOs struggle to adapt to shocks to industry growth, investment, competition, and globalization, and that the well-documented relation between firm performance and CEO turnover depends on these industry shocks. We also find that the relation between industry shocks and CEO turnover depends on various features of corporate governance and whether the CEO is identified as having “generalist” skills. Finally, we document that adaptable CEOs command a pay premium and that turnover among other top five executives is a complex function of industry shocks and the turnover decision regarding the CEO.
Phase retrieval in high dimensions : 1 Statistical and computational phase transitions

Many notations and definitions used throughout this supplementary material are given in Sec4 tions F.1,F.2. The Python code that produced the numerical data used in Figures 1,2,3, as well as 5 the data itself, are given alongside this material, and is dependent on the open-source TrAMP library 6 [1]. We provide in particular an “example” notebook which contains a detailed presentation of 7 the functions necessary to generate both the state evolution and the G-VAMP data for the complex 8 Gaussian matrix case. 9
The potential of online sampling for studying political activists around the world and across time

Parties and social movements play an important role in many theories of political science. Yet, the study of intraparty politics remains underdeveloped as random samples are difficult to conduct among political activists. This paper proposes a novel procedure to sample different parties over time and space by utilizing the advertising option of the social media webpage Facebook. As this method allows for quotas and the collection of large samples at relatively low cost, it becomes possible to improve the representativeness through poststratification and subsample robustness checks. Three examples illustrate these advantages of Facebook sampling: First, a Facebook sample approximated intraparty decisions and the outcome of a leadership contest of the Alternative for Germany. Second, a weighted Facebook sample achieved similar estimates as a representative local leader survey of the Social Democratic Party of Germany. Third, by evaluating subgroups of key demographics for parties with unknown population parameters, two Facebook samples show that the color-coded conflict in Thailand was driven by different concepts of regime type, but not by a left–right divide on economic policy-making. Facebook sampling appears to be the best and cheapest method to conduct time-series cross-sectional studies for political activists.
Scientists’ deficit perception of the public impedes their behavioral intentions to correct misinformation

This paper investigates the relationship between scientists’ communication experience and attitudes towards misinformation and their intention to correct misinformation. Specifically, the study focuses on two correction strategies: source-based correction and relational approaches. Source-based approaches combatting misinformation prioritize sharing accurate information from trustworthy sources to encourage audiences to trust reliable information over false information. On the other hand, relational approaches give priority to developing relationships or promoting dialogue as a means of addressing misinformation. In this study, we surveyed 416 scientists from U.S. land-grant universities using a self-report questionnaire. We find that scientists’ engagement in science communication activities is positively related to their intention to correct misinformation using both strategies. Moreover, the scientists’ attitude towards misinformation mediates the relationship between engagement in communication activities and intention to correct misinformation. The study also finds that the deficit model perception–that is, the assumption that scientists only need to transmit scientific knowledge to an ignorant public in order to increase understanding and support for science–moderates the indirect effect of engagement in science communication activities on behavioral intention to correct misinformation using relational strategies through attitude towards misinformation. Thus, the deficit model perception is a barrier to engaging in relational strategies to correct misinformation. We suggest that addressing the deficit model perception and providing science communication training that promotes inclusive worldviews and relational approaches would increase scientists’ behavioral intentions to address misinformation. The study concludes that scientists should recognize their dual positionality as scientists and members of their community and engage in respectful conversations with community members about science.
Linear and nonlinear associations between general intelligence and personality in Project TALENT.

Research on the relations of personality traits to intelligence has primarily been concerned with linear associations. Yet, there are no a priori reasons why linear relations should be expected over nonlinear ones, which represent a much larger set of all possible associations. Using 2 techniques, quadratic and generalized additive models, we tested for linear and nonlinear associations of general intelligence (g) with 10 personality scales from Project TALENT (PT), a nationally representative sample of approximately 400,000 American high school students from 1960, divided into 4 grade samples (Flanagan et al., 1962). We departed from previous studies, including one with PT (Reeve, Meyer, & Bonaccio, 2006), by modeling latent quadratic effects directly, controlling the influence of the common factor in the personality scales, and assuming a direction of effect from g to personality. On the basis of the literature, we made 17 directional hypotheses for the linear and quadratic associations. Of these, 53% were supported in all 4 male grades and 58% in all 4 female grades. Quadratic associations explained substantive variance above and beyond linear effects (mean R² between 1.8% and 3.6%) for Sociability, Maturity, Vigor, and Leadership in males and Sociability, Maturity, and Tidiness in females; linear associations were predominant for other traits. We discuss how suited current theories of the personality-intelligence interface are to explain these associations, and how research on intellectually gifted samples may provide a unique way of understanding them. We conclude that nonlinear models can provide incremental detail regarding personality and intelligence associations.
Observation of the Anisotropy of Cosmic Rays with HAWC

The High-Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory is sensitive to the flux and arrival direction distribution of charged cosmic rays in the TeV energy band. While the observatory is only partially deployed, with 30 out of 300 water Cherenkov detectors in data acquisition since September 2012, HAWC is recording air showers from cosmic rays at a rate above 2 kHz. As a result, we have already accumulated one of the largest data sets of TeV cosmic rays ever produced. We have analyzed the data and observed a significant anisotropy at the 10−3 level in the arrival directions of the cosmic rays on both large scales (> 60◦) and small scales (< 20◦). We present these results and compare our findings to previous observations of anisotropy by experiments such as Milagro, Tibet/ARGO, and others in the northern hemisphere, and the IceCube Neutrino Observatory in the southern hemisphere.
Mitochondrial DNA analyses revealed distinct lineages in an alpine mammal, Siberian ibex (Capra sibirica) in Xinjiang, China

Abstract Maternal lineages of mitochondrial DNA (mtDNA) are recognized as important components of intra and interspecific biodiversity and help us to disclose the phylogeny and divergence times of many taxa. Species of the genus Capra are canonical mountain dwellers. Among these is the Siberian ibex (Capra sibirica), which is regarded as a relic species whose intraspecific classification has been controversial so far. We collected 58 samples in Xinjiang, China, and analyzed the mtDNA genes to shed light on the intraspecific relationships of the C. sibirica populations and estimate the divergence time. Intriguingly, we found that the mtDNA sequences of C. sibirica split into two main lineages in both phylogenetic and network analyses: the Southern lineage, sister to Capra falconeri, consisting of samples from Ulugqat, Kagilik (both in Xinjiang), India, and Tajikistan; and the Northern lineage further divided into four monophyletic clades A–D corresponding to their geographic origins. Samples from Urumqi, Sawan, and Arturk formed a distinct monophyletic clade C within the Northern lineage. The genetic distance between the C. sibirica clades ranges from 3.0 to 8.6%, with values of F ST between 0.839 and 0.960, indicating notable genetic differentiation. The split of the genus Capra occurred approximately 6.75 Mya during the late Miocene. The Northern lineage diverged around 5.88 Mya, followed by the divergence of Clades A–D from 3.30 to 1.92 Mya during the late Pliocene and early Pleistocene. The radiation between the Southern lineage and C. falconeri occurred at 2.29 Mya during the early Pleistocene. Our results highlight the importance of extensive sampling when relating to genetic studies of alpine mammals and call for further genomic studies to draw definitive conclusions.
RIG-I immunotherapy overcomes radioresistance in p53-positive malignant melanoma

Abstract Radiotherapy induces DNA damage, resulting in cell cycle arrest and activation of cell-intrinsic death pathways. However, the radioresistance of some tumour entities such as malignant melanoma limits its clinical application. The innate immune sensing receptor retinoic acid-inducible gene I (RIG-I) is ubiquitously expressed and upon activation triggers an immunogenic form of cell death in a variety of tumour cell types including melanoma. To date, the potential of RIG-I ligands to overcome radioresistance of tumour cells has not been investigated. Here, we demonstrate that RIG-I activation enhanced the extent and immunogenicity of irradiation-induced tumour cell death in human and murine melanoma cells in vitro and improved survival in the murine B16 melanoma model in vivo. Transcriptome analysis pointed to a central role for p53, which was confirmed using p53–/– B16 cells. In vivo, the additional effect of RIG-I in combination with irradiation on tumour growth was absent in mice carrying p53–/– B16 tumours, while the antitumoural response to RIG-I stimulation alone was maintained. Our results identify p53 as a pivotal checkpoint that is triggered by RIG-I resulting in enhanced irradiation-induced tumour cell death. Thus, the combined administration of RIG-I ligands and radiotherapy is a promising approach to treating radioresistant tumours with a functional p53 pathway, such as melanoma.
BNC ‐ VLA : Bayesian Network Structure Learning Using a Team of Variable ‐ Action Set Learning Automata

Bayesian Network (BN) is a probabilistic graphical model which describes the joint probability distribution over a set of random variables. One of the most important challenges in the field of BNs is to find an optimal network structure based on an available training dataset. Since the problem of searching the optimal BN structure belongs to the class of NP-hard problems, typically greedy algorithms are used to solve it. In this paper a learning automata-based algorithm has been proposed to solve the BNs structure learning problem. There is a learning automaton corresponding with each random variable and at each stage of the proposed algorithm, named BNC-VLA, a set of learning automata is randomly activated and determined the graph edges that must be appeared in that stage. Finally, the constructed network is evaluated using a scoring function. As BNC-VLA algorithm proceeds, the learning process focuses on the BN structure with higher scores. The convergence of this algorithm is theoretically proved; and also some experiments are designed to evaluate the performance of it. Experimental results show that BNC-VLA is capable of finding the optimal structure of BN in an acceptable execution time; and comparing against other search-based methods, it outperforms them.
Validation of a field-friendly faeces drying and storage method for quantifying faecal glucocorticoid metabolites in African elephants (Loxodonta africana) opens up new perspectives for conservationists

Faecal glucocorticoid metabolites (fGCMs) provide relevant information on wildlife welfare but remain underapplied in conservation due to sample preservation issues. We validated a field-friendly drying and storage method for preserving African elephant faeces, addressing logistical challenges of fGCM quantification, while anticipating the growing interest in considering welfare in conservation management.
AIS Transactions on Human-Computer Interaction AIS Transactions on Human-Computer Interaction

: Online peer production communities such as open source software (OSS) projects attract both identified and anonymous peripheral contributions (APC) (e.g., defect reports, feature requests, or forum posts). While we can attribute identified peripheral contributions (IPC) to specific individuals and OSS projects need them to succeed, one cannot trace back anonymous peripheral contributions (APC), and they can have both positive and negative ramifications for project development. Open platforms and managers face a challenging design choice in deciding whether to allow APC and for which tasks or what type of projects. We examine the impact that the ratio between APC and IPC has on OSS project performance. Our results suggest that the OSS projects perform the best when they contain a uniform anonymity level (i.e., they contain predominantly APC or predominantly IPC). However, our results also suggest that OSS projects have lower performance when the ratio between APC and IPC nears one (i.e., they contain close to the same number of APC and IPC). Furthermore, our results suggest that these results differ depending on the type of application that a project develops. Our study contributes to the ongoing debate about the implications of anonymity for online communities and informs managers about the effect that anonymous contributions have on their projects.
Genetic polymorphisms in the serotonin, dopamine and opioid pathways influence social attention in rhesus macaques (Macaca mulatta)

Behaviour has a significant heritable component; however, unpicking the variants of interest in the neural circuits and molecular pathways that underpin these has proven difficult. Here, we present a comprehensive analysis of the relationship between known and new candidate genes from identified pathways and key behaviours for survival in 109 adult rhesus macaques (Macaca mulatta). Eight genes involved in emotion were analysed for variation at a total of nine loci. Genetic data were then correlated with cognitive and observational measures of behaviour associated with wellbeing and survival using MCMC-based Bayesian GLMM in R, to account for relatedness within the macaque population. For four loci the variants genotyped were length polymorphisms (SLC6A4 5-hydroxytryptamine transporter length-polymorphic repeat (5-HTTLPR), SLC6A4 STin polymorphism, Tryptophan 5-hydroxylase 2 (TPH2) and Monoamine oxidase A (MAOA)) whilst for the other five (5-hydroxytryptamine receptor 2A (HTR2A), Dopamine Receptor D4 (DRD4), Oxytocin receptor (OXTR), Arginine vasopressin receptor 1A (AVPR1a), Opioid receptor mu(μ) 1 (OPRM1)) SNPs were analysed. STin genotype, DRD4 haplotype and OXTR haplotype were significantly associated with the cognitive and observational measures of behaviour associated with wellbeing and survival. Genotype for 5-HTTLPR, STin and AVPR1a, and haplotype for HTR2A, DRD4 and OXTR were significantly associated with the duration of behaviours including fear and anxiety. Understanding the biological underpinnings of individual variation in negative emotion (e.g., fear and anxiety), together with their impact on social behaviour (e.g., social attention including vigilance for threat) has application for managing primate populations in the wild and captivity, as well as potential translational application for understanding of the genetic basis of emotions in humans.
Blending citizen science with natural language processing and machine learning: Understanding the experience of living with multiple sclerosis

The emergence of new digital technologies has enabled a new way of doing research, including active collaboration with the public (‘citizen science’). Innovation in machine learning (ML) and natural language processing (NLP) has made automatic analysis of large-scale text data accessible to study individual perspectives in a convenient and efficient fashion. Here we blend citizen science with innovation in NLP and ML to examine (1) which categories of life events persons with multiple sclerosis (MS) perceived as central for their MS; and (2) associated emotions. We subsequently relate our results to standardized individual-level measures. Participants (n = 1039) took part in the ’My Life with MS’ study of the Swiss MS Registry which involved telling their story through self-selected life events using text descriptions and a semi-structured questionnaire. We performed topic modeling (‘latent Dirichlet allocation’) to identify high-level topics underlying the text descriptions. Using a pre-trained language model, we performed a fine-grained emotion analysis of the text descriptions. A topic modeling analysis of totally 4293 descriptions revealed eight underlying topics. Five topics are common in clinical research: ‘diagnosis’, ‘medication/treatment’, ‘relapse/child’, ‘rehabilitation/wheelchair’, and ‘injection/symptoms’. However, three topics, ‘work’, ‘birth/health’, and ‘partnership/MS’ represent domains that are of great relevance for participants but are generally understudied in MS research. While emotions were predominantly negative (sadness, anxiety), emotions linked to the topics ‘birth/health’ and ‘partnership/MS’ was also positive (joy). Designed in close collaboration with persons with MS, the ‘My Life with MS’ project explores the experience of living with the chronic disease of MS using NLP and ML. Our study thus contributes to the body of research demonstrating the potential of integrating citizen science with ML-driven NLP methods to explore the experience of living with a chronic condition.