Miracle-dz/UniFAR-FTUs huggingface dataset:
How should we treat under-performing schools? A regression discontinuity analysis of school inspections in England

School inspections are an important part of the accountability framework for education in England. In this paper we use a panel of schools to evaluate the effect of a school failing its inspection. We collect a decadeâ€™s worth of data on how schools are judged across a very large range of sub-criteria, alongside an overall judgement of effectiveness. We use this data within a fuzzy regression discontinuity design to model the impact of â€˜justâ€™ failing the inspection, relative to the impact of â€˜justâ€™ passing. This analysis is implemented using a time-series of school performance and pupil background data. Our results suggest that schools only just failing do see an improvement in scores over the following two to three years. The effect size is moderate to large at around 10% of a pupil-level standard deviation in test scores. We also show that this improvement occurs in core compulsory subjects, suggesting that this is not all the result of course entry gaming on the part of schools. There is little positive impact on lower ability pupils, with equally large effects for those in the middle and top end of the ability distribution.
Interpretation of diagrams in dynamic geometry environments

This study addressed the use of dynamic geometry environments in students’ mathematical activity related to proofs and refutations. Using a framework of student interpretation of diagrams, we analysed a task-based interview where a triad of Japanese secondary school students (16–17 years old) tackled tasks related to conjecturing, proving, and refuting using dynamic geometry. Our study suggests that the distinction between perception and relation-oriented interpretations, as well between ‘drawing’ and ‘figure’, can help in deepening understanding of student behaviour when they are using dynamic geometry environments.
Graph-Based Change Detection for Condition Monitoring of Rotating Machines: Techniques for Graph Similarity

Detection of structural changes in machine state attracts increasing attention for the monitoring and prognosis of rotating machinery. Very recently, the graph model has been introduced and adopted for modeling of normal machine state so as to investigate the likelihood of potential changes by the use of Martingale-test. This paper expands the potential of the graph model toward correlation-analysis-based monitoring applications, where the problem of measuring graph similarity is the main challenge due to domain specificity. We first investigated six typical schemes taken from other areas for this purpose, and found that they show discriminative capacities when dealing with changes with different types in the machine condition monitoring scenarios. Subsequently, based on a procedure called “method of ranks,” we have chosen four schemes among them which are potentially promising for our usage. Meanwhile, based on these metrics, two machine learning based similarity metrics are proposed to further improve their practical values by combining them. At last, comprehensive theoretical interpretations and comparisons of presented methods are made both in simulated scenarios and real-engineering monitoring applications.
A hierarchical modeling approach to predict the distribution and density of Sierra Nevada Red Fox (Vulpes vulpes necator)

Carnivores play critical roles in ecosystems, yet many species are declining worldwide. The Sierra Nevada Red Fox (Vulpes vulpes necator; SNRF) is a rare and endangered subspecies of red fox limited to upper montane forests, subalpine, and alpine environments of California and Oregon, United States. Having experienced significant distribution contractions and population declines in the last century, the subspecies is listed as at-risk by relevant federal and state agencies. Updated information on its contemporary distribution and density is needed to guide and evaluate conservation and management actions. We combined 12 years (2009–2020) of detection and nondetection data collected throughout California and Oregon to model the potential distribution and density of SNRFs throughout their historical and contemporary ranges. We used an integrated species distribution and density modeling approach, which predicted SNRF density in sampled locations based on observed relationships between environmental covariates and detection frequencies, and then projected those predictions to unsampled locations based on the estimated correlations with environmental covariates. This approach provided predictions that serve as density estimates in sampled regions and projections in unsampled areas. Our model predicted a density of 1.06 (95% credible interval = 0.8–1.36) foxes per 100 km2 distributed throughout 22,926 km2 in three distinct regions of California and Oregon–Sierra Nevada, Lassen Peak, and Oregon Cascades. SNRFs were most likely to be found in areas with low minimum temperatures and high snow water equivalent. Our results provide a contemporary baseline to inform the development and evaluation of conservation and management actions, and guide future survey efforts.
Screamer: A Portable Efficient Implementation of Nondeterministic Common LISP

Nondeterministic LISP is a simple extension of LISP which provides automatic backtracking. Nondeterminism allows concise description of many search tasks which form the basis of much AI research. This paper discusses SCREAMER, an efficient implementation of nondeterministic LISP as a fully portable extension of COMMON LISP. In this paper we present the basic nondeterministic LISP constructs, motivate the utility of the language via numerous short examples, and discuss the compilation techniques. Comments University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCS-93-03. This technical report is available at ScholarlyCommons: http://repository.upenn. edu/ircs_reports/14
An Open-Source Photogrammetry Workflow for Reconstructing 3D Models

Acquiring accurate 3D biological models efficiently and economically is important for morphological data collection and analysis in organismal biology. In recent years, structure-from-motion (SFM) photogrammetry has become increasingly popular in biological research due to its flexibility and being relatively low cost. SFM photogrammetry registers 2D images for reconstructing camera positions as the basis for 3D modeling and texturing. However, most studies of organismal biology still rely on commercial software to reconstruct the 3D model from photographs, which impedes the adoption of this workflow in our field due the blocking issues such as cost and affordability. Also, prior investigations in photogrammetry did not sufficiently assess the geometric accuracy of the models reconstructed. Consequently, this study has two goals. First, we present an affordable and highly flexible structure-from-motion photogrammetry pipeline based on the open-source package OpenDroneMap (ODM) and its user interface WebODM. Second, we assess the geometric accuracy of the photogrammetric models acquired from the ODM pipeline by comparing them to the models acquired via microCT scanning, the de facto method to image skeleton. Our sample comprises fifteen Aplodontia rufa (mountain beaver) skulls. Using models derived from microCT scans of the samples as reference, our results show that the geometry of the models derived from ODM is sufficiently accurate for gross metric and morphometric analysis as the measurement errors are usually around or below 2%, and morphometric analysis captures consistent patterns of shape variations in both modalities. However, subtle but distinct differences between the photogrammetric and microCT-derived 3D models can affect the landmark placement, which in return affect the downstream shape analysis, especially when the variance within a sample is relatively small. At the minimum, we strongly advise not combining 3D models derived from these two modalities for geometric morphometric analysis. Our findings can be indictive of similar issues in other SFM photogrammetry tools since the underlying pipelines are similar. We recommend that users run a pilot test of geometric accuracy before using photogrammetric models for morphometric analysis. For the research community, we provide detailed guidance on using our pipeline for building 3D models from photographs.
Lexical-semantic properties of verbs and nouns used in conversation by people with Alzheimer’s disease

Alzheimer’s disease (AD) is accompanied by language impairments and communicative breakdowns. Research into language processing by people with AD (pwAD) has focused largely on production of nouns in isolation. However, impairments are consistently found in verb production at word and sentence levels, and comparatively little is known about word use by pwAD in conversation. This study investigated differences between pwAD and cognitively healthy controls in conversational use of nouns, verbs, and pronouns. Speech samples produced by 12 pwAD and 12 controls for the Carolinas Conversations Collection were analysed for noun, verb and pronoun counts and ratios, lexical diversity overall and among nouns and verbs, copula use, and frequencies and ages of acquisition (AoA) of nouns and verbs produced. pwAD used fewer nouns and a narrower range of words than controls, exhibiting signs of increased reliance on pronouns and decreased noun diversity. Age affected noun frequencies differently within each group—pwAD produced nouns of lower frequencies with age, while controls produced nouns of higher frequencies. pwAD produced nouns of higher AoA than controls. Verb use differed little by group. These findings highlight the need to account for differences between nouns and verbs, including in frequency, AoA, proportion of all words spoken, and context-dependent processing demands, when drawing conclusions on language use by pwAD. They also suggest potential for communicative interventions targeting contextual use of both nouns and verbs.
Distributed Quantile Regression Analysis and a Group Variable Selection Method

OF THE DISSERTATION Distributed Quantile Regression Analysis and a Group Variable Selection Method by Liqun Yu Doctor of Philosophy in Mathematics, Washington University in St. Louis, May, 2018. Professor Nan Lin, Chair This dissertation develops novel methodologies for distributed quantile regression analysis for big data by utilizing a distributed optimization algorithm called the alternating direction method of multipliers (ADMM). Specifically, we first write the penalized quantile regression into a specific form that can be solved by the ADMM and propose numerical algorithms for solving the ADMM subproblems. This results in the distributed QR-ADMM algorithm. Then, to further reduce the computational time, we formulate the penalized quantile regression into another equivalent ADMM form in which all the subproblems have exact closed-form solutions and hence avoid iterative numerical methods. This results in the single-loop QPADM algorithm that further improve on the computational e ciency of the QR-ADMM. Both QR-ADMM and QPADM enjoy flexible parallelization by enabling data splitting across both sample space and feature space, which make them especially appealing for the case when both sample size n and feature dimension p are large. Besides the QR-ADMM and QPADM algorithms for penalized quantile regression, we also develop a group variable selection method by approximating the Bayesian information criterion. Unlike existing penalization methods for feature selection, our proposed gMIC xi algorithm is free of parameter tuning and hence enjoys greater computational e ciency. Although the current version of gMIC focuses on the generalized linear model, it can be naturally extended to the quantile regression for feature selection. We provide theoretical analysis for our proposed methods. Specifically, we conduct numerical convergence analysis for the QR-ADMM and QPADM algorithms, and provide asymptotical theories and oracle property of feature selection for the gMIC method. All our methods are evaluated with simulation studies and real data analysis.
MatFormer

Procedural material graphs are a compact, parameteric, and resolution-independent representation that are a popular choice for material authoring. However, designing procedural materials requires significant expertise and publicly accessible libraries contain only a few thousand such graphs. We present MatFormer, a generative model that can produce a diverse set of high-quality procedural materials with complex spatial patterns and appearance. While procedural materials can be modeled as directed (operation) graphs, they contain arbitrary numbers of heterogeneous nodes with unstructured, often long-range node connections, and functional constraints on node parameters and connections. MatFormer addresses these challenges with a multi-stage transformer-based model that sequentially generates nodes, node parameters, and edges, while ensuring the semantic validity of the graph. In addition to generation, MatFormer can be used for the auto-completion and exploration of partial material graphs. We qualitatively and quantitatively demonstrate that our method outperforms alternative approaches, in both generated graph and material quality.
Combined Ultra High Vacuum Raman and Electronic Transport Characterization of Large‐Area Graphene on SiO2

An original experimental setup which allows for simultaneous sample characterization by Raman spectroscopy and electronic tranport in ultra‐high vacuum at low temperatures is presented. We show the applicability of this setup for the case of graphene that is transferred from an Ir(111) single crystal onto SiO2. The transfer of graphene is carried out using a water‐promoted electrochemical bubbling technique which is applied to graphene/Ir for the first time. The characterization prior to the transfer includes electron diffraction, photoemission spectroscopy and Raman spectroscopy using ultraviolet excitation. Following the transfer procedure, the graphene layer is electrically contacted and mounted onto a special sample carrier. This carrier allows for combined Raman and transport measurements inside an ultra high vacuum (UHV) system. UHV Raman mapping reveals a large area homogeneous graphene quality over several mm2 characterized by a D/G intensity ratio less than 0.1. UHV electrical characterization of transferred graphene in a field effect transistor geometry yields a carrier mobility of 675 cm2 V−1 s−1. Upon alkali metal doping in UHV conditions using a Cs getter, a decrease of the 4‐point resistance from above 2500 Ω to below 10 Ω is observed. The presented approach paves the way for future combined UHV Raman and transport characterization of two‐dimensional materials that are doped into superconducting or charge‐density‐wave ground states.
Predicting Caregiver Communications in a Geriatric Clinic

The current study evaluated the use of a machine learning model to determine benefit of medical record variables in predicting geriatric clinic communication requirements. Patient behavioral symptoms and global cognition, medical information, and caregiver intake assessments were extracted from 557 patient records. Two independent raters reviewed the subsequent 12 months for documented (1) incoming caregiver contacts, (2) outgoing clinic contacts, and (3) clinic communications. Random forest models’ average explained variance in training sets for incoming, outgoing, and clinic communications were 7.42%, 3.65%, and 6.23%, respectively. Permutation importances revealed the strongest predictors across outcomes were patient neuropsychiatric symptoms, global cognition, and body mass, caregiver burden, and age (caregiver and patient). Average explained variance in out-of-sample test sets for incoming, outgoing, clinic communications were 6.17%, 2.78%, and 4.28%, respectively. Findings suggest patient neuropsychiatric symptoms, caregiver burden, caregiver and patient age, patient body mass index, and global cognition may be useful predictors of communication requirements for patient care in a geriatric clinic. Future studies should consider additional caregiver variables, such as personality characteristics, and explore modifiable factors longitudinally.
INTEGRAL/IBIS observations of V404 Cygni polarimetric properties during its 2015 giant flares

V404 Cygni is a low mass X-ray binary believed to host a black hole of 12 solar masses. After 25 years of quiescence, V404 Cygni entered into a period of outburst during which it produced flares largely exceeding its Eddington luminosity. The brightness of the most luminous flares detected with the IBIS telescope has hampered the standard data treatment and produced large telemetry gaps. Polarimetric parameters were thus not measurable with the same data analysis package used with success in the studies of the Crab and Cygnus X-1. We were then forced to develop and implement a special INTEGRAL/IBIS/Compton analysis routine. Our findings show that this new process improved significantly our polarimetry sensitivity not only for very bright flaring events as for V404 Cygni but equally on more standard sources as the Crab nebula. In this paper, we compare the results of our new method with the already published Cygnus X-1 polarization. We show that V404 Cygni were highly polarized (95 ± 35 %) during INTEGRAL revolution 1555 (i.e. 2015, June 20th – 22nd) with a polarization angle of 160°, consistent with infrared measurement made at the WHT on June 23rd. This achieved sensitivity opens a new window for polarimetric studies with INTEGRAL/IBIS at energies above 200 keV.
Interferon-Alpha 2b: An Effective Treatment for Hepatic Epithelioid Hemangioendothelioma

Background: Hepatic epithelioid hemangioendothelioma (HEH) is a rare tumor and no standard treatment has been established. This study was aimed to prospectively investigate the effect of interferon-alpha 2b (IFN-a 2b) for HEH patients.Methods: Since March 2014, a total of 62 pathologically diagnosed HEH patients were followed up regularly. IFN-a 2b was suggested to HEH patients with progressed disease. Safety was assessed each month and tumor assessment scan was performed every 3 months. The primary end point was objective response rate (ORR) and disease control rate (DCR). Results: A total of 42 HEH patients were finally included in this study. Median patient age was 35.5 years (range 18-65 years old). No severe (grade ≥3) adverse event (AE) of IFN-a 2b was reported in the whole group. The most common treatment-related AEs were fever (50.0%) and fatigue (21.4%). Two patients had grade 2 level hypothyroidism and 1 patient had grade 2 level of leukopenia and thrombocytopenia. Partial response and complete response were achieved in 20 (47.6%) and 2 (4.8%) patients, respectively, and the ORR was 52.4%. Stable disease was observed in 12 (28.6%) patients and the DCR was 81.0%. Progressive disease was observed in 8 (19.0%) patients. The 1, 3 and 5-year progression free rate were 81.0%, 69.2% and 62.3%, respectively. Only 1 patient died due to the progression of disease during the study. The1, 3 and 5-year overall survival rate were 100%, 97.2% and 97.2%, respectively.Conclusion: IFN-a 2b is a safe and effective treatment for HEH patients.
Censored bimodal symmetric-asymmetric families

In this paper, we introduce two new families of distributions that are suitable for ﬁtting unimodal as well as bi-modal symmetric and asymmetric censored data. The models extend the skew normal model to bimodal symmetric and asymmetric situations and typically involves less parameters to be estimated than mixtures of normal distributions. Maximum likelihood estimation (MLE) is discussed and Fisher information matrices are derived. Results of a simulation study indicate stable parameter recovery in moderate and large samples. Applications to two real data sets are re-ported. The ﬁrst data set is related to results of a study on antiretroviral therapy (HAART) to AIDS patients with strong evidence of bimodality and asymmetry. The second data set (fetal weight of unborn children) presents bimodal symmetry, well captured by the model introduced.
Deep mutagenesis scanning using whole trimeric SARS-CoV-2 spike highlights the importance of NTD-RBD interactions in determining spike phenotype

New variants of SARS-CoV-2 are continually emerging with mutations in spike associated with increased transmissibility and immune escape. Phenotypic maps can inform the prediction of concerning mutations from genomic surveillance, however most of these maps currently derive from studies using monomeric RBD, while spike is trimeric, and contains additional domains. These maps may fail to reflect interdomain interactions in the prediction of phenotypes. To try to improve on this, we developed a platform for deep mutational scanning using whole trimeric spike. We confirmed a previously reported epistatic effect within the RBD affecting ACE2 binding, that highlights the importance of updating the base spike sequence for future mutational scanning studies. Using post vaccine sera, we found that the immune response of vaccinated individuals was highly focused on one or two epitopes in the RBD and that single point mutations at these positions can account for most of the immune escape mediated by the Omicron BA.1 RBD. However, unexpectedly we found that the BA.1 RBD alone does not account for the high level of antigenic escape by BA.1 spike. We show that the BA.1 NTD amplifies the immune evasion of its associated RBD. BA.1 NTD reduces neutralistion by RBD directed monoclonal antibodies, and impacts ACE2 interaction. NTD variation is thus an important mechanism of immune evasion by SARS-CoV-2. Such effects are not seen when pre-stabilized spike proteins are used, suggesting the interdomain effects require protein mobility to express their phenotype.
Learning Dynamical Systems from Partial Observations

Many phenomena studied in physics, computer vision, biology (Mitchell and Schaeffer (2003)), geoscience (Ferguson (1988)), finance (Y. Achdou and Lelievre (2007)), etc. obey a general equation of this form. Here the goal is to learn a θ such that the solution fits measurements. However, there is a major drawback to this approach: for most real-world applications, the variables describing the system are not fully visible to external sensors (Carrassi et al. (2018)), e.g. when studying ocean’s circulation, variables contained in the system’s state such as surface temperature or salinity are observable via satellite imaging, while others subsurface variables are substantially more difficult or costly to observe. In this case, the state is said to be partially observable.
Historiographic and biographic accounts of Faroese nurses’ training and health-promoting work from 1910 to the end of the 1930s

ABSTRACT The Nordic country Faroe Islands consists of 18 mountain islands situated in the North Atlantic Sea. In the late 1800s, nursing in the Faroe Islands was behind international standards. A transition towards organised nursing commenced when Danish deaconesses arrived in 1897; their missions were to serve as nurses and train Faroese women in nursing. The overall aim of this research is to add to the history of Faroese nurses’ training and work during the first decades of the 1900s, decades when Faroese nursing became visible through the presence of deaconesses and trained nurses. With historiographic and biographic approaches and in the context of Faroese history and nursing theory and research, we present accounts about four Faroese nurses’ living, training and work during the three first decades of the 1900s. These years were progressive for Faroese nursing in hospital care and public health around on the islands, and a struggle to combat tuberculosis took place. We conclude by emphasising the importance of considering the beginning of professional nursing in a small faraway country where resilient nurses worked with ingenuity and often exceeded geographic and professional boundaries to help and support sick and injured compatriots, promoting their health and well-being.