Miracle-dz/UniFAR-FTUs huggingface dataset:
A new decade for social

Self-repair occurring in the same turn of speaking has been subjected to analysis in studies involving first language and second language users. However, in the context of English spoken as a foreign language, the information might still need further attention. This study is conducted to first report on the self-repair strategies employed by the Arab speakers of English in the same turn of speaking and second, to describe the linguistic features of self-repair produced by the speakers. Adopting the quantitative and qualitative approaches, data for this study come from a number of videos capturing conversations in English involving Arab speakers. The conversations in the videos were later transcribed orthographically and following this, instances of self-repair were identified. Framework of self-repair strategies provided by Emrani and Hooshmand was adopted to guide the data coding while the linguistic features of self-repair in the same turn of speaking were described by looking into its context of occurrences. The first finding has shown that repetition is the most frequent type of self-repair strategy to be employed by the Arab speakers of English. This is followed by replacement, insertion, abandonment and deletion. On the other hand, the self-repair that takes place in the same turn of speaking (where trouble source can be found as well) can be marked through a number of linguistic features namely prosodic features, the presence of fillers and the repair item that comes immediately after the trouble source item. The findings have provided insight into the speakers’ ability to monitor troubles in their speech, repair the troubles and features of their self-repair which can benefit the theoretical understanding and language classroom practices.
Crop Ecology: Productivity and Management in Agricultural Systems

Preface Part I. Farming Systems and Their Biological Components: 1. Agricultural systems 2. Trophic chains 3. Community concepts 4. Genetic resources 5. Development Part II. Physical and Chemical Environments: 6. Aerial environment 7. Soil resources Part III. Production Processes: 8. Nitrogen processes 9. Water relations 10. Photosynthesis 11. Respiration and partitioning Part IV. Resource Management: 12. Soil management 13. Strategies and tactics for rainfed agriculture 14. Water management in irrigated agriculture 15. Energy and labor Part V. Farming, Then, Now and in the Future: 16. Evolution of wheat farming systems in southern Australia 17. Technological change in high-yield agriculture 18. The future of agriculture Species list Conversions and constants useful in crop ecology Index.
On the Relation between Policy Improvement and Off-Policy Minimum-Variance Policy Evaluation

Off-policy methods are the basis of a large number of effective Policy Optimization (PO) algorithms. In this setting, Importance Sampling (IS) is typically employed for off-policy evaluation, with the goal of estimating the performance of a target policy, given samples collected with a different behavioral policy. However, in Monte Carlo simulation, IS represents a variance minimization approach. In this ﬁeld, a suitable behavioral distribution is employed for sampling, allowing diminishing the variance of the estimator below the one achievable when sampling from the target distribution. In this paper, we analyze IS in these two guises in the context of PO. We provide a novel view of off-policy PO, showing a connection between the policy improvement and variance minimization objectives. Then, we illustrate how minimizing the off-policy variance can, in some circumstances, lead to a policy improvement, with the advantage, compared with direct off-policy learning, of implicitly enforcing a trust region. Finally, we present numerical simulations on continuous RL benchmarks, with a particular focus on the robustness to small batch sizes.
Insulin initiation and intensification in patients with T2DM for the primary care physician

: Type 2 diabetes mellitus (T2DM) is characterized by both insulin resistance and inadequate insulin secretion. All patients with the disease require treatment to achieve and maintain the target glycosylated hemoglobin (A1C) level of 6.5%–7%. Pharmacological management of T2DM typically begins with the introduction of oral medications, and the majority of patients require exogenous insulin therapy at some point in time. Primary care physicians play an essential role in the management of T2DM since they often initiate insulin therapy and intensify regimens over time as needed. Although insulin therapy is prescribed on an individualized basis, treatment usually begins with basal insulin added to a background therapy of oral agents. Prandial insulin injections may be added if glycemic targets are not achieved. Treatments may be intensified over time using patient-friendly titration algorithms. The goal of insulin intensification within the primary care setting is to minimize patients’ exposure to chronic hyperglycemia and weight gain, and reduce patients’ risk of hypoglycemia, while achieving individualized fasting, postprandial, and A1C targets. Simplified treatment protocols and insulin delivery devices allow physicians to become efficient prescribers of insulin intensification within the primary care arena.
A Visionary Organization: From Donor Intent to New Horizons of Race and Gender Equity

Introduction The Leeway Foundation is a unique American philanthropic organization focused on funding women and trans artists working for social justice in Philadelphia, Pennsylvania, and the surrounding five counties. The organization began as a family foundation some 25 years ago, when the founder and donor used her sizable inheritance to establish a fund to support women artists in the Philadelphia area. What is particularly notable about Leeway is the way the foundation has changed and transitioned along with — and in some cases, ahead of — mainstream understandings of gender and racial equity.
MORAL JUDGMENTS OF UNCONDITIONAL HONESTY 1 Lying is sometimes ethical, but honesty is the best policy: The desire to avoid harmful lies leads to moral preferences for unconditional honesty

People believe that some lies are ethical, while also claiming that “honesty is the best policy. ” In this article, we introduce a theory to explain this apparent inconsistency. Even though people view prosocial lies as ethical, they believe it is more important – and more moral – to avoid harmful lies than to allow prosocial lies. Unconditional honesty (simply telling the truth, without finding out how honesty will affect others) is therefore seen as ethical because it prevents the most unethical actions (i.e., harmful lies) from occurring, even though it does not optimize every moral decision. We test this theory across five focal experiments and ten supplemental studies. Consistent with our account, we find that communicators who tell the truth without finding out how honesty will affect others are viewed as more ethical, and are trusted more, than communicators who look for information about the social consequences of honesty before communicating. However, the moral preference for unconditional honesty attenuates when it is certain that looking for more information will not lead to harmful lies. Overall, this research provides a holistic understanding of how people think about honesty and suggests that moral rules are not valued because people believe all rule-violations are wrong, but rather, because they believe some violations must be avoided entirely. Abstract word count: 211 PUBLIC SIGNIFICANCE STATEMENT This research explains why people value unconditional honesty. Even though most people believe that some lies – namely, prosocial lies – are ethical, they believe that the moral costs of harmful lies outweigh the moral benefits of prosocial lies. A policy of unconditional honesty prohibits harmful lying. Consequently, communicators who are unconditionally honest are seen as more moral than communicators who seek out information about how honesty affects others.
Neuro-Symbolic Class Expression Learning

Models computed using deep learning have been effectively applied to tackle various problems in many disciplines. Yet, the predictions of these models are often at most post-hoc and locally explainable. In contrast, class expressions in description logics are ante-hoc and globally explainable. Although state-of-the-art symbolic machine learning approaches are being successfully applied to learn class expressions, their application at large scale has been hindered by their impractical runtimes. Arguably, the reliance on myopic heuristic functions contributes to this limitation. We propose a novel neuro-symbolic class expression learning model, DRILL, to mitigate this limitation. By learning non-myopic heuristic functions with deep Q-learning, DRILL efficiently steers the standard search procedure in a quasi-ordered search space towards goal states. Our extensive experiments on 4 benchmark datasets and 390 learning problems suggest that DRILL converges to goal states at least 2.7 times faster than state-of-the-art models on all learning problems. The results of our statistical significance test confirms that DRILL converges to goal states significantly faster (p-value <1%) than state-of-the-art models on all benchmark datasets. We provide an open-source implementation of DRILL, including pre-trained models, training and evaluation scripts.
Microwave and Millimeter-Wave Radiometric Studies of Temperature, Water Vapor and Clouds

The importance of accurate measurements of column amounts of water vapor and cloud liquid has been well documented by scientists within the Atmospheric Radiation Measurement (ARM) Program. At the North Slope of Alaska (NSA), both microwave radiometers (MWR) and the MWRProfiler (MWRP), been used operationally by ARM for passive retrievals of the quantities: Precipitable Water Vapor (PWV) and Liquid Water Path (LWP). However, it has been convincingly shown that these instruments are inadequate to measure low amounts of PWV and LWP. In the case of water vapor, this is especially important during the Arctic winter, when PWV is frequently less than 2 mm. For low amounts of LWP (< 50 g/m{sup 2}), the MWR and MWRP retrievals have an accuracy that is also not acceptable. To address some of these needs, in March-April 2004, NOAA and ARM conducted the NSA Arctic Winter Radiometric Experiment - Water Vapor Intensive Operational Period at the ARM NSA/Adjacent Arctic Ocean (NSA/AAO) site. After this experiment, the radiometer group at NOAA moved to the Center for Environmental Technology (CET) of the Department of Electrical and Computer Engineering of the University of Colorado at Boulder. During this 2004 experiment, a total of 220 radiosondes were launched,more » and radiometric data from 22.235 to 380 GHz were obtained. Primary instruments included the ARM MWR and MWRP, a Global Positioning System (GPS), as well as the CET Ground-based Scanning Radiometer (GSR). We have analyzed data from these instruments to answer several questions of importance to ARM, including: (a) techniques for improved water vapor measurements; (b) improved calibration techniques during cloudy conditions; (c) the spectral response of radiometers to a variety of conditions: clear, liquid, ice, and mixed phase clouds; and (d) forward modeling of microwave and millimeter wave brightness temperatures from 22 to 380 GHz. Many of these results have been published in the open literature. During the third year of this contract, we participated in another ARM-sponsored experiment at the NSA during February-March 2007. This experiment is called the Radiative Heating in Underexplored Bands Campaign (RHUBC) and the GSR was operated successfully for the duration of the campaign. One of the principal goals of the experiment was to provide retrievals of water vapor during PWV amounts less than 2 mm and to compare GSR data with ARM radiometers and radiosondes. A secondary goal was to compare the radiometric response of the microwave and millimeter wavelength radiometers to water and ice clouds. In this final report, we will include the separate progress reports for each of the three years of the project and follow with a section on major accomplishments of the project. « less
screening for autism in preterm children with extremely low and very low birth weight

Background: Studies of children with very low birth weight (VLBW, 1,000–1,500 g) and extremely low birth weight (ELBW, less than 1,000 g) indicate that this population seems to be at increased risk of autism spectrum disorder (ASD). Methods: Parents of 101 VLBW and ELBW children (age 2 years, corrected for prematurity) agreed to participate in the study and signed informed consents; however, parents of only 75 children (44 boys, 31 girls) completed the screening questionnaires. The screening battery included the Modified Checklist for Autism in Toddlers (M-CHAT), Communication and Symbolic Behavior Scales Developmental Profile Infant-Toddler Checklist (CSBS-DP-ITC), and the Infant/Toddler Sensory Profile (ITSP). Children with disabilities were excluded. All children who screened positive on any of the screening tools were subsequently invited for a detailed assessment. Results: Thirty-two children (42.7%) screened positive on at least one of the screening questionnaires. The screening tool with the most positive results was the CSBS-DP-ITC (26 positive screens), followed by the M-CHAT (19 positive screens) and the ITSP (11 positive screens). Of the 32 children who tested positive, 19 participated in the detailed follow-up assessment. A diagnosis of ASD was confirmed in eight of the 19 children. ASD prevalence, calculated from those 19 children and those with negative screening results (43 children), yielded a prevalence of 12.9% in the sample. The difference in frequency of positive screens between the tests was significant ( P = 0.011). In pair comparisons, ITSP was found to be significantly less positive than CSBS-DP-ITC ( P = 0.032). No significant differences were found between the M-CHAT and CSBS-DP-ITC or between the M-CHAT and ITSP. Conclusion: The results strongly support the hypothesis of an increased prevalence of autism in children with a birth weight less than 1,500 g.
A Smooth Distributed Feedback for Formation Control of Unicycles

This paper investigates a formation control problem in which a group of kinematic unicycles is made to converge to a desired formation with parallel heading angles and come to a stop. A control law is presented, which solves this problem for almost all initial conditions in any given compact set. The proposed control law is local and distributed, meaning that each unicycle is only required to sense its relative displacement measured in its own body frame, and the relative heading angle with respect to each of its neighbors. No communication between the unicycles is required. The sensing graph is assumed to be connected, undirected, and time invariant. The idea used to solve the above-mentioned formation control problem is to rigidly attach to the body frame of each unicycle an appropriate fixed offset vector. Stabilizing the desired formation amounts to achieving consensus of the endpoints of the offset vectors, and simultaneously synchronizing the unicycles’ heading angles. A control law achieving this goal is constructed by combining a bounded translational consensus controller with an attitude synchronizer. As a special case, the proposed solution solves the full unicycle synchronization problem, in which the unicycle positions are made to converge to each other, while the unicycle headings are made to align.
Computing Generic Abstractions from Application Datasets

Digital data plays a central role in sciences, journalism, environment, digital humanities, etc. Open Data sharing initiatives lead to many large, interesting datasets being shared online. Some of these are RDF graphs, but other formats like CSV, relational, property graphs, JSON or XML documents are also frequent. Practitioners need to understand a dataset to decide whether it is suited to their needs. Datasets may come with a schema and/or may be summarized, however the first is not always provided and the latter is often too technical for non-IT users. To overcome these limitations, we present an end-to-end dataset abstraction approach, which ( 𝑖 ) applies on any (semi)structured data model; ( 𝑖𝑖 ) computes a description meant for human users, in the form of an Entity-Relationship diagram; ( 𝑖𝑖𝑖 ) integrates Information Extraction and data profiling to classify dataset entities among a large set of intelligible categories. We implemented our approach in a system called Abstra, and detail its performance on various datasets.
RGE: A Repulsive Graph Rectification for Node Classification via Influence

In real-world graphs, noisy connections are inevitable, which makes it difficult to obtain unbiased node representations. Among various attempts to resolve this problem, a method of estimating the counterfactual effects of these connectivities has recently attracted attention, which mainly uses influence functions for single graph elements (i.e., node and edge). However, in this paper, we argue that there is a strongly interacting group effect between the influences of graph elements due to their connectivity. In the same vein, we observe that edge groups connecting to the same train node exhibit significant differences in their influences, hence no matter how negative each is, removing them at once may have a rather negative effect as a group. Based on this motivation, we propose a new edge-removing strategy, Repulsive edge Group Elimination (RGE), that preferentially removes edges with no interference in groups. Empirically, we demonstrate that RGE consistently outperforms existing methods on the various benchmark datasets.
STICH3C: Rationale and Study Protocol

BACKGROUND: Coronary artery bypass grafting (CABG) is the recommended mode of revascularization in patients with ischemic left ventricular dysfunction (iLVSD) and multivessel disease. However, contemporary percutaneous coronary intervention (PCI) outcomes have improved with the integration of novel technologies and refinement of revascularization strategies, and PCI is often used in clinical practice in this population. There is a lack of evidence from randomized trials comparing contemporary state-of-the-art PCI versus CABG for the treatment of iLVSD and multivessel disease. This was the impetus for the STICH3C trial (Canadian CABG or PCI in Patients With Ischemic Cardiomyopathy), described here. METHODS: The STICH3C trial is a prospective, unblinded, international, multicenter trial with an expected sample size of 754 participants from ≈45 centers. Patients with multivessel/left main coronary artery disease and iLVSD with left ventricular ejection fraction ≤40% considered by the local Heart Team appropriate for and amenable to revascularization by both modes of revascularization will be randomized in a 1:1 ratio to state-of-the-art PCI or CABG. RESULTS: The primary end point is the composite of death from any cause, stroke, spontaneous myocardial infarction, urgent repeat revascularization, or heart failure readmission, summarized as a time-to-event outcome. The key hierarchical end point is time to death and frequency of hospitalizations for heart failure. The key safety outcome is a composite of major adverse events. Disease-specific quality-of-life and health economics measures will be compared between groups. Participants will be followed for a median of 5 years, with a minimum follow-up of 4 years. CONCLUSIONS: STICH3C will directly inform patients, clinicians, and international practice guidelines about the efficacy and safety of CABG versus PCI in patients with iLVSD. The results will provide novel and broad evidence, including clinical events, health status, and economic assessments, to guide care for patients with iLVSD and severe coronary artery disease. REGISTRATION: URL: https://clinicaltrials.gov/; Unique identifier: NCT05427370.
DiscoBAX: Discovery of optimal intervention sets in genomic experiment design

The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms. Existing approaches search over the billions of potential interventions to maximize the expected influence on the target phenotype. However, to reduce the risk of failure in future stages of trials, practical experiment design aims to find a set of interventions that maximally change a target phenotype via diverse mechanisms. We propose DiscoBAX, a sample-efficient method for maximizing the rate of significant discoveries per experiment while simultaneously probing for a wide range of diverse mechanisms during a genomic experiment campaign. We provide theoretical guarantees of approximate optimality under standard assumptions, and conduct a comprehensive experimental evaluation covering both synthetic as well as real-world experimental design tasks. DiscoBAX outperforms existing state-of-the-art methods for experimental design, selecting effective and diverse perturbations in biological systems.
The combined effects of the aquatic herbicide fluridone and the lampricide TFM on non-target fish and invertebrates

ABSTRACT Invasive sea lamprey (Petromyzon marinus) and exotic aquatic plants like Eurasian watermilfoil (Myriophyllum spicatum) pose environmental and economic threats to aquatic ecosystems, sometimes within the same water bodies. The lampricide TFM (3-trifluoromethyl-4-nitrophenol) and the aquatic herbicide fluridone [1-methyl-3-phenyl-5-(3-trifluoromethylphenyl)-4(1 H)-pyridinone] are routinely used to control these invasives in the Great Lakes and other water bodies, but the combined toxicity of these two pesticides for non-target aquatic animals has not been reported. Here, we examine the acute toxicity of TFM in combination with fluridone for three non-target species: fathead minnows (Pimephales promelas), brown trout (Salmo trutta), and the freshwater microcrustacean Daphnia magna. The average toxicity of TFM was higher in the presence of fluridone for all three species, although this difference was not statistically significant at all concentrations and time points. Lethal times to 50% were lower for both fish species in the presence of fluridone at the TFM concentrations used in treatments. Brown trout, upon which Maximum Allowable Concentrations of TFM are based, was the least sensitive of the test species to TFM at all time points and concentrations. Although our data indicate that fluridone does increase the toxicity of TFM when close to the maximum concentration recommended by the EPA (12ppm), this increase in toxicity is likely to be within currently accepted limits of non-target mortality in most field application targets (typically <7 ppm). Given the potential presence of other contaminants, however, on-site toxicity tests should still be used whenever possible to evaluate acute mortality of non-target species.
Alzheimer-Demenz

Zusammenfassung Demenzielle Erkrankungen spielen aufgrund des demografischen Wandels eine zunehmende Rolle in unserer Gesellschaft und somit auch im klinischen Alltag. Die Weiterentwicklung diagnostischer Methoden wie das Amyloid-PET und die Biomarkerbestimmung im Liquor hat eine Wende in der Klassifikation der Demenzen herbeigeführt. Es ist mittlerweile nicht nur möglich, die Alzheimer-Erkrankung als häufigste Ursache der Demenz schon in Frühstadien sicher zu erkennen. Auch einige klinisch atypische Demenzen zeigen die charakteristische Alzheimer-Pathologie und werden dementsprechend als Alzheimer-Varianten aufgefasst. Ein relevanter Anteil alzheimerähnlicher Demenzen beruht hingegen nicht auf einer alzheimerdefinierenden Amyloid-Pathologie. In diesem Übersichtsartikel werden die Folgen der modernen diagnostischen Methoden auf die Klassifikation der Demenzen, deren Prävention und Therapie diskutiert.