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Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, $\alpha=2$ as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed $>$600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that $\alpha = 1.63 \pm 0.03$. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating., Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 71

Acquiring a better understanding of drought impacts becomes increasingly vital under a warming climate. Traditional drought indices describe mainly biophysical variables and not impacts on social, economic, and environmental systems. We utilized natural language processing and bidirectional encoder representation from Transformers (BERT) based transfer learning to fine-tune the model on the data from the news-based Drought Impact Report (DIR) and then apply it to recognize seven types of drought impacts based on the filtered Twitter data from the United States. Our model achieved a satisfying macro-F1 score of 0.89 on the DIR test set. The model was then applied to California tweets and validated with keyword-based labels. The macro-F1 score was 0.58. However, due to the limitation of keywords, we also spot-checked tweets with controversial labels. 83.5% of BERT labels were correct compared to the keyword labels. Overall, the fine-tuned BERT-based recognizer provided proper predictions and valuable information on drought impacts. The interpretation and analysis of the model were consistent with experiential domain expertise., Comment: 5 pages (+3 in appendix), 5 figures in appendix, 2 tables (+1 in appendix), ICML Workshop on Tackling Climate Change with Machine Learning Workshop, 2021

Building compositional explanations requires models to combine two or more facts that, together, describe why the answer to a question is correct. Typically, these "multi-hop" explanations are evaluated relative to one (or a small number of) gold explanations. In this work, we show these evaluations substantially underestimate model performance, both in terms of the relevance of included facts, as well as the completeness of model-generated explanations, because models regularly discover and produce valid explanations that are different than gold explanations. To address this, we construct a large corpus of 126k domain-expert (science teacher) relevance ratings that augment a corpus of explanations to standardized science exam questions, discovering 80k additional relevant facts not rated as gold. We build three strong models based on different methodologies (generation, ranking, and schemas), and empirically show that while expert-augmented ratings provide better estimates of explanation quality, both original (gold) and expert-augmented automatic evaluations still substantially underestimate performance by up to 36% when compared with full manual expert judgements, with different models being disproportionately affected. This poses a significant methodological challenge to accurately evaluating explanations produced by compositional reasoning models., Comment: Accepted to EMNLP 2021

New academic demands and challenges in schools require increased capacity to support instructional improvement. Capacity comes in many forms: technical, organizational, and political. In addition, there are different sources of capacity, such as the state government and networks. This report examines: (1) What sources of capacity to support instructional improvement operate in California?; (2) How are they distributed?; (3) How does California's capacity compare with other states?; and (4) How might capacity be strengthened? As it addresses these questions, this report strives: (1) To build on but not repeat prior analysis of California's education governance; (2) To focus on state structures for instructional support, but not consider all aspects of educational governance capacity; and (3) To focus primarily on state-level structures for support, with particular attention on the California Department of Education.

Prior studies portray support for the Common Core in California (Finkelstein et al. 2018; Jochim and Lavery 2015) as well as support for other significant reforms, like the change in California's school finance system (Koppich and Humphrey 2018). Along with enthusiasm for California's policy changes over the past decade have come calls to "stay the course" to enable the current changes to make their way into practice. How are the frontlines of instructional practice experiencing the current terrain? Do the frontlines share this view that strides are being made? This report focuses on several key questions: (1) What are California teachers' perceptions of the core elements of instructional practice--standards, curriculum, instructional materials, professional learning opportunities--in the Common Core Era?; (2) What are the resources that California teachers use in instruction and the sources of such resources?; (3) What are teachers' perceptions and experience of managing various manifestations of educational disparities as they engage with the Common Core initiative?; and (4) What are opportunities to learn as California moves forward with state policy alignment? The authors focus primarily on teachers' perspectives to address these questions, but they also include insights from California superintendents and state and regional leaders. They also strive to focus on some of the key elements of instruction--standards, curriculum, frameworks, materials, and professional development.

This study was conducted in order to identify the major reading, cognitive, and linguistic skills that contribute to individual differences in performance on the reading portion of the Florida Comprehensive Assessment Test (FCAT) at third, seventh, and tenth grades. The authors were also interested in understanding the primary deficiencies in skill and knowledge that are associated with below grade level performance on the FCAT (levels 1 and 2). The expectation was that this knowledge would be helpful in planning more effective methods to prevent performance difficulties on the FCAT and more effective remedial procedures for students who have demonstrated deficiencies on the FCAT. The results provide substantial evidence for the construct validity of the FCAT test as a measure of reading comprehension in which the proportion of items that assess "higher order thinking skills" increases substantially from third through tenth grade. (Contains 6 tables and 3 figures.) [Preliminary report was prepared December, 2004, by the Florida Center for Reading Research.]

Recent trends in the frequency of diagnoses and special education referrals for children with autism spectrum disorders necessitate the demand for evidence-based educational practices. Specifically, information related to improving social communication and social behavior domains in classrooms is needed. One method that is increasingly suggested for teaching social skills to children with autism spectrum disorders is the use of Social Stories. Although the rationale behind Social Stories is strong, little research exists examining the effectiveness of Social Story interventions for children with autism spectrum disorders. This article offers a synthesis of the available research regarding Social Stories and their effectiveness for educating children with autism spectrum disorders. In addition, future directions for research and implications for educators are provided.

Investigates differences between two types of reading probe material to monitor students' oral reading fluency over time. Reading sources were the curriculum in which the child was being instructed at school, and a set of generic (curriculum-independent) passages. Results indicated that students read significantly more words correct in the generic reading passages than in the curriculum-based passages. (Contains 36 references and 3 tables.) (GCP)

Investigates the effects of two parent tutoring reading programs upon children's reading achievement: one that used children's literature books and one that used each child's classroom basal reading materials. Results showed that although parents implemented the tutoring programs as designed, neither tutoring program had a significant effect upon student reading achievement. (Contains 74 references and 7 tables.) (GCP)

For Exploration Missions 1 and 2, the Orion capsules will be entering the Earth's atmosphere with speeds in excess of 11 km/s. In the event of a degraded Guidance, Navigation, and Control system, attempting the nominal guided entry may be inadvisable due to the potential for failures that result in a loss of vehicle (or crew, when crew are aboard). In such a case, a method of assuring Earth capture, water landing, and observence of trajectory constraints (heating, loads) is desired. Such a method should also be robust to large state uncertainty and variations in entry interface states. This document will explore four approaches evaluated and their performance in ensuring a safe return of the Orion capsule in the event of onboard system degradation.

NASA is scheduled to launch the Orion spacecraft atop the Space Launch System on Exploration Mission 1 in late 2018. When Orion returns from its lunar sortie, it will encounter Earth's atmosphere with speeds in excess of 11 kilometers per second, and Orion will attempt its first precision-guided skip entry. A suite of flight software algorithms collectively called the Entry Monitor has been developed in order to enhance crew situational awareness and enable high levels of onboard autonomy. The Entry Monitor determines the vehicle capability footprint in real-time, provides manual piloting cues, evaluates landing target feasibility, predicts the ballistic instantaneous impact point, and provides intelligent recommendations for alternative landing sites if the primary landing site is not achievable. The primary engineering challenges of the Entry Monitor is in the algorithmic implementation in making a highly reliable, efficient set of algorithms suitable for onboard applications.

Traditional ballistic entry does not scale well to higher energy entry trajectories. Clutch algorithm is a two-stage approach with the capture stage and load relief stage. Clutch may offer expansion of the operational entry corridor. Clutch is a candidate solution for Exploration Mission-2's degraded entry mode.

Recent entry guidance algorithm development123 has tended to focus on numerical integration of trajectories onboard in order to evaluate candidate bank profiles. Such methods enjoy benefits such as flexibility to varying mission profiles and improved robustness to large dispersions. A common element across many of these modern entry guidance algorithms is a reliance upon the concept of Apollo heritage lateral error (or azimuth error) deadbands in which the number of bank reversals to be performed is non-deterministic. This paper presents a closed-loop bank reversal method that operates with a fixed number of bank reversals defined prior to flight. However, this number of bank reversals can be modified at any point, including in flight, based on contingencies such as fuel leaks where propellant usage must be minimized.

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

As a backup to the GPS-aided Kalman filter and the Barometric altimeter, an "adjusted" velocity trigger is used during entry to trigger the chain of events that leads to drogue chute deploy for the Orion Multi-Purpose Crew Vehicle (MPCV) Exploration Flight Test-1 (EFT-1). Even though this scenario is multiple failures deep, the Orion Guidance, Navigation, and Control (GN&C) software makes use of a clever technique that was taken from the Mars Science Laboratory (MSL) program, which recently successfully landing the Curiosity rover on Mars. MSL used this technique to jettison the heat shield at the proper time during descent. Originally, Orion use the un-adjusted navigated velocity, but the removal of the Star Tracker to save costs for EFT-1, increased attitude errors which increased inertial propagation errors to the point where the un-adjusted velocity caused altitude dispersions at drogue deploy to be too large. Thus, to reduce dispersions, the velocity vector is projected onto a "reference" vector that represents the nominal "truth" vector at the desired point in the trajectory. Because the navigation errors are largely perpendicular to the truth vector, this projection significantly reduces dispersions in the velocity magnitude. This paper will detail the evolution of this trigger method for the Orion project and cover the various methods tested to determine the reference "truth" vector; and at what point in the trajectory it should be computed.

A method and system for determining range to a target are provided. A beam of electromagnetic energy is transmitted through an aperture in an opaque screen such that a portion of the beam passes through the aperture to generate a region of diffraction that varies as a function of distance from the aperture. An imaging system is focused on a target plane in the region of diffraction with the generated image being compared to known diffraction patterns. Each known diffraction pattern has a unique value associated therewith that is indicative of a distance from the aperture. A match between the generated image and at least one of the known diffraction patterns is indicative of a distance between the aperture and target plane.

Two versions of an optoelectronic system undergoing development are depicted. The system is expected to be capable of measuring a distance between 2 and 10 m with an error of no more than 1 micrometer. The system would be designed to exploit Fresnel diffraction of a laser beam. In particular, it would be designed to take advantage of the fact that a Fresnel diffraction pattern is ultrasensitive to distance. The two versions would differ in the following respects: In version 1, the focus of the telescope would be in the Fresnel region, and the telescope would have a small depth of focus. As a consequence, the Fresnel pattern would be imaged directly onto the photodetector array; in version 2, a multielement lens module would displace the Fresnel region from the vicinity of the pinhole to the vicinity of the optical receiver. As the distance to be measured varied, the location of the receiver relative to the displaced Fresnel-diffraction region would vary, thereby causing the Fresnel diffraction pattern on the focal plane to vary. The multielement lens module would also correct for aberrations. The processing of the digitized Fresnel diffraction pattern in the computer might be accelerated by using only parts of the pattern or even only one small part - the central pixel. As the distance from the pinhole increased, the central pixel would rapidly cycle between maximum and minimum light intensity. This in itself would not be sufficient to uniquely determine the distance. However, by varying the size of the pinhole or the wavelength of the laser, one could obtain a second cycle of variation of intensity that, in conjunction with the first cycle, could enable a unique determination of distance. Alternatively, for a single wavelength and a single pinhole size, it should suffice to consider the data from only two different key pixels in the Fresnel pattern.