Miracle-dz/UniFAR-FTUs huggingface dataset:
"These are Facts": Opportunities for and Barriers to Policy Changes that Support Learning

Barriers to high quality learning experiences are often indirectly attributed to policy, such as mandated testing, yet learning scientists seldom engage the policy arena. This study presents early-stage design-based implementation research that aims to change policy. Interactions between legislators and a collaboratory constituted to investigate performance assessments are analyzed to better understand barriers and opportunities. We found differences along party lines that can be interpreted as barriers and constraints, suggesting directions for our ongoing work. Issues Addressed Nationwide, in the United States, students take tests that are used to evaluate student progress, teacher effectiveness, and school quality; many test results are not useful in making instructional decisions and are controversial markers of learning and teacher effectiveness. To address this issue, a group of education leaders, teachers and university faculty formed a collaboratory. This group took their mission, vision, and research to the legislative education study committee (LESC) in hopes of launching policy change regarding opportunities for students to demonstrate mastery. Legislative perceptions of assessment practices and opportunities for learning are important to understand in order to make policy reform that creates opportunities for learning.
This paper is included in the Proceedings of the 19th USENIX Conference on File and Storage Technologies.

Data deduplication is widely used to reduce the size of backup workloads, but it has the known disadvantage of causing poor data locality, also referred to as the fragmentation problem, which leads to poor restore and garbage collection (GC) performance. Current research has considered writing duplicates to maintain locality (e.g. rewriting) or caching data in memory or SSD, but fragmentation continues to hurt restore and GC performance. Investigating the locality issue, we observed that most duplicate chunks in a backup are directly from its previous backup. We therefore propose a novel management-friendly deduplication framework, called MFDedup, that maintains the locality of backup workloads by using a data classiﬁcation approach to generate an optimal data layout. Speciﬁcally, we use two key techniques: Neighbor-Duplicate-Focus indexing (NDF) and Across-Version-Aware Reorganization scheme (AVAR), to perform duplicate detection against a previous backup and then rearrange chunks with an ofﬂine and iterative algorithm into a compact, sequential layout that nearly eliminates random I/O during restoration. Evaluation results with four backup datasets demonstrates that, compared with state-of-the-art techniques, MFDedup achieves deduplication ratios that are 1 . 12 × to 2 . 19 × higher and restore throughputs that are 2 . 63 × to 11 . 64 × faster due to the optimal data layout we achieve. While the rearranging stage introduces overheads, it is more than offset by a nearly-zero overhead GC process. Moreover, the NDF index only requires indexes for two backup versions, while the traditional index grows with the number of versions retained.
Exploiting Combined Locality for Wide-Stripe Erasure Coding in Distributed Storage

Erasure coding is a low-cost redundancy mechanism for distributed storage systems by storing stripes of data and parity chunks. Wide stripes are recently proposed to suppress the fraction of parity chunks in a stripe to achieve extreme storage savings. However, wide stripes aggravate the repair penalty, while existing repair-efﬁcient approaches for erasure coding cannot effectively address wide stripes. In this paper, we propose combined locality , the ﬁrst mechanism that systematically addresses the wide-stripe repair problem via the combination of both parity locality and topology locality. We further augment combined locality with efﬁcient encoding and update schemes. Experiments on Amazon EC2 show that combined locality reduces the single-chunk repair time by up to 90.5% compared to locality-based state-of-the-arts, with only a redundancy of as low as 1 . 063 × .
Telling Faults From Cyber-Attacks In A Multi-Modal Logistic System With Complex Network Analysis

We investigate the application of methodologies for the analysis of complex networks to understand the properties of systems of systems in a cybersecurity context. We are interested to resilience and attribution: the first relates to the behavior of the system in case of faults/attacks, namely to its capacity to recover full or partial functionality after a fault/attack; the second corresponds to the capability to tell faults from attacks, namely to trace the cause of an observed malfunction back to its originating cause(s). We present experiments to witness the effectiveness of our methodology considering a discrete event simulation of a multimodal logistic network featuring 40 nodes distributed across Italy and a daily traffic roughly corresponding to the number of containers shipped through in Italian ports yearly, averaged on a daily basis.
Cyberinfrastructure for Design-Based Research: Toward a Community of Practice for Learning Scientists

This symposium is part of an NSF project to help The Learning Sciences community collaboratively envision a cyberinfrastructure for design-based research (DBR). Like numerous others before us, we argue that the educational research community would be well served by a mutually created cyberinfrastructure that would encourage and support engagement by multiple design-based researchers in sharing knowledge and working toward answers to important theory-driven research questions, moving our field toward a “bigger science” approach. The vision is one in which junior and senior scholars set collaborative research agendas and establish common protocols and systems to support systematic collaborative archiving, sharing and analysis of multi-modal data. This symposium offers critique, reflection, and concrete proposals for design, helping demonstrate the possibility of implementing this idea and moving it toward becoming reality. Chair’s Introduction
Adventures in Argument: Training in Argumentation Influences Student Resource Use in Collaborative Meaning Making

: Argumentation is the primary pedagogical strategy employed in the online undergraduate course Human Abilities and Learning Online (HAL Online). We conducted a controlled in vivo experiment in this course to examine the effects, on collaborative meaning making, of providing direct training in argumentation early in the course. The performance of a group receiving the treatment, Trained Argumentation with Modest Scaffolding (TAMS), was compared with an ecological control group that did not receive argument training: Emergent Argumentation with Modest Scaffolding (EAMS). We hypothesized that argument training would influence how students attended to, used, and shared instructional resources as evidence to support explanations in collaborative meaning making. Results indicated that TAMS exerted strong influence on how deeply and thoroughly students processed, were accountable to, and integrated instructional resources.
Windows PE Malware Detection Using Ensemble Learning

In this Internet age, there are increasingly many threats to the security and safety of users daily. One of such threats is malicious software otherwise known as malware (ransomware, Trojans, viruses, etc.). The effect of this threat can lead to loss or malicious replacement of important information (such as bank account details, etc.). Malware creators have been able to bypass traditional methods of malware detection, which can be time-consuming and unreliable for unknown malware. This motivates the need for intelligent ways to detect malware, especially new malware which have not been evaluated or studied before. Machine learning provides an intelligent way to detect malware and comprises two stages: feature extraction and classification. This study suggests an ensemble learning-based method for malware detection. The base stage classification is done by a stacked ensemble of fully-connected and one-dimensional convolutional neural networks (CNNs), whereas the end-stage classification is done by a machine learning algorithm. For a meta-learner, we analyzed and compared 15 machine learning classifiers. For comparison, five machine learning algorithms were used: naïve Bayes, decision tree, random forest, gradient boosting, and AdaBoosting. The results of experiments made on the Windows Portable Executable (PE) malware dataset are presented. The best results were obtained by an ensemble of seven neural networks and the ExtraTrees classifier as a final-stage classifier.
Remap-SSD: Safely and Efficiently Exploiting SSD Address Remapping to Eliminate Duplicate Writes

Duplicate writes are prevalent in diverse storage systems, originating from data duplication, journaling, and data relocations, etc. As flash-based SSDs have been widely deployed, these writes can significantly degrade their performance and lifetime. To eliminate duplicate writes, prior studies have proposed innovative approaches that exploit the address remapping utility inside SSDs. However, remap operations lead to a mapping inconsistency problem, which may cause data loss and has not been properly addressed in existing studies. In this paper, we propose a novel SSD design, called RemapSSD, with two notable features. First, it provides a remap primitive, which allows the host software and SSD firmware to perform logical writes of duplicate data at almost zero cost. Second, a hybrid storage architecture is employed to maintain the mapping consistency. Small byte-addressable non-volatile RAM is used to persist remapping metadata in a log-structured manner and is managed synergistically with flash memory. We verify Remap-SSD on a software SSD emulator with three case studies: intra-SSD deduplication, SQLite journaling, and F2FS cleaning. Experimental results show that Remap-SSD can realize the full potential of address remapping to improve SSD performance and lifetime.
Role of carbon dioxide and ethylene during storage process.

Proces zrání obilek je doprovázen zvyšující se produkcí ethylenu, je v přímé souvislosti s obsahem abscisové kyseliny (ABA) v obilce. V průběhu dormance uvedené fytohormony klesají [1]. Po výstupu z dormance obilka plně klíčí za přítomnosti potřebného množství vody, kyslíku a tepla. Embryo vysílá gibereliny do aleuronové vrstvy, která začne vytvářet a uvolňovat do endospermu hydrolytické enzymy. Buňky aleuronové vrstvy jsou triploidní, obsahují bílkoviny, minerální látky, cukry a polyfenoly, při klíčení respirují a metabolizují [2]. Ethylen zvyšuje tvorbu xylanasy (vytváření kanálků přes stěny buněk aleuronové vrstvy), tím se podporuje uvolňování alfaamylasy, a zvyšuje se aktivita proteas podílejících se na ztrátách alfa-amylasy [3]. Plyny – ethylen a oxid uhličitý – hrají svou roli v posklizňovém dozrávání a v průběhu klíčení obilky [4]. Fyziologická účinnost ethylenu je velmi vysoká. K fyziologickým reakcím dochází v rozmezí koncentrace ethylenu 0,01–1 μg. l-1 [5]. Cílem práce bylo zjistit, jaké koncentrace ethylenu a oxidu uhličitého ovlivňují výslednou homogenitu a modifikaci sladu.
Stem Cell Therapy in Ischemic Stroke: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background and Objective: Stem cell therapy has emerged as a potential therapy for the treatment of stroke. We performed a systematic review and meta-analysis of published randomized controlled studies using various types of stem cell therapies in patients with ischemic stroke (IS). Method: Literature search was carried out using PubMed, Google Scholar, Cochrane library, and clinicaltrial.gov to identify studies on stem cell therapy in IS from its inception till January 2020. Data were extracted independently by two reviewers. STATA version 13 was used for carrying out meta- analysis. We included only randomized controlled trials (RCTs) if any of the stem cell therapy was used to treat patients with IS in any phase after the index stroke. Results: We included a total of eight randomized controlled studies involving 459 subjects (217 intervention and 242 controls) in the meta-analysis. We did not observe statistically significant reduction in mean NIHSS score in the intervention group (SMD - 0.34, 95% CI - 0.76 to 0.08) in patients with acute or sub-acute stroke. However, a statistically significant reduction (SMD - 1.57, 95% CI -2.22 to -0.92) was observed in patients with chronic ischemic stroke. Statistically non-significant reduction in mean mRS in the intervention group (SMD 0.04, 95% CI -0.20 to 0.28) in patients with acute or sub-acute ischemic stroke was observed, however a statistically significant reduction (SMD - 1.07, 95% CI - 1.94 to -0.19) was noted in patients with chronic stroke. We did not observe statistically significant reduction in mean Barthel index score (SMD 0.24, 95%CI -1.69 to 2.17) in chronic stroke. Statistically non-significant lower mortality rate was observed in intervention group compared to controls (Risk Ratio 0.84, 95% CI 0.43 to 1.66) among acute or sub-acute, as well as in the chronic stroke group (Risk Ratio 0.47, 95% CI 0.20 to 1.09). Conclusion: Our meta-analysis provides no clinically important evidence for efficacy of stem cells in reducing neurological deficit compared to control group. Well-designed large randomized controlled trials are required to provide more information on the efficacy of stem cell therapy in patients with IS.
Coronavirus anxiety scale: A validation study in an Indian population

Background and Objective: Coronavirus is spreading across the globe since December 2019. As India's first case was reported on January 30, 2020, the spread of this virus is experiencing by our population. As a result, there are extensive emotional distress and anxiety among populations due to this pandemic. Screening mental health is important for psychological well-being of any individual during this pandemic. The objective of this study was to validate the Coronavirus Anxiety Scale (CAS) in an Indian population. Materials and Methods: The research proposal was laid out before an ethical clearance committee, which approved the conduct of the research and consequent publication of the paper. Data were collected from 246 respondents through online. CAS, developed by Lee (2020), was adopted for the study. Informed consent was given by all the respondents, and their participation was voluntary. AMOS and SPSS were used to calculate confirmatory factor analysis and other statistical analyses. Bartlett's test of sphericity and Kaiser–Meyer–Olkin (KMO) test, and Cronbach's alpha, were also calculated. Convergent validity was calculated through average variance extracted (AVE) and composite reliability (CR) in Microsoft Excel. Results, and Conclusions: Bartlett's test of sphericity was highly significant measuring Chi-square = 494.004, df = 10, and P < 0.001. The KMO was acceptable at 0.805. Fit indices (P < 0.001, Chi-square/df = 3.24, goodness of fit index = 0.976, Tucker–Lewis index = 0.954, and comparative fit index = 0.977) are statistically significant. Cronbach's alpha coefficient (0.822), AVE (0.526), and CR (0.745) were adequate. The overall findings of the analyses demonstrate that the CAS is a reliable and valid scale that evaluates the severity levels of dysfunctional anxiety linked to COVID-19 in an Indian sample. CAS is applicable to measure the level of dysfunctional coronavirus anxiety in the Indian population.
Can Circulating microRNAs Identify Sudden Unexpected Death in Parkinson's Disease ?

Annals of Indian Academy of Neurology ¦ Volume 24 ¦ Issue 2 ¦ March-April 2021 290 2. Duquette A, Brais B, Bouchard JP, Mathieu J. Clinical presentation and early evolution of spastic ataxia of Charlevoix‐Saguenay. Mov Disord 2013;28:2011‐14. 3. Ogawa T, Takiyama Y, Sakoe K, Mori K, Namekawa N, Shimazaki H, et al. Identification of a SACS gene missense mutation in ARSACS. Neurology 2004;62:107‐9. 4. Baets J, Deconinck T, Smets K, Goossens D, Van den Bergh P, Dahan K, et al. Mutations in SACS cause atypical and late‐onset forms of ARSACS. Neurology 2010;75:1181‐8. 5. Vill K, Müller‐Felber W, Gläser D, Kuhn M, Teusch V, Schreiber H, et al. SACS variants are a relevant cause of autosomal recessive hereditary motor and sensory neuropathy. Hum Genet 2018;137:911‐9. 6. Agarwal PA, Ate‐Upasani P, Ramprasad VL. Autosomal recessive spastic ataxia of charlevoix–saguenay (ARSACS)‐first report of clinical and imaging features from India, and a novel SACS gene duplication. Mov Disord Clin Pract 2017;4:775‐7. 7. Hara, K, Onodera O, Endo M, Kondo H, Shiota H, Miki K, et al. Sacsin‐ related autosomal recessive ataxia without prominent retinal myelinated fibers in Japan. Mov Disord 2005;20:380‐2.
REFERENCE-DEPENDENT HEDGING: THEORY

We develop a theoretical model of optimal hedging that nests expected utility and expected target utility theories. We use this model to characterize optimal hedging with and without reference price dependence. The model’s theoretical predictions are tested with a unique database consisting of every forward contract written with a major grain marketing firm by Iowa corn producers over a fiveyear period. Our results suggest that a current December futures price higher than a reference price triggers hedging activity. A likely candidate for producers’ reference price is a rolling average of the current futures price. We then use trading activity implied by the producers to determine if they benefit from the way they hedge. The evidence is mixed. Finally, we compare the producer forward contract data to the only publicly available data on producer hedging: The Commodity Futures Trading Commission Disaggregated Commitment of Traders Report (DCOT) for Short Hedgers. A hedge ratio constructed from the open interest in new futures contracts of the DCOT report is highly correlated with the producer hedge series in the Iowa data, providing evidence that DCOT data represent farmers’ hedging behavior reasonably well. This work has important implications for future research that uses the DCOT data, and provides new evidence about producers’ hedging behavior that marketing specialists and extension agents can use to enhance their educational efforts related to risk management.
Childhood Adverse Events and the Long-Term Effects on Mental Health

Results: The results demonstrate a strong correlation between childhood adverse events and bipolar disorder specifically, sexual abuse in females, maternal separation, economic difficulties and family history of mental illness. Specific childhood adverse events show a correlation with specific types of mental illness. The recollection of abuse is associated with an increased risk of depressive symptoms. Additionally, childhood adverse events are associated with clinical outcomes of bipolar disorder including age of onset, chronicity, psychotic episodes and suicidality.
Assessing the simulated soil hydrothermal regime of the active layer from the Noah-MP land surface model (v1.1) in the permafrost regions of the Qinghai–Tibet Plateau

Abstract. Extensive and rigorous model intercomparison is of great importance before model application due to the uncertainties in current land surface models (LSMs). Without considering the uncertainties in forcing data and model parameters, this study designed an ensemble of 55 296 experiments to evaluate the Noah LSM with multi-parameterization (Noah-MP) for snow cover events (SCEs), soil temperature (ST) and soil liquid water (SLW) simulation, and investigated the sensitivity of parameterization schemes at a typical permafrost site on the Qinghai–Tibet Plateau (QTP). The results showed that Noah-MP systematically overestimates snow cover, which could be greatly resolved when adopting the sublimation from wind and a semi-implicit snow/soil temperature time scheme. As a result of the overestimated snow, Noah-MP generally underestimates ST, which is mostly influenced by the snow process. A systematic cold bias and large uncertainties in soil temperature remain after eliminating the effects of snow, particularly in the deep layers and during the cold season. The combination of roughness length for heat and under-canopy (below-canopy) aerodynamic resistance contributes to resolving the cold bias in soil temperature. In addition, Noah-MP generally underestimates top SLW. The runoff and groundwater (RUN) process dominates the SLW simulation in comparison to the very limited impacts of all other physical processes. The analysis of the model structural uncertainties and characteristics of each scheme would be constructive to a better understanding of the land surface processes in the permafrost regions of the QTP as well as to further model improvements towards soil hydrothermal regime modeling using LSMs.
Effects of International Male Migration on Wives Left Behind in Rural Tamil Nadu

Emigration by skilled and semi-skilled workers from India to the Middle East is a strategy for better economic returns. Families rarely accompany migrants. Drawing insights from primary data gathered from intensive fieldwork in Sivaganga district, Tamil Nadu, this article attempts to understand the psychological consequences on the wives left behind. A mixed sampling method was used to derive the sample size. Analysis of variance (ANOVA) and Pearson’s chi-square tests were mainly used for quantitative analysis supplemented by qualitative methods. The results indicated that wives considered loneliness the most significant psychological problem arising from their husbands’ absence. Stress was also caused by financial problems and the necessity of taking on additional roles in the family.
Optical and Handwritten Character Recognition System Using Segmentation and Neural Network

Optical character recognition has been a hot topic for decades and has been continuously being developed and improved. The following paper describes how English alphabets can be distinguished from one another based on process of segmentation and how density of marked contours helps neural networks to analyze each data. Based on experiments conducted on four skeletalized English alphabets, the following article highlights a brief description of how errors and anomalies occur during the process of handwritten character recognition.
THE INFLUENCE OF BIDENS TRIPARTITA EXTRACTS ON PSYCHO- MOTOR ABILITIES AND COGNITIVE FUNCTIONS IN RATS

The aim of the present study was the investigation of the effects of two Bidens tripartita (B. tripartita) extracts on the spatial working memory in rat. The experiments were carried out on white Wistar rats (4 groups of 6 animals each), treated intraperitoneally, as follows: Group I (Control): saline solution 0.1 mL/10 g b.w. ; Group II (BT-alcoholic): 200 mg/kg b.w. B. tripartita alcoholic extract; Group III (BT-aqueous): 250 mg/kg b.w. B. tripartita aqueous extract; Group IV (Mg): 1 mmol/kg b.w. magnesium chloride. The effects on the psycho-motor abilities and cognitive functions were evaluated using the T-maze test, in order to investigate the influence of these two vegetal extracts on the short-term memory of laboratory animals. B. tripartita aqueous extract determined a significant increase of the spontaneous alternation percentage, but less accentuated than magnesium in the T-maze test in rats. The administration of B. tripartita alcoholic extract was associated with a significant augmentation of the spontaneous alternation rate, but less intense than those produced by the aqueous extract in this behavioural model. The treatment with both B. tripartita aqueous and alcoholic extracts resulted in a facilitation of spatial learning, an improvement of the short-term memory and a weak anxiolytic effect in rat.