Miracle-dz/UniFAR-FTUs huggingface dataset:
The Convergence Rate of Finite Difference Schemes in the Presence of Shocks

Finite difference approximations generically have ${\cal O}(1)$ pointwise errors close to a shock. We show that this local error may effect the smooth part of the solution such that only first order is achieved even for formally higher-order methods. Analytic and numerical examples of this form of accuracy are given. We also show that a converging method will have the formal order of accuracy in domains where no characteristics have passed through a shock.
HDM: Heat Diffusion Mesh for Mesh Representation

The applications of 3D mesh, such as computer vision, molecular biology, and medical imaging, have received sufficient attention recently. Polygonal meshes are a significant raw representation of 3D shapes thanks to their compactness and flexibility. Though extensive research efforts are concentrating on how to represent 3D shapes well based on voxels and point clouds, there is little effort on meshes due to their irregularity. To tackle the challenge, we propose a new mesh representation called Heat Diffusion Mesh (HDM) in this paper. It represents meshes that are in non-Euclidean domains as a space-time signal by heat diffusion. Experiments of mesh classification and segmentation show the effectiveness of the proposed mesh representation.
Arbitrary Lagrangian-Eulerian thermomechanical finite-element model of material cutting

An arbitrary Lagrangian-Eulerian model is described for the prediction of metal flow in a steady-state forming process. The formulation is shown to be capable of dealing with free surface and boundary friction; the coupling with thermal effects is also considered. The numerical techniques are based on the finite elements for spatial discretization of the momentum equation and on an explicit integration scheme for discretization relative to time. The mass and energy equations are integrated by a finite-volume method. Details on the application to an orthogonal cutting process are given in the final Section of the paper.
Supporting Upper Elementary Students in Multidisciplinary Block-Based Narrative Programming

Digital storytelling, which combines traditional storytelling with digital tools, has seen growing popularity as a means of creating motivating problem-solving activities in K-12 education. Though an attractive potential solution to integrating language arts skills across topic areas such as computational thinking and science, better understanding of how to structure and support these activities is needed to increase adoption by teachers. Building on prior research on block-based programming for interactive storytelling, we present initial results from a study of 28 narrative programs created by upper elementary students that were collected in both classroom and extracurricular contexts. The narrative programs are evaluated across multiple dimensions to better understand the types of narrative programs being created by the students, characteristics of the students who created the narratives, and what types of support could most benefit the students in their narrative program construction. In addition to analyzing the student-created narrative programs, we also provide recommendations for promising system-generated and instructor-led supports.
Strangeness Changing Processes and the Limit on the Right-handed Gauge Boson Mass

We study strangeness-changing processes, in particular, the K/sub L/-K/sub S/ mass difference and K. -->. 2.pi. decays, in the context of left-right-symmetric gauge theories in a search for a lower bound on a scale of parity-breaking M/sub R/. We find that the previously claimed limit on M/sub R/ is a function of Higgs-boson masses and also the top-quark mass and its mixings to lighter quarks. For the light t quark (M/sub t/ 200 GeV (for m/sub H/approx. =100 GeV), and the bound goes down with the increasing t-quark mass. Even in the case of a very heavy neutral Higgs boson (m/sub H/approx. =1 TeV), the lower limit on M/sub R/ is only approx.400 GeV. We argue that the best limits on M/sub R/ must come from neutral-current phenomena.
Co-aligning User-Centered Design and Software Engineering Courses: A Case Study

Introducing students to different perspectives and roles in the development process allows them to engage in the work of cross-disciplinary diverse teams and even can enable them to change roles in designer-developer interactions. Industry work often places recent graduates in preexisting polarized relationship dynamics between different participants in the design and development process. This paper describes a two-stage attempt at co-alignment of software engineering and user-centered design courses: from full alignment with topic intersections and joint project to partial alignment through separate activities. We discuss challenges of both ways including time or technical constraints, increased effort from the program developers and instructors, students' and instructors' frustrations. We finalize by describing benefits of providing students with early experience identifying trade-offs between design requirements and architecture and opportunities for diverse group with different background in computer science.
Automated Structural Evaluation of Block-based Coding Assignments

As computer science is integrated into a wider variety of fields, block-based programming languages like Snap! , which assemble code with visual blocks rather than text syntax, are increasingly used to teach computational thinking (CT) to students from diverse backgrounds. Although automated evaluators (autograders) for programming assignments usually focus on runtime efficiency and output accuracy, effective evaluation of a student's CT skills requires assessing coding best practices, such as decomposition, abstraction, and algorithm design. While autograders are commonplace for text languages like Python, we present a machine learning approach to assess how effectively block-based code demonstrates understanding of CT fundamentals. Our dataset consists of Snap! programs written by students new to coding and evaluated by instructors using a CT rubric. We explore how to best transform these programs into low-dimensional features to allow encapsulation and repetition patterns to emerge. Experimentation involves comparing the effectiveness of a suite of clustering models and similarity metrics by analyzing how directly automated feedback correlates to the course staff's manual evaluation. Lastly, we demonstrate the practical application of the autograder in a classroom setting and discuss scalability and feasibility in other domains of CS education.
Scaling fit of spin pumping in various ferromagnetic materials

In this paper, we have investigated spin currents generated by the spin pumping in ferromagnetic metal/Pt and ferromagnetic insulator/Pt bilayer films using the inverse spin-Hall effect (ISHE). We found that the magnitude of the electric voltage due to the ISHE is well reproduced by a calculation based on the model of the spin pumping not only in transition magnetic metal/Pt bilayer films, but also in polycrystalline magnetic insulators BiY2Fe5O12/Pt and Y3Fe5O12/Pt. This result will show a guideline for creating a spin current in a wide range of materials.
A self‐consistent scattering model for cirrus. II: The high and low frequencies

The predictive quality of an ensemble model of cirrus ice crystals to model passive and active measurements of ice cloud, from the ultraviolet (UV) to the microwave, is tested. The ensemble model predicts m ∝ D2, where D is the maximum dimension of the ice crystal, and m is its mass. This predicted m‐D relationship is applied to a moment estimation parametrization of the particle size distribution (PSD), to estimate the PSD shape, given ice water content (IWC) and in‐cloud temperature. The same microphysics is applied across the electromagnetic spectrum to model UV, infrared, microwave and radar observations. The short‐wave measurements consist of airborne UV backscatter lidar (light detection and ranging) estimates of the volume extinction coefficient, total solar optical depth, and space‐based multi‐directional spherical albedo retrievals, at 0.865 µm, between the scattering angles 85° and 125°. The airborne long‐wave measurements consist of high‐resolution interferometer upwelling brightness temperatures, obtained between the wavelengths of about 3.45 µm and 4.1 µm, and 8.0 µm to 12.0 µm. The low‐frequency measurements consist of ground‐based Chilbolton 35 GHz radar reflectivity measurements and space‐based upwelling 190 GHz brightness temperature measurements. The predictive quality of the ensemble model is demonstrated to be generally within the experimental uncertainty of the lidar backscatter estimates of the volume extinction coefficient and total solar optical depth. The ensemble model prediction of the high‐resolution brightness temperature measurements is generally within ±2 K and ±1 K at solar and infrared wavelengths, respectively. The 35 GHz radar reflectivity and 190 GHz brightness temperatures are generally simulated to within ±2 dBZe, and ±2 K, respectively. The directional spherical albedo observations suggest that the scattering phase function of the most randomized ensemble model gives the best fit to the measurements (generally within ±3%). This article demonstrates that the ensemble model, assuming the same microphysics, is physically consistent across the electromagnetic spectrum.
Method for Identifying Early Fire Based on the Marginal End of Lightweight Neural Network

Due to the significant difference of flame color of different combustion substances, misjudgment and missed judgment of image recognition can easily occur. And the early fire flame image is a small target, characterized by low resolution, less information, as well as noise, which further increases the difficulty of fire recognition.However, the large-scale neural network architecture requires high computing power of devices, making it unsuitable to deploy marginal computing devices. Targeting the above problems, an early fire identification model based on YOLOv5s network architecture is designed in this paper, and the neural network model through three methods is improved.The key point is that SENet and CBAM modules are introduced into the backbone network to prevent neural network degradation and gradient disappearance, and the accuracy and recall of small target flame detection are improved. After optimizing the model, the recognition rate of small fire targets in the early stage reached 96.61%, and compared with the original YOLOv5s detection model, the system recognition rate increased by 19.53%, which can meet the deployment requirements of network models on mobile or marginal devices, achieving the purpose of rapid identification of early fires.
Applying Software Engineering Anti-patterns to Programming Error Messages

Programming error messages (PEMs) have long been a hindrance to novice programmers. This work aims to establish a catalog of PEM anti-patterns--- common, reoccurring features of PEMs that make them unhelpful or actively harmful to programmers. The goal is for educators to be aware of, and actively teach concrete ways that PEMs can be misleading to students; to encourage language implementers to be cognizant of these; and avoid them when designing error feedback. A pilot study is being conducted to validate the presence of anti-patterns in error messages.
Re-engineering of TNFα-NF-κB signalling dynamics in cancer cells using pathogenic E. coli effectors

Re-engineering NF-κB signalling towards enhancing beneficial outcomes such as tumour cell elimination, while minimising inflammatory damage, is a potential therapeutic avenue. In this study, we explored the ability of bacterial effectors injected into host cells by the type III secretion system to regulate NF-κB translocation dynamics. We used the enteropathogenic Escherichia coli effectors Tir (NF-κB activator), NleC (NF-κB protease) and NleE (TAB2/3 methyltransferase), to manipulate NF-κB translocation and cancer cell survival. We discovered that while these effectors have either limited or no cytotoxicity alone, they greatly enhanced caspase-8-dependent pancreatic cancer cell death in the presence of TNFα. Single cell analysis revealed that the sub-population of cells showing high NF-κB activation is less susceptible to cell death caused by NleC or NleE but instead is more susceptible to Tir. A combination of Tir, NleE and TNFα eliminated 95% cancer cells with limited NF-κB activation, potentially due to NleE-dependent blockage of the immediate pro-survival NF-κB activation without inhibiting Tir’s long-term NF-κB activation that promotes cell death. This work demonstrates that effector combinations could be used to re-engineer stress responses towards favourable outcomes.
Silicon supplementation and jasmonate activation synergistically increase phenolic defences against a legume herbivore

The accumulation of silicon (Si) is widely reported to have anti-herbivore defensive properties in grasses. There is emerging, but fragmentary, evidence that Si could play a similar role in legumes. Here, we sought to understand the effects of Si supplementation on anti-herbivore defensive properties in lucerne (Medicago sativa), especially in relation to other potential defences (i.e. phenolics) and the phytohormone that regulates anti-herbivore defences, jasmonic acid or jasmonate (JA), which is also linked to Si accumulation. We determined how growth, root nodulation and chemistry (carbon, nitrogen and phenolic concentrations) of four genotypes of lucerne responded to Si supplementation, with and without the application of JA, and we used feeding assays to determine the subsequent effects on the feeding success of adult Sitona discoideus weevils. Si supplementation increased plant mass and root nodulation of M. sativa by 61% and 227%, respectively, and reduced relative consumption (RC) and frass production by S. discoideus by 38% and 30%, respectively. Si supplementation had no effect on foliar nitrogen concentrations, most likely due to the dilution effects of increased plant growth and foliar carbon. Phenolic concentrations were negatively correlated with leaf RC; RC also decreased by 34% when JA was applied to plants. When Si was combined with JA application, phenolics were significantly enhanced, demonstrating the potential to stimulate multiple anti-herbivore properties in M. sativa. Synthesis. The novel findings suggest that Si accumulation may play a more important role in legume resistance to herbivorous animals than previously thought. The ubiquity of soil Si and its emerging functional role in plant biology, including plant–animal interactions, suggest that these patterns could be common amongst legumes.
Widespread CRISPR repeat-like RNA regulatory elements in CRISPR-Cas systems

CRISPR-cas loci typically contain CRISPR arrays with unique spacers separating direct repeats. Spacers along with portions of adjacent repeats are transcribed and processed into CRISPR(cr) RNAs that target complementary sequences (protospacers) in mobile genetic elements, resulting in cleavage of the target DNA or RNA. Additional, standalone repeats in some CRISPR-cas loci produce distinct cr-like RNAs implicated in regulatory or other functions. We developed a computational pipeline to systematically predict crRNA-like elements by scanning for standalone repeat sequences that are conserved in closely related CRISPR-cas loci. Numerous crRNA-like elements were detected in diverse CRISPR-Cas systems, mostly, of type I, but also subtype V-A. Standalone repeats often form mini-arrays containing two repeat-like sequence separated by a spacer that is partially complementary to promoter regions of cas genes, in particular cas8, or cargo genes located within CRISPR-Cas loci, such as toxins-antitoxins. We show experimentally that a mini-array from a type I-F1 CRISPR-Cas system functions as a regulatory guide. We also identified mini-arrays in bacteriophages that could abrogate CRISPR immunity by inhibiting effector expression. Thus, recruitment of CRISPR effectors for regulatory functions via spacers with partial complementarity to the target is a common feature of diverse CRISPR-Cas systems.
Incremental improvements in tractometry-based brain-age modeling with deep learning

Multivariate measurements of human brain white matter (WM) with diffusion MRI (dMRI) provide information about the role of WM in a variety of cognitive functions and in brain health. Statistical models take advantage of the regularities in these data to make inferences about individual differences. For example, dMRI data provide the basis for accurate brain-age models – models that predict the chronological age of participants from WM tissue properties. Deep learning (DL) models are powerful machine learning models, which have been shown to provide benefits in many multivariate analysis settings. We investigated whether DL would provide substantial improvements for brain-age models based on dMRI measurements of WM in a large sample of children and adolescents. We found that some DL models fit the data better than a linear baseline, but the differences are small. In particular, recurrent neural network architectures provide up to ∼6% improvement in accuracy. This suggests that information about WM development is mostly accessible with linear models, and does not require the additional invariance and non-linearity offered by DL models. However, in some applications this incremental improvement may prove critical. We provide open-source software that fits DL models to dMRI data (https://yeatmanlab.github.io/AFQ-Insight).
Sleep deprivation, sleep fragmentation, and social jet lag increase temperature preference in Drosophila

Despite the fact that sleep deprivation substantially affects the way animals regulate their body temperature, the specific mechanisms behind this phenomenon are not well understood. In both mammals and flies, neural circuits regulating sleep and thermoregulation overlap, suggesting an interdependence that may be relevant for sleep function. To investigate this relationship further, we exposed flies to 12 h of sleep deprivation, or 48 h of sleep fragmentation and evaluated temperature preference in a thermal gradient. Flies exposed to 12 h of sleep deprivation chose warmer temperatures after sleep deprivation. Importantly, sleep fragmentation, which prevents flies from entering deeper stages of sleep, but does not activate sleep homeostatic mechanisms nor induce impairments in short-term memory also resulted in flies choosing warmer temperatures. To identify the underlying neuronal circuits, we used RNAi to knock down the receptor for Pigment dispersing factor, a peptide that influences circadian rhythms, temperature preference and sleep. Expressing UAS-PdfrRNAi in subsets of clock neurons prevented sleep fragmentation from increasing temperature preference. Finally, we evaluated temperature preference after flies had undergone a social jet lag protocol which is known to disrupt clock neurons. In this protocol, flies experience a 3 h light phase delay on Friday followed by a 3 h light advance on Sunday evening. Flies exposed to social jet lag exhibited an increase in temperature preference which persisted for several days. Our findings identify specific clock neurons that are modulated by sleep disruption to increase temperature preference. Moreover, our data indicate that temperature preference may be a more sensitive indicator of sleep disruption than learning and memory.
Investigating macroecological patterns in coarse-grained microbial communities using the stochastic logistic model of growth

The structure and diversity of microbial communities is intrinsically hierarchical due to the shared evolutionary history of their constituents. This history is typically captured through taxonomic assignment and phylogenetic reconstruction, sources of information that are frequently used to group microbes into higher levels of organization in experimental and natural communities. Connecting community diversity to the joint ecological dynamics of the abundances of these groups is a central problem of community ecology. However, how microbial diversity depends on the scale of observation at which groups are defined has never been systematically examined. Here, we used a macroecological approach to quantitatively characterize the structure and diversity of microbial communities among disparate environments across taxonomic and phylogenetic scales. We found that measures of biodiversity at a given scale can be consistently predicted using a minimal model of ecology, the Stochastic Logistic Model of growth (SLM). This result suggests that the SLM is a more appropriate null-model for microbial biodiversity than alternatives such as the Unified Neutral Theory of Biodiversity. Extending these within-scale results, we examined the relationship between measures of biodiversity calculated at different scales (e.g., genus vs. family), an empirical pattern predicted by the Diversity Begets Diversity (DBD) hypothesis. We found that the relationship between richness estimates at different scales can be quantitatively predicted assuming independence among community members.Contrastingly, only by including correlations between the abundances of community members (e.g., as the consequence of interactions) can we predict the relationship between estimates of diversity at different scales. The results of this study characterize novel microbial patterns across scales of organization and establish a sharp demarcation between recently proposed macroecological patterns that are not and are affected by ecological interactions.
Synapse-specific diversity of distinct postsynaptic GluN2 subtypes defines transmission strength in spinal lamina I

The unitary postsynaptic response to presynaptic quantal glutamate release is the fundamental basis of excitatory information transfer between neurons. The view, however, of individual glutamatergic synaptic connections in a population as homogenous, fixed-strength units of neural communication is becoming increasingly scrutinized. Here, we used minimal stimulation of individual glutamatergic afferent axons to evoke single synapse resolution postsynaptic responses from central sensory lamina I neurons in an ex vivo adult rat spinal slice preparation. We detected unitary events exhibiting a NMDA receptor component with distinct kinetic properties across synapses conferred by specific GluN2 subunit composition, indicative of GluN2 subtype-based postsynaptic heterogeneity. GluN2A, 2A and 2B, or 2B and 2D synaptic predominance functioned on distinct lamina I neuron types to narrowly, intermediately, or widely tune, respectively, the duration of evoked unitary depolarization events from resting membrane potential, which enabled individual synapses to grade differentially depolarizing steps during temporally-patterned afferent input. Our results lead to a model wherein a core locus of proteomic complexity prevails at this central glutamatergic sensory synapse that involves distinct GluN2 subtype configurations. These findings have major implications for subthreshold integrative capacity and transmission strength in spinal lamina I and other CNS regions.