Miracle-dz/UniFAR-FTUs huggingface dataset:
A Decade of Antarctic Science Support Through Amps

The Antarctic Mesoscale Prediction System (AMPS) is a real-time numerical weather prediction (NWP) system covering Antarctica that has served a remarkable range of groups and activities for a decade. It employs the Weather Research and Forecasting model (WRF) on varying-resolution grids to generate numerical guidance in a variety of tailored products. While its priority mission has been to support the forecasters of the U.S. Antarctic Program, AMPS has evolved to assist a host of scientific and logistical needs for an international user base. The AMPS effort has advanced polar NWP and Antarctic science and looks to continue this into another decade. To inform those with Antarctic scientific and logistical interests and needs, the history, applications, and capabilities of AMPS are discussed.
SOP Psychopharmaka in der Schwangerschaft

100 Da viele psychiatrische Erkrankungen im jungen Erwachsenenalter manifest werden, gibt es auch immer mehr Frauen im gebärfähigen Alter bzw. mit Kinderwunsch, die bereits Psychopharmaka einnehmen. Und andere Frauen erkranken erstmalig psychisch, während sie schwanger sind. Daher ist die Frage nach Psychopharmaka in der Schwangerschaft ein Problem, mit demman im klinischen Alltag immer häufiger konfrontiert wird.
The Role of Gender in Students’ Privacy Concerns about Learning Analytics: Evidence from five countries

The protection of students’ privacy in learning analytics (LA) applications is critical for cultivating trust and effective implementations of LA in educational environments around the world. However, students’ privacy concerns and how they may vary along demographic dimensions that historically influence these concerns have yet to be studied in higher education. Gender differences, in particular, are known to be associated with people's information privacy concerns, including in educational settings. Building on an empirically validated model and survey instrument for student privacy concerns, their antecedents and their behavioral outcomes, we investigate the presence of gender differences in students’ privacy concerns about LA. We conducted a survey study of students in higher education across five countries (N = 762): Germany, South Korea, Spain, Sweden and the United States. Using multiple regression analysis, across all five countries, we find that female students have stronger trusting beliefs and they are more inclined to engage in self-disclosure behaviors compared to male students. However, at the country level, these gender differences are significant only in the German sample, for Bachelor's degree students, and for students between the ages of 18 and 24. Thus, national context, degree program, and age are important moderating factors for gender differences in student privacy concerns.
An edge-to-edge Deployment Model for Pre-Congestion Notification: Admission Control over a DiffServ Region

This document describes a deployment model for pre-congestion notification (PCN) operating in a large DiffServ-based region of the Internet. PCN-based admission control protects the quality of service of existing flows in normal circumstances, whilst if necessary (eg after a large failure) pre-emption of some flows preserves the quality of service of the remaining flows. Each link has a configured-admission-rate and a configured-pre-emption-rate, and a router marks packets that exceed these rates. Hence routers give an early warning of their own potential congestion, before packets need to be dropped. Gateways around the edges of the PCN-region convert measurements of packet rates and their markings into decisions about whether to admit new flows, and (if necessary) into the rate of excess traffic that should be pre-empted. Per-flow admission states are kept at the gateways only, while the PCN markers that are required for all routers operate on the aggregate traffic - hence there is no scalability impact on interior routers.
Using Transformer Language Models to Validate Peer-Assigned Essay Scores in Massive Open Online Courses (MOOCs)

Massive Open Online Courses (MOOCs) such as those offered by Coursera are popular ways for adults to gain important skills, advance their careers, and pursue their interests. Within these courses, students are often required to compose, submit, and peer review written essays, providing a valuable pedagogical experience for the student and a wealth of natural language data for the educational researcher. However, the scores provided by peers do not always reflect the actual quality of the text, generating questions about the reliability and validity of the scores. This study evaluates methods to increase the reliability of MOOC peer-review ratings through a series of validation tests on peer-reviewed essays. Reliability of reviewers was based on correlations between text length and essay quality. Raters were pruned based on score variance and the lexical diversity observed in their comments to create sub-sets of raters. Each subset was then used as training data to finetune distilBERT large language models to automatically score essay quality as a measure of validation. The accuracy of each language model for each subset was evaluated. We find that training language models on data subsets produced by more reliable raters based on a combination of score variance and lexical diversity produce more accurate essay scoring models. The approach developed in this study should allow for enhanced reliability of peer-reviewed scoring in MOOCS affording greater credibility within the systems.
PyTorch 2.0: The Journey to Bringing Compiler Technologies to the Core of PyTorch (Keynote)

Four and a half years after PyTorch 1.0, we announced PyTorch 2.0 at the PyTorch Conference last December. The message was simple – introducing compiled mode, torch.compile(), to the core of PyTorch. This talk shares our 5-year journey of finding the right compiler solutions for PyTorch. We answer questions like: (i) Why did it take so long? (ii) What was the biggest challenge of designing compiler solutions for PyTorch? (iii) How did we co-design the compiler w/ the core of PyTorch? (iiii) What conventions did we break in the design of TorchDynamo and TorchInductor? As an ML compiler, PyTorch 2.0 is unconventional in many ways. By sharing our thought processes, insights, and design decisions during the development of PT2, we hope to bring new thinking into the thriving landscape of ML compilers and inject a dose of real-world considerations into the research community.
On vortex strength and drag in bluff-body wakes

In a recent paper (Griffin & Ramberg 1974) the authors studied the vortex-street wakes behind forced vibrating rigid cylinders. All experimental conditions were within the regime of wake capture or synchronization between the vibration and vortex frequencies. Both mean and fluctuating velocities in the wake together with the length of the vortex formation region were measured as functions of vibration amplitude and frequency at a Reynolds number of 144. The viscous vortex strength, age and spacing at this Reynolds number were then obtained by matching a model for the vortex street with the mean and r.m.s. velocity profiles obtained from hot-wire measurements. These results are employed here to determine the steady drag force on the vibrating cylinder by means of the von Kármán drag formulation. The drag coefficients determined in this way are in agreement with the recently published direct force measurements of Tanida, Okajima & Watanabe (1973) and Griffin, Skop & Koopmann (1973) at Reynolds numbers of 80, 500-900 and 4000. From these results a direct relation is drawn between the increased drag on resonantly vibrating structures and changes in the vortex strength, spacing and formation in their wakes.
A Game-Based Framework to Compare Program Classifiers and Evaders

Algorithm classification consists in determining which algorithm a program implements, given a finite set of candidates. Classifiers are used in applications such malware identification and plagiarism detection. There exist many ways to implement classifiers. There are also many ways to implement evaders to deceive the classifiers. This paper analyzes the state-of-the-art classification and evasion techniques. To organize this analysis, this paper brings forward a system of four games that matches classifiers and evaders. Games vary according to the amount of information that is given to each player. This setup lets us analyze a space formed by the combination of nine program encodings; seven obfuscation passes; and six stochastic classification models. Observations from this study include: (i) we could not measure substantial advantages of recent vector-based program representations over simple histograms of opcodes; (ii) deep neural networks recently proposed for program classification are no better than random forests; (iii) program optimizations are almost as effective as classic obfuscation techniques to evade classifiers; (iv) off-the-shelf code optimizations can completely remove the evasion power of naïve obfuscators; (v) control-flow flattening and bogus-control flow tend to resist the normalizing power of code optimizations.
AArch64 Atomics: Might They Be Harming Your Performance?

Atomic operations are indivisible operations guaranteed to execute as a whole. One of the most important and widely used atomic operations is "compare-and-swap" (CAS), which allows threads to perform concurrent read-modify-write operations on the same memory location, free of data races. On recent Arm architectures, CAS operations can be implemented either directly via CAS instructions, or via load-linked/store-conditional (LL-SC) instruction pairs. In this work we explore the performance of the CAS and LL-SC approaches to implement CAS operations on recent high-performance AArch64 CPUs, namely the A64FX, ThunderX2 (TX2), and Graviton3. We observe that these instructions can lead to fundamentally different performance profiles. On A64FX, for example, the newer CAS instructions---often preferred by compilers over the older LL-SC pairs---can lead to a quadratic increase in average time per successful CAS operation as the number of threads increases, whereas the older LL-SC pairs show the expected linear increase. For high thread counts, this translates into LL-SC being more than 20x faster than CAS. On TX2 and Graviton3, LL-SC can bring more conservative (but still significant) 2--3x speedups. We characterise the conditions under which each approach delivers better performance on each CPU.
Analysis of Functional Modules in Protein Networks Using Graph Clustering Method

Interaction detection methods have led to the discovery of thousands of interactions between proteins, and discerning relevance within large-scale data sets is important to present-day biology. As an important means for knowledge discovery, graph clustering attracts much attention in analysis of protein-protein interaction networks. Here, a modularity-based method was used to find communities of protein-protein interaction networks. Using this method, 177 communities were detected from a network involving 11,855 interactions among 2617 proteins in yeast and annotated according to MIPS hierarchical functional categories. We validated that these communities are indeed densely connected subgraphs.
Logs or Self-Reports? Misalignment Between Behavioral Trace Data and Surveys When Modeling Learner Achievement Goal Orientation

While learning analytics researchers have been diligently integrating trace log data into their studies, learners’ achievement goals are still predominantly measured by self-reported surveys. This study investigated the properties of trace data and survey data as representations of achievement goals. Through the lens of goal complex theory, we generated achievement goal clusters using latent variable mixture modeling applied to each kind of data. Findings show significant misalignment between these two data sources. Self-reported goals stated before learning do not translate into goal-relevant behaviors tracked using trace data collected during learning activities. While learners generally articulate an orientation towards mastery learning in self-report surveys, behavioral trace data showed a higher incidence of less engaged learning activities. These findings call into question the utility of survey-based measures when up-to-date achievement goal data are needed. Our results advance methodological and theoretical understandings of achievement goals in the modern age of learning analytics.
Fast Symmetric Eigenvalue Decomposition via WY Representation on Tensor Core

Symmetric eigenvalue decomposition (EVD) is a fundamental analytic and numerical tool used in many scientific areas. The state-of-the-art algorithm in terms of performance is typically the two-stage tridiagonalization method. The first stage in the two-stage tridiagonalization is called successive band reduction (SBR), which reduces a symmetric matrix to a band form, and its computational cost usually dominates. When Tensor Core (specialized matrix computational accelerator) is used to accelerate the expensive EVD, the conventional ZY-representation-based method results in suboptimal performance due to unfavorable shapes of the matrix computations. In this paper, we propose a new method that uses WY representation instead of ZY representation (see Section 3.2 for details), which can provide a better combination of locality and parallelism so as to perform better on Tensor Cores. Experimentally, the proposed method can bring up to 3.7x speedup in SBR and 2.3x in the entire EVD compared to state-of-the-art implementations.
A new FDTD algorithm based on alternating-direction implicit method

In this paper, a new finite-difference time-domain (FDTD) algorithm is proposed in order to eliminate the Courant-Friedrich-Levy (CFL) condition restraint. The new algorithm is based on an alternating-direction implicit method. It is shown that the new algorithm is quite stable both analytically and numerically even when the CFL condition is not satisfied. Therefore, if the minimum cell size in the computational domain is required to be much smaller than the wavelength, this new algorithm is more efficient than conventional FDTD schemes in terms of computer resources such as central-processing-unit time. Numerical formulations are presented and simulation results are compared to those using the conventional FDTD method.
A ug 2 00 8 Quantum Phase Transition in the Itinerant Antiferromagnet ( V 0

Hiroaki Kadowaki, ∗ Kiyoichiro Motoya, Taku J. Sato, J. W. Lynn, J. A. Fernandez-Baca, and Jun Kikuchi Department of Physics, Tokyo Metropolitan University, Hachioji, Tokyo 192-0397, Japan Department of Physics, Tokyo University of Science, Noda, Chiba 278-8510, Japan NSL, Institute for Solid State Physics, University of Tokyo, Tokai, Ibaraki 319-1106, Japan NIST Center for Neutron Research, Gaithersburg, Maryland 20899-6102, USA Neutron Scattering Science Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6393, USA Department of Physics, Meiji University, Kawasaki, Kanagawa 214-8571, Japan (Dated: August 23, 2008)
TL4x: Buffered Durable Transactions on Disk as Fast as in Memory

The arrival of persistent memory devices to consumer market has revived the interest in transactional durable algorithms. Persistent memory (PM) is touted as having two attributes that distinguish it from other storage technologies: byte-addressability and fast transactional persistence. In this work we investigate how these attributes differentiate PM from block storage in the context of buffered durability. We present a novel algorithm, TL4x, capable of providing buffered durable linearizable transactions with high scalability for disjoint writes and efficient persistence on either PM or block storage devices. TL4x is a software-only user-space solution that optimizes writes to persistent storage, providing buffered durable transactions whose cost is negligible compared to similar non-durable transactions. TL4x maintains a volatile consistent snapshot which is used for buffered durability and shared with irrevocable read-only transactions, allowing long range-query operations to run in parallel with write transactions. We use TL4x to implement a transactional database engine that can outperform RocksDB by an order of magnitude.
How to Build More Generalizable Models for Collaboration Quality? Lessons Learned from Exploring Multi-Context Audio-Log Datasets using Multimodal Learning Analytics

Multimodal learning analytics (MMLA) research for building collaboration quality estimation models has shown significant progress. However, the generalizability of such models is seldom addressed. In this paper, we address this gap by systematically evaluating the across-context generalizability of collaboration quality models developed using a typical MMLA pipeline. This paper further presents a methodology to explore modelling pipelines with different configurations to improve the generalizability of the model. We collected 11 multimodal datasets (audio and log data) from face-to-face collaborative learning activities in six different classrooms with five different subject teachers. Our results showed that the models developed using the often-employed MMLA pipeline degraded in terms of Kappa from Fair (.20 < Kappa < .40) to Poor (Kappa < .20) when evaluated across contexts. This degradation in performance was significantly ameliorated with pipelines that emerged as high-performing from our exploration of 32 pipelines. Furthermore, our exploration of pipelines provided statistical evidence that often-overlooked contextual data features improve the generalizability of a collaboration quality model. With these findings, we make recommendations for the modelling pipeline which can potentially help other researchers in achieving better generalizability in their collaboration quality estimation models.
Regime of Improved Confinement and High Beta in Neutral-Beam-Heated Divertor Discharges of the ASDEX Tokamak

A new operational regime has been observed in neutral-injection-heated ASDEX divertor discharges. This regime is characterized by high ${\ensuremath{\beta}}_{p}$ values comparable to the aspect ratio $A({\ensuremath{\beta}}_{p}l~0.65A)$ and by confinement times close to those of Ohmic discharges. The high-${\ensuremath{\beta}}_{p}$ regime develops at an injection power \ensuremath{\ge}1.9 MW, a mean density ${\overline{n}}_{e}g~3\ifmmode\times\else\texttimes\fi{}{10}^{13}$ ${\mathrm{cm}}^{\ensuremath{-}3}$, and a $q(a)$ value \ensuremath{\ge}2.6. Beyond these limits or in discharges with material limiter, low ${\ensuremath{\beta}}_{p}$ values and reduced particle and energy confinement times are obtained compared to the Ohmic heating phase.
The effect of local magnetic field gradient on coercive field of domain walls

Local magnetic field gradient /spl beta/ keeping a domain wall (DW) close to the equilibrium position has very strong effect on the value of coercive field H/sub cw/ of DW. We present a model calculation of the dependence of H/sub cw/ on /spl beta/ using the pinning field H/sub p/ expressed as a stochastic function of the DW coordinate x. H/sub p/ is described by the Wiener-Levy process with two correlation lengths /spl xi//sub 1/ and /spl xi//sub 2/ such that H/sub p/ is stationary for /spl Delta/x/spl Gt//spl xi//sub 2/ and dHp/dx is well defined for /spl Delta/x/spl Lt//spl xi//sub 1/. In the case of /spl xi//sub 1//spl rarr/0 and /spl xi//sub 2//spl rarr//spl infin/ we obtained hyperbolic decrease H/sub cw//spl prop/1//spl beta/ while finite /spl xi//sub 1/ and /spl xi//sub 2/ yield H/sub cw/(/spl beta/) closer to the experimental dependence, in particular: (i) finite H/sub cw/ for any finite DW velocity and for /spl beta//spl rarr/0, (ii) for higher /spl beta/ H/sub cw/ decreases much steeper than /spl prop/1//spl beta/. >
Phase problem in crystallography

Phase recovery from unphased data is the central problem in the interpretation of crystal diffraction data. There are a number of experimental methods for determining phases, and for small molecules computational methods work very well. The problem of developing robust computational methods for large molecules is not yet solved. Most of the approaches for large molecules are variants of the Grechburg–Saxton algorithm [ Optik35, 237– 246 ( 1972)]; with sufficiently good initial phase estimates these converge, but in general they fail with phase stagnation similar to that seen in image processing [ DaintyJ. C.FienupJ. R. , in Image Recovery: Theory and Application, StarkH. , ed. ( Academic, Orlando, Fla., 1987); FienupJ. R.WackermanC. C. , J. Opt. Soc. Am. A3, 1897– 1907 ( 1986)].
Rie1 and Sgn1 form an RNA-binding complex that enforces the meiotic entry cell fate decision

Budding yeast cells have the capacity to adopt few but distinct physiological states depending on environmental conditions. Vegetative cells proliferate rapidly by budding while spores can survive prolonged periods of nutrient deprivation and/or desiccation. Whether or not a yeast cell will enter meiosis and sporulate represents a critical decision which could be lethal if made in error. Most cell fate decisions, including those of yeast, are understood as being triggered by the activation of master transcription factors. However, mechanisms that enforce cell fates post-transcriptionally have been more difficult to attain. Here, we perform a forward genetic screen to determine RNA-binding proteins that affect meiotic entry at the post-transcriptional level. Our screen revealed several candidates with meiotic entry phenotypes, the most significant being RIE1 which encodes an RRM-containing protein. We demonstrate that Rie1 binds RNA, is associated with the translational machinery, and acts post-transcriptionally to enhance protein levels of the master transcription factor Ime1 in sporulation conditions. We also identified a physical binding partner of Rie1, Sgn1, which is another RRM-containing protein that plays a role in timely Ime1 expression. We demonstrate that these proteins act independently of cell size regulation pathways to promote meiotic entry. We propose a model explaining how constitutively expressed RNA-binding proteins, such as Rie1 and Sgn1, can act in cell-fate decisions both as switch-like enforcers and as repressors of spurious cell fate activation.