Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward more accurate measurements and a better understanding of students' transfer ability in solving physics problems online

Two earlier studies demonstrated that students' behavior data from a sequence of online learning modules can be analyzed to measure their ability to transfer their knowledge on solving one physics problem to a similar new one. In addition, adding an on-ramp module that develops basic skills improved students' transfer ability. In the current study, we improved the accuracy of the transfer measurement by identifying and excluding students who interacted with the learning modules differently from what was expected and examined two possible mechanisms by which the on-ramp module could improve student transfer. Based on a two by two framework of self-regulated learning, we hypothesized that students with a performance-avoidance oriented goal are more likely to consistently guess on their initial attempts, leaving a distinctive pattern in the log data and resulting in an underestimation of students' actual transfer ability. We divided the remaining student sample according to whether they passed the on-ramp module before or after accessing the instructional materials and compared their performance to a propensity score-matched sample from a previous semester. Improvement in transfer ability was found to primarily occur among students who passed the on-ramp module before learning. A possible explanation is that the on-ramp module served as an effective reminder for students who already possess the essential skills, but may be insufficient to develop those skills for other students. Our results suggest that online learning modules can be an accurate and flexible tool in assessing students' transfer ability. Further, our results demonstrate that the analysis of online learning data can produce more accurate and insightful results when taking into account details of student learning behavior and learning strategy.
The unreasonable effectiveness of machine learning in Moldavian versus Romanian dialect identification

Motivated by the seemingly high accuracy levels of machine learning (ML) models in Moldavian versus Romanian dialect identification and the increasing research interest on this topic, we provide a follow‐up on the Moldavian versus Romanian Cross‐Dialect Topic Identification (MRC) shared task of the VarDial 2019 evaluation campaign. The shared task included two subtask types: one that consisted in discriminating between the Moldavian and Romanian dialects and one that consisted in classifying documents by topic across the two dialects of Romanian. Participants achieved impressive scores, for example, the top model for Moldavian versus Romanian dialect identification obtained a macro‐ F1 score of 0.895. We conduct a subjective evaluation by human annotators, showing that humans attain much lower accuracy rates compared with ML models. Hence, it remains unclear why the methods proposed by participants attain such high accuracy rates. Our goal is to understand (i) why the proposed methods work so well (by visualizing the discriminative features) and (ii) to what extent these methods can keep their high accuracy levels, for example, when we shorten the text samples to single sentences or when we use tweets at inference time. A secondary goal of our work is to propose an improved ML model using ensemble learning. Our experiments show that ML models can accurately identify the dialects, even at the sentence level and across different domains (news articles vs. tweets). We also analyze the most discriminative features of the best performing models, providing some explanations behind the decisions taken by these models. Interestingly, we learn new dialectal patterns previously unknown to us or to our human annotators. Furthermore, we conduct experiments showing that the ML performance on the MRC shared task can be improved through an ensemble based on stacking.
Opportunities for probing 
U(1)T3R
 with light mediators

We consider strategies for using new datasets to probe scenarios in which light right-handed SM fermions couple to a new gauge group, $U(1)_{T3R}$. This scenario provides a natural explanation for the light flavor sector scale, and a motivation for sub-GeV dark matter. There is parameter space which is currently allowed, but we find that much of it can be probed with future experiments. In particular, cosmological and astrophysical observations, neutrino experiments and experiments which search for displaced visible decay or invisible decay can all play a role. Still, there is a small region of parameter space which even these upcoming experiments will not be able to probe. This model can explain the observed 2.4-3$\sigma$ excess of events at the COHERENT experiment in the parameter space allowed by current laboratory experiments, but the ongoing/upcoming laboratory experiments will decisively probe this possibility.
A Compositional Model of Consciousness Based on Consciousness-Only

Scientific studies of consciousness rely on objects whose existence is assumed to be independent of any consciousness. On the contrary, we assume consciousness to be fundamental, and that one of the main features of consciousness is characterized as being other-dependent. We set up a framework which naturally subsumes this feature by defining a compact closed category where morphisms represent conscious processes. These morphisms are a composition of a set of generators, each being specified by their relations with other generators, and therefore co-dependent. The framework is general enough and fits well into a compositional model of consciousness. Interestingly, we also show how our proposal may become a step towards avoiding the hard problem of consciousness, and thereby address the combination problem of conscious experiences.
Exploring Swedish & English fastText Embeddings with the Transformer

In this paper, our main contributions are that embeddings from relatively smaller corpora can outperform ones from far larger corpora and we present the new Swedish analogy test set. To achieve a good network performance in natural language processing (NLP) downstream tasks, several factors play important roles: dataset size, the right hyper-parameters, and well-trained embedding. We show that, with the right set of hyper-parameters, good network performance can be reached even on smaller datasets. We evaluate the embeddings at the intrinsic level and extrinsic level, by deploying them on the Transformer in named entity recognition (NER) task and conduct significance tests.This is done for both Swedish and English. We obtain better performance in both languages on the downstream task with far smaller training data, compared to recently released, common crawl versions and character n-grams appear useful for Swedish, a morphologically rich language.
TEAM: We Need More Powerful Adversarial Examples for DNNs

Although deep neural networks (DNNs) have achieved success in many application fields, it is still vulnerable to imperceptible adversarial examples that can lead to misclassification of DNNs easily. To overcome this challenge, many defensive methods are proposed. Indeed, a powerful adversarial example is a key benchmark to measure these defensive mechanisms. In this paper, we propose a novel method (TEAM, Taylor Expansion-Based Adversarial Methods) to generate more powerful adversarial examples than previous methods. The main idea is to craft adversarial examples by minimizing the confidence of the ground-truth class under untargeted attacks or maximizing the confidence of the target class under targeted attacks. Specifically, we define the new objective functions that approximate DNNs by using the second-order Taylor expansion within a tiny neighborhood of the input. Then the Lagrangian multiplier method is used to obtain the optimize perturbations for these objective functions. To decrease the amount of computation, we further introduce the Gauss-Newton (GN) method to speed it up. Finally, the experimental result shows that our method can reliably produce adversarial examples with 100% attack success rate (ASR) while only by smaller perturbations. In addition, the adversarial example generated with our method can defeat defensive distillation based on gradient masking.
The role of elevated terrain and the Gulf of Mexico in the production of severe local storm environments over North America

The prevailing conceptual model for the production of severe local storm (SLS) environments over North America asserts that upstream elevated terrain and the Gulf of Mexico are both essential to their formation. This work tests this hypothesis using two prescribed-ocean climate model experiments with North American topography removed or the Gulf of Mexico converted to land and analyzes how SLS environments and associated synoptic-scale drivers (southerly Great Plains low-level jets, drylines, elevated mixed layers, and extratropical cyclones) change relative to a control historical run. Overall, SLS environments depend strongly on upstream elevated terrain but weakly on the Gulf of Mexico. Removing elevated terrain substantially reduces SLS environments especially over the continental interior due to broad reductions in both thermodynamic and kinematic parameters, leaving a more zonally-uniform residual distribution that is maximized near the Gulf coast and decays toward the continental interior. This response is associated with a strong reduction in synoptic-scale drivers and a cooler and drier mean-state atmosphere. Replacing the Gulf of Mexico with land modestly reduces SLS environments thermodynamically over the Great Plains and increases them kinematically over the eastern U.S, shifting the primary local maximum eastward into Illinois; it also eliminates the secondary, smaller local maximum over southern Texas. This response is associated with modest changes in synoptic-scale drivers and a warmer and drier lower-tropospheric mean state. These experiments provide insight into the role of elevated terrain and the Gulf of Mexico in modifying the spatial distribution and seasonality of SLS environments.
An empirical survey of data augmentation for time series classification with neural networks

In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.
Realization of tropical curves in abelian surfaces

We construct algebraic curves in abelian surfaces starting from tropical curves in real tori. We give a necessary and sufficient condition for a tropical curve in a real torus to be realizable by an algebraic curve in an abelian surface. When the condition is satisfied, the number of algebraic curves can be computed by a combinatorial formula. This gives us an algebraic-tropical correspondence theorem for abelian surfaces analogous to Mikhalkin's correspondence theorem for toric surfaces. In other words, the number of algebraic curves passing through generic points in an abelian surface can be computed purely combinatorially via tropical curves.
Machine learning for complete intersection Calabi-Yau manifolds: a methodological study

We revisit the question of predicting both Hodge numbers $h^{1,1}$ and $h^{2,1}$ of complete intersection Calabi-Yau (CICY) 3-folds using machine learning (ML), considering both the old and new datasets built respectively by Candelas-Dale-Lutken-Schimmrigk / Green-Hubsch-Lutken and by Anderson-Gao-Gray-Lee. In real world applications, implementing a ML system rarely reduces to feed the brute data to the algorithm. Instead, the typical workflow starts with an exploratory data analysis (EDA) which aims at understanding better the input data and finding an optimal representation. It is followed by the design of a validation procedure and a baseline model. Finally, several ML models are compared and combined, often involving neural networks with a topology more complicated than the sequential models typically used in physics. By following this procedure, we improve the accuracy of ML computations for Hodge numbers with respect to the existing literature. First, we obtain 97% (resp. 99%) accuracy for $h^{1,1}$ using a neural network inspired by the Inception model for the old dataset, using only 30% (resp. 70%) of the data for training. For the new one, a simple linear regression leads to almost 100% accuracy with 30% of the data for training. The computation of $h^{2,1}$ is less successful as we manage to reach only 50% accuracy for both datasets, but this is still better than the 16% obtained with a simple neural network (SVM with Gaussian kernel and feature engineering and sequential convolutional network reach at best 36%). This serves as a proof of concept that neural networks can be valuable to study the properties of geometries appearing in string theory.
Inner-outer decomposition and universal near-wall motions in turbulent channels

Near-wall turbulent velocities in turbulent channel flows are decomposed into small-scale and large-scale components at $y^+<100$, where $y^+$ is the viscous-normalized wall-normal height. The small-scale one is obtained by fully removing outer influences and can be reminiscent of the well-known near-wall turbulence cycle. On the other hand, the large-scale one represents the near-wall footprints of outer energy-containing motions. We present plenty of evidences that demonstrate the small-scale motions are universal (Reynolds-number invariant) with the viscous scaling, at friction Reynolds numbers between 1000 and 5200. At lower Reynolds numbers from 180 to 600, the small scales can not be scaled by the viscous units, and the vortical structures are progressively strengthened as Reynolds number increases, which is proposed as the main mechanism responsible for the anomalous scaling behavior. The viscous-scaled energetic significance and length scales of outer footprints grow with Reynolds number, but their small-scale parts can be approximately scaled by the viscous units, implying possible relations with the inherently near-wall motions. Finally, the average effect of amplitude modulation may be negligible at least at the Reynolds numbers we consider, since the statistics of the small-scale velocities before and after the demodulation procedure are almost identical. The finding of the universal small-scale motions in wall turbulence may be akin to the universal small scales in homogeneous and isotropic turbulence, possibly suggesting the universality of the existence of universal small-scales in different turbulent flows.
Possible Time Correlation between Jet Ejection and Mass Accretion for RW Aur A

For the active T-Taur star RW Aur A we have performed long-term (∼10 yr) monitoring observations of (1) jet imaging in the [Fe II] 1.644 μm emission line using Gemini-NIFS and VLT-SINFONI; (2) optical high-resolution spectroscopy using CFHT-ESPaDOnS; and (3) V-band photometry using the CrAO 1.25-m telescope and AAVSO. The latter two observations confirm the correlation of time variabilities between (A) the Ca II 8542 Å and O I 7772 Å line profiles associated with magnetospheric accretion, and (B) optical continuum fluxes. The jet images and their proper motions show that four knot ejections occurred at the star over the past ∼15 yr with an irregular interval of 2–6 yr. The timescale and irregularity of these intervals are similar to those of the dimming events seen in the optical photometry data. Our observations show a possible link between remarkable (ΔV < −1) photometric rises and jet knot ejections. Observations over another few years may confirm or reject this trend. If confirmed, this would imply that the location of the jet launching region is very close to the star (r ≲ 0.1 au) as predicted by some jet launching models. Such a conclusion would be crucial for understanding disk evolution within a few astronomical units of the star, and therefore possible ongoing planet formation at these radii.
High Salt Inclusion Reduces Concentrate Intake Without Major Effects on Renal Function in Young Bulls

Beef producers prefer to feed concentrates on an ad libitum basis to increase the flexibility of their work. Including salt, which is a self-limiting supplement, could control or reduce concentrate intake without increasing the workforce. The aim of the study was to evaluate the effect of including 10%NaCl in the concentrate on intake, growth, blood ions (sodium, potassium and chlorine), renal function (through creatinine and urea concentrations in blood), and daytime behaviour of bulls over 6 weeks. Bulls consuming the control concentrate (Control bulls) had greater weight gain (P<0.05) and concentrate intake (P<0.001) than those consuming the concentrate with 10%NaCl (10%NaCl bulls). Lower plasma sodium concentration was found in Control bulls after 6 weeks (P<0.05), while potassium concentration was lower after 4 (P<0.05) and 6 weeks (P<0.01). Blood urea did not differ between the groups, and creatinine only differed at week 4 (P<0.01). Control bulls spent less time eating hay (P<0.001) and more time idling (P<0.01) during daylight hours. In conclusion, the inclusion of 10%NaCl in the concentrate for short periods could be used to reduce concentrate intake without major effects on renal function; however, a concomitant decrease in weight gain should be expected.
High-energy Neutrinos and Gamma Rays from Nonrelativistic Shock-powered Transients

Shock interaction has been argued to play a role in powering a range of optical transients, including supernovae, classical novae, stellar mergers, tidal disruption events, and fast blue optical transients. These same shocks can accelerate relativistic ions, generating high-energy neutrino and gamma-ray emission via hadronic pion production. The recent discovery of time-correlated optical and gamma-ray emission in classical novae has revealed the important role of radiative shocks in powering these events, enabling an unprecedented view of the properties of ion acceleration, including its efficiency and energy spectrum, under similar physical conditions to shocks in extragalactic transients. Here we introduce a model for connecting the radiated optical fluence of nonrelativistic transients to their maximal neutrino and gamma-ray fluence. We apply this technique to a wide range of extragalactic transient classes in order to place limits on their contributions to the cosmological high-energy gamma-ray and neutrino backgrounds. Based on a simple model for diffusive shock acceleration at radiative shocks, calibrated to novae, we demonstrate that several of the most luminous transients can accelerate protons up to 1016 eV, sufficient to contribute to the IceCube astrophysical background. Furthermore, several of the considered sources—particularly hydrogen-poor supernovae—may serve as “gamma-ray-hidden” neutrino sources owing to the high gamma-ray opacity of their ejecta, evading constraints imposed by the nonblazar Fermi Large Area Telescope background. However, adopting an ion acceleration efficiency of ∼0.3%–1% motivated by nova observations, we find that currently known classes of nonrelativistic, potentially shock-powered transients contribute at most a few percent of the total IceCube background.
Deep Direct Likelihood Knockoffs

Predictive modeling often uses black box machine learning methods, such as deep neural networks, to achieve state-of-the-art performance. In scientific domains, the scientist often wishes to discover which features are actually important for making the predictions. These discoveries may lead to costly follow-up experiments and as such it is important that the error rate on discoveries is not too high. Model-X knockoffs [2] enable important features to be discovered with control of the false discovery rate (fdr). However, knockoffs require rich generative models capable of accurately modeling the knockoff features while ensuring they obey the so-called "swap" property. We develop Deep Direct Likelihood Knockoffs (ddlk), which directly minimizes the KL divergence implied by the knockoff swap property. ddlk consists of two stages: it first maximizes the explicit likelihood of the features, then minimizes the KL divergence between the joint distribution of features and knockoffs and any swap between them. To ensure that the generated knockoffs are valid under any possible swap, ddlk uses the Gumbel-Softmax trick to optimize the knockoff generator under the worst-case swap. We find ddlk has higher power than baselines while controlling the false discovery rate on a variety of synthetic and real benchmarks including a task involving a large dataset from one of the epicenters of COVID-19.
Phase transitions of photon fluid flows driven by a virtual all-optical piston

The piston problem, i.e. the dynamics in a uniform gas at rest under the action of a moving piston is fundamental problem of physics and a canonical case study in shock wave physics. We investigate theoretically and experimentally the analogous problem for a photon fluid, which turns out to be strongly influenced by the dispersive character of the problem. The experiment makes use of a fiber optics setup where an all-optically controlled quasi instantaneous change of frequency of the input light mimics the piston action. We show that the flow exhibits phase transitions which involve cross-over from regimes characterized by 2-shocks (pushing piston) to 2-rarefaction waves (retracting piston), with the appearance of cavitating states at critical amplitudes of the jump. Importantly, the appearance of vacuum points into the 2-shock marks the transition to a regime which is unique to the photon fluid. Our observations allow for the extensive and quantitative test of the state of the art description of the dispersive Riemann problem, i.e. Whitham modulation theory, applied to the universal defocusing nonlinear Schrodinger equation.
Life history characteristics of an invasive cyprinid fish (Carassius gibelio) in Chimaditis Lake (northern Greece)

Summary The life history characteristics of an invasive cyprinid fish, the Prussian carp (Carassius gibelio) were examined in Lake Chimaditis (northern Greece). The population is dominated by females that are able to reproduce gynogenetically. Fish samples were collected using trammel nets from August 2004 to July 2005. Their total length (TL) ranged between 21.9 and 37.0 cm. The length–weight relationship was W = 0.0336TL2. 81 (r2 = 0.92, n = 600) and the mean Fulton condition factor exhibited monthly variation from 0.172 (May) to 0.186 (August). According to the annual growth marks present, the lifespan of the Prussian carp in Lake Chimaditis is 6 years. The growth parameters were estimated as L∞ = 34.46 cm, K = 0.297 year−1, t0 = −1.994 year. Absolute fecundity (FA) ranged between 26 000 and 176 600 oocytes (mean = 66990) and relative fecundity between 78 and 251 oocytes per gram of total weight (mean = 158). Absolute fecundity increased exponentially with length (FA = 0.286 × TL3.66, r2 = 0.66) and weight (FA = 17.93 × W1.36, r2 = 0.73) of the fish.