Reviewerly/exHarmony huggingface dataset:
A Survey of Computational Tools in Solar Physics

Abstract The SunPy Project developed a 13-question survey to understand the software and hardware usage of the solar-physics community. Of the solar-physics community, 364 members across 35 countries responded to our survey. We found that $99\pm 0.5$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>99</mml:mn><mml:mo>±</mml:mo><mml:mn>0.5</mml:mn></mml:math> % of respondents use software in their research and 66% use the Python scientific-software stack. Students are twice as likely as faculty, staff scientists, and researchers to use Python rather than Interactive Data Language (IDL). In this respect, the astrophysics and solar-physics communities differ widely: 78% of solar-physics faculty, staff scientists, and researchers in our sample uses IDL, compared with 44% of astrophysics faculty and scientists sampled by Momcheva and Tollerud (2015). $63\pm 4$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>63</mml:mn><mml:mo>±</mml:mo><mml:mn>4</mml:mn></mml:math> % of respondents have not taken any computer-science courses at an undergraduate or graduate level. We also found that most respondents use consumer hardware to run software for solar-physics research. Although 82% of respondents work with data from space-based or ground-based missions, some of which ( e.g. the Solar Dynamics Observatory and Daniel K. Inouye Solar Telescope ) produce terabytes of data a day, 14% use a regional or national cluster, 5% use a commercial cloud provider, and 29% use exclusively a laptop or desktop. Finally, we found that $73\pm 4$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>73</mml:mn><mml:mo>±</mml:mo><mml:mn>4</mml:mn></mml:math> % of respondents cite scientific software in their research, although only $42\pm 3$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>42</mml:mn><mml:mo>±</mml:mo><mml:mn>3</mml:mn></mml:math> % do so routinely.
COMPLETE ADDITIVITY AND MODAL INCOMPLETENESS

Abstract In this article, we tell a story about incompleteness in modal logic. The story weaves together an article of van Benthem (1979), “Syntactic aspects of modal incompleteness theorems,” and a longstanding open question: whether every normal modal logic can be characterized by a class of completely additive modal algebras, or as we call them, ${\cal V}$ - bao s. Using a first-order reformulation of the property of complete additivity, we prove that the modal logic that starred in van Benthem’s article resolves the open question in the negative. In addition, for the case of bimodal logic, we show that there is a naturally occurring logic that is incomplete with respect to ${\cal V}$ - bao s, namely the provability logic $GLB$ (Japaridze, 1988; Boolos, 1993). We also show that even logics that are unsound with respect to such algebras do not have to be more complex than the classical propositional calculus. On the other hand, we observe that it is undecidable whether a syntactically defined logic is ${\cal V}$ -complete. After these results, we generalize the Blok Dichotomy (Blok, 1978) to degrees of ${\cal V}$ -incompleteness. In the end, we return to van Benthem’s theme of syntactic aspects of modal incompleteness.
Embedding Words in Non-Vector Space with Unsupervised Graph Learning

It has become a de-facto standard to represent words as elements of a vector space (word2vec, GloVe).While this approach is convenient, it is unnatural for language: words form a graph with a latent hierarchical structure, and this structure has to be revealed and encoded by word embeddings.We introduce Graph-Glove: unsupervised graph word representations which are learned end-to-end.In our setting, each word is a node in a weighted graph and the distance between words is the shortest path distance between the corresponding nodes.We adopt a recent method learning a representation of data in the form of a differentiable weighted graph and use it to modify the GloVe training algorithm.We show that our graph-based representations substantially outperform vector-based methods on word similarity and analogy tasks.Our analysis reveals that the structure of the learned graphs is hierarchical and similar to that of WordNet, the geometry is highly non-trivial and contains subgraphs with different local topology.
Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography

Deep learning object detection algorithm has been widely used in medical image analysis. Currently all the object detection tasks are based on the data annotated with object classes and their bounding boxes. On the other hand, medical images such as mammography usually contain normal regions or objects that are similar to the lesion region, and may be misclassified in the testing stage if they are not taken care of. In this paper, we address such problem by introducing a novel top likelihood loss together with a new sampling procedure to select and train the suspected target regions, as well as proposing a similarity loss to further identify suspected targets from targets. Mean average precision (mAP) according to the predicted targets and specificity, sensitivity, accuracy, AUC values according to classification of patients are adopted for performance comparisons. We firstly test our proposed method on a private dense mammogram dataset. Results show that our proposed method greatly reduce the false positive rate and the specificity is increased by 0.25 on detecting mass type cancer. It is worth mention that dense breast typically has a higher risk for developing breast cancers and also are harder for cancer detection in diagnosis, and our method outperforms a reported result from performance of radiologists. Our method is also validated on the public Digital Database for Screening Mammography (DDSM) dataset, brings significant improvement on mass type cancer detection and outperforms the most state-of-the-art work.
Calibrated Reliable Regression using Maximum Mean Discrepancy

Accurate quantification of uncertainty is crucial for real-world applications of machine learning. However, modern deep neural networks still produce unreliable predictive uncertainty, often yielding over-confident predictions. In this paper, we are concerned with getting well-calibrated predictions in regression tasks. We propose the calibrated regression method using the maximum mean discrepancy by minimizing the kernel embedding measure. Theoretically, the calibration error of our method asymptotically converges to zero when the sample size is large enough. Experiments on non-trivial real datasets show that our method can produce well-calibrated and sharp prediction intervals, which outperforms the related state-of-the-art methods.
Using Mobile Educational Apps to Foster Work and Play in Learning: A Systematic Review

&lt;p&gt;The paper examined the use of mobile educational applications to foster work and play in learning. Three research questions were asked to guide the study. Fifty (50) articles pertaining to the use of mobile educational applications in classrooms were downloaded. Out of all these papers, thirty-five (35) relevant articles were selected for review after which twenty-five (25) were carefully read and searched so as to capture the required information for the analysis. The outcome of the review indicated that there are lots of mobile educational apps that can be used by educators to foster work and play in classroom teaching and learning thereby making the teaching and learning become mobile and attractive. This means that teachers make use of apps, internet and computers to teach in classroom, which makes teaching and learning more interactive, collaborative and replaces the conventional chalkboards. The study applied systematic literature methodology in order to achieve the goal of the research. The research outcome showed that some educators have not yet embraced the use of mobile educational apps for teaching and learning in classrooms especially in third world countries. It was recommended that teachers, curriculum planners, policy makers, administrators and caregivers should be provided with knowledge on the new trend of mobile learning and its importance to the education sector.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
PatchBERT: Just-in-Time, Out-of-Vocabulary Patching

Large scale pre-trained language models have shown groundbreaking performance improvements for transfer learning in the domain of natural language processing.In our paper, we study a pre-trained multilingual BERT model and analyze the OOV rate on downstream tasks, how it introduces information loss, and as a side-effect, obstructs the potential of the underlying model.We then propose multiple approaches for mitigation and demonstrate that it improves performance with the same parameter count when combined with finetuning.
'A Modern Up-To-Date Laptop' - Vagueness in Natural Language Queries for Product Search

With the rise of voice assistants and an increase in mobile search usage, natural language has become an important query language. So far, most of the current systems are not able to process these queries because of the vagueness and ambiguity in natural language. Users have adapted their query formulation to what they think the search engine is capable of, which adds to their cognitive burden. With our research, we contribute to the design of interactive search systems by investigating the genuine information need in a product search scenario. In a crowd-sourcing experiment, we collected 132 information needs in natural language. We examine the vagueness of the formulations and their match to retailer-generated content and user-generated product reviews. Our findings reveal high variance on the level of vagueness and the potential of user reviews as a source for supporting users with rather vague search intents.
Short Proofs for Slow Consistency

Let $\operatorname{Con}(\mathbf T)\!\restriction\!x$ denote the finite consistency statement "there are no proofs of contradiction in $\mathbf T$ with $\leq x$ symbols". For a large class of natural theories $\mathbf T$, Pudl\'ak has shown that the lengths of the shortest proofs of $\operatorname{Con}(\mathbf T)\!\restriction\!n$ in the theory $\mathbf T$ itself are bounded by a polynomial in $n$. At the same time he conjectures that $\mathbf T$ does not have polynomial proofs of the finite consistency statements $\operatorname{Con}(\mathbf T+\operatorname{Con}(\mathbf T))\!\restriction\!n$. In contrast we show that Peano arithmetic ($\mathbf{PA}$) has polynomial proofs of $\operatorname{Con}(\mathbf{PA}+\operatorname{Con}^*(\mathbf{PA}))\!\restriction\!n$, where $\operatorname{Con}^*(\mathbf{PA})$ is the slow consistency statement for Peano arithmetic, introduced by S.-D. Friedman, Rathjen and Weiermann. We also obtain a new proof of the result that the usual consistency statement $\operatorname{Con}(\mathbf{PA})$ is equivalent to $\varepsilon_0$ iterations of slow consistency. Our argument is proof-theoretic, while previous investigations of slow consistency relied on non-standard models of arithmetic.
Enhancing Self-Regulated Learning Strategy via Handheld Devices for Improving English Writing Skills and Motivation

This study investigated the impact of developing learning app via handheld devices based on some self-regulated learning strategies for improving English writing skills and motivation among intensive English language preparatory program students. The sample was chosen from English language preparation program students at college of sciences and Arts. The random sample consisted of (n=40) students. Those students were divided into two similar groups, Experimental and Control groups. To collect the data of the study, the Authors used a pre-posttest, interviews with the students and a motivation scale. T-test was used to compare between the two groups at the pre and post-test. The results revealed an observed improvement in writing skills, motivation, and attitudes for the benefit of experimental group. Moreover, the findings revealed that there were statistical differences between pre and post-test for the experimental group. No differences were found, according to the control group.
Modular specification of monads through higher-order presentations

In their work on second-order equational logic, Fiore and Hur have studied presentations of simply typed languages by generating binding constructions and equations among them. To each pair consisting of a binding signature and a set of equations, they associate a category of `models', and they give a monadicity result which implies that this category has an initial object, which is the language presented by the pair. In the present work, we propose, for the untyped setting, a variant of their approach where monads and modules over them are the central notions. More precisely, we study, for monads over sets, presentations by generating (`higher-order') operations and equations among them. We consider a notion of 2-signature which allows to specify a monad with a family of binding operations subject to a family of equations, as is the case for the paradigmatic example of the lambda calculus, specified by its two standard constructions (application and abstraction) subject to $\beta$- and $\eta$-equalities. Such a 2-signature is hence a pair $(\Sigma,E)$ of a binding signature $\Sigma$ and a family $E$ of equations for $\Sigma$. This notion of 2-signature has been introduced earlier by Ahrens in a slightly different context. We associate, to each 2-signature $(\Sigma,E)$, a category of `models of $(\Sigma,E)$; and we say that a 2-signature is `effective' if this category has an initial object; the monad underlying this (essentially unique) object is the `monad specified by the 2-signature'. Not every 2-signature is effective; we identify a class of 2-signatures, which we call `algebraic', that are effective. Importantly, our 2-signatures together with their models enjoy `modularity': when we glue (algebraic) 2-signatures together, their initial models are glued accordingly. We provide a computer formalization for our main results.
Event-Based Control for Online Training of Neural Networks

Convolutional Neural Network (CNN) has become the most used method for image classification tasks. During its training the learning rate and the gradient are two key factors to tune for influencing the convergence speed of the model. Usual learning rate strategies are time-based i.e. monotonous decay over time. Recent state-of-the-art techniques focus on adaptive gradient algorithms i.e. Adam and its versions. In this paper we consider an online learning scenario and we propose two Event-Based control loops to adjust the learning rate of a classical algorithm E (Exponential)/PD (Proportional Derivative)-Control. The first Event-Based control loop will be implemented to prevent sudden drop of the learning rate when the model is approaching the optimum. The second Event-Based control loop will decide, based on the learning speed, when to switch to the next data batch. Experimental evaluationis provided using two state-of-the-art machine learning image datasets (CIFAR-10 and CIFAR-100). Results show the Event-Based E/PD is better than the original algorithm (higher final accuracy, lower final loss value), and the Double-Event-BasedE/PD can accelerate the training process, save up to 67% training time compared to state-of-the-art algorithms and even result in better performance.
Machine Reasoning: Technology, Dilemma and Future

Machine reasoning research aims to build interpretable AI systems that can solve problems or draw conclusions from what they are told (i.e. facts and observations) and already know (i.e. models, common sense and knowledge) under certain constraints. In this tutorial, we will (1) describe the motivation of this tutorial and give our definition on machine reasoning; (2) introduce typical machine reasoning frameworks, including symbolic reasoning, probabilistic reasoning, neural-symbolic reasoning and neural-evidence reasoning, and show their successful applications in real-world scenarios; (3) talk about the dilemma between black-box neural networks with state-of-the-art performance and machine reasoning approaches with better interpretability; (4) summarize the content of this tutorial and discuss possible future directions.
Phase diagram for the one-way quantum deficit of two-qubit X states

The one-way quantum deficit, a measure of quantum correlation, can exhibit for X quantum states the regions (subdomains) with the phases $Δ_0$ and $Δ_{π/2}$ which are characterized by constant (i.e., universal) optimal measurement angles, correspondingly, zero and $π/2$ with respect to the $z$-axis and a third phase $Δ_\vartheta$ with the variable (state-dependent) optimal measurement angle $\vartheta$. We build the complete phase diagram of one-way quantum deficit for the XXZ subclass of symmetric X states. In contrast to the quantum discord where the region for the phase with variable optimal measurement angle is very tiny (more exactly, it is a very thin layer), the similar region $Δ_\vartheta$ is large and achieves the sizes comparable to those of regions $Δ_0$ and $Δ_{π/2}$. This instils hope to detect the mysterious fraction of quantum correlation with the variable optimal measurement angle experimentally.
High-Throughput Synchronous Deep RL

Deep reinforcement learning (RL) is computationally demanding and requires processing of many data points. Synchronous methods enjoy training stability while having lower data throughput. In contrast, asynchronous methods achieve high throughput but suffer from stability issues and lower sample efficiency due to `stale policies.' To combine the advantages of both methods we propose High-Throughput Synchronous Deep Reinforcement Learning (HTS-RL). In HTS-RL, we perform learning and rollouts concurrently, devise a system design which avoids `stale policies' and ensure that actors interact with environment replicas in an asynchronous manner while maintaining full determinism. We evaluate our approach on Atari games and the Google Research Football environment. Compared to synchronous baselines, HTS-RL is 2-6$\times$ faster. Compared to state-of-the-art asynchronous methods, HTS-RL has competitive throughput and consistently achieves higher average episode rewards.
Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home

Health smart home, as a typical application of Internet of things, provides a new solution for remote medical treatment. It can effectively relieve pressure from shortage of medical resources caused by aging population and help elderly people live at home more independently and safely. Activity recognition is the core of health smart home. This technology aims to recognize the activity patterns of users from a series of observations on the user’ actions and the environmental conditions, so as to avoid distress situations as much as possible. However, most of the existing researches focus on offline activity recognition, but not good at online real-time activity recognition. Besides, the feature representation techniques used for offline activity recognition are generally not suitable for online scenarios. In this article, the authors propose a real-time online activity recognition approach based on the genetic algorithm–optimized support vector machine classifier. In order to support online real-time activity recognition, a new sliding window-based feature representation technique enhanced by mutual information between sensors is devised. In addition, the genetic algorithm is used to automatically select optimal hyperparameters for the support vector machine model, thereby reducing the recognition inaccuracy caused by manual tuning of hyperparameters. Finally, a series of comprehensive experiments are conducted on freely available data sets to validate the effectiveness of the proposed approach.
Cavity-enhanced Ramsey spectroscopy at a Rydberg-atom–superconducting-circuit interface

The coherent interaction of Rydberg helium atoms with microwave fields in a $\lambda/4$ superconducting coplanar waveguide resonator has been exploited to probe the spectral characteristics of an individual resonator mode. This was achieved by preparing the atoms in the 1s55s$^3$S$_1$ Rydberg level by resonance enhanced two-color two-photon excitation from the metastable 1s2s$^3$S$_1$ level. The atoms then travelled over the resonator in which the third harmonic microwave field, at a frequency of $\omega_{\mathrm{res}}=2\pi\times19.556$ GHz, drove the two-photon 1s55s$^3$S$_1\rightarrow$1s56s$^3$S$_1$ transition. By injecting a sequence of Ramsey pulses into the resonator, and monitoring the coherent evolution of the Rydberg state population by state-selective pulsed electric field ionization as the frequency of the microwave field was tuned, spectra were recorded that allowed the resonator resonance frequency and quality factor to be determined with the atoms acting as microscopic quantum sensors.
Constant-Size Lattice-Based Group Signature with Forward Security in the Standard Model

One important property of group signatures is forward-security, which prevents an attacker in possession of a group signing key to forge signatures produced in the past. In case of exposure of one group member’s signing key, group signatures lacking forward-security need to invalidate all group public and secret keys (by re-initializing the whole system) but also invalidate all previously issued group signatures. Most of the existing forward-secure group signatures (FS-GS) are built from number-theoretic security assumptions which are vulnerable to quantum computers. The only post-quantum secure FS-GS scheme is built from lattices by Ling et al. (PQCrypto 19) in the random oracle model, following the classical framework of encrypt-then-prove, thus using non-interactive zero-knowledge (NIZK) proofs. In this work, we achieve the first FS-GS from lattices in the standard model. Our starting point is the group signature of Katsumada and Yamada (Eurocrypt 19) which replaces NIZK by attribute-based signatures (ABS), thus removing the need for random oracles. We first modify the underlying ABS of Tsabary (TCC 17) to equip it with forward-security property. We then prove that by plugging it back in the group signature framework of Katsumada and Yamada (Eurocrypt 19), we can design a FS-GS scheme secure in the standard model with public key and signature size constant in the number of users. Our constant size is achieved by relying on complexity leveraging, which further implies relying on the subexponential hardness of the Short Integers Solution (SIS) assumption.
Quantum Public Key Distribution using Randomized Glauber States

State-of-the-art Quantum Key Distribution (QKD) is based on the uncertainty principle of qubits on quantum measurements and is theoretically proven to be unconditionally secure. Over the past three decades, QKD has been explored with single photons as the information carrier. More recently, attention has shifted towards using weak coherent laser pulses as the information carrier. In this paper, we propose a novel quantum key distribution mechanism over a pure optical channel using randomized Glauber states. The proposed mechanism closely resembles a quantum mechanical implementation of the public key envelope idea. For the proposed solution, we explore physical countermeasures to provide path authentication and to avoid man-in-the-middle attacks. Other attack vectors can also be effectively mitigated by leveraging the QPKE, the uncertainty principle and the DPSK modulation technique.