Miracle-dz/UniFAR-FTUs huggingface dataset:
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis

Evaluation metrics play a vital role in the growth of an area as it defines the standard of distinguishing between good and bad models. In the area of code synthesis, the commonly used evaluation metric is BLEU or perfect accuracy, but they are not suitable enough to evaluate codes, because BLEU is originally designed to evaluate the natural language, neglecting important syntactic and semantic features of codes, and perfect accuracy is too strict thus it underestimates different outputs with the same semantic logic. To remedy this, we introduce a new automatic evaluation metric, dubbed CodeBLEU. It absorbs the strength of BLEU in the n-gram match and further injects code syntax via abstract syntax trees (AST) and code semantics via data-flow. We conduct experiments by evaluating the correlation coefficient between CodeBLEU and quality scores assigned by the programmers on three code synthesis tasks, i.e., text-to-code, code translation, and code refinement. Experimental results show that our proposed CodeBLEU can achieve a better correlation with programmer assigned scores compared with BLEU and accuracy.
From things' modeling language (ThingML) to things' machine learning (ThingML2)

In this paper, we illustrate how to enhance an existing state-of-the-art modeling language and tool for the Internet of Things (IoT), called ThingML, to support machine learning on the modeling level. To this aim, we extend the Domain-Specific Language (DSL) of ThingML, as well as its code generation framework. Our DSL allows one to define things, which are in charge of carrying out data analytics. Further, our code generators can automatically produce the complete implementation in Java and Python. The generated Python code is responsible for data analytics and employs APIs of machine learning libraries, such as Keras, Tensorflow and Scikit Learn. Our prototype is available as open source software on Github.
Systematic errors in dust mass determinations: insights from laboratory opacity measurements

The thermal emission of dust is one of the most important tracers of the interstellar medium: multi-wavelength photometry in the far-infrared (FIR) and submillimeter (submm) can be fitted with a model, providing estimates of the dust mass. The fit results depend on the assumed value for FIR/submm opacity, which in most models - due to the scarcity, until recently, of experimental measurements - is extrapolated from shorter wavelengths. Lab measurements of dust analogues, however, show that FIR opacities are usually higher than the values used in models and depend on temperature, which suggests that dust mass estimates may be biased. To test the extent of this bias, we create multi-wavelength synthetic photometry for dusty galaxies at different temperatures and redshifts, using experimental results for FIR/submm dust opacity, then we fit the synthetic data using standard dust models. We find that the dust masses recovered by typical models are overestimated by a factor 2 to 20, depending on how the experimental opacities are treated. If the experimental dust samples are accurate analogues of interstellar dust, therefore, current dust masses are overestimated by up to a factor 20. The implications for our understanding of dust, both Galactic and at high redshift, are discussed.
Limit theorems for trawl processes

In this work we derive limit theorems for trawl processes. First,we study the asymptotic behaviour of the partial sums of the discretized trawl process $(X_{i\Delta_{n}})_{i=0}^{\lfloor nt\rfloor-1}$, under the assumption that as $n\uparrow\infty$, $\Delta_{n}\downarrow0$ and $n\Delta_{n}\rightarrow\mu\in[0,+\infty]$. Second, we derive a functional limit theorem for trawl processes as the Levy measure of the trawl seed grows to infinity and show that the limiting process has a Gaussian moving average representation.
The Ising antiferromagnet and max cut on random regular graphs

The Ising antiferromagnet is an important statistical physics model with close connections to the {\sc Max Cut} problem. Combining spatial mixing arguments with the method of moments and the interpolation method, we pinpoint the replica symmetry breaking phase transition predicted by physicists. Additionally, we rigorously establish upper bounds on the {\sc Max Cut} of random regular graphs predicted by Zdeborova and Boettcher [Journal of Statistical Mechanics 2010]. As an application we prove that the information-theoretic threshold of the disassortative stochastic block model on random regular graphs coincides with the Kesten-Stigum bound.
Implications of gauge freedom for nonrelativistic quantum electrodynamics

We review gauge-freedom in quantum electrodynamics (QED) outside of textbook regimes. We emphasise that QED subsystems are defined {\em relative} to a choice of gauge. Each definition uses different gauge-invariant observables. We show that this relativity is only eliminated if a sufficient number of Markovian and weak-coupling approximations are employed. All physical predictions are gauge-invariant, including subsystem properties such as photon number and entanglement. However, subsystem properties naturally differ for different physical subsystems. Gauge-ambiguities arise not because it is unclear how to obtain gauge-invariant predictions, but because it is not always clear which physical observables are the most operationally relevant. The gauge-invariance of a prediction is necessary but not sufficient to ensure its operational relevance. We show that in controlling which gauge-invariant observables are used to define a material system, the choice of gauge affects the balance between the material system's localisation and its electromagnetic dressing. We review various implications of subsystem gauge-relativity for deriving effective models, for describing time-dependent interactions, for photodetection theory, and for describing matter within a cavity.
Online geolocalized emotion across US cities during the COVID crisis: Universality, policy response, and connection with local mobility

As the COVID-19 pandemic began to sweep across the US it elicited a wide spectrum of responses, both online and offline, across the population. To aid the development of effective spatially targeted interventions in the midst of this turmoil, it is important to understand the geolocalization of these online emotional responses, as well as their association with offline behavioral responses. Here, we analyze around 13 million geotagged tweets in 49 cities across the US from the first few months of the pandemic to assess regional dependence in online sentiments with respect to a few major topics, and how these sentiments correlate with policy development and human mobility. Surprisingly, we observe universal trends in overall and topic-based sentiments across cities over the time period studied, with variability primarily seen only in the immediate impact of federal guidelines and local lockdown policies. We also find that these local sentiments are highly correlated with and predictive of city-level mobility, while the correlations between sentiments and local cases and deaths are relatively weak. Our findings point to widespread commonalities in the online public emotional responses to COVID across the US, both temporally and relative to offline indicators, in contrast with the high variability seen in early local containment policies. This study also provides new insights into the use of social media data in crisis management by integrating offline data to gain an in-depth understanding of public emotional responses, policy development, and local mobility.
Improving Convergence for Nonconvex Composite Programming

High-dimensional nonconvex problems are popular in today's machine learning and statistical genetics research. Recently, Ghadimi and Lan [1] proposed an algorithm to optimize nonconvex high-dimensional problems. There are several parameters in their algorithm that are to be set before running the algorithm. It is not trivial how to choose these parameters nor there is, to the best of our knowledge, an explicit rule on how to select the parameters to make the algorithm converges faster. We analyze Ghadimi and Lan's algorithm to gain an interpretation based on the inequality constraints for convergence and the upper bound for the norm of the gradient analogue. Our interpretation of their algorithm suggests this to be a damped Nesterov's acceleration scheme. Based on this, we propose an approach on how to select the parameters to improve convergence of the algorithm. Our numerical studies using high-dimensional nonconvex sparse learning problems, motivated by image denoising and statistical genetics applications, show that convergence can be made, on average, considerably faster than that of the conventional ISTA algorithm for such optimization problems with over $10000$ variables should the parameters be chosen using our proposed approach.
Contamination of cereal grain by Fusarium micromycetes and their mycotoxins under Lithuanian climatic conditions

In 2003–2005, cereal grains grown in Lithuanian climatic conditions were strongly contaminated by mould fungi. Fusarium was among the most common fungal genera identified in wheat and barley grains. Fungi of the genus Fusarium contaminated 92.1% of the grain samples tested. The spread and subsequent development of fusaria in cereal grains depend on climatic conditions, particularly rainfall and temperature. The vegetation period in 2005 was sufficiently warm and wet and thus most convenient for the distribution of Fusarium, especially of F. graminearum. Twenty four taxa of Fusarium were identified in wheat and barley grains. F. graminearum, F. poae, F. avenaceum, F. equiseti, F. sporotrichioides and F. sambucinum were most frequently isolated from grain surface, while F. poae, F. sporotrichioides, F. avenaceum, F. graminearum and F. culmorum dominated in the internal tissues with a decreasing prevalence. The complex of Fusarium species ascertained in wheat and barley grains in various years differed in composition as well as in isolation frequency and the relative density of separate species. These indexes depended on climatic conditions of a separate year and on the species of cereal as well. Different Fusarium complexes which developed on the surface and in the internal tissues of grains were established. F. sporotrichioides and F. graminearum were ascertained as the main producers of T-2 toxin and its derivates in wheat and barley grains. F. culmorum can be the main source of nivalenol, F. avenaceum produces enniatins and beauvericin. Producers of trichotecenes A dominated in the grain samples studied.
On the Spectral Peak Energy of Swift Gamma-Ray Bursts

Owing to the narrow energy band of the Swift Burst Alert Telescope (BAT), several urgent issues remain unsolved. We systematically study the properties of a refined sample of 283 Swift/BAT gamma-ray bursts (GRBs) with well-measured spectral peak energy (Ep) at a high confidence level greater than 3σ. We find that the duration (T90) distribution of Swift bursts still exhibits an evident bimodality with a more reliable boundary of T90 ≃ 1.06 s instead of 2 s as found for previously contaminated samples, including bursts without well-peaked spectra, which is very close to the ∼1.27 and ∼0.8 s values suggested in the literature for the Fermi/Gamma-ray Burst Monitor and Swift/BAT catalogs, respectively. The Swift/BAT short and long bursts have comparable mean Ep values of and keV, similar to what was found for both types of BATSE bursts, which indicates that the traditional short–hard/long–soft scheme may not be tenable for certain detector energy windows. We also statistically investigate the consistency of distinct methods for Ep estimates and find that a Bayesian approach and BAND function (Band et al.) can always provide consistent evaluations. In contrast, the frequently used cutoff power-law model matches two other methods for lower Ep and overestimates the Ep by more than 70%, as Ep > 100 keV. Peak energies of X-ray flashes, X-ray-rich bursts, and classical GRBs could be an evolutionary consequence of moving from thermal-dominated to nonthermal-dominated radiation mechanisms. Finally, we find that the Ep and the observed fluence (Sγ) in the observer frame are correlated as keV, which might be a useful indicator of GRB peak energies.
MockFog 2.0: Automated Execution of Fog Application Experiments in the Cloud

Fog computing is an emerging computing paradigm that uses processing and storage capabilities located at the edge, in the cloud, and possibly in between. Testing and benchmarking fog applications, however, is hard since runtime infrastructure will typically be in use or may not exist, yet. While approaches for the emulation of infrastructure testbeds do exist, their focus is typically the emulation of edge devices. Other approaches also emulate infrastructure within the core network or the cloud, but they miss support for automated experiment orchestration. In this article, we propose to evaluate fog applications on an emulated infrastructure testbed created in the cloud which can be manipulated based on a pre-defined orchestration schedule. Developers can freely design the infrastructure, configure performance characteristics, manage application components, and orchestrate their experiments. We also present our proof-of-concept implementation MockFog 2.0. We use MockFog 2.0 to evaluate a fog-based smart factory application and showcase how its features can be used to study the impact of infrastructure changes and workload variations. With these experiments, we also show that MockFog can achieve good experiment reproducibility, even in a public cloud environment.
A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline

We present an open-source speech corpus for the Kazakh language. The Kazakh speech corpus (KSC) contains around 332 hours of transcribed audio comprising over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. It was carefully inspected by native Kazakh speakers to ensure high quality. The KSC is the largest publicly available database developed to advance various Kazakh speech and language processing applications. In this paper, we first describe the data collection and preprocessing procedures followed by a description of the database specifications. We also share our experience and challenges faced during the database construction, which might benefit other researchers planning to build a speech corpus for a low-resource language. To demonstrate the reliability of the database, we performed preliminary speech recognition experiments. The experimental results imply that the quality of audio and transcripts is promising (2.8% character error rate and 8.7% word error rate on the test set). To enable experiment reproducibility and ease the corpus usage, we also released an ESPnet recipe for our speech recognition models.
PodSumm - Podcast Audio Summarization

The diverse nature, scale, and specificity of podcasts present a unique challenge to content discovery systems. Listeners often rely on text descriptions of episodes provided by the podcast creators to discover new content. Some factors like the presentation style of the narrator and production quality are significant indicators of subjective user preference but are difficult to quantify and not reflected in the text descriptions provided by the podcast creators. We propose the automated creation of podcast audio summaries to aid in content discovery and help listeners to quickly preview podcast content before investing time in listening to an entire episode. In this paper, we present a method to automatically construct a podcast summary via guidance from the text-domain. Our method performs two key steps, namely, audio to text transcription and text summary generation. Motivated by a lack of datasets for this task, we curate an internal dataset, find an effective scheme for data augmentation, and design a protocol to gather summaries from annotators. We fine-tune a PreSumm[10] model with our augmented dataset and perform an ablation study. Our method achieves ROUGE-F(1/2/L) scores of 0.63/0.53/0.63 on our dataset. We hope these results may inspire future research in this direction.
Deep N-ary Error Correcting Output Codes

Ensemble learning consistently improves the performance of multi-class classification through aggregating a series of base classifiers. To this end, data-independent ensemble methods like Error Correcting Output Codes (ECOC) attract increasing attention due to its easiness of implementation and parallelization. Specifically, traditional ECOCs and its general extension N-ary ECOC decompose the original multi-class classification problem into a series of independent simpler classification subproblems. Unfortunately, integrating ECOCs, especially N-ary ECOC with deep neural networks, termed as deep N-ary ECOC, is not straightforward and yet fully exploited in the literature, due to the high expense of training base learners. To facilitate the training of N-ary ECOC with deep learning base learners, we further propose three different variants of parameter sharing architectures for deep N-ary ECOC. To verify the generalization ability of deep N-ary ECOC, we conduct experiments by varying the backbone with different deep neural network architectures for both image and text classification tasks. Furthermore, extensive ablation studies on deep N-ary ECOC show its superior performance over other deep data-independent ensemble methods.
Ethical Machine Learning in Health Care

The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis

In this paper, we focus on the imbalance issue, which is rarely studied in aspect term extraction and aspect sentiment classification when regarding them as sequence labeling tasks. Besides, previous works usually ignore the interaction between aspect terms when labeling polarities. We propose a GRadient hArmonized and CascadEd labeling model (GRACE) to solve these problems. Specifically, a cascaded labeling module is developed to enhance the interchange between aspect terms and improve the attention of sentiment tokens when labeling sentiment polarities. The polarities sequence is designed to depend on the generated aspect terms labels. To alleviate the imbalance issue, we extend the gradient harmonized mechanism used in object detection to the aspect-based sentiment analysis by adjusting the weight of each label dynamically. The proposed GRACE adopts a post-pretraining BERT as its backbone. Experimental results demonstrate that the proposed model achieves consistency improvement on multiple benchmark datasets and generates state-of-the-art results.
Recommending stack overflow posts for fixing runtime exceptions using failure scenario matching

Using online Q&A forums, such as Stack Overflow (SO), for guidance to resolve program bugs, among other development issues, is commonplace in modern software development practice. Runtime exceptions (RE) is one such important class of bugs that is actively discussed on SO. In this work we present a technique and prototype tool called MAESTRO that can automatically recommend an SO post that is most relevant to a given Java RE in a developer's code. MAESTRO compares the exception-generating program scenario in the developer's code with that discussed in an SO post and returns the post with the closest match. To extract and compare the exception scenario effectively, MAESTRO first uses the answer code snippets in a post to implicate a subset of lines in the post's question code snippet as responsible for the exception and then compares these lines with the developer's code in terms of their respective Abstract Program Graph (APG) representations. The APG is a simplified and abstracted derivative of an abstract syntax tree, proposed in this work, that allows an effective comparison of the functionality embodied in the high-level program structure, while discarding many of the low-level syntactic or semantic differences. We evaluate MAESTRO on a benchmark of 78 instances of Java REs extracted from the top 500 Java projects on GitHub and show that MAESTRO can return either a highly relevant or somewhat relevant SO post corresponding to the exception instance in 71% of the cases, compared to relevant posts returned in only 8% - 44% instances, by four competitor tools based on state-of-the-art techniques. We also conduct a user experience study of MAESTRO with 10 Java developers, where the participants judge MAESTRO reporting a highly relevant or somewhat relevant post in 80% of the instances. In some cases the post is judged to be even better than the one manually found by the participant.
Cloud Atlas: Unraveling the Vertical Cloud Structure with the Time-series Spectrophotometry of an Unusually Red Brown Dwarf

Rotational modulations of emission spectra in brown dwarf and exoplanet atmospheres show that clouds are often distributed non-uniformly in these ultracool atmospheres. The spatial heterogeneity in cloud distribution demonstrates the impact of atmospheric dynamics on cloud formation and evolution. In this study, we update the Hubble Space Telescope (HST) time-series data analysis of the previously reported rotational modulations of WISEP J004701+680352—an unusually red late-L brown dwarf with a spectrum similar to that of the directly imaged planet HR 8799e. We construct a self-consistent spatially heterogeneous cloud model to explain the HST and the Spitzer time-series observations, as well as the time-averaged spectra of WISE 0047. In the heterogeneous cloud model, a cloud thickness variation of around one pressure scale height explains the wavelength dependence in the HST near-IR spectral variability. By including disequilibrium CO/CH4 chemistry, our models also reproduce the redder color of WISE 0047 compared to that of field brown dwarfs. We discuss the impact of vertical cloud structure on atmospheric profile and estimate the minimum eddy diffusivity coefficient for other objects with redder colors. Our data analysis and forward modeling results demonstrate that time-series spectrophotometry with a broad wavelength coverage is a powerful tool for constraining heterogeneous atmospheric structure.
On Gluing CFT Correlators to Higher-Spin Amplitudes in AdS.

We demonstrate how three-point correlation functions of the free scalar U(N) model involving two scalar operators and one spin-$s$ conserved current organize themselves into corresponding AdS amplitudes involving two scalar and one spin-$s$ bulk to boundary propagators, coupled via the bulk gauge invariant interaction vertex. Our analysis relies on the general program advocated in hep-th/0308184 and some features of the embedding space formalism also play an important role.