Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of the initial mass function on Galactic chemical enrichment

Context. We have been seeing mounting evidence that the stellar initial mass function (IMF) might extend far beyond the canonical Mi ∼ 100 M⊙ limit, but the impact of such a hypothesis on the chemical enrichment of galaxies is yet to be clarified. Aims. We aim to address this question by analysing the observed abundances of thin- and thick-disc stars in the Milky Way with chemical evolution models that account for the contribution of very massive stars dying as pair instability supernovae. Methods. We built new sets of chemical yields from massive and very massive stars up to Mi ∼ 350 M⊙ by combining the wind ejecta extracted from our hydrostatic stellar evolution models with explosion ejecta from the literature. Using a simple chemical evolution code, we analysed the effects of adopting different yield tables by comparing predictions against observations of stars in the solar vicinity. Results. After several tests, we set our focus on the [O/Fe] ratio that best separates the chemical patterns of the two Milky Way components. We find that with a standard IMF, truncated at Mi ∼ 100 M⊙, we can reproduce various observational constraints for thin-disc stars; however, the same IMF fails to account for the [O/Fe] ratios of thick-disc stars. The best results are obtained by extending the IMF up to Mi = 350 M⊙, while including the chemical ejecta of very massive stars in the form of winds and pair instability supernova (PISN) explosions. Conclusions. Our study indicates that PISN may have played a significant role in shaping the chemical evolution of the thick disc of the Milky Way. Including their chemical yields makes it easier to reproduce not only the level of the α-enhancement, but also the observed slope of thick-disc stars in the [O/Fe] vs. [Fe/H] diagram. The bottom line is that the contribution of very massive stars to the chemical enrichment of galaxies is potentially quite important and should not be neglected in models of chemical evolution.
BART based semantic correction for Mandarin automatic speech recognition system

Although automatic speech recognition (ASR) systems achieved significantly improvements in recent years, spoken language recognition error occurs which can be easily spotted by human beings. Various language modeling techniques have been developed on post recognition tasks like semantic correction. In this paper, we propose a Transformer based semantic correction method with pretrained BART initialization, Experiments on 10000 hours Mandarin speech dataset show that character error rate (CER) can be effectively reduced by 21.7% relatively compared to our baseline ASR system. Expert evaluation demonstrates that actual improvement of our model surpasses what CER indicates.
On Analyzing Churn Prediction in Mobile Games

In subscription-based businesses, the churn rate refers to the percentage of customers who discontinue their subscriptions within a given time period. Particularly, in the mobile games industry, the churn rate is often pronounced due to the high competition and cost in customer acquisition; therefore, the process of minimizing the churn rate is crucial. This needs churn prediction, predicting users who will be churning within a given time period. Accurate churn prediction can enable the businesses to devise and engage strategic remediations to maintain a low churn rate. The paper presents our highly accurate churn prediction method. We designed this method to take into account each individual user's distinct usage period in churn prediction. As presented in the paper, this approach was able to achieve 96.6\% churn prediction accuracy on a real game business. In addition, the paper shows that other existing churn prediction algorithms are improved in prediction accuracy when this method is applied.
Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets

We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Min- ing for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Dis- tillBERT on each task, as well as first fine- tuning the model on the other task. In this paper, we additionally explore how much fine- tuning is necessary for accurately classifying tweets as containing self-reported COVID-19 symptoms (Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal reporting, or a literature/news mention of the virus (Task 6).
AI Powered Compiler Techniques for DL Code Optimization

Creating high performance implementations of deep learning primitives on CPUs is a challenging task. Multiple considerations including multi-level cache hierarchy, and wide SIMD units of CPU platforms influence the choice of program transformations to apply for performance optimization. In this paper, we present machine learning powered compiler techniques to optimize loop nests. We take a two-pronged approach to code optimization: We first apply high level optimizations to optimize the code to take optimal advantage of the cache memories. Then, we perform low level, target-specific optimizations to effectively vectorize the code to run well on the SIMD units of the machine. For high level optimizations, we use polyhedral compilation techniques and deep learning approaches. For low level optimization, we use a target specific code generator that generates code using vector intrinsics and Reinforcement Learning (RL) techniques to find the optimal parameters for the code generator. We perform experimental evaluation of the developed techniques on various matrix multiplications that occur in popular deep learning workloads. The experimental results show that the compiler techniques presented in the paper achieve 7.6X and 8.2X speed-ups over a baseline for sequential and parallel runs respectively.
Print Error Detection using Convolutional Neural Networks

This paper discusses the need of an automated system for detecting print errors and the efficacy of Convolutional Neural Networks in such an application. We recognise the need of a dataset containing print error samples and propose a way to generate one artificially. We discuss the algorithms to generate such data along with the limitaions and advantages of such an apporach. Our final trained network gives a remarkable accuracy of 99.83\% in testing. We further evaluate how such efficiency was achieved and what modifications can be tested to further the results.
Generating Code with the Help of Retrieved Template Functions and Stack Overflow Answers

We approach the important challenge of code autocompletion as an open-domain task, in which a sequence-to-sequence code generator model is enhanced with the ability to attend to reference code snippets supplied by a semantic code search engine. In this work, we present a novel framework to precisely retrieve template functions as well as intent-snippet pairs and effectively train such a retrieval-guided code generator. To demonstrate the effectiveness of our model designs, we perform extensive experiments with CodeSearchNet which contains template functions and CoNaLa which contains Stack Overflow intent-snippet pairs. We also investigate different retrieval models, including Elasticsearch, DPR, and our fusion representation search model, which currently holds the number one spot on the CodeSearchNet leaderboard. We observe improvements by leveraging multiple database elements and further gain from retrieving diverse data points by using Maximal Marginal Relevance. Overall, we see a 4% improvement to cross-entropy loss, a 15% improvement to edit distance, and a 44% improvement to BLEU score when retrieving template functions. We see subtler improvements of 2%, 11%, and 6% respectively when retrieving Stack Overflow intent-snippet pairs. We also create a novel Stack Overflow-Function Alignment dataset, which consists of 150K tuples of functions and Stack Overflow intent-snippet pairs that are of help in writing the associated function, of which 1.7K are manually curated.
A Probabilistic Framework for Lexicon-based Keyword Spotting in Handwritten Text Images

Query by String Keyword Spotting (KWS) is here considered as a key technology for indexing large collections of handwritten text images to allow fast textual access to the contents of these colections. Under this prespective, a probabilistic framework for lexicon-based KWS in text images is presented. The presentation aims at providing a tutorial view which helps understanding the relations between classical statements of KWS and the relative challenges entailed by these statements. More specifically, the development of the proposed framework makes it self-evident that word recognition or classification implicitly or explicitly underlies any formulation of KWS. Moreover, it clearly suggests that the same statistical models and training methods successfully used for handwriting text recognition, can advantageously used also for KWS, even though KWS does not generally require or rely on any kind of previously produced image transcripts. These ideas are developped into a specific, probabilistically sound approach for segmentation-free, lexicon-based, query-by-string KWS. Experiments carried out using this approach are presented, which support the consistency and general interest of the proposed framework. Several datasets, traditionally used for KWS benchmarking are considered, with results significantly better than those previously published for these datasets. In addition, results on two new, larger handwritten text image datasets are reported, showing the great potential of the methods proposed in this paper for indexing and textual search in large collections of handwritent documents. 1 ar X iv :2 10 4. 04 55 6v 1 [ cs . I R ] 9 A pr 2 02 1
The DKU System Description for The Interspeech 2021 Auto-KWS Challenge

This paper introduces the system submitted by the DKU-SMIIP team for the Auto-KWS 2021 Challenge. Our implementation consists of a two-stage keyword spotting system based on query-by-example spoken term detection and a speaker verification system. We employ two different detection algorithms in our proposed keyword spotting system. The first stage adopts subsequence dynamic time warping for template matching based on frame-level language-independent bottleneck feature and phoneme posterior probability. We use a sliding window template matching algorithm based on acoustic word embeddings to further verify the detection from the first stage. As a result, our KWS system achieves an average score of 0.61 on the feedback dataset, which outperforms the baseline1 system by 0.25.
On the optimal and partial optimal dual fusion frames

Abstract. The purpose of this work is to examine the structure of optimal dual fusion frames and get more flexibility in the use of dual fusion frames for erasures of subspaces. The problem of optimal Q-component preserving dual fusion frames was considered by S. B. Heineken and P. M. Morillas. We deal with differences and advantages of dual fusion frames with respect to Q-component preserving duals to show that our approach provides concrete structures for reconstruction of elements underlying Hilbert spaces. We then study the relation between local and global optimal duals, by a new notion called partial optimal duals which leads to some applicable results. In the sense that, we obtain an overcomplete frame and a family of associated optimal duals by a given Riesz fusion basis. We present some examples to exhibit the effect of error rate when dual fusion frames and Q-component preserving duals are used to reconstruction.
DexYCB: A Benchmark for Capturing Hand Grasping of Objects

We introduce DexYCB, a new dataset for capturing hand grasping of objects. We first compare DexYCB with a related one through cross-dataset evaluation. We then present a thorough benchmark of state-of-the-art approaches on three relevant tasks: 2D object and keypoint detection, 6D object pose estimation, and 3D hand pose estimation. Finally, we evaluate a new robotics-relevant task: generating safe robot grasps in human-to-robot object handover. 1
High-Throughput Virtual Screening of Small Molecule Inhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Models

Structure-based Deep Fusion models were recently shown to outperform several physics- and machine learning-based protein-ligand binding affinity prediction methods. As part of a multi-institutional COVID-19 pandemic response, over 500 million small molecules were computationally screened against four protein structures from the novel coronavirus (SARS-CoV-2), which causes COVID-19. Three enhancements to Deep Fusion were made in order to evaluate more than 5 billion docked poses on SARS-CoV-2 protein targets. First, the Deep Fusion concept was refined by formulating the architecture as one, coherently backpropagated model (Coherent Fusion) to improve binding-affinity prediction accuracy. Secondly, the model was trained using a distributed, genetic hyper-parameter optimization. Finally, a scalable, high-throughput screening capability was developed to maximize the number of ligands evaluated and expedite the path to experimental evaluation. In this work, we present both the methods developed for machine learning-based high-throughput screening and results from using our computational pipeline to find SARS-CoV-2 inhibitors.
Hierarchical Prior Regularized Matrix Factorization for Image Completion

The recent low-rank prior based models solve the tensor completion problem efficiently. However, these models fail to exploit the local patterns of tensors, which compromises the performance of tensor completion. In this paper, we propose a novel hierarchical prior regularized matrix factorization model for tensor completion. This model hierarchically incorporates the low-rank prior, total variation prior, and sparse coding prior into a matrix factorization, simultaneously characterizing both the global low-rank property and the local smoothness of tensors. For solving the proposed model, we use the alternating direction method of multipliers to establish our algorithm. Besides, the complexity and convergence are investigated to further validate the algorithm effectiveness. The proposed scheme is then evaluated through various data sets. Experiment results verify that, the proposed method outperforms several state-of-the-art approaches.
Likely Detection of GeV Gamma-Ray Emission from the Composite Supernova Remnant COMP G327.1+1.1 with Fermi-LAT

We report the likely GeV γ-ray emission from the composite supernova remnant (SNR) COMP G327.1+1.1 by analyzing ∼12.2 yr of the Fermi Large Area Telescope (Fermi-LAT) Pass 8 data. We found the features of its spectrum and luminosity are well consistent with those of the observed COMP SNRs in the Milky Way. Moreover, the position of the source matches those in radio and TeV energy bands; we propose that the γ-ray source is likely to be a GeV counterpart of COMP G327.1+1.1. Considering the case of the association from COMP G327.1+1.1 and the γ-ray source, we interpreted its broadband spectral energy distribution (SED) by using three simple stationary models including one-zone and two-zone leptonic models and a one-zone leptohadronic model. We found that the simple two-zone model dominated by leptons can better explain its SED. More high-energy data are expected to firmly confirm the association between the γ-ray source and COMP G327.1+1.1 in the future.
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer

Text style transfer aims to controllably generate text with targeted stylistic changes while maintaining core meaning from the source sentence constant. Many of the existing style transfer benchmarks primarily focus on individual high-level semantic changes (e.g. positive to negative), which enable controllability at a high level but do not offer fine-grained control involving sentence structure, emphasis, and content of the sentence. In this paper, we introduce a large-scale benchmark, StylePTB, with (1) paired sentences undergoing 21 fine-grained stylistic changes spanning atomic lexical, syntactic, semantic, and thematic transfers of text, as well as (2) compositions of multiple transfers which allow modeling of fine-grained stylistic changes as building blocks for more complex, high-level transfers. By benchmarking existing methods on StylePTB, we find that they struggle to model fine-grained changes and have an even more difficult time composing multiple styles. As a result, StylePTB brings novel challenges that we hope will encourage future research in controllable text style transfer, compositional models, and learning disentangled representations. Solving these challenges would present important steps towards controllable text generation.
A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions

This paper introduces the a framework that simplifies the process of designing and describing autonomous vehicle platooning manoeuvres which implements four design principles: Standardisation, Encapsulation, Abstraction, and Decoupling (SEAD). Although a large body of research has been formulating platooning manoeuvres, it is still challenging to design, describe, read, and understand them. This difficulty largely arises from missing formalisation. To fill this gap, we analysed existing ways of describing manoeuvres, derived the causes of difficulty, and designed a framework that simplifies the manoeuvre design process. Alongside, a Manoeuvre Design Language was developed to structurally describe manoeuvres in a machine-readable format. Unlike state-of-the-art manoeuvre descriptions that require one state machine for every participating vehicle, the SEAD framework allows describing any manoeuvre from the single perspective of the platoon leader. We hope that the SEAD framework will pave the way for further research in the area of new manoeuvre design and optimisation by largely simplifying and unifying platooning manoeuvre representation.
Quantitative Analysis of Behavior in Rotenone-Induced Parkinsonism in Rats Using Force Plate Actimeter

Rotenone is a chemical that has been used to induce Parkinsonism-like behavioral symptoms such as tremor, bradykinesia, rigidity and postural instability in rats. In this study, we used rotenone to induce parkinsonism-like symptoms in rats. Eighteen Wistar rats were randomly divided into three groups: control group, 2.5 mg/kg, rotenone-treated group and 2.5 mg/kg rotenone+10 mg/kg Sinemet â group. Rotenone was dissolved in soybean oil and injected subcutaneously every day excluding weekends. After 9 doses of soybean oil or rotenone, a series of oral doses of 3% (w/v) acacia or 10 mg/kg Sinemet â was administered daily for 5 days, followed by a dose of acacia+soybean oil, acacia+rotenone, or Sinemet â +rotenone. Animal behavior was assessed by using force plate actimeter. The results showed a significant decrease in motor ability of rats injected with rotenone compared to control group. Oral treatment of 10 mg/kg Sinemet â improved the movement of rotenone-treated rats. Power spectra revealed that the parkinsonian features induced by rotenone possessed the frequency between 0.5-2.5 Hz and 4-12 Hz. This is the first model that applies the behavioral assessment using force plate actimeter in rotenone-induced parkinsonism in rats and thus may be used for evaluating new anti-parkinsonian drugs.
Model‐assisted analyses of cluster‐randomized experiments

Cluster‐randomized experiments are widely used due to their logistical convenience and policy relevance. To analyse them properly, we must address the fact that the treatment is assigned at the cluster level instead of the individual level. Standard analytic strategies are regressions based on individual data, cluster averages and cluster totals, which differ when the cluster sizes vary. These methods are often motivated by models with strong and unverifiable assumptions, and the choice among them can be subjective. Without any outcome modelling assumption, we evaluate these regression estimators and the associated robust standard errors from the design‐based perspective where only the treatment assignment itself is random and controlled by the experimenter. We demonstrate that regression based on cluster averages targets a weighted average treatment effect, regression based on individual data is suboptimal in terms of efficiency and regression based on cluster totals is consistent and more efficient with a large number of clusters. We highlight the critical role of covariates in improving estimation efficiency and illustrate the efficiency gain via both simulation studies and data analysis. The asymptotic analysis also reveals the efficiency‐robustness trade‐off by comparing the properties of various estimators using data at different levels with and without covariate adjustment. Moreover, we show that the robust standard errors are convenient approximations to the true asymptotic standard errors under the design‐based perspective. Our theory holds even when the outcome models are misspecified, so it is model‐assisted rather than model‐based. We also extend the theory to a wider class of weighted average treatment effects.
Contrastive Learning of Global and Local Audio-Visual Representations

Contrastive learning has delivered impressive re-sults in many audio-visual representation learning scenarios. However, existing approaches optimize for learning either global representations useful for tasks such as classiﬁcation, or local representations useful for tasks such as audio-visual source localization and separation. While they produce satisfactory results in their intended downstream scenarios, they often fail to generalize to tasks that they were not originally designed for. In this work, we propose a versatile self-supervised approach to learn audio-visual representations that generalize to both the tasks which require global semantic information (e.g., classiﬁcation) and the tasks that require ﬁne-grained spatio-temporal information (e.g. localization). We achieve this by optimizing two cross-modal contrastive objectives that together encourage our model to learn discriminative global-local visual information given audio signals. To show that our approach learns generalizable video representations, we evaluate it on various downstream scenarios including action/sound classiﬁcation, lip reading, deepfake detection, and sound source localization.
DockerMock: Pre-Build Detection of Dockerfile Faults through Mocking Instruction Execution

Continuous Integration (CI) and Continuous Deployment (CD) are widely adopted in software engineering practice. In reality, the CI/CD pipeline execution is not yet reliably continuous because it is often interrupted by Docker build failures. However, the existing trial-and-error practice to detect faults is time-consuming. To timely detect Dockerfile faults, we propose a context-based pre-build analysis approach, named DockerMock, through mocking the execution of common Dockerfile instructions. A Dockerfile fault is declared when an instruction conflicts with the approximated and accumulated running context. By explicitly keeping track of whether the context is fuzzy, DockerMock strikes a good balance of detection precision and recall. We evaluated DockerMock with 53 faults in 41 Dockerfiles from open source projects on GitHub and 130 faults in 105 Dockerfiles from student course projects. On average, DockerMock detected 68.0% Dockerfile faults in these two datasets. While baseline hadolint detected 6.5%, and baseline BuildKit detected 60.5% without instruction execution. In the GitHub dataset, DockerMock reduces the number of builds to 47, outperforming that of hadolint (73) and BuildKit (74).