Miracle-dz/UniFAR-FTUs huggingface dataset:
A Statistical Study of Short-period Decayless Oscillations of Coronal Loops in an Active Region

Coronal loop oscillations are common phenomena in the solar corona, which are often classified as decaying and decayless oscillations. Using the high-resolution observation measured by the Extreme Ultraviolet Imager (EUI) on board the Solar Orbiter, we statistically investigate small-scale transverse oscillations with short periods (<200 s) of coronal loops in an active region (AR), i.e., NOAA AR 12965. A total of 111 coronal loops are identified in EUI 174 Å images, and they all reveal transverse oscillations without any significant decaying, regarded as decayless oscillations. Oscillatory periods are measured from ∼11 to ∼185 s, with a median period of 40 s. Thus, they are also termed short-period oscillations. The corresponding loop lengths are measured from ∼10.5 to ∼30.2 Mm, and a strong dependence of oscillatory periods on loop lengths is established, indicating that the short-period oscillations are standing kink-mode waves in nature. Based on the coronal seismology, kink speeds are measured to be ∼330–1910 km s−1, and magnetic field strengths in coronal loops are estimated to be ∼4.1–25.2 G, while the energy flux carried by decayless kink oscillations lies in the range from roughly 7 to 9220 W m−2. Our estimations suggest that the wave energy carried by short-period decayless kink oscillations cannot support the coronal heating in the AR.
Restricted log-exp-analytic power functions

A preparation theorem for compositions of restricted log-exp-analytic functions and power functions of the form $$h: \mathbb{R} \to \mathbb{R}, x \mapsto \left\{\begin{array}{ll} x^r,&x>0, \\ 0,&\textnormal{ else, } \end{array}\right. $$ for $r \in \mathbb{R}$ is given. Consequently we obtain a parametric version of Tamm's theorem for this class of functions which is indeed a full generalisation of the parametric version of Tamm's theorem for $\mathbb{R}_{\textnormal{an}}^{\mathbb{R}}$-definable functions.
BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting

The BLOOM model is a large publicly available multilingual language model, but its pretraining was limited to 46 languages. To extend the benefits of BLOOM to other languages without incurring prohibitively large costs, it is desirable to adapt BLOOM to new languages not seen during pretraining. In this work, we apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages in a resource-constrained setting. We find language adaptation to be effective at improving zero-shot performance in new languages. Surprisingly, we find that adapter-based finetuning is more effective than continued pretraining for large models. In addition, we discover that prompting performance is not significantly affected by language specifics, such as the writing system. It is primarily determined by the size of the language adaptation data. We also add new languages to BLOOMZ, which is a multitask finetuned version of BLOOM capable of following task instructions zero-shot. We find including a new language in the multitask fine-tuning mixture to be the most effective method to teach BLOOMZ a new language. We conclude that with sufficient training data language adaptation can generalize well to diverse languages. Our code is available at https://github.com/bigscience-workshop/multilingual-modeling.
Enriching Relation Extraction with OpenIE

Relation extraction (RE) is a sub-discipline of information extraction (IE) which focuses on the prediction of a relational predicate from a natural-language input unit (such as a sentence, a clause, or even a short paragraph consisting of multiple sentences and/or clauses). Together with named-entity recognition (NER) and disambiguation (NED), RE forms the basis for many advanced IE tasks such as knowledge-base (KB) population and verification. In this work, we explore how recent approaches for open information extraction (OpenIE) may help to improve the task of RE by encoding structured information about the sentences' principal units, such as subjects, objects, verbal phrases, and adverbials, into various forms of vectorized (and hence unstructured) representations of the sentences. Our main conjecture is that the decomposition of long and possibly convoluted sentences into multiple smaller clauses via OpenIE even helps to fine-tune context-sensitive language models such as BERT (and its plethora of variants) for RE. Our experiments over two annotated corpora, KnowledgeNet and FewRel, demonstrate the improved accuracy of our enriched models compared to existing RE approaches. Our best results reach 92% and 71% of F1 score for KnowledgeNet and FewRel, respectively, proving the effectiveness of our approach on competitive benchmarks.
On an inverse boundary problem for mean field games

In this paper, we propose and study an inverse boundary problem for the mean ﬁeld games (MFGs) governed by the ﬁrst-order master equation in a bounded domain. We establish a novel unique identiﬁability result by showing that the running cost function within any given proper (state) space-time subdomain is uniquely determined by the MFG boundary data of this subdomain. There are several salient features of our study. The MFG system couples two hyperbolic PDEs with one forward in time and the other one backward in time, and moreover there is a probability measure constraint. These make the inverse problem study new to the literature and highly challenging. We develop an eﬀective and eﬃcient scheme in tackling the inverse problem including the high-order variation in the probability space around a nontrivial distribution, and the construction of a novel class of CGO solutions to serve as the “probing modes”. Our study opens up a new ﬁeld of research on inverse problems for MFGs.
Optimal Treatment Regimes for Proximal Causal Learning

A common concern when a policymaker draws causal inferences from and makes decisions based on observational data is that the measured covariates are insufficiently rich to account for all sources of confounding, i.e., the standard no confoundedness assumption fails to hold. The recently proposed proximal causal inference framework shows that proxy variables that abound in real-life scenarios can be leveraged to identify causal effects and therefore facilitate decision-making. Building upon this line of work, we propose a novel optimal individualized treatment regime based on so-called outcome and treatment confounding bridges. We then show that the value function of this new optimal treatment regime is superior to that of existing ones in the literature. Theoretical guarantees, including identification, superiority, excess value bound, and consistency of the estimated regime, are established. Furthermore, we demonstrate the proposed optimal regime via numerical experiments and a real data application.
OASum: Large-Scale Open Domain Aspect-based Summarization

Aspect or query-based summarization has recently caught more attention, as it can generate differentiated summaries based on users' interests. However, the current dataset for aspect or query-based summarization either focuses on specific domains, contains relatively small-scale instances, or includes only a few aspect types. Such limitations hinder further explorations in this direction. In this work, we take advantage of crowd-sourcing knowledge on Wikipedia.org and automatically create a high-quality, large-scale open-domain aspect-based summarization dataset named OASum, which contains more than 3.7 million instances with around 1 million different aspects on 2 million Wikipedia pages. We provide benchmark results on OASum and demonstrate its ability for diverse aspect-based summarization generation. To overcome the data scarcity problem on specific domains, we also perform zero-shot, few-shot, and fine-tuning on seven downstream datasets. Specifically, zero/few-shot and fine-tuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model. Our dataset and pre-trained checkpoints are publicly available.
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning

—Federated learning (FL) allows multiple clients co- operatively train models without disclosing local data. However, the existing works fail to address all these practical concerns in FL: limited communication resources, dynamic network con- ditions and heterogeneous client properties, which slow down the convergence of FL. To tackle the above challenges, we propose a heterogeneity-aware FL framework, called FedCG, with adaptive client selection and gradient compression. Specif- ically, the parameter server (PS) selects a representative client subset considering statistical heterogeneity and sends the global model to them. After local training, these selected clients upload compressed model updates matching their capabilities to the PS for aggregation, which signiﬁcantly alleviates the communication load and mitigates the straggler effect. We theoretically analyze the impact of both client selection and gradient compression on convergence performance. Guided by the derived convergence rate, we develop an iteration-based algorithm to jointly optimize client selection and compression ratio decision using submodular maximization and linear programming. Extensive experiments on both real-world prototypes and simulations show that FedCG can provide up to 5.3 × speedup compared to other methods.
High-efficiency electrophosphorescent organic light-emitting diodes with double light-emitting layers

We demonstrate high-efficiency electrophosphorescent organic light-emitting diodes (PHOLEDs) with double light-emitting layers (D–EMLs) by doping both hole and electron transport hosts with fac tris(2-phenylpyridine)iridium [Ir(ppy)3] simultaneously. The D–EMLs PHOLEDs show significantly improved efficiency (peak external quantum efficiency of about 12.6%, corresponding to a current efficiency of 44.3 cd/A) compared to the conventional PHOLEDs with a single EML and either hole or electron transport host doped with Ir(ppy)3. We attribute this improvement mainly to reduced losses of triplet excitons into regions that are not doped by phosphorescent emitter molecules.
Human Image Generation: A Comprehensive Survey

Image and video synthesis has become a blooming topic in computer vision and machine learning communities along with the developments of deep generative models, due to its great academic and application value. Many researchers have been devoted to synthesizing high-fidelity human images as one of the most commonly seen object categories in daily lives, where a large number of studies are performed based on various deep generative models, task settings and applications. Thus, it is necessary to give a comprehensive overview on these variant methods on human image generation. In this paper, we divide human image generation techniques into three paradigms, i.e., data-driven methods, knowledge-guided methods and hybrid methods. For each route, the most representative models and the corresponding variants are presented, where the advantages and characteristics of different methods are summarized in terms of model architectures and input/output requirements. Besides, the main public human image datasets and evaluation metrics in the literature are also summarized. Furthermore, due to the wide application potentials, two typical downstream usages of synthesized human images are covered, i.e., data augmentation for person recognition tasks and virtual try-on for fashion customers. Finally, we discuss the challenges and potential directions of human image generation to shed light on future research.
Revamping Kaluza-Klein dark matter in an orbifold theory of flavor

We suggest a common origin for dark matter, neutrino mass and family symmetry within the orbifold theory proposed in [arXiv:1910.05605v2,arXiv:2004.06735v2]. Flavor physics is described by an $A_4$ family symmetry that results naturally from compactification. WIMP Dark matter emerges from the first Kaluza-Klein excitation of the same scalar that drives family symmetry breaking and neutrino masses through the inverse seesaw mechanism. In addition to the"golden"quark-lepton mass relation and neutrino predictions for $0\nu\beta\beta$ decay, the model provides a good global description of all flavor observables.
Natural Language Processing in Customer Service: A Systematic Review

Artificial intelligence and natural language processing (NLP) are increasingly being used in customer service to interact with users and answer their questions. The goal of this systematic review is to examine existing research on the use of NLP technology in customer service, including the research domain, applications, datasets used, and evaluation methods. The review also looks at the future direction of the field and any significant limitations. The review covers the time period from 2015 to 2022 and includes papers from five major scientific databases. Chatbots and question-answering systems were found to be used in 10 main fields, with the most common use in general, social networking, and e-commerce areas. Twitter was the second most commonly used dataset, with most research also using their own original datasets. Accuracy, precision, recall, and F1 were the most common evaluation methods. Future work aims to improve the performance and understanding of user behavior and emotions, and address limitations such as the volume, diversity, and quality of datasets. This review includes research on different spoken languages and models and techniques.
Natural Language to Code Generation in Interactive Data Science Notebooks

Computational notebooks, such as Jupyter notebooks, are interactive computing environments that are ubiquitous among data scientists to perform data wrangling and analytic tasks. To measure the performance of AI pair programmers that automatically synthesize programs for those tasks given natural language (NL) intents from users, we build ARCADE, a benchmark of 1078 code generation problems using the pandas data analysis framework in data science notebooks. ARCADE features multiple rounds of NL-to-code problems from the same notebook. It requires a model to understand rich multi-modal contexts, such as existing notebook cells and their execution states as well as previous turns of interaction. To establish a strong baseline on this challenging task, we develop PaChiNCo, a 62B code language model (LM) for Python computational notebooks, which significantly outperforms public code LMs. Finally, we explore few-shot prompting strategies to elicit better code with step-by-step decomposition and NL explanation, showing the potential to improve the diversity and explainability of model predictions. Arcade is publicly available at https://github.com/google-research/arcade-nl2code/.
Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization

Domain shift widely exists in the visual world, while modern deep neural networks commonly suffer from severe performance degradation under domain shift due to the poor generalization ability, which limits the real-world applications. The domain shift mainly lies in the limited source environmental variations and the large distribution gap between source and unseen target data. To this end, we propose a unified framework, Style-HAllucinated Dual consistEncy learning (SHADE), to handle such domain shift in various visual tasks. Specifically, SHADE is constructed based on two consistency constraints, Style Consistency (SC) and Retrospection Consistency (RC). SC enriches the source situations and encourages the model to learn consistent representation across style-diversified samples. RC leverages general visual knowledge to prevent the model from overfitting to source data and thus largely keeps the representation consistent between the source and general visual models. Furthermore, we present a novel style hallucination module (SHM) to generate style-diversified samples that are essential to consistency learning. SHM selects basis styles from the source distribution, enabling the model to dynamically generate diverse and realistic samples during training. Extensive experiments demonstrate that our versatile SHADE can significantly enhance the generalization in various visual recognition tasks, including image classification, semantic segmentation and object detection, with different models, i.e., ConvNets and Transformer.
The ACT Reference Catalog

The US Naval Observatory has completed the compilation of the ACT Reference Catalog, containing 988,758 stars covering the entire sky. The motivation behind the ACT was to provide accurate proper motions for the majority of the stars in the Tycho Catalogue. To do this, positions from new reductions of the Astrographic Catalogue (AC 2000) were combined with those of the Tycho Catalogue. The large epoch span between the two catalogs yields proper motions about an order of magnitude more accurate than those found in the Tycho Catalogue. The astrometric data contained in the ACT Reference Catalog include positions and proper motions and their corresponding errors. These are on the Hipparcos system (ICRS) for epoch J2000.0. Photometric and variability data from Tycho are included. In addition, cross-references to the Tycho, AC 2000, Bonner Durchmusterung (BD), Cordoba Durchmusterung (CD), Cape Durchmusterung (CPD), Henry Draper (HD), and Hipparcos catalogs are given. The ACT is now available at the international data centers and by contacting the authors via electronic mail.
"Zoology'' of non-invertible duality defects: the view from class $\mathcal{S}$

We study generalizations of the non-invertible duality defects present in $\mathcal{N} = 4$ SU(N) SYM by studying theories with larger duality groups. We focus on 4d $\mathcal{N} = 2$ theories of class $\mathcal{S}$ obtained by the dimensional reduction of the 6d $\mathcal{N} = (2, 0)$ theory of $A_{N-1}$ type on a Riemann surface $\Sigma_g$ without punctures. We discuss their non-invertible duality symmetries and provide two ways to compute their fusion algebra: either using discrete topological manipulations or a 5d TQFT description. We also introduce the concept of"rank"of a non-invertible duality symmetry and show how it can be used to (almost) completely fix the fusion algebra with little computational effort.
ProNet: Adaptive Process Noise Estimation for INS/DVL Fusion

Inertial and Doppler velocity log sensors are commonly used to provide the navigation solution for autonomous underwater vehicles (AUV). To this end, a nonlinear filter is adopted for the fusion task. The filter’s process noise covariance matrix is critical for filter accuracy and robustness. While this matrix varies over time during the AUV mission, the filter assumes a constant matrix. Several models and learning approaches in the literature suggest tuning the process noise covariance during operation. In this work, we propose ProNet, a hybrid, adaptive process, noise estimation approach for a velocity-aided navigation filter. ProNet requires only the inertial sensor reading to regress the process noise covariance. Once learned, it is fed into the model-based navigation filter, resulting in a hybrid filter. Simulation results show the benefits of our approach compared to other models and learning adaptive approaches.
RISE: Leveraging Retrieval Techniques for Summarization Evaluation

Evaluating automatically-generated text summaries is a challenging task. While there have been many interesting approaches, they still fall short of human evaluations. We present RISE, a new approach for evaluating summaries by leveraging techniques from information retrieval. RISE is first trained as a retrieval task using a dual-encoder retrieval setup, and can then be subsequently utilized for evaluating a generated summary given an input document, without gold reference summaries. RISE is especially well suited when working on new datasets where one may not have reference summaries available for evaluation. We conduct comprehensive experiments on the SummEval benchmark (Fabbri et al., 2021) and the results show that RISE has higher correlation with human evaluations compared to many past approaches to summarization evaluation. Furthermore, RISE also demonstrates data-efficiency and generalizability across languages.
Zenix: Efficient Execution of Bulky Serverless Applications

Serverless computing, commonly offered as Function-as-a-Service, was initially designed for small, lean applications. However, there has been an increasing desire to run larger, more complex applications (what we call bulky applications) in a serverless manner. Existing strategies for enabling such applications are to either increase function sizes or to rewrite applications as DAGs of functions. These approaches cause significant resource wastage, manual efforts, and/or performance overhead. We argue that the root cause of these issues is today's function-centric serverless model, where a function is the resource allocation and scaling unit. We propose a new, resource-centric serverless-computing model for executing bulky applications in a resource- and performance-efficient way, and we build the Zenix serverless platform following this model. Our results show that Zenix reduces resource consumption by up to 90% compared to today's function-centric serverless systems, while improving performance by up to 64%.
DDO68 C: The Actual Appearance of a Ghost Satellite Dwarf through Adaptive Optics at the Large Binocular Telescope

Through adaptive optics (AO) imaging with the SOUL+LUCI instrument at the Large Binocular Telescope we were able to resolve, for the first time, individual stars in the gas-rich galaxy DDO68 C. This system was already suggested to be interacting with the extremely metal-poor dwarf DDO68, but its nature has remained elusive so far because of the presence of a bright foreground star close to its line of sight that hampers a detailed study of its stellar population and distance. In our study, we turned this interloper star into an opportunity to have a deeper insight on DDO68 C, using it as a guide star for the AO correction. Although the new data do not allow for a direct distance measurement through the red giant branch tip method, the combined analysis of the resolved-star color–magnitude diagram, of archival GALEX far-UV and near-UV photometry, and of Hα data provides a self-consistent picture in which DDO68 C is at the same ∼13 Mpc distance as its candidate companion DDO68. These results indicate that DDO68 is a unique case of a low-mass dwarf, less massive than the Magellanic Clouds, interacting with three satellites (DDO68 C and two previously confirmed accreting systems), providing useful constraints on cosmological models and a potential explanation for its anomalous extremely low metallicity.
Pure-Oxygen Radiative Shocks with Electron Thermal Conduction

Steady state radiative shock models in gas composed entirely of oxygen are calculated with the purpose of explaining observations of fast-moving knots in Cas A and other oxygen-rich SNRs. Models with electron thermal conduction differ significantly from models in which conduction is neglected. Conduction reduces postshock electron temperatures by a factor of 7-10 and flattens temperature gradients. The O III ion, whose forbidden emission usually dominates the observed spectra, is present over a wide range of shock velocities, from 100 to 170 km/s. The electron temperature in the O III forbidden line formation region is 30,000 K, in agreement with the 20,000 K derived from observations. All models with conduction have extensive warm (T above 4000 K) photoionization zones, which provides better agreement with observed optical O I line strengths. 86 refs.