Miracle-dz/UniFAR-FTUs huggingface dataset:
SNR G292.0+1.8: A Remnant of a Low-mass-progenitor Stripped-envelope Supernova

We present a study of the Galactic supernova remnant (SNR) G292.0+1.8, a classic example of a core-collapse SNR that contains oxygen-rich ejecta, circumstellar material, a rapidly moving pulsar, and a pulsar wind nebula (PWN). We use hydrodynamic simulations of the remnant’s evolution to show that the SNR reverse shock is interacting with the PWN and has most likely shocked the majority of the supernova ejecta. In our models, such a scenario requires a total ejecta mass of ≲3 M ⊙ and implies that there is no significant quantity of cold ejecta in the interior of the reverse shock. In light of these results, we compare the estimated elemental masses and abundance ratios in the reverse-shocked ejecta to nucleosynthesis models, and further conclude that they are consistent with a progenitor star with an initial mass of 12–16 M ⊙. We conclude that the progenitor of G292.0+1.8 was likely a relatively low-mass star that experienced significant mass loss through a binary interaction and would have produced a stripped-envelope supernova explosion. We also argue that the region known as the “spur” in G292.0+1.8 arises as a result of the pulsar’s motion through the supernova ejecta, and that its dynamical properties may suggest a line-of-sight component to the pulsar’s velocity, leading to a total space velocity of ∼600 km s−1 and implying a significant natal kick. Finally, we discuss binary mass-loss scenarios relevant to G292.0+1.8 and their implications for the binary companion properties and future searches.
Dual-Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager

Sea fog significantly threatens the safety of maritime activities. This article develops a sea fog detection dataset (SFDD) and a dual-branch sea fog detection network (DB-SFNet). We investigate all the observed sea fog events in the Yellow Sea and the Bohai Sea (118.1°E–128.1°E, 29.5°N–43.8°N) from 2010 to 2020 and collect the sea fog images for each event from the Geostationary Ocean Color Imager (GOCI) to comprise the dataset SFDD. The location of the sea fog in each image in SFDD is accurately marked. The proposed dataset is characterized by a long-time span, a large number of samples, and accurate labeling, which can substantially improve the robustness of various sea fog detection models. Furthermore, this article proposes a DB-SFNet to achieve accurate and holistic sea fog detection. The proposed DB-SFNet is composed of a knowledge extraction module and a dual-branch optional encoding decoding module. The two modules jointly extract discriminative features from both visual and statistical domains. Experiments show promising sea fog detection results with an F1-score of 0.77 and a critical success index of 0.63. Compared with existing advanced deep learning networks, DB-SFNet is superior in detection performance and stability, particularly in the mixed cloud and fog areas.
Utilitarianism on the front lines: COVID-19, public ethics, and the “hidden assumption” problem

Abstract How should we think of the preferences of citizens? Whereas self-optimal policy is relatively straightforward to produce, socially optimal policy often requires a more detailed examination. In this paper, we identify an issue that has received far too little attention in welfarist modelling of public policy, which we name the “hidden assumptions” problem. Hidden assumptions can be deceptive because they are not expressed explicitly and the social planner (e.g. a policy maker, a regulator, a legislator) may not give them the critical attention they need. We argue that ethical expertise has a direct role to play in public discourse because it is hard to adopt a position on major issues like public health policy or healthcare prioritisation without making contentious assumptions about population ethics. We then postulate that ethicists are best situated to critically evaluate these hidden assumptions, and can therefore play a vital role in public policy debates.
Adaptive constant-depth circuits for manipulating non-abelian anyons

We consider Kitaev's quantum double model based on a finite group $G$ and describe quantum circuits for (a) preparation of the ground state, (b) creation of anyon pairs separated by an arbitrary distance, and (c) non-destructive topological charge measurement. We show that for any solvable group $G$ all above tasks can be realized by constant-depth adaptive circuits with geometrically local unitary gates and mid-circuit measurements. Each gate may be chosen adaptively depending on previous measurement outcomes. Constant-depth circuits are well suited for implementation on a noisy hardware since it may be possible to execute the entire circuit within the qubit coherence time. Thus our results could facilitate an experimental study of exotic phases of matter with a non-abelian particle statistics. We also show that adaptiveness is essential for our circuit construction. Namely, task (b) cannot be realized by non-adaptive constant-depth local circuits for any non-abelian group $G$. This is in a sharp contrast with abelian anyons which can be created and moved over an arbitrary distance by a depth-$1$ circuit composed of generalized Pauli gates.
Logical Metatheorems for Accretive and (Generalized) Monotone Set-Valued Operators

Accretive and monotone operator theory are central branches of nonlinear functional analysis and constitute the abstract study of set-valued mappings between function spaces. This paper deals with the computational properties of certain large classes of operators, namely accretive and (generalized) monotone set-valued ones. In particular, we develop (and extend) for this field the theoretical framework of proof mining, a program in mathematical logic that seeks to extract computational information from prima facie `non-computational' proofs from the mainstream literature. To this end, we establish logical metatheorems that guarantee and quantify the computational content of theorems pertaining to accretive and (generalized) monotone set-valued operators. On one hand, our results unify a number of recent case studies, while they also provide characterizations of central analytical notions in terms of proof theoretic ones on the other, which provides a crucial perspective on needed quantitative assumptions in future applications of proof mining to these branches.
Visual Commonsense in Pretrained Unimodal and Multimodal Models

Our commonsense knowledge about objects includes their typical visual attributes; we know that bananas are typically yellow or green, and not purple. Text and image corpora, being subject to reporting bias, represent this world-knowledge to varying degrees of faithfulness. In this paper, we investigate to what degree unimodal (language-only) and multimodal (image and language) models capture a broad range of visually salient attributes. To that end, we create the Visual Commonsense Tests (ViComTe) dataset covering 5 property types (color, shape, material, size, and visual co-occurrence) for over 5000 subjects. We validate this dataset by showing that our grounded color data correlates much better than ungrounded text-only data with crowdsourced color judgments provided by Paik et al. (2021). We then use our dataset to evaluate pretrained unimodal models and multimodal models. Our results indicate that multimodal models better reconstruct attribute distributions, but are still subject to reporting bias. Moreover, increasing model size does not enhance performance, suggesting that the key to visual commonsense lies in the data.
Sequencer: Deep LSTM for Image Classification

In recent computer vision research, the advent of the Vision Transformer (ViT) has rapidly revolutionized various architectural design efforts: ViT achieved state-of-the-art image classification performance using self-attention found in natural language processing, and MLP-Mixer achieved competitive performance using simple multi-layer perceptrons. In contrast, several studies have also suggested that carefully redesigned convolutional neural networks (CNNs) can achieve advanced performance comparable to ViT without resorting to these new ideas. Against this background, there is growing interest in what inductive bias is suitable for computer vision. Here we propose Sequencer, a novel and competitive architecture alternative to ViT that provides a new perspective on these issues. Unlike ViTs, Sequencer models long-range dependencies using LSTMs rather than self-attention layers. We also propose a two-dimensional version of Sequencer module, where an LSTM is decomposed into vertical and horizontal LSTMs to enhance performance. Despite its simplicity, several experiments demonstrate that Sequencer performs impressively well: Sequencer2D-L, with 54M parameters, realizes 84.6% top-1 accuracy on only ImageNet-1K. Not only that, we show that it has good transferability and the robust resolution adaptability on double resolution-band.
A Novel Fully Annotated Thermal Infrared Face Dataset: Recorded in Various Environment Conditions and Distances From The Camera

Facial thermography is one of the most popular research areas in infrared thermal imaging, with diverse applications in medical, surveillance, and environmental monitoring. However, in contrast to facial imagery in the visual spectrum, the lack of public datasets on facial thermal images is an obstacle to research improvement in this area. Thermal face imagery is still a relatively new research area to be evaluated and studied in different domains.The current thermal face datasets are limited in regards to the subjects' distance from the camera, the ambient temperature variation, and facial landmarks' localization. We address these gaps by presenting a new facial thermography dataset. This article makes two main contributions to the body of knowledge. First, it presents a comprehensive review and comparison of current public datasets in facial thermography. Second, it introduces and studies a novel public dataset on facial thermography, which we call it Charlotte-ThermalFace. Charlotte-ThermalFace contains more than10000 infrared thermal images in varying thermal conditions, several distances from the camera, and different head positions. The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image. Our dataset is the first publicly available thermal dataset annotated with the thermal sensation of each subject in different thermal conditions and one of the few datasets in raw 16-bit format. Finally, we present a preliminary analysis of the dataset to show the applicability and importance of the thermal conditions in facial thermography. The full dataset, including annotations, are freely available for research purpose at https://github.com/TeCSAR-UNCC/UNCC-ThermalFace
SMLT: A Serverless Framework for Scalable and Adaptive Machine Learning Design and Training

In today's production machine learning (ML) systems, models are continuously trained, improved, and deployed. ML design and training are becoming a continuous workflow of various tasks that have dynamic resource demands. Serverless computing is an emerging cloud paradigm that provides transparent resource management and scaling for users and has the potential to revolutionize the routine of ML design and training. However, hosting modern ML workflows on existing serverless platforms has non-trivial challenges due to their intrinsic design limitations such as stateless nature, limited communication support across function instances, and limited function execution duration. These limitations result in a lack of an overarching view and adaptation mechanism for training dynamics and an amplification of existing problems in ML workflows. To address the above challenges, we propose SMLT, an automated, scalable, and adaptive serverless framework to enable efficient and user-centric ML design and training. SMLT employs an automated and adaptive scheduling mechanism to dynamically optimize the deployment and resource scaling for ML tasks during training. SMLT further enables user-centric ML workflow execution by supporting user-specified training deadlines and budget limits. In addition, by providing an end-to-end design, SMLT solves the intrinsic problems in serverless platforms such as the communication overhead, limited function execution duration, need for repeated initialization, and also provides explicit fault tolerance for ML training. SMLT is open-sourced and compatible with all major ML frameworks. Our experimental evaluation with large, sophisticated modern ML models demonstrate that SMLT outperforms the state-of-the-art VM based systems and existing serverless ML training frameworks in both training speed (up to 8X) and monetary cost (up to 3X)
DEAR: A Novel Deep Learning-based Approach for Automated Program Repair

The existing deep learning (DL)-based automated program repair (APR) models are limited in fixing general software defects. We present DEAR, a DL-based approach that supports fixing for the general bugs that require dependent changes at once to one or mul-tiple consecutive statements in one or multiple hunks of code. We first design a novel fault localization (FL) technique for multi-hunk, multi-statement fixes that combines traditional spectrum-based (SB) FL with deep learning and data-flow analysis. It takes the buggy statements returned by the SBFL model, detects the buggy hunks to be fixed at once, and expands a buggy statement $s$ in a hunk to include other suspicious statements around s. We design a two-tier, tree-based LSTM model that incorporates cycle training and uses a divide-and-conquer strategy to learn proper code transformations for fixing multiple statements in the suitable fixing context consisting of surrounding subtrees. We conducted several experiments to evaluate DEAR on three datasets: Defects4J (395 bugs), BigFix (+26k bugs), and CPatMiner (+44k bugs). On Defects4J dataset, DEAR outperforms the baselines from 42%-683% in terms of the number of auto-fixed bugs with only the top-1 patches. On BigFix dataset, it fixes 31–145 more bugs than existing DL-based APR models with the top-1 patches. On CPatMiner dataset, among 667 fixed bugs, there are 169 (25.3%) multi-hunk/multi-statement bugs. DEAR fixes 71 and 164 more bugs, including 52 and 61 more multi-hunk/multi-statement bugs, than the state-of-the-art, DL-based APR models.
AmbiPun: Generating Humorous Puns with Ambiguous Context

In this paper, we propose a simple yet effective way to generate pun sentences that does not require any training on existing puns. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. Given a pair of definitions of a pun word, our model first produces a list of related concepts through a reverse dictionary. We then utilize one-shot GPT3 to generate context words and then generate puns incorporating context words from both concepts. Human evaluation shows that our method successfully generates pun 52% of the time, outperforming well-crafted baselines and the state-of-the-art models by a large margin.
Relative Gorenstein flat modules and Foxby classes and their model structures

A model structure on a category is a formal way of introducing a homotopy theory on that category, and if the model structure is abelian and hereditary, its homotopy category is known to be triangulated. So a good way to both build and model a triangulated category is to build a hereditary abelian model structure. Given a ring $R$ and a (non necessarily semidualizing) left $R$-module $C$, we introduce and study new concepts of relative Gorenstein cotorsion and cotorsion modules: $\rm G_C$-cotorsion and (strongly) $\mathcal{C}_C$-cotorsion. As an application, we prove that there is a unique hereditary abelian model structure on the category of left $R$-modules, in which the cofibrations are the monomorphisms with $\rm G_C$-flat cokernel and the fibrations are the epimorphisms with $\mathcal{C}_C$-cotorsion kernel belonging to the Bass class $\mathcal{B}_C(R)$. In the second part, when $C$ is a semidualizing $(R,S)$-bimodule, we investigate the existence of abelian model structures on the category of left (resp., right) $R$-modules where the cofibrations are the epimorphisms (resp., monomorphisms) with kernel (resp., cokernel) belonging to the Bass (resp., Auslander) class $\mathcal{B}_C(R)$ (resp., $\mathcal{A}_C(R)$). We also study the class of $\rm G_C$-flat modules and the Bass class from the Auslander-Buchweitz approximation theory point of view. We show that they are part of weak AB-contexts. As the concept of weak AB-context can be dualized, we also give dual results that involve the class of $\rm G_C$-cotorsion modules and the Auslander class.
Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion Cause Pair Extraction

The emotion cause pair extraction (ECPE) task aims to extract emotions and causes as pairs from documents. We observe that the relative distance distribution of emotions and causes is extremely imbalanced in the typical ECPE dataset. Existing methods have set a fixed size window to capture relations between neighboring clauses. However, they neglect the effective semantic connections between distant clauses, leading to poor generalization ability towards position-insensitive data. To alleviate the problem, we propose a novel \textbf{M}ulti-\textbf{G}ranularity \textbf{S}emantic \textbf{A}ware \textbf{G}raph model (MGSAG) to incorporate fine-grained and coarse-grained semantic features jointly, without regard to distance limitation. In particular, we first explore semantic dependencies between clauses and keywords extracted from the document that convey fine-grained semantic features, obtaining keywords enhanced clause representations. Besides, a clause graph is also established to model coarse-grained semantic relations between clauses. Experimental results indicate that MGSAG surpasses the existing state-of-the-art ECPE models. Especially, MGSAG outperforms other models significantly in the condition of position-insensitive data.
Efficient Few-Shot Fine-Tuning for Opinion Summarization

Abstractive summarization models are typically pre-trained on large amounts of generic texts, then fine-tuned on tens or hundreds of thousands of annotated samples. However, in opinion summarization, large annotated datasets of reviews paired with reference summaries are not available and would be expensive to create. This calls for fine-tuning methods robust to overfitting on small datasets. In addition, generically pre-trained models are often not accustomed to the specifics of customer reviews and, after fine-tuning, yield summaries with disfluencies and semantic mistakes. To address these problems, we utilize an efficient few-shot method based on adapters which, as we show, can easily store in-domain knowledge. Instead of fine-tuning the entire model, we add adapters and pre-train them in a task-specific way on a large corpus of unannotated customer reviews, using held-out reviews as pseudo summaries. Then, fine-tune the adapters on the small available human-annotated dataset. We show that this self-supervised adapter pre-training improves summary quality over standard fine-tuning by 2.0 and 1.3 ROUGE-L points on the Amazon and Yelp datasets, respectively. Finally, for summary personalization, we condition on aspect keyword queries, automatically created from generic datasets. In the same vein, we pre-train the adapters in a query-based manner on customer reviews and then fine-tune them on annotated datasets. This results in better-organized summary content reflected in improved coherence and fewer redundancies.
Chasing Streams with Existential Rules

We study reasoning with existential rules to perform query answering over streams of data. On static databases, this problem has been widely studied, but its extension to rapidly changing data has not yet been considered. To bridge this gap, we extend LARS, a well-known framework for rule-based stream reasoning, to support existential rules. For that, we show how to translate LARS with existentials into a semantics-preserving set of existential rules. As query answering with such rules is undecidable in general, we describe how to leverage the temporal nature of streams and present suitable notions of acyclicity that ensure decidability.
DEGREE: A Data-Efficient Generation-Based Event Extraction Model

Event extraction requires high-quality expert human annotations, which are usually expensive. Therefore, learning a data-efficient event extraction model that can be trained with only a few labeled examples has become a crucial challenge. In this paper, we focus on low-resource end-to-end event extraction and propose DEGREE, a data-efficient model that formulates event extraction as a conditional generation problem. Given a passage and a manually designed prompt, DEGREE learns to summarize the events mentioned in the passage into a natural sentence that follows a predefined pattern. The final event predictions are then extracted from the generated sentence with a deterministic algorithm. DEGREE has three advantages to learn well with less training data. First, our designed prompts provide semantic guidance for DEGREE to leverage DEGREE and thus better capture the event arguments. Moreover, DEGREE is capable of using additional weakly-supervised information, such as the description of events encoded in the prompts. Finally, DEGREE learns triggers and arguments jointly in an end-to-end manner, which encourages the model to better utilize the shared knowledge and dependencies among them. Our experimental results demonstrate the strong performance of DEGREE for low-resource event extraction.
Molecular formations and spectra due to electron correlations in three-electron hybrid double-well qubits

We show that systematic full configuration-interaction (FCI) calculations enable prediction of the energy spectra and the intrinsic spatial and spin structures of the many-body wave functions as a function of the detuning parameter for the case of three-electron hybrid qubits based on GaAs asymmetric double quantum dots. Specifically, in comparison with the case of weak interactions and treating the entire three-electron double-dot hybrid qubit as an integral unit, it is shown that the predicted spectroscopic patterns, originating from strong electron correlations, manifest the formation of Wigner molecules (WMs). Signatures of WM formation include: (1) a strong suppression of the energy gaps relative to the non-interacting-electrons modeling, and (2) the appearance of a pair of avoided crossings arising between states associated with two-electron occupancies in the left and right wells. The Wigner molecule is a physical entity associated with electron localization within each well and it cannot be captured by the previously employed independent-particle or two-site-Hubbard theoretical modeling of the hybrid qubits. The emergence of strong WMs is investigated in depth through the concerted use of FCI-adapted diagnostic tools like charge and spin densities, as well as conditional probability distributions. Furthermore, the energy spectrum as a function of the strength of the Coulomb repulsion (at constant detuning) is calculated in order to complement the thorough analysis of the factors contributing to WM emergence. We report remarkable agreement with recent experimental measurements. The present FCI methodology for multi-well quantum dots can be straightforwardly extended to treat valleytronic two-band Si/SiGe hybrid qubits, where the central role of the WMs was confirmed recently.