Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiwavelength variability and broad-band SED modelling of BL Lac during a bright flaring period MJD 59000–59943

We carried out a detailed temporal and spectral study of the BL Lacertae (BL Lac) by using the long-term Fermi-Large Area Telescope (LAT) and Swift-X-ray Telescope (XRT)/Ultraviolet Optical Telescope (UVOT) observations, during the period MJD 59000–59943. The daily-binned γ-ray light curve displays a maximum flux of $1.74\pm 0.09\times 10^{-5} \,\rm photons\, cm^{-2}\, s^{-1}$ on MJD 59868, which is the highest daily γ-ray flux observed from BL Lac. The γ-ray variability is characterized by power spectral density (PSD), rms–flux relation, and flux distribution study. We find that the power-law model fits the PSD with index ∼1, which suggests a long-memory process at work. The observed rms–flux relation exhibits a linear trend, which indicates that the γ-ray flux distribution follows a lognormal distribution. The skewness/Anderson–Darling test and histogram fit reject the normality of flux distribution, and instead suggest that the flux distribution is a lognormal distribution. The fractional variability amplitude shows that the source is more variable in the X-ray band than in optical/ultraviolet/γ-ray bands. In order to obtain an insight into the underlying physical process, we extracted broad-band spectra from different time periods of the light curve. The broad-band spectra are statistically fitted with the convolved one-zone leptonic model with different forms of the particle energy distribution. We found that spectral energy distribution during different flux states can be reproduced well with the synchrotron, synchrotron self-Compton, and external Compton emissions from a broken power-law electron distribution, ensuring equipartition condition. A comparison between the best-fitting physical parameters shows that the variation in different flux states is mostly related to an increase in the bulk Lorentz factor and spectral hardening of the particle distribution.
Towards a Transportable Causal Network Model Based on Observational Healthcare Data

Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods.
Degradation Estimation Recurrent Neural Network with Local and Non-Local Priors for Compressive Spectral Imaging

In the Coded Aperture Snapshot Spectral Imaging (CASSI) system, deep unfolding networks (DUNs) have demonstrated excellent performance in recovering 3D hyperspectral images (HSIs) from 2D measurements. However, some noticeable gaps exist between the imaging model used in DUNs and the real CASSI imaging process, such as the sensing error as well as photon and dark current noise, compromising the accuracy of solving the data subproblem and the prior subproblem in DUNs. To address this issue, we propose a Degradation Estimation Network (DEN) to correct the imaging model used in DUNs by simultaneously estimating the sensing error and the noise level, thereby improving the performance of DUNs. Additionally, we propose an efficient Local and Non-local Transformer (LNLT) to solve the prior subproblem, which not only effectively models local and non-local similarities but also reduces the computational cost of the window-based global Multi-head Self-attention (MSA). Furthermore, we transform the DUN into a Recurrent Neural Network (RNN) by sharing parameters of DNNs across stages, which not only allows DNN to be trained more adequately but also significantly reduces the number of parameters. The proposed DERNN-LNLT achieves state-of-the-art (SOTA) performance with fewer parameters on both simulation and real datasets.
End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions

End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) \textbf{\textit{First survey}}: to our knowledge, we take the first step to present a thorough survey of this research field; (2) \textbf{\textit{New taxonomy}}: we first introduce a unified perspective for EToD, including (i) \textit{Modularly EToD} and (ii) \textit{Fully EToD}; (3) \textbf{\textit{New Frontiers}}: we discuss some potential frontier areas as well as the corresponding challenges, hoping to spur breakthrough research in EToD field; (4) \textbf{\textit{Abundant resources}}: we build a public website\footnote{We collect the related papers, baseline projects, and leaderboards for the community at \url{https://etods.net/}. }, where EToD researchers could directly access the recent progress. We hope this work can serve as a thorough reference for the EToD research community.
Frustrated magnets in the limit of infinite dimensions: dynamics and disorder-free glass transition

We study the statistical mechanics and the equilibrium dynamics of a system of classical Heisenberg spins with frustrated interactions on a $d$-dimensional simple hypercubic lattice, in the limit of infinite dimensionality $d \to \infty$. In the analysis we consider a class of models in which the matrix of exchange constants is a linear combination of powers of the adjacency matrix. This choice leads to a special property: the Fourier transform of the exchange coupling $J(\mathbf{k})$ presents a $(d-1)$-dimensional surface of degenerate maxima in momentum space. Using the cavity method, we find that the statistical mechanics of the system presents for $d \to \infty$ a paramagnetic solution which remains locally stable at all temperatures down to $T = 0$. To investigate whether the system undergoes a glass transition we study its dynamical properties assuming a purely dissipative Langevin equation, and mapping the system to an effective single-spin problem subject to a colored Gaussian noise. The conditions under which a glass transition occurs are discussed including the possibility of a local anisotropy and a simple type of anisotropic exchange. The general results are applied explicitly to a simple model, equivalent to the isotropic Heisenberg antiferromagnet on the $d$-dimensional fcc lattice with first and second nearest-neighbour interactions tuned to the point $J_{1} = 2J_{2}$. In this model, we find a dynamical glass transition at a temperature $T_{\rm g}$ separating a high-temperature liquid phase and a low temperature vitrified phase. At the dynamical transition, the Edwards-Anderson order parameter presents a jump demonstrating a first-order phase transition.
EMF-Aware Power Control for Massive MIMO: Cell-Free versus Cellular Networks

The impressive growth of wireless data networks has recently led to increased attention to the issue of electromagnetic pollution. Specific absorption rates and incident power densities have become popular indicators for measuring electromagnetic field (EMF) exposure. This paper tackles the problem of power control in user-centric cell-free massive multiple-input-multiple-output (CF-mMIMO) systems under EMF constraints. Specifically, the power allocation maximizing the minimum data rate across users is derived for both the uplink and the downlink under EMF constraints. The developed solution is also applied to a cellular mMIMO system and compared to other benchmark strategies. Simulation results prove that EMF safety restrictions can be easily met without jeopardizing the minimum data rate, that the CF-mMIMO outperforms the multi-cell massive MIMO deployment, and that the proposed power control strategy greatly improves the system fairness.
On semi-supervised estimation using exponential tilt mixture models

Consider a semi-supervised setting with a labeled dataset of binary responses and predictors and an unlabeled dataset with only the predictors. Logistic regression is equivalent to an exponential tilt model in the labeled population. For semi-supervised estimation, we develop further analysis and understanding of a statistical approach using exponential tilt mixture (ETM) models and maximum nonparametric likelihood estimation, while allowing that the class proportions may differ between the unlabeled and labeled data. We derive asymptotic properties of ETM-based estimation and demonstrate improved efficiency over supervised logistic regression in a random sampling setup and an outcome-stratified sampling setup previously used. Moreover, we reconcile such efficiency improvement with the existing semiparametric efficiency theory when the class proportions in the unlabeled and labeled data are restricted to be the same. We also provide a simulation study to numerically illustrate our theoretical findings.
Proving Calculational Proofs Correct

Teaching proofs is a crucial component of any undergraduate-level program that covers formal reasoning. We have developed a calculational reasoning format and refined it over several years of teaching a freshman-level course,"Logic and Computation", to thousands of undergraduate students. In our companion paper, we presented our calculational proof format, gave an overview of the calculational proof checker (CPC) tool that we developed to help users write and validate proofs, described some of the technical and implementation details of CPC and provided several publicly available proofs written using our format. In this paper, we dive deeper into the implementation details of CPC, highlighting how proof validation works, which helps us argue that our proof checking process is sound.
Low-Frequency Load Identification using CNN-BiLSTM Attention Mechanism

Non-intrusive Load Monitoring (NILM) is an established technique for effective and cost-efficient electricity consumption management. The method is used to estimate appliance-level power consumption from aggregated power measurements. This paper presents a hybrid learning approach, consisting of a convolutional neural network (CNN) and a bidirectional long short-term memory (BILSTM), featuring an integrated attention mechanism, all within the context of disaggregating low-frequency power data. While prior research has been mainly focused on high-frequency data disaggregation, our study takes a distinct direction by concentrating on low-frequency data. The proposed hybrid CNN-BILSTM model is adept at extracting both temporal (time-related) and spatial (location-related) features, allowing it to precisely identify energy consumption patterns at the appliance level. This accuracy is further enhanced by the attention mechanism, which aids the model in pinpointing crucial parts of the data for more precise event detection and load disaggregation. We conduct simulations using the existing low-frequency REDD dataset to assess our model performance. The results demonstrate that our proposed approach outperforms existing methods in terms of accuracy and computation time.
Multi-objective Non-intrusive Hearing-aid Speech Assessment Model

Without the need for a clean reference, non-intrusive speech assessment methods have caught great attention for objective evaluations. While deep learning models have been used to develop non-intrusive speech assessment methods with promising results, there is limited research on hearing-impaired subjects. This study proposes a multi-objective non-intrusive hearing-aid speech assessment model, called HASA-Net Large, which predicts speech quality and intelligibility scores based on input speech signals and specified hearing-loss patterns. Our experiments showed the utilization of pre-trained SSL models leads to a significant boost in speech quality and intelligibility predictions compared to using spectrograms as input. Additionally, we examined three distinct fine-tuning approaches that resulted in further performance improvements. Furthermore, we demonstrated that incorporating SSL models resulted in greater transferability to OOD dataset. Finally, this study introduces HASA-Net Large, which is a non-invasive approach for evaluating speech quality and intelligibility. HASA-Net Large utilizes raw waveforms and hearing-loss patterns to accurately predict speech quality and intelligibility levels for individuals with normal and impaired hearing and demonstrates superior prediction performance and transferability.
Social Meme-ing: Measuring Linguistic Variation in Memes

Much work in the space of NLP has used computational methods to explore sociolinguistic variation in text. In this paper, we argue that memes, as multimodal forms of language comprised of visual templates and text, also exhibit meaningful social variation. We construct a computational pipeline to cluster individual instances of memes into templates and semantic variables, taking advantage of their multimodal structure in doing so. We apply this method to a large collection of meme images from Reddit and make available the resulting \textsc{SemanticMemes} dataset of 3.8M images clustered by their semantic function. We use these clusters to analyze linguistic variation in memes, discovering not only that socially meaningful variation in meme usage exists between subreddits, but that patterns of meme innovation and acculturation within these communities align with previous findings on written language.
Ab Initio Construction of Poincar\'e and AdS Particle

We study the construction of a manifestly covariant worldline action from a coadjoint orbit. A coadjoint orbit is a submanifold in the dual vector space of the Lie algebra and generated by coadjoint actions. Since a coadjoint orbit is a symplectic space, we derive the worldline particle action from the symplectic two-form. One subtlety in formulating worldline particle actions from coadjoint orbits is choosing the coordinate system that sufficiently illustrates physical properties of the particles. We introduce Hamiltonian constraints by the defining conditions of the isometry. This allows us to write a manifestly covariant worldline action. We demonstrate our method to the both massive and massless particles in Minkowski and AdS spacetime.
Cultural context shapes the carbon footprints of recipes

Food systems are responsible for a third of global anthropogenic greenhouse gas emissions central to global warming and climate change. Increasing awareness of the environmental impact of food-centric emissions has led to the carbon footprint quantification of food products. However, food consumption is dictated by traditional dishes, the cultural capsules that encode traditional protocols for culinary preparations. Carbon footprint estimation of recipes will provide actionable insights into the environmental sustainability of culturally influenced patterns in recipe compositions. By integrating the carbon footprint data of food products with a gold-standard repository of recipe compositions, we show that the ingredient constitution dictates the carbon load of recipes. Beyond the prevalent focus on individual food products, our analysis quantifies the carbon footprint of recipes within the cultural contexts that shape culinary protocols. While emphasizing the widely understood harms of animal-sourced ingredients, this article presents a nuanced perspective on the environmental impact of culturally influenced dietary practices. Along with the grasp of taste and nutrition correlates, such an understanding can help design palatable and environmentally sustainable recipes. Systematic compilation of fine-grained carbon footprint data is the way forward to address the challenge of sustainably feeding an anticipated population of 10 billion.
Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning

The development of 6G/B5G wireless networks, which have requirements that go beyond current 5G networks, is gaining interest from academia and industry. However, to increase 6G/B5G network quality, conventional cellular networks that rely on terrestrial base stations are constrained geographically and economically. Meanwhile, NOMA allows multiple users to share the same resources, which improves the spectral efficiency of the system and has the advantage of supporting a larger number of users. Additionally, by intelligently manipulating the phase and amplitude of both the reflected and transmitted signals, STAR-RISs can achieve improved coverage, increased spectral efficiency, and enhanced communication reliability. However, STAR-RISs must simultaneously optimize the amplitude and phase shift corresponding to reflection and transmission, which makes the existing terrestrial networks more complicated and is considered a major challenging issue. Motivated by the above, we study the joint user pairing for NOMA and beamforming design of Multi-STAR-RISs in an indoor environment. Then, we formulate the optimization problem with the objective of maximizing the total throughput of MUs by jointly optimizing the decoding order, user pairing, active beamforming, and passive beamforming. However, the formulated problem is a MINLP. To address this challenge, we first introduce the decoding order for NOMA networks. Next, we decompose the original problem into two subproblems, namely: 1) MU pairing and 2) Beamforming optimization under the optimal decoding order. For the first subproblem, we employ correlation-based K-means clustering to solve the user pairing problem. Then, to jointly deal with beamforming vector optimizations, we propose MAPPO, which can make quick decisions in the given environment owing to its low complexity.
Reinforcement Learning with Model Predictive Control for Highway Ramp Metering

In the backdrop of an increasingly pressing need for effective urban and highway transportation systems, this work explores the synergy between model-based and learning-based strategies to enhance traffic flow management by use of an innovative approach to the problem of highway ramp metering control that embeds Reinforcement Learning techniques within the Model Predictive Control framework. The control problem is formulated as an RL task by crafting a suitable stage cost function that is representative of the traffic conditions, variability in the control action, and violations of a safety-critical constraint on the maximum number of vehicles in queue. An MPC-based RL approach, which merges the advantages of the two paradigms in order to overcome the shortcomings of each framework, is proposed to learn to efficiently control an on-ramp and to satisfy its constraints despite uncertainties in the system model and variable demands. Finally, simulations are performed on a benchmark from the literature consisting of a small-scale highway network. Results show that, starting from an MPC controller that has an imprecise model and is poorly tuned, the proposed methodology is able to effectively learn to improve the control policy such that congestion in the network is reduced and constraints are satisfied, yielding an improved performance compared to the initial controller.
All-optical measurement of magnetic fields for quantum gas experiments

We present an all-optical method to measure and compensate for residual magnetic fields present in a cloud of ultracold atoms trapped in an optical dipole trap. Our approach leverages the increased loss from the trapped atomic sample through electromagnetically induced absorption. Modulating the excitation laser provides coherent sidebands, resulting in {\Lambda}-type pump-probe scheme. Scanning an additional magnetic offset field leads to pairs of sub-natural linewidth resonances, whose positions encode the magnetic field in all three spatial directions. Our measurement scheme is readily implemented in a typical quantum gas experiments and has no particular hardware requirements.
Explaining Explanation: An Empirical Study on Explanation in Code Reviews

Code review is an important process for quality assurance in software development. For an effective code review, the reviewers must explain their feedback to enable the authors of the code change to act on them. However, the explanation needs may differ among developers, who may require different types of explanations. It is therefore crucial to understand what kind of explanations reviewers usually use in code reviews. To the best of our knowledge, no study published to date has analyzed the types of explanations used in code review. In this study, we present the first analysis of explanations in useful code reviews. We extracted a set of code reviews based on their usefulness and labeled them based on whether they contained an explanation, a solution, or both a proposed solution and an explanation thereof. Based on our analysis, we found that a significant portion of the code review comments (46%) only include solutions without providing an explanation. We further investigated the remaining 54% of code review comments containing an explanation and conducted an open card sorting to categorize the reviewers' explanations. We distilled seven distinct categories of explanations based on the expression forms developers used. Then, we utilize large language models, specifically ChatGPT, to assist developers in getting a code review explanation that suits their preferences. Specifically, we created prompts to transform a code review explanation into a specific type of explanation. Our evaluation results show that ChatGPT correctly generated the specified type of explanation in 88/90 cases and that 89/90 of the cases have the correct explanation. Overall, our study provides insights into the types of explanations that developers use in code review and showcases how ChatGPT can be leveraged during the code review process to generate a specific type of explanation.