Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Properties of Two Complementary Samples of Intermediate Seyfert Galaxies

We present first results of the analysis of optical spectra of two complementary samples of Seyfert galaxies (Seyferts). The first sample was extracted from a selection of the 4th Fermi Gamma-ray Large Area Telescope (4FGL) catalog and consists of 11 γ-ray-emitting jetted Seyfert galaxies. The second one was extracted from the Swift-BAT AGN Spectroscopic Survey (BASS) and is composed of 38 hard-X-ray-selected active galactic nuclei (AGN). These two samples are complementary, with the former being expected to have smaller viewing angles, while the latter may include objects with larger viewing angles. We measured emission-line ratios to investigate whether the behavior of these Seyferts can be explained in terms of obscuration, as suggested by the Unified Model (UM) of AGN, or if there are intrinsic differences due to the presence of jets or outflows, or due to evolution. We found no indications of intrinsic differences. The UM remains the most plausible interpretation for these classes of objects, even if some results can be challenging for this model.
Reproducible Parameter Inference Using Bagged Posteriors

Under model misspecification, it is known that Bayesian posteriors often do not properly quantify uncertainty about true or pseudo-true parameters. Even more fundamentally, misspecification leads to a lack of reproducibility in the sense that the same model will yield contradictory posteriors on independent data sets from the true distribution. To define a criterion for reproducible uncertainty quantification under misspecification, we consider the probability that two confidence sets constructed from independent data sets have nonempty overlap, and we establish a lower bound on this overlap probability that holds for any valid confidence sets. We prove that credible sets from the standard posterior can strongly violate this bound, particularly in high-dimensional settings (i.e., with dimension increasing with sample size), indicating that it is not internally coherent under misspecification. To improve reproducibility in an easy-to-use and widely applicable way, we propose to apply bagging to the Bayesian posterior ("BayesBag"'); that is, to use the average of posterior distributions conditioned on bootstrapped datasets. We motivate BayesBag from first principles based on Jeffrey conditionalization and show that the bagged posterior typically satisfies the overlap lower bound. Further, we prove a Bernstein--Von Mises theorem for the bagged posterior, establishing its asymptotic normal distribution. We demonstrate the benefits of BayesBag via simulation experiments and an application to crime rate prediction.
ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language

The use of natural language interfaces (NLIs) to create charts is becoming increasingly popular due to the intuitiveness of natural language interactions. One key challenge in this approach is to accurately capture user intents and transform them to proper chart specifications. This obstructs the wide use of NLI in chart generation, as users' natural language inputs are generally abstract (i.e., ambiguous or under-specified), without a clear specification of visual encodings. Recently, pre-trained large language models (LLMs) have exhibited superior performance in understanding and generating natural language, demonstrating great potential for downstream tasks. Inspired by this major trend, we propose ChartGPT, generating charts from abstract natural language inputs. However, LLMs are struggling to address complex logic problems. To enable the model to accurately specify the complex parameters and perform operations in chart generation, we decompose the generation process into a step-by-step reasoning pipeline, so that the model only needs to reason a single and specific sub-task during each run. Moreover, LLMs are pre-trained on general datasets, which might be biased for the task of chart generation. To provide adequate visualization knowledge, we create a dataset consisting of abstract utterances and charts and improve model performance through fine-tuning. We further design an interactive interface for ChartGPT that allows users to check and modify the intermediate outputs of each step. The effectiveness of the proposed system is evaluated through quantitative evaluations and a user study.
Obtaining Explainable Classification Models using Distributionally Robust Optimization

Model explainability is crucial for human users to be able to interpret how a proposed classifier assigns labels to data based on its feature values. We study generalized linear models constructed using sets of feature value rules, which can capture nonlinear dependencies and interactions. An inherent trade-off exists between rule set sparsity and its prediction accuracy. It is computationally expensive to find the right choice of sparsity -- e.g., via cross-validation -- with existing methods. We propose a new formulation to learn an ensemble of rule sets that simultaneously addresses these competing factors. Good generalization is ensured while keeping computational costs low by utilizing distributionally robust optimization. The formulation utilizes column generation to efficiently search the space of rule sets and constructs a sparse ensemble of rule sets, in contrast with techniques like random forests or boosting and their variants. We present theoretical results that motivate and justify the use of our distributionally robust formulation. Extensive numerical experiments establish that our method improves over competing methods -- on a large set of publicly available binary classification problem instances -- with respect to one or more of the following metrics: generalization quality, computational cost, and explainability.
Enhancing Computer Science Education with Pair Programming and Problem Solving Studios

This study examines the adaptation of the problem-solving studio to computer science education by combining it with pair programming. Pair programming is a software engineering practice in industry, but has seen mixed results in the classroom. Recent research suggests that pair programming has promise and potential to be an effective pedagogical tool, however what constitutes good instructional design and implementation for pair programming in the classroom is not clear. We developed a framework for instructional design for pair programming by adapting the problem-solving studio (PSS), a pedagogy originally from biomedical engineering. PSS involves teams of students solving open-ended problems with real-time feedback given by the instructor. Notably, PSS uses problems of adjustable difficulty to keep students of all levels engaged and functioning within the zone of proximal development. The course structure has three stages, first starting with demonstration, followed by a PSS session, then finishing with a debrief. We studied the combination of PSS and pair programming in a CS1 class over three years. Surveys of the students report a high level of engagement, learning, and motivation.
Spatial prediction of diameter distributions for the alpine protection forests in Ebensee, Austria, using ALS/PLS and spatial distributional regression models

A spatial distributional regression model is presented to predict the forest structural diversity in terms of the distributions of the stem diameter at breast height (DBH) in the protection forests in Ebensee, Austria. In total 36,338 sample trees were measured via a handheld mobile personal laser scanning system (PLS) on 273 sample plots each having a 20 m radius. Recent airborne laser scanning (ALS) data was used to derive regression covariates from the normalized digital vegetation height model (DVHM) and the digital terrain model (DTM). Candidate models were constructed that differed in their linear predictors of the two gamma distribution parameters. Non-linear smoothing splines outperformed linear parametric slope coefficients, and the best implementation of spatial structured effects was achieved by a Gaussian process smooth. Model fitting and posterior parameter inference was achieved by using full Bayesian methodology and MCMC sampling algorithms implemented in the R-package BAMLSS. Spatial predictions of stem count proportions per DBH classes revealed that regeneration of smaller trees was lacking in certain areas of the protection forest landscape.
Efficient Algorithms for Monte Carlo Particle Transport on AI Accelerator Hardware

The recent trend toward deep learning has led to the development of a variety of highly innovative AI accelerator architectures. One such architecture, the Cerebras Wafer-Scale Engine 2 (WSE-2), features 40 GB of on-chip SRAM, making it a potentially attractive platform for latency- or bandwidth-bound HPC simulation workloads. In this study, we examine the feasibility of performing continuous energy Monte Carlo (MC) particle transport on the WSE-2 by porting a key kernel from the MC transport algorithm to Cerebras's CSL programming model. New algorithms for minimizing communication costs and for handling load balancing are developed and tested. The WSE-2 is found to run 130 times faster than a highly optimized CUDA version of the kernel run on an NVIDIA A100 GPU -- significantly outpacing the expected performance increase given the difference in transistor counts between the architectures.
A symmetry-based theory for the mean velocity profiles of turbulent boundary layers subjected to pressure gradient and historical turbulence effects

The pressure gradient has a significant influence on the mean-flow properties of turbulent boundary layers. Conventional analytical studies on PG turbulent boundary layers are conducted separately for the viscous sub-layer and overlap layer only, with the remaining large portion of the boundary layers less described theoretically. Here a symmetry approach is proposed for modeling whole mean velocity profiles of turbulent boundary layers with both PG and historical turbulence effects. First, a modified defect law is constructed for the total stress profile to capture the Reynolds stress overshoot owing to the PG and historical turbulence effects. Second, the multi-layered power-law formulation of the stress length function in the structural ensemble dynamics theory of the canonical zero-PG turbulent boundary layer (J. Fluid Mechanics, 2017, Vol. 827, pp. 322-35) is extended to describe the PG turbulent boundary layer. Comparing with that of the zero-PG turbulent boundary layers, the stress length of the PG turbulent boundary layer possesses a variable (in magnitude and extension) plateau in the defect layer, which are characterized by three parameters: the buffer layer thickness, the (nominal) K'arm'an constant, and the defect-law exponent. In the case of intense turbulence from upstream flow, a newly-identified"bulk turbulent layer"replaces the conventional buffer layer and overlap layer. With the above formulations, the entire mean velocity profile is predicted analytically, and validated to accurately describe the published direct numerical simulation data on a two-dimensional separation bubble. The study provides a novel method to parameterize PG turbulent boundary layers with high accuracy and sound physics, and paves a way for quantifying complicated boundary-layer flows.
Adversarial Examples in the Physical World: A Survey

Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial examples. Besides the attacks in the digital world, the practical implications of adversarial examples in the physical world present significant challenges and safety concerns. However, current research on physical adversarial examples (PAEs) lacks a comprehensive understanding of their unique characteristics, leading to limited significance and understanding. In this paper, we address this gap by thoroughly examining the characteristics of PAEs within a practical workflow encompassing training, manufacturing, and re-sampling processes. By analyzing the links between physical adversarial attacks, we identify manufacturing and re-sampling as the primary sources of distinct attributes and particularities in PAEs. Leveraging this knowledge, we develop a comprehensive analysis and classification framework for PAEs based on their specific characteristics, covering over 100 studies on physical-world adversarial examples. Furthermore, we investigate defense strategies against PAEs and identify open challenges and opportunities for future research. We aim to provide a fresh, thorough, and systematic understanding of PAEs, thereby promoting the development of robust adversarial learning and its application in open-world scenarios.
Antisymmetric tensor fields: actions, symmetries and first order Duffin-Kemmer-Petiau formulations

Analyzing the representations of the Lorentz group, we give a systematic count and construction of all the possible Lagrangians describing an antisymmetric rank two tensor field. The count yields two scalars: the gauge invariant Kalb-Ramond model, equivalent to the sigma model and familiar from super gravity and string theory, and the conformally invariant Avdeev-Chizhov model, which describes self-dual tensors. The count also includes a third invariant, a pseudoscalar, which is an antisymmetrized form of the Avdeev-Chizhov Lagrangian, first noticed in the $SU(2/1)$ superalgebraic model of the weak interactions. This model is also conformally invariant, and naturally implements the Landau $CP$ symmetry. Then, by extending the Duffin-Kemmer-Petiau 10 component formalism, we recover the model Lagrangians as first order systems. To complete the analysis we classify all local Lorentz invariant potentials (mass terms and quartic couplings) for charged antisymmetric tensor fields coupled to a Yang-Mills field.
Assist Is Just as Important as the Goal: Image Resurfacing to Aid Model's Robust Prediction

Adversarial patches threaten visual AI models in the real world. The number of patches in a patch attack is variable and determines the attack's potency in a specific environment. Most existing defenses assume a single patch in the scene, and the multiple patch scenarios are shown to overcome them. This paper presents a model-agnostic defense against patch attacks based on total variation for image resurfacing (TVR). The TVR is an image-cleansing method that processes images to remove probable adversarial regions. TVR can be utilized solely or augmented with a defended model, providing multi-level security for robust prediction. TVR nullifies the influence of patches in a single image scan with no prior assumption on the number of patches in the scene. We validate TVR on the ImageNet-Patch benchmark dataset and with real-world physical objects, demonstrating its ability to mitigate patch attack.
KG-FRUS: a Novel Graph-based Dataset of 127 Years of US Diplomatic Relations

In the current paper, we present the KG-FRUS dataset, comprised of more than 300,000 US government diplomatic documents encoded in a Knowledge Graph (KG). We leverage the data of the Foreign Relations of the United States (FRUS) (available as XML files) to extract information about the documents and the individuals and countries mentioned within them. We use the extracted entities, and associated metadata, to create a graph-based dataset. Further, we supplement the created KG with additional entities and relations from Wikidata. The relations in the KG capture the synergies and dynamics required to study and understand the complex fields of diplomacy, foreign relations, and politics. This goes well beyond a simple collection of documents which neglects the relations between entities in the text. We showcase a range of possibilities of the current dataset by illustrating different approaches to probe the KG. In the paper, we exemplify how to use a query language to answer simple research questions and how to use graph algorithms such as Node2Vec and PageRank, that benefit from the complete graph structure. More importantly, the chosen structure provides total flexibility for continuously expanding and enriching the graph. Our solution is general, so the proposed pipeline for building the KG can encode other original corpora of time-dependent and complex phenomena. Overall, we present a mechanism to create KG databases providing a more versatile representation of time-dependent related text data and a particular application to the all-important FRUS database.
Revealing Local Field Effects at the Electrical Double Layer with Efficient Open Boundary Simulations under Potential Control

A major challenge in modelling interfacial processes in electrochemical (EC) devices is performing simulations at constant potential. This requires an open-boundary description of the electrons, so that they can enter and leave the computational cell. To enable realistic modelling of EC processes under potential control we have interfaced Density Functional Theory with the Hairy Probe method in the weak coupling limit (DOI: 10.1103/PhysRevB.97.045116). Our implementation was systematically tested using simple parallel-plate capacitor models with pristine surfaces and a single layer of adsorbed water molecules. Remarkably, our code's efficiency is comparable with a standard DFT calculation. We reveal that local field effects at the electrical double layer induced by the change of applied potential can significantly affect the energies of chemical steps in heterogeneous electrocatalysis. Our results demonstrate the importance of an explicit modelling of the applied potential in a simulation and provide an efficient tool to control this critical parameter.
Fermi-LAT Detection of the Supernova Remnant G312.4-0.4 in the Vicinity of 4FGL J1409.1-6121e

Gamma-ray emission provides constraints on the nonthermal radiation processes at play in astrophysical particle accelerators. This allows both the nature of accelerated particles and the maximum energy that they can reach to be determined. Notably, it remains an open question to what extent supernova remnants contribute to the sea of Galactic cosmic rays. In the Galactic plane, at around 312° of Galactic longitude, Fermi-LAT observations show an extended source (4FGL J1409.1−6121e) around five powerful pulsars. This source is described by one large disk of 0. °7 radius with a high significance of 45σ in the 4FGL-DR3 catalog. Using 14 yr of Fermi-LAT observations, we revisited this region with a detailed spectro-morphological analysis in order to disentangle its underlying structure. Three sources have been distinguished, including the supernova remnant G312.4−0.4 whose gamma-ray emission correlates well with the shell observed at radio energies. The hard spectrum detected by the LAT, extending up to 100 GeV without any sign of a cutoff, is well reproduced by a purely hadronic model.
Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers

Recent studies have demonstrated the great potential of Large Language Models (LLMs) serving as zero-shot relevance rankers. The typical approach involves making comparisons between pairs or lists of documents. Although effective, these listwise and pairwise methods are not efficient and also heavily rely on intricate prompt engineering. To tackle this problem, we introduce a novel instruction distillation method. The key idea is to distill the pairwise ranking ability of open-sourced LLMs to a simpler but more efficient pointwise ranking. Specifically, given the same LLM, we first rank documents using the effective pairwise approach with complex instructions, and then distill the teacher predictions to the pointwise approach with simpler instructions. Evaluation results on the BEIR, TREC, and ReDial datasets demonstrate that instruction distillation can improve efficiency by 10 to 100x and also enhance the ranking performance of LLMs. Furthermore, our approach surpasses the performance of existing supervised methods like monoT5 and is on par with the state-of-the-art zero-shot methods. The code to reproduce our results is available at www.github. com/sunnweiwei/RankGPT.
The two upper critical dimensions of the Ising and Potts models

We derive the exact actions of the $Q$-state Potts model valid on any graph, first for the spin degrees of freedom, and second for the Fortuin-Kasteleyn clusters. In both cases the field is a traceless $Q$-component scalar field $\Phi^\alpha$. For the Ising model ($Q=2$), the field theory for the spins has upper critical dimension $d_{\rm c}^{\rm spin}=4$, whereas for the clusters it has $d_{\rm c}^{\rm cluster}=6$. As a consequence, the probability for three points to be in the same cluster is not given by mean-field theory theory for $d$ within $4<d<6$. We estimate the associated universal structure constant as $C=\sqrt{6-d}+ {\cal O}(6-d)^{3/2}$. This shows that some observables in the Ising model have an upper critical dimension of 4, while others have an upper critical dimension of $6$. Combining perturbative results from the $\epsilon=6-d$ expansion with a non-perturbative treatment close to dimension $d=4$ allows us to locate the shape of the critical domain of the Potts model in the whole $(Q,d)$ plane.
Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer

This paper introduces a groundbreaking classification model called the Controllable Ensemble Transformer and CNN (CETC) for the analysis of medical images. The CETC model combines the powerful capabilities of convolutional neural networks (CNNs) and transformers to effectively capture both high and low frequency features present in medical images. The model architecture comprises three main components: a transformer classification block (TCB), a transposed-convolutional decoder block (TDB), and a convolutional encoder block (CEB). The CEB is responsible for capturing multi-local features at different scales and draws upon components from VGGNet, ResNet, and MobileNet as backbones. By leveraging this combination, the CEB is able to effectively detect and encode local features. The TDB, on the other hand, consists of sub-decoders that decode and sum the captured features using ensemble coefficients. This enables the model to efficiently integrate the information from multiple scales. Finally, the TCB utilizes the SwT backbone and a specially designed prediction head to capture global features, ensuring a comprehensive understanding of the entire image. The paper provides detailed information on the experimental setup and implementation, including the use of transfer learning, data preprocessing techniques, and training settings. The CETC model is trained and evaluated using two publicly available COVID-19 datasets. Moreover, the model is able to outperform existing state-of-the-art models across a variety of evaluation metrics, which is encouraging. Experimental results clearly demonstrate that the CETC model performs better than other deep learning models, emphasizing its potential for accurate and efficient image analysis in the field of medicine.
Amplification, Mitigation and Energy Storage via Constrained Thermalization

Amplification (mitigation) is the increase (decrease) in the change of thermodynamic quantities when an initial thermal state is thermalized to a different temperature in the presence of constraints, studied thus far only for permutationally invariant baths. In this manuscript, we generalize amplification and mitigation to accommodate generic strong symmetries of open quantum systems and connect the phenomenon to Landauer's erasure. We exemplify our general theory with a new bath-induced battery charging protocol that overcomes the passivity of KMS-preserving transitions.