Miracle-dz/UniFAR-FTUs huggingface dataset:
The Spatial Distribution of Type Ia Supernovae within Host Galaxies

We study how type Ia supernovae (SNe Ia) are spatially distributed within their host galaxies, using data taken from the Sloan Digital Sky Survey (SDSS). This paper specifically tests the hypothesis that the SNe Ia rate traces the r-band light of the morphological component to which supernovae belong. A sample of supernovae is taken from the SDSS SN Survey, and host galaxies are identified. Each host galaxy is decomposed into a bulge and disk, and the distribution of supernovae is compared to the distribution of disk and bulge light. Our methodology is relatively unaffected by seeing. We find that, in galaxies dominated by disk light, SNe Ia trace light closely. The situation is less clear for bulges and ellipticals, because of resolution effects, but the available evidence is also consistent with the hypothesis that bulge/elliptical SNe Ia follow light.
Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras

Overlapping cameras offer exciting opportunities to view a scene from different angles, allowing for more advanced, comprehensive and robust analysis. However, existing visual analytics systems for multi-camera streams are mostly limited to (i) per-camera processing and aggregation and (ii) workload-agnostic centralized processing architectures. In this paper, we present Argus, a distributed video analytics system with cross-camera collaboration on smart cameras. We identify multi-camera, multi-target tracking as the primary task of multi-camera video analytics and develop a novel technique that avoids redundant, processing-heavy identification tasks by leveraging object-wise spatio-temporal association in the overlapping fields of view across multiple cameras. We further develop a set of techniques to perform these operations across distributed cameras without cloud support at low latency by (i) dynamically ordering the camera and object inspection sequence and (ii) flexibly distributing the workload across smart cameras, taking into account network transmission and heterogeneous computational capacities. Evaluation of three real-world overlapping camera datasets with two Nvidia Jetson devices shows that Argus reduces the number of object identifications and end-to-end latency by up to 7.13x and 2.19x (4.86x and 1.60x compared to the state-of-the-art), while achieving comparable tracking quality.
Testing Alpha in High Dimensional Linear Factor Pricing Models with Dependent Observations

In this study, we introduce three distinct testing methods for testing alpha in high dimensional linear factor pricing model that deals with dependent data. The first method is a sum-type test procedure, which exhibits high performance when dealing with dense alternatives. The second method is a max-type test procedure, which is particularly effective for sparse alternatives. For a broader range of alternatives, we suggest a Cauchy combination test procedure. This is predicated on the asymptotic independence of the sum-type and max-type test statistics. Both simulation studies and practical data application demonstrate the effectiveness of our proposed methods when handling dependent observations.
Computationally-Efficient Linear Periodically Time-Variant Digital PLL Modeling Using Conversion Matrices and Uncorrelated Upsampling

This paper introduces a conversion matrix method for linear periodically time-variant (LPTV) digital phase-locked loop (DPLL) phase noise modeling that offers precise and computationally efficient results to enable rapid design iteration and optimization. Unlike many previous studies, which either assume linear time-invariance (LTI) and therefore overlook phase noise aliasing effects, or solve LPTV systems with noise folding and multiple sampling rate conversions that heightens modeling and computational complexity, the proposed conversion matrix method allows the designer to represent the LPTV systems using intuitive LTI-like transfer functions with excellent accuracy. Additionally, computational efficiency is improved through the uncorrelated upsampling method, which eliminates the need to consider beat frequency of noise sources with different sampling rates. The proposed algorithm is applied to modeling a DPLL with time-varying proportional loop gain, and the modeling accuracy is validated with Simulink transient simulations.
Diffusion of light in structurally anisotropic media with uniaxial symmetry

Anisotropic light transport is extremely common among scattering materials, yet a comprehensive picture of how macroscopic diffusion is determined by microscopic tensor scattering coefficients is not fully established yet. In this work, we present a theoretical and experimental study of diffusion in structurally anisotropic media with uniaxially symmetric scattering coefficients. Exact analytical relations are derived in the case of index-matched turbid media, unveiling the general relation between microscopic scattering coefficients and the resulting macroscopic diffusion tensor along different directions. Excellent agreement is found against anisotropic Monte Carlo simulations up to high degrees of anisotropy, in contrast with previously proposed approaches. The obtained solutions are used to analyze experimental measurements of anisotropic light transport in polystyrene foam samples under different degrees of uniaxial compression, providing a practical example of their applicability.
On Sparse Covers of Minor Free Graphs, Low Dimensional Metric Embeddings, and other applications

Given a metric space $(X,d_X)$, a $(\beta,s,\Delta)$-sparse cover is a collection of clusters $\mathcal{C}\subseteq P(X)$ with diameter at most $\Delta$, such that for every point $x\in X$, the ball $B_X(x,\frac\Delta\beta)$ is fully contained in some cluster $C\in \mathcal{C}$, and $x$ belongs to at most $s$ clusters in $\mathcal{C}$. Our main contribution is to show that the shortest path metric of every $K_r$-minor free graphs admits $(O(r),O(r^2),\Delta)$-sparse cover, and for every $\epsilon>0$, $(4+\epsilon,O(\frac1\epsilon)^r,\Delta)$-sparse cover (for arbitrary $\Delta>0$). We then use this sparse cover to show that every $K_r$-minor free graph embeds into $\ell_\infty^{\tilde{O}(\frac1\epsilon)^{r+1}\cdot\log n}$ with distortion $3+\eps$ (resp. into $\ell_\infty^{\tilde{O}(r^2)\cdot\log n}$ with distortion $O(r)$). Further, we provide applications of these sparse covers into padded decompositions, sparse partitions, universal TSP / Steiner tree, oblivious buy at bulk, name independent routing, and path reporting distance oracles.
Modular Adaptation of Multilingual Encoders to Written Swiss German Dialect

Creating neural text encoders for written Swiss German is challenging due to a dearth of training data combined with dialectal variation. In this paper, we build on several existing multilingual encoders and adapt them to Swiss German using continued pre-training. Evaluation on three diverse downstream tasks shows that simply adding a Swiss German adapter to a modular encoder achieves 97.5% of fully monolithic adaptation performance. We further find that for the task of retrieving Swiss German sentences given Standard German queries, adapting a character-level model is more effective than the other adaptation strategies. We release our code and the models trained for our experiments.
Statistical Characterization of RIS-assisted UAV Communications in Terrestrial and Non-Terrestrial Networks Under Channel Aging

This paper studies the statistical characterization of ground-to-air (G2A) and reconfigurable intelligent surface (RIS)-assisted air-to-ground (A2G) communications with unmanned aerial vehicles (UAVs) in terrestrial and non-terrestrial networks under the impact of channel aging. We first model the G2A and A2G signal-to-noise ratios (SNRs) as non-central complex Gaussian quadratic random variables (RVs) and derive their exact probability density functions, offering a unique characterization for the A2G SNR as the product of two scaled non-central chi-square RVs. Moreover, we also find that, for a large number of RIS elements, the RIS-assisted A2G channel can be characterized as a single Rician fading channel. Our results reveal the presence of channel hardening in A2G communication under low UAV speeds, where we derive the maximum target spectral efficiency (SE) for a system to maintain a consistent required outage level. Meanwhile, high UAV speeds, exceeding 50 m/s, lead to a significant performance degradation, which cannot be mitigated by increasing the number of RIS elements.
Deep Clustering with Diffused Sampling and Hardness-aware Self-distillation

Deep clustering has gained significant attention due to its capability in learning clustering-friendly representations without labeled data. However, previous deep clustering methods tend to treat all samples equally, which neglect the variance in the latent distribution and the varying difficulty in classifying or clustering different samples. To address this, this paper proposes a novel end-to-end deep clustering method with diffused sampling and hardness-aware self-distillation (HaDis). Specifically, we first align one view of instances with another view via diffused sampling alignment (DSA), which helps improve the intra-cluster compactness. To alleviate the sampling bias, we present the hardness-aware self-distillation (HSD) mechanism to mine the hardest positive and negative samples and adaptively adjust their weights in a self-distillation fashion, which is able to deal with the potential imbalance in sample contributions during optimization. Further, the prototypical contrastive learning is incorporated to simultaneously enhance the inter-cluster separability and intra-cluster compactness. Experimental results on five challenging image datasets demonstrate the superior clustering performance of our HaDis method over the state-of-the-art. Source code is available at https://github.com/Regan-Zhang/HaDis.
Generating Likely Counterfactuals Using Sum-Product Networks

Due to user demand and recent regulation (GDPR, AI Act), decisions made by AI systems need to be explained. These decisions are often explainable only post hoc, where counterfactual explanations are popular. The question of what constitutes the best counterfactual explanation must consider multiple aspects, where"distance from the sample"is the most common. We argue that this requirement frequently leads to explanations that are unlikely and, therefore, of limited value. Here, we present a system that provides high-likelihood explanations. We show that the search for the most likely explanations satisfying many common desiderata for counterfactual explanations can be modeled using mixed-integer optimization (MIO). In the process, we propose an MIO formulation of a Sum-Product Network (SPN) and use the SPN to estimate the likelihood of a counterfactual, which can be of independent interest. A numerical comparison against several methods for generating counterfactual explanations is provided.
ODC and ROC curves, comparison curves, and stochastic dominance

We discuss two novel approaches to the classical two-sample problem. Our starting point are properly standardized and combined, very popular in several areas of statistics and data analysis, ordinal dominance and receiver characteristic curves, denoted by ODC and ROC, respectively. The proposed new curves are termed the comparison curves. Their estimates, being weighted rank processes on (0,1), form the basis of inference. These weighted processes are intuitive, well-suited for visual inspection of data at hand, and are also useful for constructing some formal inferential procedures. They can be applied to several variants of two-sample problem. Their use can help to improve some existing procedures both in terms of power and the ability to identify the sources of departures from the postulated model. To simplify interpretation of finite sample results we restrict attention to values of the processes on a finite grid of points. This results in the so-called bar plots (B-plots) which readably summarize the information contained in the data. What is more, we show that B-plots along with adjusted simultaneous acceptance regions provide principled information about where the model departs from the data. This leads to a framework which facilitates identification of regions with locally significant differences. We show an implementation of the considered techniques to a standard stochastic dominance testing problem. Some min-type statistics are introduced and investigated. A simulation study compares two tests pertinent to the comparison curves to well-established tests in the literature and demonstrates the strong and competitive performance of the former in many typical situations. Some real data applications illustrate simplicity and practical usefulness of the proposed approaches. A range of other applications of considered weighted processes is briefly discussed too.
AR-GAN: Generative Adversarial Network-Based Defense Method Against Adversarial Attacks on the Traffic Sign Classification System of Autonomous Vehicles

This study developed a generative adversarial network (GAN)-based defense method for traffic sign classification in an autonomous vehicle (AV), referred to as the attack-resilient GAN (AR-GAN). The novelty of the AR-GAN lies in (i) assuming zero knowledge of adversarial attack models and samples and (ii) providing consistently high traffic sign classification performance under various adversarial attack types. The AR-GAN classification system consists of a generator that denoises an image by reconstruction, and a classifier that classifies the reconstructed image. The authors have tested the AR-GAN under no-attack and under various adversarial attacks, such as Fast Gradient Sign Method (FGSM), DeepFool, Carlini and Wagner (C&W), and Projected Gradient Descent (PGD). The authors considered two forms of these attacks, i.e., (i) black-box attacks (assuming the attackers possess no prior knowledge of the classifier), and (ii) white-box attacks (assuming the attackers possess full knowledge of the classifier). The classification performance of the AR-GAN was compared with several benchmark adversarial defense methods. The results showed that both the AR-GAN and the benchmark defense methods are resilient against black-box attacks and could achieve similar classification performance to that of the unperturbed images. However, for all the white-box attacks considered in this study, the AR-GAN method outperformed the benchmark defense methods. In addition, the AR-GAN was able to maintain its high classification performance under varied white-box adversarial perturbation magnitudes, whereas the performance of the other defense methods dropped abruptly at increased perturbation magnitudes.
A New Image Quality Database for Multiple Industrial Processes

Recent years have witnessed a broader range of applications of image processing technologies in multiple industrial processes, such as smoke detection, security monitoring, and workpiece inspection. Different kinds of distortion types and levels must be introduced into an image during the processes of acquisition, compression, transmission, storage, and display, which might heavily degrade the image quality and thus strongly reduce the final display effect and clarity. To verify the reliability of existing image quality assessment methods, we establish a new industrial process image database (IPID), which contains 3000 distorted images generated by applying different levels of distortion types to each of the 50 source images. We conduct the subjective test on the aforementioned 3000 images to collect their subjective quality ratings in a well-suited laboratory environment. Finally, we perform comparison experiments on IPID database to investigate the performance of some objective image quality assessment algorithms. The experimental results show that the state-of-the-art image quality assessment methods have difficulty in predicting the quality of images that contain multiple distortion types.
Exploring Adversarial Threat Models in Cyber Physical Battery Systems

Technological advancements like the Internet of Things (IoT) have facilitated data exchange across various platforms. This data exchange across various platforms has transformed the traditional battery system into a cyber physical system. Such connectivity makes modern cyber physical battery systems vulnerable to cyber threats where a cyber attacker can manipulate sensing and actuation signals to bring the battery system into an unsafe operating condition. Hence, it is essential to build resilience in modern cyber physical battery systems (CPBS) under cyber attacks. The first step of building such resilience is to analyze potential adversarial behavior, that is, how the adversaries can inject attacks into the battery systems. However, it has been found that in this under-explored area of battery cyber physical security, such an adversarial threat model has not been studied in a systematic manner. In this study, we address this gap and explore adversarial attack generation policies based on optimal control framework. The framework is developed by performing theoretical analysis, which is subsequently supported by evaluation with experimental data generated from a commercial battery cell.
Longitudinal Sentiment Topic Modelling of Reddit Posts

In this study, we analyze texts of Reddit posts written by students of four major Canadian universities. We gauge the emotional tone and uncover prevailing themes and discussions through longitudinal topic modeling of posts textual data. Our study focuses on four years, 2020-2023, covering COVID-19 pandemic and after pandemic years. Our results highlight a gradual uptick in discussions related to mental health.
Investigating Organic Carbon and Thermal History of CM Carbonaceous Chondrites Using Spectroscopy and Laboratory Techniques

The CM chondrites are characterized as primary accretionary rocks which originate from primitive water-rich asteroids formed during the early Solar System. Here, we study the mineralogy and organic characteristics of right CM and one ungrouped chondrite to better understand their alteration history; Queen Alexandra Range 93005 (QUE 93005), Murchison, LaPaz Icefield 02333 (LAP 02333), Miller Range (MIL 13005), Mackay Glacier 05231 (MCY 05231), Northwest Africa 8534 (NWA 8534), Northwest Africa 3340 (NWA 3340), Yamato 86695 (Y-86695), and the ungrouped carbonaceous chondrite Belgica 7904 (B-7904). Raman spectroscopy has been employed to detect the presence of organic carbon in the samples, specifically through the G band at approximately 1580 cm-1 and D band at around 1350 cm-1. The properties of organic matter in meteorites serve as valuable indicators for characterizing the structure and crystallinity of carbonaceous materials and estimating their thermal metamorphism degree. The R1 parameter, defined as the peak height ratio of the D and G bands, provides a quantifiable measure of this structural organization. Raman spectra are used to show the general mineralogy, thermal history and heating stage of CM and ungrouped chondrites. X-ray diffraction patterns further indicate the mineralogical compositions of the samples. Visible to near-infrared (VNIR) and attenuated total reflection (ATR) reflectance spectra illustrate the trends related to their mineralogy and furthermore infer aqueous alteration, thermal history of CM carbonaceous chondrites, formation and evolution of their parent bodies.
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images

Capturing the diversity of people in images is challenging: recent literature tends to focus on diversifying one or two attributes, requiring expensive attribute labels or building classifiers. We introduce a diverse people image ranking method which more flexibly aligns with human notions of people diversity in a less prescriptive, label-free manner. The Perception-Aligned Text-derived Human representation Space (PATHS) aims to capture all or many relevant features of people-related diversity, and, when used as the representation space in the standard Maximal Marginal Relevance (MMR) ranking algorithm, is better able to surface a range of types of people-related diversity (e.g. disability, cultural attire). PATHS is created in two stages. First, a text-guided approach is used to extract a person-diversity representation from a pre-trained image-text model. Then this representation is fine-tuned on perception judgments from human annotators so that it captures the aspects of people-related similarity that humans find most salient. Empirical results show that the PATHS method achieves diversity better than baseline methods, according to side-by-side ratings from human annotators.
Mapping the Design Space of Teachable Social Media Feed Experiences

Social media feeds are deeply personal spaces that reflect individual values and preferences. However, top-down, platform-wide content algorithms can reduce users' sense of agency and fail to account for nuanced experiences and values. Drawing on the paradigm of interactive machine teaching (IMT), an interaction framework for non-expert algorithmic adaptation, we map out a design space for teachable social media feed experiences to empower agential, personalized feed curation. To do so, we conducted a think-aloud study (N=24) featuring four social media platforms -- Instagram, Mastodon, TikTok, and Twitter -- to understand key signals users leveraged to determine the value of a post in their feed. We synthesized users' signals into taxonomies that, when combined with user interviews, inform five design principles that extend IMT into the social media setting. We finally embodied our principles into three feed designs that we present as sensitizing concepts for teachable feed experiences moving forward.
Mesoscale self-organization of polydisperse magnetic nanoparticles at the water surface.

In this study, we investigated the self-ordering process in Langmuir films of polydisperse iron oxide nanoparticles on a water surface, employing in situ x-ray scattering, surface pressure-area isotherm analysis, and Brewster angle microscopy. X-ray reflectometry confirmed the formation of a monolayer, while grazing incidence small-angle x-ray scattering revealed short-range lateral correlations with a characteristic length equal to the mean particle size. Remarkably, our findings indicated that at zero surface pressure, the particles organized into submicrometer clusters, merging upon compression to form a homogeneous layer. These layers were subsequently transferred to a solid substrate using the Langmuir-Schaefer technique and further characterized via scanning electron microscopy and polarized neutron reflectometry. Notably, our measurements revealed a second characteristic length in the lateral correlations, orders of magnitude longer than the mean particle diameter, with polydisperse particles forming circular clusters densely packed in a hexagonal lattice. Furthermore, our evidence suggests that the lattice constant of this mesocrystal depends on the characteristics of the particle size distribution, specifically the mean particle size and the width of the size distribution. In addition, we observed internal size separation within these clusters, where larger particles were positioned closer to the center of the cluster. Finally, polarized neutron reflectometry measurements provided valuable insights into the magnetization profile across the layer.