Miracle-dz/UniFAR-FTUs huggingface dataset:
Secondary standard stars for absolute spectrophotometry

Based on an adopted absolute spectral energy distribution for the primary standard star Alpha Lyrae, absolute fluxes are given for the four very metal-deficient F type subdwarfs HD 19445, HD 84937, BD + 26.2606 deg, and BD + 17.4703 deg. Somewhat inferior data are also given for HD 140283. The data are given for 40-A bands and cover the wavelength range from 3080 A to 12,000 A. The four stars, all near magnitude 9 and distributed around the sky, are intended as secondary standards for absolute spectrophotometry.
A metric for tradable biodiversity credits linked to the Living Planet Index and global species conservation

Difficulties identifying appropriate biodiversity impact metrics remain a major barrier to inclusion of biodiversity considerations in environmentally responsible investment. We propose and analyse a simple science-based local metric: the sum of proportional changes in local species abundances relative to their global species abundances, with a correction for species close to extinction. As we show, this metric quantifies changes in the mean long-term global survival probability of species. It links mathematically to a widely cited global biodiversity indicator, the Living Planet Index, for which we propose an improved formula that directly addresses the known problem of singularities caused by extinctions. We show that, in an ideal market, trade in our metric would lead to near-optimal allocation of resources to species conservation. We further show that the metric is closely related to several other metrics and indices already in use. Barriers to adoption are therefore low. Used in conjunction with metrics addressing ecosystem functioning and services, potential areas of application include biodiversity related financial disclosures and voluntary or legislated no net biodiversity loss policies.
Wavefront Sets of Unipotent Representations of Reductive $p$-adic Groups I

The wavefront set is a fundamental invariant arising from the Harish-Chandra-Howe local character expansion of an admissible representation. We prove a precise formula for the wavefront set of an irreducible Iwahori-spherical representation with `real infinitesimal character' and determine a lower bound for this invariant in terms of the Deligne-Langlands-Lusztig parameters. In particular, for the Iwahori-spherical representations with real infinitesimal character, we deduce that the algebraic wavefront set is a singleton, as conjectured by Moeglin and Waldspurger. As a corollary, we obtain an explicit description of the wavefront set of an irreducible spherical representation with real Satake parameter.
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This article reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey.
Rt-Track: Robust Tricks for Multi-Pedestrian Tracking

Object tracking is divided into single-object tracking (SOT) and multi-object tracking (MOT). MOT aims to maintain the identities of multiple objects across a series of continuous video sequences. In recent years, MOT has made rapid progress. However, modeling the motion and appearance models of objects in complex scenes still faces various challenging issues. In this paper, we design a novel direction consistency method for smooth trajectory prediction (STP-DC) to increase the modeling of motion information and overcome the lack of robustness in previous methods in complex scenes. Existing methods use pedestrian re-identification (Re-ID) to model appearance, however, they extract more background information which lacks discriminability in occlusion and crowded scenes. We propose a hyper-grain feature embedding network (HG-FEN) to enhance the modeling of appearance models, thus generating robust appearance descriptors. We also proposed other robustness techniques, including CF-ECM for storing robust appearance information and SK-AS for improving association accuracy. To achieve state-of-the-art performance in MOT, we propose a robust tracker named Rt-track, incorporating various tricks and techniques. It achieves 79.5 MOTA, 76.0 IDF1 and 62.1 HOTA on the test set of MOT17.Rt-track also achieves 77.9 MOTA, 78.4 IDF1 and 63.3 HOTA on MOT20, surpassing all published methods.
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks. This finding implies that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models. We conclude that LLMs such as GPTs exhibit traits of general-purpose technologies, indicating that they could have considerable economic, social, and policy implications.
Non-Fermi-liquid behavior in d - and f -electron metals

A relatively new class of materials has been found in which the basic assumption of Landau Fermi-liquid theory—that at low energies the electrons in a metal should behave essentially as a collection of weakly interacting particles—is violated. These ‘‘non-Fermi-liquid’’ systems exhibit unusual temperature dependences in their low-temperature properties, including several examples in which the specific heat divided by temperature shows a singular log T temperature dependence over more than two orders of magnitude, from the lowest measured temperatures in the milliKelvin regime to temperatures over 10 K. These anomalous properties, with their often pure power-law or logarithmic temperature dependences over broad temperature ranges and inherent low characteristic energies, have attracted active theoretical interest from the first experimental report in 1991. This article first describes the various theoretical approaches to trying to understand the source of strong temperatureand frequency-dependent electron-electron interactions in non-Fermi-liquid systems. It then discusses the current experimental body of knowledge, including a compilation of data on non-Fermi-liquid behavior in over 50 systems. The disparate data reveal some interesting correlations and trends and serve to point up a number of areas where further theoretical and experimental work is needed.
Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding

Predicting attention regions of interest is an important yet challenging task for self-driving systems. Existing methodologies rely on large-scale labeled traffic datasets that are labor-intensive to obtain. Besides, the huge domain gap between natural scenes and traffic scenes in current datasets also limits the potential for model training. To address these challenges, we are the first to introduce an unsupervised way to predict self-driving attention by uncertainty modeling and driving knowledge integration. Our approach’s Uncertainty Mining Branch (UMB) discovers commonalities and differences from multiple generated pseudo-labels achieved from models pre-trained on natural scenes by actively measuring the uncertainty. Meanwhile, our Knowledge Embedding Block (KEB) bridges the domain gap by incorporating driving knowledge to adaptively refine the generated pseudo-labels. Quantitative and qualitative results with equivalent or even more impressive performance compared to fully-supervised state-of-the-art approaches across all three public datasets demonstrate the effectiveness of the proposed method and the potential of this direction. The code is available at https://github.com/zaplm/DriverAttention.
Autopsy of Ethereum's Post-Merge Reward System

Like most modern blockchain networks, Ethereum has relied on economic incentives to promote honest participation in the chain's consensus. The distributed character of the platform, together with the “randomness” or “luck” factor that both proof of work (PoW) and proof of stake (PoS) provide when electing the next block proposer, pushed the industry to model and improve the reward system of the system. With several improvements to predict PoW block proposal rewards and to maximize the extractable rewards of the same ones, the ultimate Ethereum's transition to PoS applied in the Paris Hard-Fork, more generally known as “The Merge”, has meant a significant modification on the reward system in the platform. In this paper, we aim to break down both theoretically and empirically the new reward system in this post-merge era. We present a highly detailed description of the different rewards and their share among validators' rewards. Ultimately, we offer a study that uses the presented reward model to analyze the performance of the network during this transition.
AVERAGE TRANSIENT TEMPERATURE WITHIN A BODY HEATED BY A DISK HEAT SOURCE

An exact solution is given for a transient average temperature in a semi-infinite body that is heated by a constant heat flux over a circular surface region and insulated elsewhere at the surface. The average temperature is for any circular region parallel to and directly below the heated disk. The solution has direct application to thermal conductance, electric contacts, laser heating, and other cases. Though studied by others, no exact solution has previously been given. The exact solution is compared with several approximate solutions and substantial differences are noted, particularly at small dimensionless times. In addition to effective recursive methods for evaluating the infinite series solution, some simple but very accurate expressions are given for the heated surface and other locations. The basic solution for the semiinfinite geometry with a heat flux boundary condition is shown to be a building block for a number of other boundary conditions for both semi-infinite and infinite geometries. Nomenclature A = contact area Cn = specific heat erfc(x; = complementary error function ierfc(x) = integrated error function J(<(r) = Bessel function I|<(r) = Bessel function k = thermal conductivity
A cyclic sieving phenomenon for symplectic tableaux

We give a cyclic sieving phenomenon for symplectic $\lambda$-tableaux $SP(\lambda,2m)$, where $\lambda$ is a partition of an odd integer $n$ and $gcd(m,p)=1$ for any odd prime $p\leq n$. We use the crystal structure on Kashiwara-Nakashima symplectic tableaux to get a cyclic sieving action as the product $\sigma$ of simple reflections in the Weyl group. The cyclic sieving polynomial is the $q$-anologue of the hook-content formula for symplectic tableaux. More generally, we give a CSP for symplectic skew tableaux with analogous conditions on the shape and a cyclic group action that rotates tableaux weights in a way motivated by the $\sigma$-action.
Disentangling the Link Between Image Statistics and Human Perception

In the 1950s Horace Barlow and Fred Attneave suggested a connection between sensory systems and how they are adapted to the environment: early vision evolved to maximise the information it conveys about incoming signals. Following Shannon's definition, this information was described using the probability of the images taken from natural scenes. Previously, direct accurate predictions of image probabilities were not possible due to computational limitations. Despite the exploration of this idea being indirect, mainly based on oversimplified models of the image density or on system design methods, these methods had success in reproducing a wide range of physiological and psychophysical phenomena. In this paper, we directly evaluate the probability of natural images and analyse how it may determine perceptual sensitivity. We employ image quality metrics that correlate well with human opinion as a surrogate of human vision, and an advanced generative model to directly estimate the probability. Specifically, we analyse how the sensitivity of full-reference image quality metrics can be predicted from quantities derived directly from the probability distribution of natural images. First, we compute the mutual information between a wide range of probability surrogates and the sensitivity of the metrics and find that the most influential factor is the probability of the noisy image. Then we explore how these probability surrogates can be combined using a simple model to predict the metric sensitivity, giving an upper bound for the correlation of 0.85 between the model predictions and the actual perceptual sensitivity. Finally, we explore how to combine the probability surrogates using simple expressions, and obtain two functional forms (using one or two surrogates) that can be used to predict the sensitivity of the human visual system given a particular pair of images.
Image comparison and scaling via nonlinear elasticity

A nonlinear elasticity model for comparing images is formulated and analyzed, in which optimal transformations between images are sought as minimizers of an integral functional. The existence of minimizers in a suitable class of homeomorphisms between image domains is established under natural hypotheses. We investigate whether for linearly related images the minimization algorithm delivers the linear transformation as the unique minimizer.
LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Grounding

Humans excel at acquiring knowledge through observation. For example, we can learn to use new tools by watching demonstrations. This skill is fundamental for intelligent systems to interact with the world. A key step to acquire this skill is to identify what part of the object affords each action, which is called affordance grounding. In this paper, we address this problem and propose a framework called LOCATE that can identify matching object parts across images, to transfer knowledge from images where an object is being used (exocentric images used for learning), to images where the object is inactive (egocentric ones used to test). To this end, we first find interaction areas and extract their feature embeddings. Then we learn to aggregate the embeddings into compact prototypes (human, object part, and background), and select the one representing the object part. Finally, we use the selected prototype to guide affordance grounding. We do this in a weakly supervised manner, learning only from image-level affordance and object labels. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods by a large margin on both seen and unseen objects.11Project page: https://reagan1311.github. io/locate.
The Use of the Deviation Angle Variance Technique on Geostationary Satellite Imagery to Estimate Tropical Cyclone Size Parameters

AbstractThis study extends past research based on the deviation angle variance (DAV) technique that utilizes digital brightness temperatures from longwave infrared satellite images to objectively measure the symmetry of a tropical cyclone (TC). In previous work, the single-pixel DAV values were used as an objective estimator of storm intensity while maps of the DAV values indicated areas where tropical cyclogenesis was occurring. In this study the spatial information in the DAV maps is utilized along with information from the Cooperative Institute for Research in the Atmosphere’s extended best-track archive and the Statistical Hurricane Intensity Prediction Scheme model to create multiple linear regression models of wind radii parameters for TCs in the North Atlantic basin. These models are used to estimate both symmetric, and by quadrant, 34-, 50-, and 64-kt wind radii (where 1 kt = 0.51 m s−1 1) on a half-hourly time scale. The symmetric model assumes azimuthal symmetry and has mean absolute errors of 3.
Understanding Frontline Workers’ and Unhoused Individuals’ Perspectives on AI Used in Homeless Services

Recent years have seen growing adoption of AI-based decision-support systems (ADS) in homeless services, yet we know little about stakeholder desires and concerns surrounding their use. In this work, we aim to understand impacted stakeholders’ perspectives on a deployed ADS that prioritizes scarce housing resources. We employed AI lifecycle comicboarding, an adapted version of the comicboarding method, to elicit stakeholder feedback and design ideas across various components of an AI system’s design. We elicited feedback from county workers who operate the ADS daily, service providers whose work is directly impacted by the ADS, and unhoused individuals in the region. Our participants shared concerns and design suggestions around the AI system’s overall objective, specific model design choices, dataset selection, and use in deployment. Our findings demonstrate that stakeholders, even without AI knowledge, can provide specific and critical feedback on an AI system’s design and deployment, if empowered to do so.
Vortex dynamics: A variational approach using the principle of least action

The study of vortex dynamics using a variational formulation has an extensive history and a rich literature. The standard Hamiltonian function that describes the dynamics of interacting point vortices of constant strength is the Kirchhoff-Routh (KR) function. This function was not obtained from basic definitions of classical mechanics (i.e., in terms of kinetic and potential energies), but it was rather devised to match the already known differential equations of motion for constant-strength point vortices given by the Bio-Savart law. Instead, we develop a new variational formulation for vortex dynamics based on the principle of least action. As an application, we consider a system of non-deforming, free vortex patches of constant-strength. Interestingly, the obtained equations of motion are second-order differential equations defining vortex accelerations, not velocities. In the special case of constant-strength point vortices, the new formulation reduces to the motion determined from the KR function. However, for finite-core vortices, the resulting dynamics are more complex than those obtained from the KR formulation. For example, a pair of equal-strength, counter-rotating vortices could be initialized with different velocities, resulting in interesting patterns which cannot be handled by the KR approach. Also, the developed model easily admits external body forces (e.g., electromagnetic). The interaction between the electrodynamic force and the hydrodynamic vortex force leads to a rich, counter-intuitive behavior that could not be handled by the KR formulation. Finally, this new variational formulation, which is derived from first principles in mechanics, can be easily extended to deforming and/or arbitrary time-varying vortices.
Algorithm Design: A Fairness-Accuracy Frontier

Algorithm designers increasingly optimize not only for accuracy, but also for the fairness of the algorithm across pre-defined groups. We study the tradeoff between fairness and accuracy for any given set of inputs to the algorithm. We propose and characterize a fairness-accuracy frontier, which consists of the optimal points across a broad range of preferences over fairness and accuracy. Our results identify a simple property of the inputs, group-balance, which qualitatively determines the shape of the frontier. We further study an information-design problem where the designer flexibly regulates the inputs (e.g., by coarsening an input or banning its use) but the algorithm is chosen by another agent. Whether it is optimal to ban an input generally depends on the designer's preferences. But when inputs are group-balanced, then excluding group identity is strictly suboptimal for all designers, and when the designer has access to group identity, then it is strictly suboptimal to exclude any informative input.
The Combinatorics of Causality

We introduce and explore the notion of"spaces of input histories", a broad family of combinatorial objects which can be used to model input-dependent, dynamical causal order. We motivate our definition with reference to traditional partial order- and preorder-based notions of causal order, adopted by the majority of previous literature on the subject, and we proceed to explore the novel landscape of combinatorial complexity made available by our generalisation of those notions. In the process, we discover that the fine-grained structure of causality is significantly more complex than we might have previously believed: in the simplest case of binary inputs, the number of available"causally complete"spaces grows from 7 on 2 events, to 2644 on 3 events, to an unknown number on 4 events (likely around a billion). For perspective, previous literature on non-locality and contextuality used a single one of the 2644 available spaces on 3 events, work on definite causality used 19 spaces, derived from partial orders, and work on indefinite causality used only 6 more, for a grand total of 25. This paper is the first instalment in a trilogy: the sheaf-theoretic treatment of causal distributions is detailed in Part 2,"The Topology of Causality"[arXiv:2303.07148], while the polytopes formed by the associated empirical models are studied in Part 3,"The Geometry of Causality"[arXiv:2303.09017]. An exhaustive classification of the 2644 causally complete spaces on 3 events with binary inputs is provided in the supplementary work"Classification of causally complete spaces on 3 events with binary inputs", together with the algorithm used for the classification and partial results from the ongoing search on 4 events.