Miracle-dz/UniFAR-FTUs huggingface dataset:
Photometric metallicities of fundamental-mode RR Lyr stars from Gaia G-band photometry of globular-cluster variables

Photometric metallicity formulae of fundamental-mode RR Lyr (RRab) stars are presented using globular-cluster data exclusively. The aim is to check whether this selection may help increasing the overall accuracy of the fits and eliminating the systematic bias of the photometric results, namely that they tend to overestimate [Fe/H] of the most metal-poor variables. The G-band time-series data available in the Gaia DR3 archive and a new compilation of the published spectroscopic globular cluster [Fe/H] values on a uniform solar reference metallicity scale are utilized. We have derived a new [Fe/H]phot − P, ϕ31 formula, and have diagnosed that no significant increase in the accuracy of the fit can be achieved using non-linear or multi-parameter formulae. The best result is obtained when different formulae are applied for variables with Oosterhoff-type I and II properties. However, even this solution cannot eliminate the systematic bias of the results completely. This separation of the variables has also led to the conclusion that the photometric estimates of the [Fe/H] are less reliable for the Oo-type II variables than for the Oo-type I sample. Published [Fe/H]phot values and the results of the available photometric formulae in the Gaia G-band are compared with the present results. It is found that each of the solutions yields very similar results, with similar accuracy and systematic biases. Major differences are detected only in the zero-points of the [Fe/H] scales, and these offsets are larger than differences in the accepted solar reference values would explain.
Compositional Semantics of Finite Petri Nets

Structure-preserving bisimilarity is a truly concurrent behavioral equivalence for finite Petri nets, which relates markings (of the same size only) generating the same causal nets, hence also the same partial orders of events. The process algebra FNM truly represents all (and only) the finite Petri nets, up to isomorphism. We prove that structure-preserving bisimilarity is a congruence w.r. t. the FMN operators, In this way, we have defined a compositional semantics, fully respecting causality and the branching structure of systems, for the class of all the finite Petri nets. Moreover, we study some algebraic properties of structure-preserving bisimilarity, that are at the base of a sound (but incomplete) axiomatization over FNM process terms.
Causally Linking Health Application Data and Personal Information Management Tools

The proliferation of consumer health devices such as smart watches, sleep monitors, smart scales, etc, in many countries, has not only led to growing interest in health monitoring, but also to the development of a countless number of ``smart'' applications to support the exploration of such data by members of the general public, sometimes with integration into professional health services. While a variety of health data streams has been made available by such devices to users, these streams are often presented as separate time-series visualizations, in which the potential relationships between health variables are not explicitly made visible. Furthermore, despite the fact that other aspects of life, such as work and social connectivity, have become increasingly digitised, health and well-being applications make little use of the potentially useful contextual information provided by widely used personal information management tools, such as shared calendar and email systems. This paper presents a framework for the integration of these diverse data sources, analytic and visualization tools, with inference methods and graphical user interfaces to help users by highlighting causal connections among such time-series.
Effective Action for Dissipative and Nonholonomic Systems

We show that the action of a dynamical system can be supplemented by an effective action for its environment to reproduce arbitrary coordinate dependent ohmic dissipation and gyroscopic forces. The action is a generalization of the harmonic bath model and describes a set of massless interacting scalar fields in an auxiliary space coupled to the original system at the boundary. A certain limit of the model implements nonholonomic constraints. In the case of dynamics with nonlinearly realized symmetries the effective action takes the form of a two-dimensional nonlinear sigma-model. It provides a basis for application of path integral methods to general dissipative and nonholonomic systems.
Learning Reward Machines through Preference Queries over Sequences

Reward machines have shown great promise at capturing non-Markovian reward functions for learning tasks that involve complex action sequencing. However, no algorithm currently exists for learning reward machines with realistic weak feedback in the form of preferences. We contribute REMAP, a novel algorithm for learning reward machines from preferences, with correctness and termination guarantees. REMAP introduces preference queries in place of membership queries in the L* algorithm, and leverages a symbolic observation table along with unification and constraint solving to narrow the hypothesis reward machine search space. In addition to the proofs of correctness and termination for REMAP, we present empirical evidence measuring correctness: how frequently the resulting reward machine is isomorphic under a consistent yet inexact teacher, and the regret between the ground truth and learned reward machines.
Deep Boosting Multi-Modal Ensemble Face Recognition with Sample-Level Weighting

Deep convolutional neural networks have achieved remarkable success in face recognition (FR), partly due to the abundant data availability. However, the current training benchmarks exhibit an imbalanced quality distribution; most images are of high quality. This poses issues for generalization on hard samples since they are underrepresented during training. In this work, we employ the multi-model boosting technique to deal with this issue. Inspired by the well-known AdaBoost, we propose a sample-level weighting approach to incorporate the importance of different samples into the FR loss. Individual models of the proposed framework are experts at distinct levels of sample hardness. Therefore, the combination of models leads to a robust feature extractor without losing the discriminability on the easy samples. Also, for incorporating the sample hardness into the training criterion, we analytically show the effect of sample mining on the important aspects of current angular margin loss functions, i.e., margin and scale. The proposed method shows superior performance in comparison with the state-of-the-art algorithms in extensive experiments on the CFP-FP, LFW, CPLFW, CALFW, AgeDB, TinyFace, IJB-B, and IJB-C evaluation datasets.
Utilizing relational and social network analysis to inform community-based climate change adaptation : a central Oregon case study

approved: ______________________________________________________ Lori A. Cramer Central Oregon’s landscape is rich in dry forests prone to frequent wildfires. Climate change studies and improved modeling indicate this region could experience conditions that result in an increase in number and severity of wildfires. With the potential for increased environmental hazards, the nearby communities face potential risks and vulnerabilities to their social, political, cultural and economic conditions. This dissertation utilizes an innovative social network methodological framework to examine current socio-political conditions and information network systems associated with wildfire and climate change in two Central Oregon communities. Research focused on the communities’ social and information networks on climate change and governmental activities, the degree of network connections to professionals, and measures of community social capital. The results illustrate that both communities have a high percentage of community members that are disengaged with the subject of climate change. It was also found that within the community social network exchanges, climate change is rarely discussed nor did it overlap with other informational issues such as wildfires and community politics. Information networks on climate change are complex, and ties to climate change information sources do not always reflect one’s position on climate change or its local risks. Informational resources regarding governmental activities are mostly media based in the incorporated community and social systems appear more important in the rural communities. When expressed, social capital and more specifically, governmental trust appears to be linked to the degree of concern respondents have on climate change, suggesting that addressing scientific illiteracy may not be as important as establishing or maintaining positive governmental relations. Furthermore, this research may have policy implications for increasing local adaptive capacity, as well as research implications for the use of social network analysis as a tool to address future research.
Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement

Phase information has a significant impact on speech perceptual quality and intelligibility. However, existing speech enhancement methods encounter limitations in explicit phase estimation due to the non-structural nature and wrapping characteristics of the phase, leading to a bottleneck in enhanced speech quality. To overcome the above issue, in this paper, we proposed MP-SENet, a novel Speech Enhancement Network which explicitly enhances Magnitude and Phase spectra in parallel. The proposed MP-SENet adopts a codec architecture in which the encoder and decoder are bridged by time-frequency Transformers along both time and frequency dimensions. The encoder aims to encode time-frequency representations derived from the input distorted magnitude and phase spectra. The decoder comprises dual-stream magnitude and phase decoders, directly enhancing magnitude and wrapped phase spectra by incorporating a magnitude estimation architecture and a phase parallel estimation architecture, respectively. To train the MP-SENet model effectively, we define multi-level loss functions, including mean square error and perceptual metric loss of magnitude spectra, anti-wrapping loss of phase spectra, as well as mean square error and consistency loss of short-time complex spectra. Experimental results demonstrate that our proposed MP-SENet excels in high-quality speech enhancement across multiple tasks, including speech denoising, dereverberation, and bandwidth extension. Compared to existing phase-aware speech enhancement methods, it successfully avoids the bidirectional compensation effect between the magnitude and phase, leading to a better harmonic restoration. Notably, for the speech denoising task, the MP-SENet yields a state-of-the-art performance with a PESQ of 3.60 on the public VoiceBank+DEMAND dataset.
Experimental quantum e-commerce

E-commerce, a type of trading that occurs at a high frequency on the internet, requires guaranteeing the integrity, authentication, and nonrepudiation of messages through long distance. As current e-commerce schemes are vulnerable to computational attacks, quantum cryptography, ensuring information-theoretic security against adversary's repudiation and forgery, provides a solution to this problem. However, quantum solutions generally have much lower performance compared to classical ones. Besides, when considering imperfect devices, the performance of quantum schemes exhibits a notable decline. Here, we demonstrate the whole e-commerce process of involving the signing of a contract and payment among three parties by proposing a quantum e-commerce scheme, which shows resistance of attacks from imperfect devices. Results show that with a maximum attenuation of 25 dB among participants, our scheme can achieve a signature rate of 0.82 times per second for an agreement size of approximately 0.428 megabit. This proposed scheme presents a promising solution for providing information-theoretic security for e-commerce.
Scope is all you need: Transforming LLMs for HPC Code

With easier access to powerful compute resources, there is a growing trend in the field of AI for software development to develop larger and larger language models (LLMs) to address a variety of programming tasks. Even LLMs applied to tasks from the high-performance computing (HPC) domain are huge in size (e.g., billions of parameters) and demand expensive compute resources for training. We found this design choice confusing - why do we need large LLMs trained on natural languages and programming languages unrelated to HPC for HPC-specific tasks? In this line of work, we aim to question design choices made by existing LLMs by developing smaller LLMs for specific domains - we call them domain-specific LLMs. Specifically, we start off with HPC as a domain and propose a novel tokenizer named Tokompiler, designed specifically for preprocessing code in HPC and compilation-centric tasks. Tokompiler leverages knowledge of language primitives to generate language-oriented tokens, providing a context-aware understanding of code structure while avoiding human semantics attributed to code structures completely. We applied Tokompiler to pre-train two state-of-the-art models, SPT-Code and Polycoder, for a Fortran code corpus mined from GitHub. We evaluate the performance of these models against the conventional LLMs. Results demonstrate that Tokompiler significantly enhances code completion accuracy and semantic understanding compared to traditional tokenizers in normalized-perplexity tests, down to ~1 perplexity score. This research opens avenues for further advancements in domain-specific LLMs, catering to the unique demands of HPC and compilation tasks.
Leak Proof PDBBind: A Reorganized Dataset of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction

Many physics-based and machine-learned scoring functions (SFs) used to predict protein-ligand binding free energies have been trained on the PDBBind dataset. However, it is controversial as to whether new SFs are actually improving since the general, refined, and core datasets of PDBBind are cross-contaminated with proteins and ligands with high similarity, and hence they may not perform comparably well in binding prediction of new protein-ligand complexes. In this work we have carefully prepared a cleaned PDBBind data set of non-covalent binders that are split into training, validation, and test datasets to control for data leakage. The resulting leak-proof (LP)-PDBBind data is used to retrain four popular SFs: AutoDock vina, Random Forest (RF)-Score, InteractionGraphNet (IGN), and DeepDTA, to better test their capabilities when applied to new protein-ligand complexes. In particular we have formulated a new independent data set, BDB2020+, by matching high quality binding free energies from BindingDB with co-crystalized ligand-protein complexes from the PDB that have been deposited since 2020. Based on all the benchmark results, the retrained models using LP-PDBBind that rely on 3D information perform consistently among the best, with IGN especially being recommended for scoring and ranking applications for new protein-ligand systems.
Fourier modal method for inverse design of metasurface-enhanced micro-LEDs.

We present a simulation capability for micro-scale light-emitting diodes (µLEDs) that achieves comparable accuracy to CPU-based finite-difference time-domain simulation but is more than 107 times faster. Our approach is based on the Fourier modal method (FMM)-which, as we demonstrate, is well suited to modeling thousands of incoherent sources-with extensions that allow rapid convergence for µLED structures that are challenging to model with standard approaches. The speed of our method makes the inverse design of µLEDs tractable, which we demonstrate by designing a metasurface-enhanced µLED that doubles the light extraction efficiency of an unoptimized device.
Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks

Physics-informed neural networks (PINNs) have been demonstrated to be efficient in solving partial differential equations (PDEs) from a variety of experimental perspectives. Some recent studies have also proposed PINN algorithms for PDEs on surfaces, including spheres. However, theoretical understanding of the numerical performance of PINNs, especially PINNs on surfaces or manifolds, is still lacking. In this paper, we establish rigorous analysis of the physics-informed convolutional neural network (PICNN) for solving PDEs on the sphere. By using and improving the latest approximation results of deep convolutional neural networks and spherical harmonic analysis, we prove an upper bound for the approximation error with respect to the Sobolev norm. Subsequently, we integrate this with innovative localization complexity analysis to establish fast convergence rates for PICNN. Our theoretical results are also confirmed and supplemented by our experiments. In light of these findings, we explore potential strategies for circumventing the curse of dimensionality that arises when solving high-dimensional PDEs.
First spatio-spectral Bayesian imaging of SN1006 in X-ray

Supernovae are an important source of energy in the interstellar medium. Young remnants of supernovae have a peak emission in the X-ray region, making them interesting objects for X-ray observations. In particular, the supernova remnant SN1006 is of great interest due to its historical record, proximity and brightness. It has therefore been studied by several X-ray telescopes. Improving the X-ray imaging of this and other remnants is important but challenging as it requires to address a spatially varying instrument response in order to achieve a high signal-to-noise ratio. Here, we use Chandra observations to demonstrate the capabilities of Bayesian image reconstruction using information field theory. Our objective is to reconstruct denoised, deconvolved and spatio-spectral resolved images from X-ray observations and to decompose the emission into different morphologies, namely diffuse and point-like. Further, we aim to fuse data from different detectors and pointings into a mosaic and quantify the uncertainty of our result. Utilizing prior knowledge on the spatial and spectral correlation structure of the two components, diffuse emission and point sources, the presented method allows the effective decomposition of the signal into these. In order to accelerate the imaging process, we introduce a multi-step approach, in which the spatial reconstruction obtained for a single energy range is used to derive an informed starting point for the full spatio-spectral reconstruction. The method is applied to 11 Chandra observations of SN1006 from 2008 and 2012, providing a detailed, denoised and decomposed view of the remnant. In particular, the separated view of the diffuse emission should provide new insights into its complex small-scale structures in the center of the remnant and at the shock front profiles.
A Mathematical Characterization of Minimally Sufficient Robot Brains

This paper addresses the lower limits of encoding and processing the information acquired through interactions between an internal system (robot algorithms or software) and an external system (robot body and its environment) in terms of action and observation histories. Both are modeled as transition systems. We want to know the weakest internal system that is sufficient for achieving passive (filtering) and active (planning) tasks. We introduce the notion of an information transition system (ITS) for the internal system which is a transition system over a space of information states that reflect a robot’s or other observer’s perspective based on limited sensing, memory, computation, and actuation. An ITS is viewed as a filter and a policy or plan is viewed as a function that labels the states of this ITS. Regardless of whether internal systems are obtained by learning algorithms, planning algorithms, or human insight, we want to know the limits of feasibility for given robot hardware and tasks. We establish, in a general setting, that minimal information transition systems (ITSs) exist up to reasonable equivalence assumptions, and are unique under some general conditions. We then apply the theory to generate new insights into several problems, including optimal sensor fusion/filtering, solving basic planning tasks, and finding minimal representations for modeling a system given input-output relations.
Sharp minimax optimality of LASSO and SLOPE under double sparsity assumption

This paper introduces a rigorous approach to establish the sharp minimax optimalities of both LASSO and SLOPE within the framework of double sparse structures, notably without relying on RIP-type conditions. Crucially, our findings illuminate that the achievement of these optimalities is fundamentally anchored in a sparse group normalization condition, complemented by several novel sparse group restricted eigenvalue (RE)-type conditions introduced in this study. We further provide a comprehensive comparative analysis of these eigenvalue conditions. Furthermore, we demonstrate that these conditions hold with high probability across a wide range of random matrices. Our exploration extends to encompass the random design, where we prove the random design properties and optimal sample complexity under both weak moment distribution and sub-Gaussian distribution.
Multi-Period Portfolio Optimisation Using a Regime-Switching Predictive Framework

Regime-switching poses both problems and opportunities for portfolio managers. If a switch in the behaviour of the markets is not quickly detected it can be a source of loss, since previous trading positions may be inappropriate in the new regime. However, if a regime-switch can be detected quickly, and especially if it can be predicted ahead of time, these changes in market behaviour can instead be a source of substantial profit. The work of this paper builds on two previous works by the authors, the first of these dealing with regime detection and the second, which is an extension of the first, with regime prediction. Specifically, this work uses our previous regime-prediction model (KMRF) within a framework of multi-period portfolio optimisation, achieved by model predictive control, (MPC), with the KMRF-derived return estimates accuracy-boosted by means of a novel use of a Kalman filter. The resulting proposed model, which we term the KMRF+MPC model, to reflect its constituent methodologies, is demonstrated to outperform industry-standard benchmarks, even though it is restricted, in order to be acceptable to the widest range of investors, to long-only positions.