Miracle-dz/UniFAR-FTUs huggingface dataset:
SIGGMA: A SURVEY OF IONIZED GAS IN THE GALAXY, MADE WITH THE ARECIBO TELESCOPE

A Survey of Ionized Gas in the Galaxy, made with the Arecibo telescope (SIGGMA), uses the Arecibo L-band Feed Array (ALFA) to fully sample the Galactic plane (30° ⩽ l ⩽ 75° and −2° ⩽ b ⩽ 2°; 175° ⩽ l ⩽ 207° and −2° ⩽ b ⩽ 1°) observable with the telescope in radio recombination lines (RRLs). Processed data sets are being produced in the form of data cubes of 2° (along l) × 4° (along b) × 151 (number of channels), archived and made public. The 151 channels cover a velocity range of 600 km s−1 and the velocity resolution of the survey changes from 4.2 km s−1 to 5.1 km s−1 from the lowest frequency channel to the highest frequency channel. RRL maps with 3. ′4 resolution and a line flux density sensitivity of ∼0.5 mJy will enable us to identify new H ii regions, measure their electron temperatures, study the physics of photodissociation regions with carbon RRLs, and investigate the origin of the extended low-density medium. Twelve Hnα lines fall within the 300 MHz bandpass of ALFA; they are resampled to a common velocity resolution to improve the signal-to-noise ratio (S/N) by a factor of three or more and preserve the line width. SIGGMA will produce the most sensitive fully sampled RRL survey to date. Here, we discuss the observing and data reduction techniques in detail. A test observation toward the H ii region complex S255/S257 has detected Hnα and Cnα lines with S/N > 10.
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning

This work focuses on equilibrium selection in no-conflict multi-agent games, where we specifically study the problem of selecting a Pareto-optimal Nash equilibrium among several existing equilibria. It has been shown that many state-of-the-art multi-agent reinforcement learning (MARL) algorithms are prone to converging to Pareto-dominated equilibria due to the uncertainty each agent has about the policy of the other agents during training. To address sub-optimal equilibrium selection, we propose Pareto Actor-Critic (Pareto-AC), which is an actor-critic algorithm that utilises a simple property of no-conflict games (a superset of cooperative games): the Pareto-optimal equilibrium in a no-conflict game maximises the returns of all agents and, therefore, is the preferred outcome for all agents. We evaluate Pareto-AC in a diverse set of multi-agent games and show that it converges to higher episodic returns compared to seven state-of-the-art MARL algorithms and that it successfully converges to a Pareto-optimal equilibrium in a range of matrix games. Finally, we propose PACDCG, a graph neural network extension of Pareto-AC, which is shown to efficiently scale in games with a large number of agents.
Sequential Attention for Feature Selection

Feature selection is the problem of selecting a subset of features for a machine learning model that maximizes model quality subject to a budget constraint. For neural networks, prior methods, including those based on $\ell_1$ regularization, attention, and other techniques, typically select the entire feature subset in one evaluation round, ignoring the residual value of features during selection, i.e., the marginal contribution of a feature given that other features have already been selected. We propose a feature selection algorithm called Sequential Attention that achieves state-of-the-art empirical results for neural networks. This algorithm is based on an efficient one-pass implementation of greedy forward selection and uses attention weights at each step as a proxy for feature importance. We give theoretical insights into our algorithm for linear regression by showing that an adaptation to this setting is equivalent to the classical Orthogonal Matching Pursuit (OMP) algorithm, and thus inherits all of its provable guarantees. Our theoretical and empirical analyses offer new explanations towards the effectiveness of attention and its connections to overparameterization, which may be of independent interest.
Open charm mesons at nonzero temperature: results in the hadronic phase from lattice QCD

We study what happens to D and D_s mesons as the temperature increases, using lattice QCD simulations with N_f=2+1 dynamical flavours on anistropic lattices. We have access to five temperatures in the hadronic phase. Using the determined groundstate mass at the lowest temperature, we investigate the effect of rising temperature by analysing ratios of mesonic correlators, without the need for further fitting or spectral reconstruction. In the pseudoscalar and vector channels, we demonstrate that temperature effects are at the percent level and can be captured by a reduction of the groundstate mass as the thermal crossover is approached. In the axial-vector and scalar channels on the other hand, temperature effects are prominent throughout the hadronic phase.
A review of univariate and multivariate multifractal analysis illustrated by the analysis of marathon runners physiological data

We review the central results concerning wavelet methods in multifractal analysis, which consists in analysis of the pointwise singularities of a signal, and we describe its recent extension to multivariate multifractal analysis, which deals with the joint analysis of several signals; we focus on the mathematical questions that this new techniques motivate. We illustrate these methods by an application to data recorded on marathon runners.
Analyzing Diffusion as Serial Reproduction

Diffusion models are a class of generative models that learn to synthesize samples by inverting a diffusion process that gradually maps data into noise. While these models have enjoyed great success recently, a full theoretical understanding of their observed properties is still lacking, in particular, their weak sensitivity to the choice of noise family and the role of adequate scheduling of noise levels for good synthesis. By identifying a correspondence between diffusion models and a well-known paradigm in cognitive science known as serial reproduction, whereby human agents iteratively observe and reproduce stimuli from memory, we show how the aforementioned properties of diffusion models can be explained as a natural consequence of this correspondence. We then complement our theoretical analysis with simulations that exhibit these key features. Our work highlights how classic paradigms in cognitive science can shed light on state-of-the-art machine learning problems.
NEAR OPTIMALITY OF FINITE SPACE AND DISCRETE-TIME APPROXIMATIONS IN CONTROLLED DIFFUSIONS UNDER SEVERAL COST CRITERIA USING THE BORKAR CONTROL TOPOLOGY

We ﬁrst show that the discounted cost, cost up to an exit time, and ergodic cost involving controlled non-degenerate diﬀusions are continuous on the space of stationary control policies when the policies are given a topology introduced by Borkar [V. S. Borkar, A topology for markov controls, Applied Mathematics and Optimization 20 (1989), 55–62]. The same applies for ﬁnite horizon problems when the control policies are Markov and the topology is revised to include time also as a parameter. We then establish that ﬁnite action/piecewise constant stationary policies are dense in the space of stationary Markov policies under this topology. Using the above mentioned continuity and denseness results we establish that ﬁnite action/piecewise constant policies approximate optimal stationary policies with arbitrary precision. This gives rise to the applicability of many numerical methods such as policy iteration and stochastic learning methods for discounted cost, cost up to an exit time, and ergodic cost optimal control problems in continuous-time. For the ﬁnite-horizon setup, we establish additionally near optimality of time-discretized policies by an analogous argument. We thus present a uniﬁed and concise approach for approximations directly applicable under several commonly adopted cost criteria.
Transmission Model for Resonant Beam SWIPT with Telescope Internal Modulator

To satisfy the long-range and energy self-sustaining communication needs of electronic devices in the Internet of Things (IoT), we introduce a simultaneous wireless information and power transfer (SWIPT) system using the resonant beam that incorporates a telescope modulator inside a cavity for suppressing diffraction losses. We theoretically analyze power transfer in the resonant beam system with telescope internal modulator (TIM-RBS) considering the electromagnetic field propagation, the end-to-end (E2E) power transfer, and power and information reception. The numerical evaluation demonstrates that the TIM can effectively compress the beam spot, which allows the TIM-RBS to transmit energy twice as far as the RBS without TIM at higher power. Additionally, the largest transmission distance and maximum output power are proportional to the input power, and about 34m transmission distance, 4W electric power, and 12bps/Hz spectral efficiency can be achieved in the TIM-RBS with 200W input power. Hence, TIM-RBS can be considered as a promising option for realizing long-range, high-power, and high-rate SWIPT.
Statistical analysis of the Si i 6560.58 Å line observed by CHASE

The Si I 6560.58 \r{A} line in the H$\alpha$ blue wing is blended with a telluric absorption line from water vapor in ground-based observations. Recent observations with the space-based telescope CHASE provide a new window to study this line. We aim to study the Si I line statistically and to explore possible diagnostics. We select three scannings in the CHASE observations, and measure the equivalent width (EW) and the full width at half maximum (FWHM) for each pixel on the solar disk. We then calculate the theoretical EW and FWHM from the VALC model. An active region is also studied in particular for difference in the quiet Sun and the sunspots. The Si I line is formed at the bottom of the photosphere. The EW of this line increases from the disk center to $\mu$ = 0.2, and then decreases toward the solar limb, while the FWHM shows a monotonically increasing trend. Theoretically predicted EW agrees well with observations, while the predicted FWHM is far smaller due to the absence of unresolved turbulence in models. The macroturbulent velocity is estimated to be 2.80 km s$^{-1}$ at the disk center, and increases to 3.52 km s$^{-1}$ at $\mu$ = 0.2. We do not find any response to flare heating in current observations. Doppler shifts and line widths of the Si I 6560.58 \r{A} and Fe I 6569.21 \r{A} lines can be used to study the mass flows and turbulence of the different photospheric layers. The Si I line has good potentials to diagnose the dynamics and energy transport in the photosphere.
A Semianalytical Line Transfer Model. III. Galactic Inflows

We present calculations of ultraviolet spectra resulting from the scattering of photons by gas infalling onto an isotropically emitting source of radiation. The model is based on an adaptation of the semianalytical line transfer (SALT) code of Scarlata & Panagia, and designed to interpret the inverse P Cygni profiles observed in the spectra of partially ionized galactic inflows. In addition to presenting the model, we explore the parameter space of the inflowing SALT model and recreate various physically motivated scenarios including spherical inflows, inflows with covering fractions less than unity, and galactic fountains (i.e., galactic systems with both an inflowing and outflowing component). The resulting spectra from inflowing gas show spectral features that could be misinterpreted as interstellar medium features in low resolution spectroscopy (σ ≈ 120 km s−1), suggesting that the total number of galactic systems with inflows is undercounted. Our models suggest that observations at medium resolution (R = 6000 or σ ≈ 50 km s−1) that can be obtained with 8 m class telescopes will be able to resolve the characteristic inverse P Cygni profiles necessary to identify inflows.
Measurement of three-dimensional coherent fluid structure in high Reynolds number turbulent boundary layers

The turbulent boundary layer is an aspect of fluid flow which dominates the performance of many engineering systems yet the analytic solution of such flows is intractable for most applications. Our understanding of boundary layers is therefore limited by our ability to simulate and measure them. Tomographic Particle Image Velocimetry (TPIV) is a recently developed technique for direct measurement of fluid velocity within a 3D region. This allows new insight into the topological structure of turbulent boundary layers. Increasing Reynolds Number increases the range of scales at which turbulence exists; a measurement technique must have a larger ’dynamic range’ to fully resolve the flow. Tomographic PIV is currently limited in spatial dynamic range (which is also linked to the spatial and temporal resolution) due to a high degree of noise. Results also contain significant bias error. This work proposes a modification of the technique to use more than two exposures in the PIV process, which (for four exposures) is shown to improve random error by a factor of 2 to 7 depending on experimental setup parameters. The dynamic range increases correspondingly and can be doubled again in highly turbulent flows. Bias error is reduced by up to 40%. An alternative reconstruction approach is also presented, based on application of a reduction strategy (elimination of coefficients based on a first guess) to the tomographic weightings matrixWij. This facilitates a potentially significant increase in computational efficiency. Despite the achieved reduction in error, measurements contain non-zero divergence due to noise and sampling errors. The same problem affects visualisation of topology and coherent fluid structures. Using Projection Onto Convex Sets, a framework for post-processing operators is implemented which includes a divergence minimisation procedure and a scale-limited denoising strategy which is resilient to ’false’ vectors contained in the data. Finally, developed techniques are showcased by visualisation of topological information in the inner region of a high Reynolds Number boundary layer (δ+ = 1890, Reθ = 3650). Comments are made on the visible flow structures and tentative conclusions are drawn.
Testing SALT Approximations with Numerical Radiation Transfer Code. I. Validity and Applicability

Absorption line spectroscopy offers one of the best opportunities to constrain the properties of galactic outflows and the environment of the circumgalactic medium. Extracting physical information from line profiles is difficult; however, for the physics governing the underlying radiation transfer is complicated and depends on many different parameters. Idealized analytical models are necessary to constrain the large parameter spaces efficiently, but are typically plagued by model degeneracy and systematic errors. Comparison tests with idealized numerical radiation transfer codes offer an excellent opportunity to confront both of these issues. In this paper, we present a detailed comparison between SALT, an analytical radiation transfer model for predicting UV spectra of galactic outflows, with the numerical radiation transfer software, RASCAS. Our analysis has led to upgrades to both models including an improved derivation of SALT and a customizable adaptive mesh refinement routine for RASCAS. We explore how well SALT, when paired with a Monte Carlo fitting procedure, can recover flow parameters from nonturbulent and turbulent flows. Overall we find that turbulence leads to biases in the recovery of kinematic parameters and the optical depth, but find that derived quantities (e.g., mass outflow rates, column density, etc.) are still well recovered. From the analysis, we estimate average uncertainties in our ability to measure metal flow rates spanning 0.65 (0.95) dex in M ⊙ yr−1 and uncertainties spanning 0.54 (0.94) dex in cm−2 for column densities at a resolution of 20 (100) km s−1 and signal-to-noise ratio = 10.
Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning

In recent years, the geospatial industry has been developing at a steady pace. This growth implies the addition of satellite constellations that produce a copious supply of satellite imagery and other Remote Sensing data on a daily basis. Sometimes, this information, even if in some cases we are referring to publicly available data, it sits unaccounted for due to the sheer size of it. Processing such large amounts of data with the help of human labour or by using traditional automation methods is not always a viable solution from the standpoint of both time and other resources. Within the present work, we propose an approach for creating a multi-modal and spatio-temporal dataset comprised of publicly available Remote Sensing data and testing for feasibility using state of the art Machine Learning (ML) techniques. Precisely, the usage of Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation that are present in the proposed dataset. Popularity and success of similar methods in the context of Geographical Information Systems (GIS) and Computer Vision (CV) more generally indicate that methods alike should be taken in consideration and further analysed and developed. For the first part, we will start off with an overview of the current problem space and the argument necessity of studying such techniques. Following this, Chapter 2 begins by providing extended details regarding the current state of research for GIS starting from characteristics of Remote Sensing data to how the acquisition of such data is performed and to additional preprocessings that could be required. Within Chapter 2 we also present informations about the inner workings of ML with respect to current heuristics that are used in the literature for solving various CV tasks. Chapter 3 showcases the approach taken for developing an appropriate multi-modal spatio-temporal Remote Sensing dataset publicly available data. Some results on experimenting with ML techniques for training Convolutional Neural Networks (CNN) on the task of semantic segmentation of vegetation related classes are also presented for validating the usability and relevance of the aforementioned dataset. Chapter 4 illustrates the results of training such models on the proposed dataset on how they perform on solving the task of semantic segmentation of vegetation.
A case study of spatiotemporal forecasting techniques for weather forecasting

The majority of real-world processes are spatiotemporal, and the data generated by them exhibits both spatial and temporal evolution. Weather is one of the most important processes that fall under this domain, and forecasting it has become a crucial part of our daily routine. Weather data analysis is considered the most complex and challenging task. Although numerical weather prediction models are currently state-of-the-art, they are resource intensive and time-consuming. Numerous studies have proposed time-series-based models as a viable alternative to numerical forecasts. Recent research has primarily focused on forecasting weather at a specific location. Therefore, models can only capture temporal correlations. This self-contained paper explores various methods for regional data-driven weather forecasting, i.e., forecasting over multiple latitude-longitude points to capture spatiotemporal correlations. The results showed that spatiotemporal prediction models reduced computational cost while improving accuracy; in particular, the proposed tensor train dynamic mode decomposition-based forecasting model has comparable accuracy to ConvLSTM without the need for training. We use the NASA POWER meteorological dataset to evaluate the models and compare them with the current state of the art.
IvySyn: Automated Vulnerability Discovery in Deep Learning Frameworks

We present IvySyn, the first fully-automated framework for discovering memory error vulnerabilities in Deep Learning (DL) frameworks. IvySyn leverages the statically-typed nature of native APIs in order to automatically perform type-aware mutation-based fuzzing on low-level kernel code. Given a set of offending inputs that trigger memory safety (and runtime) errors in low-level, native DL (C/C++) code, IvySyn automatically synthesizes code snippets in high-level languages (e.g., in Python), which propagate error-triggering input via high(er)-level APIs. Such code snippets essentially act as"Proof of Vulnerability", as they demonstrate the existence of bugs in native code that an attacker can target through various high-level APIs. Our evaluation shows that IvySyn significantly outperforms past approaches, both in terms of efficiency and effectiveness, in finding vulnerabilities in popular DL frameworks. Specifically, we used IvySyn to test TensorFlow and PyTorch. Although still an early prototype, IvySyn has already helped the TensorFlow and PyTorch framework developers to identify and fix 61 previously-unknown security vulnerabilities, and assign 39 unique CVEs.
Overcoming temperature limits in the optical cooling of solids using light-dressed states

Laser cooling of solids currently has a temperature floor of 50 - 100 K. We propose a method that could overcome this using defects, such as diamond color centers, with narrow electronic manifolds and bright optical transitions. It exploits the dressed states formed in strong fields which extend the set of phonon transitions and have tunable energies. This allows an enhancement of the cooling power and diminishes the effect of inhomogeneous broadening. We demonstrate these effects theoretically for the silicon-vacancy and the germanium-vacancy, and discuss the role of background absorption, phonon-assisted emission, and non-radiative decay.
Quantum invariants for the graph isomorphism problem

Graph Isomorphism is such an important problem in computer science, that it has been widely studied over the last decades. It is well known that it belongs to NP class, but is not NP-complete. It is thought to be of comparable difficulty to integer factorisation. The best known proved algorithm to solve this problem in general, was proposed by L\'aszl\'o Babai and Eugene Luks in 1983. Recently, there has been some research in the topic by using quantum computing, that also leads the present piece of research. In fact, we present a quantum computing algorithm that defines an invariant over Graph Isomorphism characterisation. This quantum algorithm is able to distinguish more non-isomorphic graphs than most of the known invariants so far. The proof of correctness and some hints illustrating the extent and reason of the improvement are also included in this paper.