Miracle-dz/UniFAR-FTUs huggingface dataset:
Hartle formalism for rotating Newtonian configurations

We apply the Hartle formalism to study equilibrium configurations in the framework of Newtonian gravity. This approach allows one to study in a simple manner the properties of the interior gravitational field in the case of static as well as stationary rotating stars in hydrostatic equilibrium. It is shown that the gravitational equilibrium conditions reduce to a system of ordinary differential equations which can be integrated numerically. We derive all the relevant equations up to the second order in the angular velocity. Moreover, we find explicitly the total mass, the moment of inertia, the quadrupole moment, the polar and equatorial radii, the eccentricity and the gravitational binding energy of the rotating body. We also present the procedure to calculate the gravitational Love number. We test the formalism in the case of white dwarfs and show its compatibility with the known results in the literature.
Constraining the distribution of dark matter at the Galactic centre using the high-resolution Event Horizon Telescope

We investigate constraints on the distribution of dark matter in the neighbourhood of the Galactic centre that may eventually be attained with the high-resolution Event Horizon Telescope (EHT). The shadow of a black hole in vacuum is used to generate a toy model describing how dark matter a ects the size of the shadow of the supermassive black hole located at the Galactic centre. Observations by the EHT may constrain the properties of the dark matter distribution in a possible density spike around the black hole. Current uncertainties due to both the resolution of the telescope and the analysis of stellar orbits prevent one from discerning the e ect of dark matter on the measured size of the shadow. The change in the size of the shadow induced by dark matter can be seen as an additional uncertainty in any test of general relativity that relies on using the angular size of the shadow to estimate the Schwarzschild radius of the black hole. In the framework of the standard model of cosmology, dark matter comprises about 23 percent of the energy content of the universe. Consequently, unravelling the mystery of the nature of dark matter is one of the most challenging problems of modern cosmology. In this context, astronomical observations can provide significant constraints on the dark matter distribution. The dawning of a new era of telescopes with high resolution and sensitivity has widened the scope of precision experiments that may be used to set constraints on the properties of dark matter. In this work, we focus on one such experiment. We consider an experiment focusing on the Galactic centre, since the centre of the Milky Way has become a favoured region for searches for dark matter signatures. The dark matter density is indeed expected to be higher in the neighbourhood of the Galactic centre, according to N-body simulations. Moreover, if dark matter is indeed made of weakly interacting particles, there is likely to be an enhancement of dark matter around the supermassive black hole Sgr A* located at the centre of the Milky Way.
Experimental investigation of the radial structure of energetic particle driven modes

Alfvén eigenmodes (AEs) and energetic particle modes (EPMs) are often excited by energetic particles (EPs) in tokamak plasmas. One of the main open questions concerning EP driven instabilities is the non-linear evolution of the mode structure. The aim of the present paper is to investigate the properties of beta-induced AEs (BAEs) and EP driven geodesic acoustic modes (EGAMs) observed in the ramp-up phase of off-axis NBI heated ASDEX Upgrade (AUG) discharges. This paper focuses on the changes in the mode structure of BAEs/EGAMs during the non-linear chirping phase. Our investigation has shown that in the case of the observed down-chirping BAEs the changes in the radial structure are smaller than the uncertainty of our measurement. This behaviour is most probably the consequence of the fact that BAEs are normal modes, thus their radial structure strongly depends on the background plasma parameters rather than on the EP distribution. In the case of rapidly upward chirping EGAMs the analysis consistently shows shrinkage of the mode structure. The proposed explanation is that the resonance in the velocity space moves towards more passing particles which have narrower orbit widths.
Spacetime approach to force-free magnetospheres

Force-Free Electrodynamics (FFE) describes magnetically dominated relativistic plasma via non-linear equations for the electromagnetic field alone. Such plasma is thought to play a key role in the physics of pulsars and active black holes. Despite its simple covariant formulation, FFE has primarily been studied in 3+1 frameworks, where spacetime is split into space and time. In this article we systematically develop the theory of force-free magnetospheres taking a spacetime perspective. Using a suite of spacetime tools and techniques (notably exterior calculus) we cover 1) the basics of the theory, 2) exact solutions that demonstrate the extraction and transport of the rotational energy of a compact object (in the case of a black hole, the Blandford-Znajek mechanism), 3) the behavior of current sheets, 4) the general theory of stationary, axisymmetric magnetospheres and 5) general properties of pulsar and black hole magnetospheres. We thereby synthesize, clarify and generalize known aspects of the physics of force-free magnetospheres, while also introducing several new results.
Raman-scattered Ne vii λ973 at 4881 Å in the symbiotic star V1016 Cygni

We present the high resolution spectra of the symbiotic star V1016 Cygni obtained with the Bohyunsan Optical Echelle Spectrograph in 2003 and 2005, from which we find a broad emission feature at 4881 \AA. We propose that this broad feature is formed from Raman scattering of Ne VII$\lambda973$ by atomic hydrogen. Thus far, the detection of Raman scattered lines by atomic hydrogen is limited to O VI$\lambda\lambda$1032, 1038 and He II$\lambda\lambda$940, 972 and 1025. With the adoption of the center wavelength 973.302 \AA\ of Ne VII$\lambda$973 and consideration of the air refractive index of $n_{air}=1.000279348$, the atomic line center of the Raman scattered Ne VII feature is determined to be 4880.53 \AA. The total cross section at the line center of Ne VII$\lambda973$ is computed to be $2.62\times 10^{-22}{\rm\ cm^2}$ with the branching ratio of 0.17. We perform Monte Carlo simulations to fit the Raman scattered Ne VII$\lambda973$. Assuming that the Ne VII and He II emission regions share the same kinematics with respect to the neutral scattering region, we find that the Raman scattered He II$\lambda$972 at 4850 \AA\ and Ne VII$\lambda$973 at 4881 \AA\ are excellently fitted. We also propose that the He II and Ne VII emission regions were stationary with respect to the H I region in 2003 but that they were receding from it with a velocity $\sim 20{\rm\ km\ s^{-1}}$ in 2005.
GammaTile: Comprehensive Review of a Novel Radioactive Intraoperative Seed-Loading Device for the Treatment of Brain Tumors

GammaTile is a Food and Drug Administration (FDA)-licensed device consisting of four cesium-131 (Cs-131) radiation-emitting seeds in the collagen tile about the postage stamp size. The tiles are utilized to line the brain cavity immediately after tumor resection. GammaTile therapy is a surgically targeted radiation therapy (STaRT) that helps provide instant, dose-intense treatment after the completion of resection. The objective of this study is to explore the safety and efficacy of GammaTile surgically targeted radiation therapy for brain tumors. This study also reviews the differences between GammaTile surgically targeted radiation therapy (STaRT) and other traditional treatment options for brain tumors. The electronic database searches utilized in this study include PubMed, Google Scholar, and ScienceDirect. A total of 4,150 articles were identified based on the search strategy. Out of these articles, 900 articles were retrieved. A total of 650 articles were excluded for various reasons, thus retrieving 250 citations. We applied the exclusion and inclusion criteria to these retrieved articles by screening their full text and excluding 180 articles. Therefore, 70 citations were retrieved and included in this comprehensive literature review, as outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram. Based on the findings of this study, GammaTile surgically targeted radiation therapy (STaRT) is safe and effective for treating brain tumors. Similarly, the findings have also shown that the efficacy of GammaTile therapy can be enhanced by combining it with other standard-of-care treatment options/external beam radiation therapy (EBRT). Also, the results show that patients diagnosed with recurrent glioblastoma (GBM) exhibit poor median overall survival because of the possibility of the tumor returning. Therefore, combining STaRT with other standard-of-care treatment options/EBRT can improve the patient's overall survival (OS). GammaTile therapy enhances access to care, guarantees 100% compliance, and eliminates patients' need to travel regularly to hospitals for radiation treatments. Its implementation requires collaboration from various specialties, such as radiation oncology, medical physics, and neurosurgery.
Shift‐share Analyses of Malaysia’s Export Competitiveness: The Case of Oleochemical Products

Palm oil is the most commonly used edible oil globally and is found in many consumer products. In Malaysia, this industry is one of the key economic drivers of the Malaysian economy. Despite the recent covid-19 pandemic year, Malaysia's palm oil industry has faced challenges from reduced demand from China, as well as unfavourable prices.
THE NGC 404 NUCLEUS: STAR CLUSTER AND POSSIBLE INTERMEDIATE-MASS BLACK HOLE

We examine the nuclear morphology, kinematics, and stellar populations in nearby S0 galaxy NGC 404 using a combination of adaptive optics assisted near-IR integral-field spectroscopy, optical spectroscopy, and Hubble Space Telescope imaging. These observations enable study of the NGC 404 nucleus at a level of detail possible only in the nearest galaxies. The surface brightness profile suggests the presence of three components: a bulge, a nuclear star cluster (NSC), and a central light excess within the cluster at radii < 3 pc. These components have distinct kinematics with modest rotation seen in the NSC and counter-rotation seen in the central excess. Molecular hydrogen emission traces a disk with rotation nearly orthogonal to that of the stars. The stellar populations of the three components are also distinct, with half of the mass of the NSC having ages of ∼1 Gyr (perhaps resulting from a galaxy merger), while the bulge is dominated by much older stars. Dynamical modeling of the stellar kinematics gives a total NSC mass of 1.1 × 107 M☉. Dynamical detection of a possible intermediate-mass black hole (BH) is hindered by uncertainties in the central stellar mass profile. Assuming a constant mass-to-light ratio, the stellar dynamical modeling suggests a BH mass of <1 × 105 M☉, while the molecular hydrogen gas kinematics are best fitted by a BH with a mass of 4.5+3.5−2.0 × 105 M☉. Unresolved and possibly variable dust emission in the near-infrared and active galactic nucleus-like molecular hydrogen emission-line ratios do suggest the presence of an accreting BH in this nearby LINER galaxy.
Using machine learning and clinical registry data to uncover variation in clinical decision making

Clinical registry data contains a wealth of information on patients, clinical practice, outcomes and interventions. Machine learning algorithms are able to learn complex patterns from data. We present methods for using machine learning with clinical registry data to carry out retrospective audit of clinical practice. Using a registry of stroke patients, we demonstrate how machine learning can be used to: investigate whether patients would have been treated differently had they attended a different hospital; group hospitals according to clinical decision making practice; identify where there is variation in decision making between hospitals; characterise patients that hospitals find it hard to agree on how to treat. Our methods should be applicable to any clinical registry and any machine learning algorithm to investigate the extent to which clinical practice is standardized and identify areas for improvement at a hospital level.
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization

Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as it removes the need for expert knowledge or pre-solved instances. However, it is unrealistic to expect an agent to solve these (often NP-)hard problems in a single shot at inference due to their inherent complexity. Thus, leading approaches often implement additional search strategies, from stochastic sampling and beam search to explicit fine-tuning. In this paper, we argue for the benefits of learning a population of complementary policies, which can be simultaneously rolled out at inference. To this end, we introduce Poppy, a simple training procedure for populations. Instead of relying on a predefined or hand-crafted notion of diversity, Poppy induces an unsupervised specialization targeted solely at maximizing the performance of the population. We show that Poppy produces a set of complementary policies, and obtains state-of-the-art RL results on four popular NP-hard problems: traveling salesman, capacitated vehicle routing, 0-1 knapsack, and job-shop scheduling.
Pion Oscillations

Pions produced via the D+ → π+K0 decay are in an entangled superposition of energy states because the K0 is not a mass eigenstate. This presents an opportunity to access kaon mixing parameters via π decay accompanied by flavor tagged kaons. The pion oscillation frequency is ∆mK mK mD , about 224 MHz. This method may help refine measurements of ∆mD0 via B + → π+D0. PACS numbers: 14.40. -n 13.20. Fc 14.40. Df
Dynamic Latent Separation for Deep Learning

A core problem in machine learning is to learn expressive latent variables for model prediction on complex data that involves multiple sub-components in a flexible and interpretable fashion. Here, we develop an approach that improves expressiveness, provides partial interpretation, and is not restricted to specific applications. The key idea is to dynamically distance data samples in the latent space and thus enhance the output diversity. Our dynamic latent separation method, inspired by atomic physics, relies on the jointly learned structures of each data sample, which also reveal the importance of each sub-component for distinguishing data samples. This approach, atom modeling, requires no supervision of the latent space and allows us to learn extra partially interpretable representations besides the original goal of a model. We empirically demonstrate that the algorithm also enhances the performance of small to larger-scale models in various classification and generation problems.
A short proof of Kotzig's theorem

A new shortest proof of Kotzig's Theorem about graphs with unique perfect matching is presented in this paper. It is well known that Kotzig's theorem is a consequence of Yeo's Theorem about edge-colored graph without alternating cycle. We present a proof of Yeo's Theorem based on the same ideas as our proof of Kotzig's theorem.
Artificial Intelligence and Natural Language Processing and Understanding in Space: A Methodological Framework and Four ESA Case Studies

The European Space Agency is well known as a powerful force for scientific discovery in numerous areas related to Space. The amount and depth of the knowledge produced throughout the different missions carried out by ESA and their contribution to scientific progress is enormous, involving large collections of documents like scientific publications, feasibility studies, technical reports, and quality management procedures, among many others. Through initiatives like the Open Space Innovation Platform, ESA also acts as a hub for new ideas coming from the wider community across different challenges, contributing to a virtuous circle of scientific discovery and innovation. Handling such wealth of information, of which large part is unstructured text, is a colossal task that goes beyond human capabilities, hence requiring automation. In this paper, we present a methodological framework based on artificial intelligence and natural language processing and understanding to automatically extract information from Space documents, generating value from it, and illustrate such framework through several case studies implemented across different functional areas of ESA, including Mission Design, Quality Assurance, Long-Term Data Preservation, and the Open Space Innovation Platform. In doing so, we demonstrate the value of these technologies in several tasks ranging from effortlessly searching and recommending Space information to automatically determining how innovative an idea can be, answering questions about Space, and generating quizzes regarding quality procedures. Each of these accomplishments represents a step forward in the application of increasingly intelligent AI systems in Space, from structuring and facilitating information access to intelligent systems capable to understand and reason with such information.
Leveraging Structure from Motion to Localize Inaccessible Bus Stops

—The detection of hazardous conditions near pub- lic transit stations is necessary for ensuring the safety and accessibility of public transit. Smart city infrastructures aim to facilitate this task among many others through the use of computer vision. However, most state-of-the-art computer vision models require thousands of images in order to perform accurate detection, and there exist few images of hazardous conditions as they are generally rare. In this paper, we examine the detection of snow-covered sidewalks along bus routes. Previous work has focused on detecting other vehicles in heavy snowfall or simply detect- ing the presence of snow. However, our application has an added complication of determining if the snow covers areas of importance and can cause falls or other accidents (e.g. snow covering a sidewalk) or simply covers some background area (e.g. snow on a neighboring ﬁeld). This problem involves localizing the positions of the areas of importance when they are not necessarily visible. We introduce a method that utilizes Structure from Motion (SfM) rather than additional annotated data to address this issue. Speciﬁcally, our method learns the locations of sidewalks in a given scene by applying a segmentation model and SfM to images from bus cameras during clear weather. Then, we use the learned locations to detect if and where the sidewalks become obscured with snow. After evaluating across various threshold parameters, we identify an optimal range at which our method consistently classiﬁes different categories of side- walk images correctly. Although we demonstrate an application for snow coverage along bus routes, this method can extend to other hazardous conditions as well. Code for this project is available at https://github.com/ind1010/SfM_for_ BusEdge .
Shape, Velocity, and Exact Controllability for the Wave Equation

Exact controllability is proven on a graph with cycle. The controls can be a mix of controls applied at the boundary and interior vertices. The method of proof ﬁrst uses a dynamical argument to prove shape controllability and velocity controllability, thereby solving their associated moment problems. This enables one to solve the moment problem associated to exact controllability. In the case of a single control, either boundary or interior, it is shown that exact controllability fails.
Low-Latency Adiabatic Quantum-Flux-Parametron Circuit Integrated With a Hybrid Serializer/Deserializer

Adiabatic quantum-flux-parametron (AQFP) logic is an ultra-low-power superconductor logic family. AQFP logic gates are powered and clocked by dedicated clocking schemes using ac excitation currents to implement an energy-efficient switching process, adiabatic switching. We have proposed a low-latency clocking scheme, delay-line clocking, and demonstrated basic AQFP logic gates. In order to test more complex circuits, a serializer/deserializer (SerDes) should be incorporated into the AQFP circuit under test, since the number of input/output (I/O) cables is limited by equipment. Therefore, in the present study we propose and develop a novel SerDes for testing delay-line-clocked AQFP circuits by combining AQFP and rapid single-flux-quantum (RSFQ) logic families, which we refer to as the AQFP/RSFQ hybrid SerDes. The hybrid SerDes comprises RSFQ shift registers to facilitate the data storage during serial-to-parallel and parallel-to-serial conversion. Furthermore, all the component circuits in the hybrid SerDes are clocked by the identical excitation current to synchronize the AQFP and RSFQ parts. We fabricate and demonstrate a delay-line-clocked AQFP circuit (8-to-3 encoder, which is the largest delay-line-clocked circuit ever designed) integrated with the hybrid SerDes at 4.2 K up to 4.5 GHz. Our measurement results indicate that the hybrid SerDes enables the testing of delay-line-clocked AQFP circuits with only a few I/O cables and is thus a powerful tool for the development of very large-scale integration AQFP circuits.
Competing ground states of strongly correlated bosons in the Harper-Hofstadter-Mott model

Using an efficient cluster approach, we study the physics of two-dimensional lattice bosons in a strong magnetic field in the regime where the tunneling is much weaker than the on-site interaction strength. We study both dilute, hard core bosons at filling factors much smaller than unity occupation per site, and the physics in the vicinity of the superfluid-Mott lobes as the density is tuned away from unity. For hardcore bosons, we carry out extensive numerics for a fixed flux per plaquette $\phi=1/5$ and $\phi = 1/3$. At large flux, the lowest energy state is a strongly correlated superfluid, analogous to He-$4$, in which the order parameter is dramatically suppressed, but non-zero. At filling factors $\nu=1/2,1$, we find competing incompressible states which are metastable. These appear to be commensurate density wave states. For small flux, the situation is reversed, and the ground state at $\nu = 1/2$ is an incompressible density-wave solid. Here, we find a metastable lattice supersolid phase, where superfluidity and density-wave order coexist. We then perform careful numerical studies of the physics near the vicinity of the Mott lobes for $\phi = 1/2$ and $\phi = 1/4$. At $\phi = 1/2$, the superfluid ground state has commensurate density-wave order. At $\phi = 1/4$, incompressible phases appear outside the Mott lobes at densities $n = 1.125$ and $n = 1.25$, corresponding to filling fractions $\nu = 1/2$ and $1$ respectively. These phases, which are absent in single-site mean-field theory are metastable, and have slightly higher energy than the superfluid, but the energy difference between them shrinks rapidly with increasing cluster size, suggestive of an incompressible ground state. We thus explore the interplay between Mott physics, magnetic Landau levels, and superfluidity, finding a rich phase diagram of competing compressible and incompressible states.
Stochastic normalizing flows for lattice field theory

Stochastic normalizing flows are a class of deep generative models that combine normalizing flows with Monte Carlo updates and can be used in lattice field theory to sample from Boltzmann distributions. In this proceeding, we outline the construction of these hybrid algorithms, pointing out that the theoretical background can be related to Jarzynski's equality, a non-equilibrium statistical mechanics theorem that has been successfully used to compute free energy in lattice field theory. We conclude with examples of applications to the two-dimensional $\phi^4$ field theory.