Miracle-dz/UniFAR-FTUs huggingface dataset:
Electronic structure and magnetism in P4/nmm KCoO2

KCoO2 has been found in 1975 to exist in a unique structure with P4/nmm spacegroup with Co in a square pyramidal coordination with the Co atoms in the plane linked by O in a square arrangement reminiscent of the cuprates but its electronic structure has not been studied until now. Unlike Co atoms in LiCoO2 and NaCoO2 in octahedral coordination, which are non-magnetic band structure insulators, the unusual coordination of d6 Co^{3+} in KCoO2 is here shown to lead to a magnetic stabilization of an insulating structure with high magnetic moments of 4{\mu}B per Co. The electronic band structure is calculated using the quasiparticle self-consistent (QS)GW method and the basic formation of magnetic moments is explained in terms of the orbital decomposition of the bands. The optical dielectric function is calculated using the Bethe-Salpeter equation including only transitions between equal spin bands. The magnetic moments are shown to prefer an antiferromagnetic ordering along the [110] direction. Exchange interactions are calculated from the transverse spin susceptibility and a rigid spin approximation. The N\'eel temperature is estimated using the mean-field and Tyablikov methods and found to be between approximately 100 and 250 K. The band structure in the AFM ordering can be related to the FM ordering by band folding effects. The optical spectra are similar in both structures and show evidence of excitonic features below the quasiparticle gap of about 4 eV.
On the isomorphism problem for central extensions I

Let $G_{2}$ be a group which acts trivially on an abelian group $G_{1}$. As is well known, each perturbed direct product of $G_{1}$ and $G_{2}$ under a 2-cocycle $\varepsilon\in Z^{2}(G_{2},G_{1})$ determines a central extension of $G_{1}$ by $G_{2}$. The purpose of this paper is to study perturbed direct products of groups and to decide in some cases how the isomorphism of these groups can be decided. Furthermore, we show that the study of the isomorphism of perturbed direct products of an abelian torsion group and a finite group is reduced to the study of the isomorphism of $p$-subgroups. We characterize such isomorphisms in various situations with some assumptions on the quotient group.
Generative Kaleidoscopic Networks

We discovered that the neural networks, especially the deep ReLU networks, demonstrate an `over-generalization' phenomenon. That is, the output values for the inputs that were not seen during training are mapped close to the output range that were observed during the learning process. In other words, the neural networks learn a many-to-one mapping and this effect is more prominent as we increase the number of layers or the depth of the neural network. We utilize this property of neural networks to design a dataset kaleidoscope, termed as `Generative Kaleidoscopic Networks'. Briefly, if we learn a model to map from input $x\in\mathbb{R}^D$ to itself $f_\mathcal{N}(x)\rightarrow x$, the proposed `Kaleidoscopic sampling' procedure starts with a random input noise $z\in\mathbb{R}^D$ and recursively applies $f_\mathcal{N}(\cdots f_\mathcal{N}(z)\cdots )$. After a burn-in period duration, we start observing samples from the input distribution and the quality of samples recovered improves as we increase the depth of the model. Scope: We observed this phenomenon to various degrees for the other deep learning architectures like CNNs, Transformers&U-Nets and we are currently investigating them further.
Prompt Black Hole Formation in Binary Neutron Star Mergers

We carry out an in-depth analysis of the prompt-collapse behaviour of binary neutron star (BNS) mergers. To this end, we perform more than $80$ general relativistic BNS merger simulations using a family of realistic Equations of State (EOS) with different stiffness, which feature a first order deconfinement phase transition between hadronic and quark matter. From these simulations we infer the critical binary mass $M_{\rm crit}$ that separates the prompt from the non-prompt collapse regime. We show that the critical mass increases with the stiffness of the EOS and obeys a tight quasi-universal relation, $M_{\rm crit}/M_{\rm TOV}\approx 1.41\pm 0.06$, which links it to the maximum mass $M_{\rm TOV}$ of static neutron stars, and therefore provides a straightforward estimate for the total binary mass beyond which prompt collapse becomes inevitable. In addition, we introduce a novel gauge independent definition for a one-parameter family of threshold masses in terms of curvature invariants of the Riemann tensor which characterizes the development toward a more rapid collapse with increasing binary mass. Using these diagnostics, we find that the amount of matter remaining outside the black hole sharply drops in supercritical mass mergers compared to subcritical ones and is further reduced in mergers where the black hole collapse is induced by the formation of a quark matter core. This implies that $M_{\rm crit}$, particularly for merger remnants featuring quark matter cores, imposes a strict upper limit on the emission of any detectable electromagnetic counterpart in BNS mergers.
Stochastic Hessian Fittings on Lie Groups

This paper studies the fitting of Hessian or its inverse for stochastic optimizations using a Hessian fitting criterion from the preconditioned stochastic gradient descent (PSGD) method, which is intimately related to many commonly used second order and adaptive gradient optimizers, e.g., BFGS, Gaussian-Newton and natural gradient descent, AdaGrad, etc. Our analyses reveal the efficiency and reliability differences among a wide range of preconditioner fitting methods, from closed-form to iterative solutions, using Hessian-vector products or stochastic gradients only, with Hessian fittings in the Euclidean space, the manifold of symmetric positive definite (SPL) matrices, or a variety of Lie groups. The most intriguing discovery is that the Hessian fitting itself as an optimization problem is strongly convex under mild conditions on a specific yet general enough Lie group. This discovery turns Hessian fitting into a well behaved optimization problem, and facilitates the designs of highly efficient and elegant Lie group sparse preconditioner fitting methods for large scale stochastic optimizations.
Real-World Planning with PDDL+ and Beyond

Real-world applications of AI Planning often require a highly expressive modeling language to accurately capture important intricacies of target systems. Hybrid systems are ubiquitous in the real-world, and PDDL+ is the standardized modeling language for capturing such systems as planning domains. PDDL+ enables accurate encoding of mixed discrete-continuous system dynamics, exogenous activity, and many other interesting features exhibited in realistic scenarios. However, the uptake in usage of PDDL+ has been slow and apprehensive, largely due to a general shortage of PDDL+ planning software, and rigid limitations of the few existing planners. To overcome this chasm, we present Nyx, a novel PDDL+ planner built to emphasize lightness, simplicity, and, most importantly, adaptability. The planner is designed to be effortlessly customizable to expand its capabilities well beyond the scope of PDDL+. As a result, Nyx can be tailored to virtually any potential real-world application requiring some form of AI Planning, paving the way for wider adoption of planning methods for solving real-world problems.
Signed-Perturbed Sums Estimation of ARX Systems: Exact Coverage and Strong Consistency (Extended Version)

Sign-Perturbed Sums (SPS) is a system identification method that constructs confidence regions for the unknown system parameters. In this paper, we study SPS for ARX systems, and establish that the confidence regions are guaranteed to include the true model parameter with exact, user-chosen, probability under mild statistical assumptions, a property that holds true for any finite number of observed input-output data. Furthermore, we prove the strong consistency of the method, that is, as the number of data points increases, the confidence region gets smaller and smaller and will asymptotically almost surely exclude any parameter value different from the true one. In addition, we also show that, asymptotically, the SPS region is included in an ellipsoid which is marginally larger than the confidence ellipsoid obtained from the asymptotic theory of system identification. The results are theoretically proven and illustrated in a simulation example.
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence

Plain language summarization with LLMs can be useful for improving textual accessibility of technical content. But how factual are these summaries in a high-stakes domain like medicine? This paper presents FactPICO, a factuality benchmark for plain language summarization of medical texts describing randomized controlled trials (RCTs), which are the basis of evidence-based medicine and can directly inform patient treatment. FactPICO consists of 345 plain language summaries of RCT abstracts generated from three LLMs (i.e., GPT-4, Llama-2, and Alpaca), with fine-grained evaluation and natural language rationales from experts. We assess the factuality of critical elements of RCTs in those summaries: Populations, Interventions, Comparators, Outcomes (PICO), as well as the reported findings concerning these. We also evaluate the correctness of the extra information (e.g., explanations) added by LLMs. Using FactPICO, we benchmark a range of existing factuality metrics, including the newly devised ones based on LLMs. We find that plain language summarization of medical evidence is still challenging, especially when balancing between simplicity and factuality, and that existing metrics correlate poorly with expert judgments on the instance level.
Stable Menus of Public Goods: A Matching Problem

We study a matching problem between agents and public goods, in settings without monetary transfers. Since goods are public, they have no capacity constraints. There is no exogenously defined budget of goods to be provided. Rather, each provided good must justify its cost, leading to strong complementarities in the"preferences"of goods. Furthermore, goods that are in high demand given other already-provided goods must also be provided. The question of the existence of a stable solution (a menu of public goods to be provided) exhibits a rich combinatorial structure. We uncover sufficient conditions and necessary conditions for guaranteeing the existence of a stable solution, and derive both positive and negative results for strategyproof stable matching.
Non-equilibrium pathways to emergent polar supertextures

Ultrafast stimuli can stabilize metastable states of matter inaccessible by equilibrium means. Establishing the spatiotemporal link between ultrafast excitation and metastability is crucial to understanding these phenomena. Here, we use single-shot optical-pump, X-ray-probe measurements to provide snapshots of the emergence of a persistent polar vortex supercrystal in a heterostructure that hosts a fine balance between built-in electrostatic and elastic frustrations by design. By perturbing this balance with photoinduced charges, a starting heterogenous mixture of polar phases disorders within a few picoseconds, resulting in a soup state composed of disordered ferroelectric and suppressed vortex orders. On the pico-to-nanosecond timescales, transient labyrinthine fluctuations form in this soup along with a recovering vortex order. On longer timescales, these fluctuations are progressively quenched by dynamical strain modulations, which drive the collective emergence of a single supercrystal phase. Our results, corroborated by dynamical phase-field modeling, reveal how ultrafast excitation of designer systems generates pathways for persistent metastability.
A System-Dynamic Based Simulation and Bayesian Optimization for Inventory Management

Inventory management is a fundamental challenge in supply chain management. The challenge is compounded when the associated products have unpredictable demands. This study proposes an innovative optimization approach combining system-dynamic Monte-Carlo simulation and Bayesian optimization. The proposed algorithm is tested with a real-life, unpredictable demand dataset to find the optimal stock to meet the business objective. The findings show a considerable improvement in inventory policy. This information is helpful for supply chain analytics decision-making, which increases productivity and profitability. This study further adds sensitivity analysis, considering the variation in demand and expected output in profit percentage. This paper makes a substantial contribution by presenting a simple yet robust approach to addressing the fundamental difficulty of inventory management in a dynamic business environment.
Photoelectron Polarization Vortexes in Strong-Field Ionization

The spin polarization of photoelectrons induced by an intense linearly polarized laser field is investigated using numerical solutions of the time-dependent Schr\"odinger equation in companion with our analytic treatment via the spin-resolved strong-field approximation and classical trajectory Monte Carlo simulations. We demonstrate that, even though the total polarization vanishes upon averaging over the photoelectron momentum, momentum-resolved spin polarization is significant, typically exhibiting a vortex structure relative to the laser polarization axis. The polarization arises from the transfer of spin-orbital coupling in the bound state to the spin-correlated quantum orbits in the continuum. The rescattering of photoelectrons at the atomic core plays an important role in forming the polarization vortex structure, while there is no significant effect of the spin-orbit coupling during the continuum dynamics. Furthermore, spin-polarized electron holography is demonstrated, feasible for extracting fine structural information about the atom.
SpeCrawler: Generating OpenAPI Specifications from API Documentation Using Large Language Models

In the digital era, the widespread use of APIs is evident. However, scalable utilization of APIs poses a challenge due to structure divergence observed in online API documentation. This underscores the need for automatic tools to facilitate API consumption. A viable approach involves the conversion of documentation into an API Specification format. While previous attempts have been made using rule-based methods, these approaches encountered difficulties in generalizing across diverse documentation. In this paper we introduce SpeCrawler, a comprehensive system that utilizes large language models (LLMs) to generate OpenAPI Specifications from diverse API documentation through a carefully crafted pipeline. By creating a standardized format for numerous APIs, SpeCrawler aids in streamlining integration processes within API orchestrating systems and facilitating the incorporation of tools into LLMs. The paper explores SpeCrawler's methodology, supported by empirical evidence and case studies, demonstrating its efficacy through LLM capabilities.
Modified RRT* for Path Planning in Autonomous Driving

Essential tasks in autonomous driving includes environment perception, detection and tracking, path planning and action control. This paper focus on path planning, which is one of the challenging task as it needs to find optimal path in highly complex and dynamic environments. Usually, a driving scenario has large number of obstacles in their route. In this paper, we propose a two-stage path planning algorithm named Angle-based Directed Rapidly exploring Random Trees (AD-RRT*) to address the problem of optimal path in complex environment. The proposed algorithm uses A* algorithm for global path planning and modifies RRT* to bound the samples using angle. The efficiency of the proposed algorithm is evaluated through experiments in different scenarios based on the location and number of obstacles. The proposed algorithm showed higher rate of convergence with reduced time and less number of nodes than the base RRT* algorithm.
Point-Wise Vibration Pattern Production via a Sparse Actuator Array for Surface Tactile Feedback

Surface vibration tactile feedback is capable of conveying various semantic information to humans via the handheld electronic devices, like smartphone, touch panel,and game controller. However, covering the whole device contacting surface with dense actuator arrangement can affect its normal use, how to produce desired vibration patterns at any contact point with only several sparse actuators deployed on the handled device surface remains a significant challenge. In this work, we develop a tactile feedback board with only five actuators in the size of a smartphone, and achieve the precise vibration pattern production that can focus at any desired position all over the board. Specifically, we investigate the vibration characteristics of single passive coil actuator, and construct its vibration pattern model at any position on the feedback board surface. Optimal phase and amplitude modulation, found with the simulated annealing algorithm, is employed with five actuators in a sparse array. And all actuators' vibration patterns are superimposed linearly to synthetically generate different onboard vibration energy distribution for tactile sensing. Experiments demonstrated that for point-wise vibration pattern production on our tactile board achieved an average level of about 0.9 in the Structural Similarity Index Measure (SSIM) evaluation, when compared to the ideal single-point-focused target vibration pattern. The sparse actuator array can be easily embedded into usual handheld electronic devices, which shows a good significant implication for enriching their haptic interaction functionalities.
Light storage in wavy dielectric grating with Kerr nonlinearity

Periodical corrugation in dielectric slab transfers the two waveguide modes at zero Bloch wave number into a leaky resonant mode and a symmetry protected bound states in the continuum (BIC) with small frequency detune. The leaky resonant mode can be directly excited by weak linearly polarized normally incident optical field. In the presence of Kerr nonlinearity, the BIC can be indirectly excited by an optical bistable response. Two types of bistable operations are considered. For the first type, the intensity of the incident field gradually increases to exceed a critical value, and then decreases to zero. For the second type, the intensity is fixed, while the linear polarization angle of the incident field gradually increases to exceed a critical value, and then decreases to 0$^{o}$. Theoretically, the indirectly excited BIC can store the optical energy without loss, even though the intensity of the incident field decreases to zero. Incidence of an optical field with double frequency or orthogonal linear polarization can erase the stored optical field by destroying the BIC. The proposed optical system could function as optical storage and switching device.
FOD-Swin-Net: angular super resolution of fiber orientation distribution using a transformer-based deep model

Identifying and characterizing brain fiber bundles can help to understand many diseases and conditions. An important step in this process is the estimation of fiber orientations using Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI). However, obtaining robust orientation estimates demands high-resolution data, leading to lengthy acquisitions that are not always clinically available. In this work, we explore the use of automated angular super resolution from faster acquisitions to overcome this challenge. Using the publicly available Human Connectome Project (HCP) DW-MRI data, we trained a transformer-based deep learning architecture to achieve angular super resolution in fiber orientation distribution (FOD). Our patch-based methodology, FOD-Swin-Net, is able to bring a single-shell reconstruction driven from 32 directions to be comparable to a multi-shell 288 direction FOD reconstruction, greatly reducing the number of required directions on initial acquisition. Evaluations of the reconstructed FOD with Angular Correlation Coefficient and qualitative visualizations reveal superior performance than the state-of-the-art in HCP testing data. Open source code for reproducibility is available at https://github.com/MICLab-Unicamp/FOD-Swin-Net.
Rate of convergence for first-order singular perturbation problems: Hamilton-Jacobi-Isaacs equations and mean field games of acceleration

This work focuses on the rate of convergence for singular perturbation problems for first-order Hamilton-Jacobi equations. As an application we derive the rate of convergence for singularly perturbed two-players zero-sum deterministic differential games (i.e., leading to Hamilton-Jacobi-Isaacs equations) and, subsequently, in case of singularly perturbed mean field games of acceleration. Namely, we show that in both the models the rate of convergence is $\varepsilon$.
Dirac field, van der Waals gas, Weyssenhoff fluid, Newton particle

We consider the Dirac field in polar formulation, showing that when torsion is taken in effective approximation the theory has the thermodynamic properties of a van der Waals gas, that when the limit of zero chiral angle is taken the theory reduces to that of a Weyssenhoff fluid, and that under the spinless condition it gives the Newtonian particle. This nesting of approximations will allow us to interpret the various objects pertaining to the spinor, with torsion providing a form of negative pressure, and the chiral angle being related to a type of temperature.
Magnetic anisotropy and GGG substrate stray field in YIG films down to millikelvin temperatures

Quantum magnonics investigates the quantum-mechanical properties of magnons such as quantum coherence or entanglement for solid-state quantum information technologies at the nanoscale. The most promising material for quantum magnonics is the ferrimagnetic yttrium iron garnet (YIG), which hosts magnons with the longest lifetimes. YIG films of the highest quality are grown on a paramagnetic gadolinium gallium garnet (GGG) substrate. The literature has reported that ferromagnetic resonance (FMR) frequencies of YIG/GGG decrease at temperatures below 50 K despite the increase in YIG magnetization. We investigated a 97 nm-thick YIG film grown on 500 $\mathrm{\mu}$m-thick GGG substrate through a series of experiments conducted at temperatures as low as 30 mK, and using both analytical and numerical methods. Our findings suggest that the primary factor contributing to the FMR frequency shift is the stray magnetic field created by the partially magnetized GGG substrate. This stray field is antiparallel to the applied external field and is highly inhomogeneous, reaching up to 40 mT in the center of the sample. At temperatures below 500 mK, the GGG field exhibits a saturation that cannot be described by the standard Brillouin function for a paramagnet. Including the calculated GGG field in the analysis of the FMR frequency versus temperature dependence allowed the determination of the cubic and uniaxial anisotropies. We find that the total anisotropy increases more than three times with the decrease in temperature down to 2 K. Our findings enable accurate predictions of the YIG/GGG magnetic systems behavior at low and ultra-low millikelvin temperatures, crucial for developing quantum magnonic devices.