Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact solar UV burst triggered in a magnetic field with a fan-spine topology

Solar UV bursts are small-scale features that exhibit intermittent brightenings that are thought to be due to magnetic reconnection. They are observed abundantly in the chromosphere and transition region, in particular in active regions. We investigate in detail a UV burst related to a magnetic feature that is advected by the moat flow from a sunspot towards a pore. The moving feature is parasitic in that its magnetic polarity is opposite to that of the spot and the pore. We used UV spectroscopic and slit-jaw observations from the IRIS to identify and study chromospheric and transition region spectral signatures of said UV burst. To investigate the magnetic topology surrounding the UV burst, we used a two-hour-long time sequence of simultaneous line-of-sight magnetograms from the HMI and performed data-driven 3D magnetic field extrapolations by means of a magnetofrictional relaxation technique. We can connect UV burst signatures to the overlying EUV coronal loops observed by the AIA. The UV burst shows a variety of extremely broad line profiles indicating plasma flows in excess of $\pm$200 km s$^{-1}$ at times. The whole structure is divided into two spatially distinct zones of predominantly up- and downflows. The magnetic field extrapolations show a persistent fan-spine magnetic topology at the UV burst. The associated 3D magnetic null point exists at a height of about 500 km above the photosphere and evolves co-spatially with the observed UV burst. The EUV emission at the footpoints of coronal loops is correlated with the evolution of the underlying UV burst. The magnetic field around the null point is sheared by photospheric motions, triggering magnetic reconnection that ultimately powers the observed UV burst and energises the overlying coronal loops. The location of the null point suggests that the burst is triggered low in the solar chromosphere.
New developments in fission studies within the time-dependent density functional theory framework

We have extended significantly the microscopic description of the fission process by examining a larger set of observables. We extract neutron and proton numbers of fission fragments, their spins and fission fragment relative orbital angular momentum and their correlations, investigate neutrons emitted at or shortly after scission, excitation energy sharing mechanism, total kinetic energy of fission fragments, and the entanglement entropy. I will present a short overview of our simulations obtained with two independent nuclear energy density functionals.
Magnetic order and spin fluctuations in low-dimensional insulating systems

The current theoretical and experimental situations are reviewed for low-dimensional insulating systems with a low magnetic transition temperature TM and pronounced short-range magnetic order above this temperature. Both the standard and self-consistent spin-wave theories are shown to be insufficient to quantitatively describe the experimental data on these systems. Field-theoretical approaches taking into account the contribution of the spin-fluctuation excitations (neglected in the spin-wave theories) to the thermodynamic properties of ferro- and antiferromagnets are discussed.
High-pressure CO2 dissociation with nanosecond pulsed discharges

We investigate the conversion of CO2 into CO and O2 with nanosecond repetitively pulsed (NRP) discharges in a high-pressure batch reactor. Stable discharges are obtained at up to 12 bar. By-products are measured with gas chromatography. The energy efficiency is determined for a range of processing times, pulse energy, and fill pressures. It is only weakly sensitive to the plasma operating parameters, i.e the extent of CO2 conversion is almost linearly-dependent on the specific energy invested. A conversion rate as high as 14% is achieved with an energy efficiency of 23%. For long processing times, saturation in the yield and a drop in efficiency are observed, due to the increasing role of three-body recombination reactions, as described by zero-dimensional detailed kinetic modeling. The modeling reveals the presence of three-stage kinetics between NRP pulses, controlled by electron-impact CO2 dissociation, vibrational relaxation, and neutral elementary kinetics. Transport effects are shown to be important for CO2 conversion at high pressures. For fill pressures beyond 10 bar, CO2 may locally transit into supercritical states. The supercritical plasma kinetics may bypass atomic oxygen pathways and directly convert CO2 into O2. This work provides a detailed analysis of plasma-based high-pressure CO2 conversion, which is of great relevance to future large-scale sustainable carbon capture, utilization, and storage.
Optimal Liquidation with Signals: the General Propagator Case

We consider a class of optimal liquidation problems where the agent’s trans-actions create transient price impact driven by a Volterra-type propagator along with temporary price impact. We formulate these problems as minimization of a revenue-risk functionals, where the agent also exploits available information on a progressively measurable price predicting signal. By using an inﬁnite dimensional stochastic control approach, we characterize the value function in terms of a solution to a free-boundary L 2 -valued backward stochastic diﬀerential equation and an operator-valued Riccati equation. We then derive analytic solutions to these equations which yields an explicit expression for the optimal trading strategy. We show that our formulas can be implemented in a straightforward and eﬃcient way for a large class of price impact kernels with possible singularities such as the power-law kernel.
Augmentation Invariant Manifold Learning

Data augmentation is a widely used technique and an essential ingredient in the recent advance in self-supervised representation learning. By preserving the similarity between augmented data, the resulting data representation can improve various downstream analyses and achieve state-of-the-art performance in many applications. Despite the empirical effectiveness, most existing methods lack theoretical understanding under a general nonlinear setting. To fill this gap, we develop a statistical framework on a low-dimension product manifold to model the data augmentation transformation. Under this framework, we introduce a new representation learning method called augmentation invariant manifold learning and design a computationally efficient algorithm by reformulating it as a stochastic optimization problem. Compared with existing self-supervised methods, the new method simultaneously exploits the manifold's geometric structure and invariant property of augmented data and has an explicit theoretical guarantee. Our theoretical investigation characterizes the role of data augmentation in the proposed method and reveals why and how the data representation learned from augmented data can improve the $k$-nearest neighbor classifier in the downstream analysis, showing that a more complex data augmentation leads to more improvement in downstream analysis. Finally, numerical experiments on simulated and real datasets are presented to demonstrate the merit of the proposed method.
AI explainability and governance in smart energy systems: A review

Traditional electrical power grids have long suffered from operational unreliability, instability, inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the energy sector with emerging technologies, renewable energy sources, and other trends. Artificial intelligence (AI) is being applied to smart energy systems to process massive and complex data in this sector and make smart and timely decisions. However, the lack of explainability and governability of AI is a major concern for stakeholders hindering a fast uptake of AI in the energy sector. This paper provides a review of AI explainability and governance in smart energy systems. We collect 3,568 relevant papers from the Scopus database, automatically discover 15 parameters or themes for AI governance in energy and elaborate the research landscape by reviewing over 150 papers and providing temporal progressions of the research. The methodology for discovering parameters or themes is based on “deep journalism,” our data-driven deep learning-based big data analytics approach to automatically discover and analyse cross-sectional multi-perspective information to enable better decision-making and develop better instruments for governance. The findings show that research on AI explainability in energy systems is segmented and narrowly focussed on a few AI traits and energy system problems. This paper deepens our knowledge of AI governance in energy and is expected to help governments, industry, academics, energy prosumers, and other stakeholders to understand the landscape of AI in the energy sector, leading to better design, operations, utilisation, and risk management of energy systems.
Fermionic Isometric Tensor Network States in Two Dimensions

We generalize isometric tensor network states to fermionic systems, paving the way for efficient adaptations of 1D tensor network algorithms to 2D fermionic systems. As the first application of this formalism, we developed and benchmarked a time-evolution block-decimation (TEBD) algorithm for real-time and imaginary-time evolution. The imaginary-time evolution produces ground-state energies for gapped systems, systems with a Dirac point, and systems with gapless edge modes to good accuracy. The real-time TEBD captures the scattering of two fermions and the chiral edge dynamics on the boundary of a Chern insulator.
Testing CAN for Robotic Control

This project elaborates link drivers for the ADSP-21992. They are link drivers for Analog to Digital Converter (ADC), Encoder Interface Unit (EIU), Auxiliary PWM and the Control Area Network (CAN) bus. All link drivers are derived from the abstract LinkDriver class available in the CT Library. These link drivers are applied to a DC motor speed control with the PID Controller. This project will use two ADSP-21992 EZ-KIT LITE boards (evaluation board). There are two configurations, i.e. one-way and two-way configurations. For the one-way configuration, board 1 reads reference signal (from ADCLinkDriver) and actual speed (from EIULinkDriver) then send the difference between those two to board 2 via CAN bus (CANLinkDriver). Before sending this data to board 2, board 1 send synchronization pulse to board 2 to write PWM value. Upon receiving this data, board 2 calculates the PID Controller output, then wait for synchronization pulse from board 1. There is only one communication between two boards. For the two-way configuration, board 1 reads value from ADC and encoder, actuates the PWM by writing PWM value and sends the difference between reference signal and actual speed as in one-way configuration. The process on board 2 is almost the same as in the one-way configuration, except the result is not written to PWM but to CAN bus. The main issue is the highest sampling frequency for each configuration. The sampling frequency also depends on the CAN speed. Therefore, this is presented based on the CAN speed. From the experiment, it can be concluded that sampling frequency depends on the CAN speed. The relationship between CAN speed and sampling frequency is not linear. This is due to the fact that reading data from ADC and EIU (encoder) do not depend on CAN speed.
Data-Efficient Cross-Lingual Transfer with Language-Specific Subnetworks

Large multilingual language models typically share their parameters across all languages, which enables cross-lingual task transfer, but learning can also be hindered when training updates from different languages are in conflict. In this paper, we propose novel methods for using language-specific subnetworks, which control cross-lingual parameter sharing, to reduce conflicts and increase positive transfer during fine-tuning. We introduce dynamic subnetworks, which are jointly updated with the model, and we combine our methods with meta-learning, an established, but complementary, technique for improving cross-lingual transfer. Finally, we provide extensive analyses of how each of our methods affects the models.
Bayesian Analysis of Neutron-star Properties with Parameterized Equations of State: The Role of the Likelihood Functions

We have investigated the systematic differences introduced when performing a Bayesian-inference analysis of the equation of state (EOS) of neutron stars employing either variable- or constant-likelihood functions. The former has the advantage of retaining the full information on the distributions of the measurements, making exhaustive usage of the data. The latter, on the other hand, has the advantage of a much simpler implementation and reduced computational costs. In both approaches, the EOSs have identical priors and have been built using the sound speed parameterization method so as to satisfy the constraints from X-ray and gravitational waves observations, as well as those from chiral effective theory and perturbative quantum chromodynamics. In all cases, the two approaches lead to very similar results and the 90% confidence levels essentially overlap. Some differences do appear, but in regions where the probability density is extremely small and are mostly due to the sharp cutoff on the binary tidal deformability Λ˜≤720 set in the constant-likelihood approach. Our analysis has also produced two additional results. First, an inverse correlation between the normalized central number density, n c,TOV/n s , and the radius of a maximally massive star, R TOV. Second, and most importantly, it has confirmed the relation between the chirp mass and the binary tidal deformability. The importance of this result is that it relates chirp , which is measured very accurately, and Λ˜ , which contains important information on the EOS. Hence, when chirp is measured in future detections, our relation can be used to set tight constraints on Λ˜ .
Generating Sequences by Learning to Self-Correct

Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content. Language models, whether fine-tuned or prompted with few-shot demonstrations, frequently violate these constraints, and lack a mechanism to iteratively revise their outputs. Moreover, some powerful language models are of extreme scale or inaccessible, making it inefficient, if not infeasible, to update their parameters for task-specific adaptation. We present Self-Correction, an approach that decouples an imperfect base generator (an off-the-shelf language model or supervised sequence-to-sequence model) from a separate corrector that learns to iteratively correct imperfect generations. To train the corrector, we propose an online training procedure that can use either scalar or natural language feedback on intermediate imperfect generations. We show that Self-Correction improves upon the base generator in three diverse generation tasks - mathematical program synthesis, lexically-constrained generation, and toxicity control - even when the corrector is much smaller than the base generator.
Investigating Content-Aware Neural Text-to-Speech MOS Prediction Using Prosodic and Linguistic Features

Current state-of-the-art methods for automatic synthetic speech evaluation are based on MOS prediction neural models. Such MOS prediction models include MOSNet and LDNet that use spectral features as input, and SSL-MOS that relies on a pretrained selfsupervised learning model that directly uses the speech signal as input. In modern high-quality neural TTS systems, prosodic appropriateness with regard to the spoken content is a decisive factor for speech naturalness. For this reason, we propose to include prosodic and linguistic features as additional inputs in MOS prediction systems, and evaluate their impact on the prediction outcome. We consider phoneme-level F0 and duration features as prosodic inputs, as well as Tacotron encoder outputs, POS tags and BERT embeddings as higher-level linguistic inputs. All MOS prediction systems are trained on SOMOS, a neural TTS-only dataset with crowdsourced naturalness MOS evaluations. Results show that the proposed additional features are beneficial in the MOS prediction task, by improving the predicted MOS scores’ correlation with the ground truths, both at utterance-level and system-level predictions.
Parametrization and thermodynamic scaling of pair correlation functions for the fractional quantum Hall effect

The calculation of pair correlations and density profiles of quasiholes are routine steps in the study of proposed fractional quantum Hall states. Nevertheless, the field has not adopted a standard way to present the results of such calculations in an easily reproducible form. We develop a polynomial expansion that allows for easy quantitative comparison between different candidate wavefunctions, as well as reliable scaling of correlation and quasihole profiles to the thermodynamic limit. We start from the well-known expansion introduced by Girvin [PRB, 30 (1984)] (see also [Girvin, MacDonald and Platzman, PRB, 33 (1986)]), which is physically appealing but, as we demonstrate, numerically unstable. We orthogonalize their basis set to obtain a new basis of modified Jacobi polynomials, whose coefficients can be stably calculated. We then apply our expansion to extract pair correlation expansion coefficients and quasihole profiles in the thermodynamic limit for a wide range of fractional quantum Hall wavefunctions. These include the Laughlin series, composite fermion states with both reverse and direct flux attachment, the Moore-Read Pfaffian state, and BS hierarchy states. The expansion procedure works for both abelian and non-abelian quasiholes, even when the density at the core is not zero. We find that the expansion coefficients for all quantum Hall states considered can be fit remarkably well using a cosine oscillation with exponentially decaying amplitude. The frequency and the decay length are related in an intuitive, but not elementary way to the filling fraction. Different states at the same filling fraction can have distinct values for these parameters. Finally, we also use our scaled correlation functions to calculate estimates for the magneto-roton gaps of the various states.
Least-Squares Solution of Linear Inequalities.

Abstract : The paper deals with a system of linear inequalities in a finite dimensional space. When the system is inconsistent, we are interested in vectors that satisfy the system in a least-squares sense. We characterize such least-squares solutions and propose a method to find one of these solutions. It is shown that for any given system of linear inequalities and for any starting point, the method can produce a solution in a finite number of iterations. Computational results are very satisfactory in terms of accuracy and number of iterations. (Author)
Transonic cascade flow calculations using non-periodic C-type grids

A new kind of C-type grid is proposed for turbomachinery flow calculations. This grid is nonperiodic on the wake and results in minimum skewness for cascades with high turning and large camber. Euler and Reynolds averaged Navier-Stokes equations are discretized on this type of grid using a finite volume approach. The Baldwin-Lomax eddy-viscosity model is used for turbulence closure. Jameson's explicit Runge-Kutta scheme is adopted for the integration in time, and computational efficiency is achieved through accelerating strategies such as multigriding and residual smoothing. A detailed numerical study was performed for a turbine rotor and for a vane. A grid dependence analysis is presented and the effect of artificial dissipation is also investigated. Comparison of calculations with experiments clearly demonstrates the advantage of the proposed grid.
Electron-phonon correlation effects in molecular transistors

The interplay of electron-electron and electron-phonon interactions is studied analytically in the Kondo regime. A Holstein electron-phonon coupling is shown to produce a weakening of the gate voltage dependence of the Kondo temperature and may explain the observed anomalies in some of these devices. A molecular center-of-mass mode opens a new channel for charge and spin fluctuations and in the antiadabatic limit the latter are described by an asymmetric two-channel Kondo model. Below the Kondo temperature the system develops a dynamical Jahn-Teller distortion and a low energy peak emerges in the phonon spectral density that could be observed in Raman microscopy experiments.
On the zeroes of hypergraph independence polynomials

We study the locations of complex zeroes of independence polynomials of bounded-degree hypergraphs. For graphs, this is a long-studied subject with applications to statistical physics, algorithms, and combinatorics. Results on zero-free regions for bounded-degree graphs include Shearer’s result on the optimal zero-free disc, along with several recent results on other zero-free regions. Much less is known for hypergraphs. We make some steps towards an understanding of zero-free regions for bounded-degree hypergaphs by proving that all hypergraphs of maximum degree $\Delta$ have a zero-free disc almost as large as the optimal disc for graphs of maximum degree $\Delta$ established by Shearer (of radius $\sim 1/(e \Delta )$ ). Up to logarithmic factors in $\Delta$ this is optimal, even for hypergraphs with all edge sizes strictly greater than $2$ . We conjecture that for $k\ge 3$ , $k$ -uniform linear hypergraphs have a much larger zero-free disc of radius $\Omega (\Delta ^{- \frac{1}{k-1}} )$ . We establish this in the case of linear hypertrees.
Modeling noise in global Mølmer-Sørensen interactions applied to quantum approximate optimization

Many-qubit Molmer-Sorensen (MS) interactions applied to trapped ions offer unique capabilities for quantum information processing, with applications including quantum simulation and the quantum approximate optimization algorithm (QAOA). Here, we develop a physical model to describe many-qubit MS interactions under four sources of experimental noise: vibrational mode frequency fluctuations, laser power fluctuations, thermal initial vibrational states, and state preparation and measurement errors. The model parameterizes these errors from simple experimental measurements, without free parameters. We validate the model in comparison with experiments that implement sequences of MS interactions on two and three $^{171}$Yb$^+$ ions. The model shows good agreement after several MS interactions as quantified by the reduced chi-squared statistic $\chi^2_\mathrm{red}<2$. As an application we examine MaxCut QAOA experiments on three and six ions. The experimental performance is quantified by approximation ratios that are $91\%$ and $83\%$ of the optimal theoretical values. Our model predicts $0.93^{+0.03}_{-0.02}$ and $0.92^{+0.06}_{-0.06}$, respectively, with disagreement in the latter value attributable to secondary noise sources beyond those considered in our analysis. With realistic experimental improvements to reduce measurement error and radial trap frequency variations the model achieves approximation ratios that are 99$\%$ of the optimal. Incorporating these improvements into future experiments is expected to reveal new aspects of noise for future modeling and experimental improvements.