Miracle-dz/UniFAR-FTUs huggingface dataset:
$\ell_1$-penalized Multinomial Regression: Estimation, inference, and prediction, with an application to risk factor identification for different dementia subtypes

High-dimensional multinomial regression models are very useful in practice but receive less research attention than logistic regression models, especially from the perspective of statistical inference. In this work, we analyze the estimation and prediction error of the contrast-based $\ell_1$-penalized multinomial regression model and extend the debiasing method to the multinomial case, which provides a valid confidence interval for each coefficient and $p$-value of the individual hypothesis test. We apply the debiasing method to identify some important predictors in the progression into dementia of different subtypes. Results of intensive simulations show the superiority of the debiasing method compared to some other inference methods.
Super-equipartition fields in simulations of photospheric magnetoconvection

Abstract Observations of magnetic fields in the quiet Sun indicate that kilogauss-strength fields can be found in the intergranular lanes. Since the magnetic energy of these localised features greatly exceeds estimates of the kinetic energy of the surrounding granular convection, it is difficult to see how these features could be formed simply by convective flux concentration. Idealised, high-resolution simulations of three-dimensional compressible magnetoconvection are used to investigate the formation of these features numerically. Initially we take a fully developed non-magnetic convective state into which we insert a weak, uniform, vertical magnetic field. Magnetic flux is rapidly swept into the convective downflows, where it is concentrated into localised regions. As the field strength within these regions becomes dynamically significant, the high magnetic pressure leads to partial evacuation (via the convective downflows). Provided that the magnetic Reynolds number is large enough, the strength of the resulting magnetic fields significantly exceeds the (so called) “equipartition” value, with the dynamical effects of the surrounding convection playing an important role in confining these magnetic features to localised regions. These results can be related to the well-known convective collapse instability, although there are some important differences between the two models.
Counterfactual Identifiability of Bijective Causal Models

We study counterfactual identifiability in causal models with bijective generation mechanisms (BGM), a class that generalizes several widely-used causal models in the literature. We establish their counterfactual identifiability for three common causal structures with unobserved confounding, and propose a practical learning method that casts learning a BGM as structured generative modeling. Learned BGMs enable efficient counterfactual estimation and can be obtained using a variety of deep conditional generative models. We evaluate our techniques in a visual task and demonstrate its application in a real-world video streaming simulation task.
Bilevel inverse problems in neuromorphic imaging

Event or neuromorphic cameras are novel biologically inspired sensors that record data based on the change in light intensity at each pixel asynchronously. They have a temporal resolution of microseconds. This is useful for scenes with fast moving objects that can cause motion blur in traditional cameras, which record the average light intensity over an exposure time for each pixel synchronously. This paper presents a bilevel inverse problem framework for neuromorphic imaging. Existence of solution to the inverse problem is established. Second order sufficient conditions are derived under special situations for this nonconvex problem. A second order Newton type solver is derived to solve the problem. The efficacy of the approach is shown on several examples.
Augmentation of stagnation region heat transfer due to turbulence from a DLN can combustor

Heat transfer measurements have been made in the stagnation region of a flat plate with a circular leading edge. Electrically heated aluminum strips placed symmetrically about the leading edge stagnation region were used to measure spanwise-averaged heat transfer coefficients. The maximum Reynolds number obtained, based on leading edge diameter, was about 100,000. The model was immersed in the flow field downstream of an approximately half-scale model of a can-type combustor from a low NO(x), ground-based power-generating turbine. The tests were conducted with room temperature air; no fuel was added. Room air flowed into the combustor through six vane-type fuel/air swirlers. The combustor can contained no dilution holes. The fuel/air swirlers all swirled the incoming airflow in a counterclockwise direction (facing downstream). A five-hole probe flow field survey in the plane of the model stagnation point showed the flow was one big vortex with flow angles up to 36 deg at the outer edges of the rectangular test section. Hot-wire measurements showed test section flow had very high levels of turbulence, around 28.5%, and had a relatively large axial-length scale-to-leading edge diameter ratio of 0.5. X-wire measurements showed the turbulence to be nearly isotropic. Stagnation heat transfer augmentation over laminar levels was around 77% and was about 14% higher than predicted by a previously developed correlation for isotropic grid-generated turbulence.
SU UMa-type supercycle in the IW And-type dwarf nova BO Cet above the period gap

BO Cet is renowned cataclysmic variable having an orbital period above the period gap (0.139835 d) and showing both features of a Z Cam/IW And star and an SU UMa star. Using the Asteroid Terrestrial-impact Last Alert System (ATLAS) forced photometry and the All-Sky Automated Survey for Supernovae (ASAS-SN) Sky Patrol data, I found that BO Cet underwent a superoutburst in 2022 October-November after a series of short, normal outbursts with increasing amplitudes. This sequence of outbursts (supercycle) is what is seen in many SU UMa stars and this observation strengthened the suggestion that the accumulating mass and angular momentum in the disk during repeated normal outbursts caused a superoutburst even in the unusual system BO Cet. The outburst just preceding the superoutburst bore characteristics of an IW And-type standstill. This phenomenon reinforces the suggestion that the terminal outburst in IW And stars occurs when the disk radius reaches a certain limit. I consider that this outburst was a failed superoutburst, during which the disk reached the radius of the 3:1 resonance but the outburst faded before superhumps developed. In BO Cet with a mass ratio on the borderline of the stability of the 3:1 resonance, there may have been a competition between the effects of tidal truncation and the 3:1 resonance as the disk radius grew and the latter won in the current case. This finding in BO Cet might suggest that IW And-type and SU UMa-type phenomena are more strongly physically related than have been thought.
Cross section measurement of the 144Sm(alpha,n)147Gd reaction for studying the alpha-nucleus optical potential at astrophysical energies

Nuclear reactions involving alpha particles play an important role in various astrophysical processes such as the gamma-process of heavy element nucleosynthesis. The poorly known low-energy alpha-nucleus optical (AOMP) potential is a key parameter to estimate the rates of these reactions. The AOMP can be tested by measuring the cross section of alpha-scattering as well as alpha-induced reactions. Low energy elastic alpha-scattering on 144Sm has recently been measured with high precision. The aim of the present work was to complement that work by measuring the (a,n) cross sections on 144Sm at low energies. The experimental data shall be used to constrain the AOMP. From this potential the 144Sm(a,g)148Gd reaction rate can be derived with reduced uncertainties. The 144Sm(a,n)147Gd reaction was studied by bombarding Sm targets with alpha-beams provided by the cyclotron accelerator of Atomki. The cross section was determined using the activation method. The gamma-radiation following the decay of the 147Gd reaction product was measured with a HPGe detector. The experimental data are analyzed within the statistical model. The cross section was measured in the alpha-energy range between 13 and 20 MeV in 1 MeV steps, i.e., from close above the (a,n) threshold. The results were compared with statistical model calculations using various approaches and parametrizations for the AOMP, and excellent agreement was obtained for two recent potentials. However, these potentials cannot reproduce literature data for the 144Sm(a,g)148Gd reaction with the same accuracy. Constraints for the AOMP were derived from an analysis of the new 144Sm(a,n)147Gd data and literature data for 144Sm(a,g)148Gd. These constraints enable a determination of the reaction rate of the 144Sm(a,g)148Gd reaction with significantly reduced uncertainties of less than a factor of two.
z-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning

Federated Learning (FL) is a promising privacy-preserving distributed learning paradigm but suffers from high communi- cation cost when training large-scale machine learning models. Sign-based methods, such as SignSGD, have been proposed as a biased gradient compression technique for reducing the communication cost. However, sign-based algorithms could diverge under heterogeneous data, which thus motivated the de- velopment of advanced techniques, such as the error-feedback method and stochastic sign-based compression, to fix this issue. Nevertheless, these methods still suffer from slower convergence rates, and none of them allows multiple local SGD updates like FedAvg. In this paper, we propose a novel noisy perturbation scheme with a general symmetric noise distribution for sign-based compression, which not only al- lows one to flexibly control the bias-variance tradeoff for the compressed gradient, but also provides a unified viewpoint to existing stochastic sign-based methods. More importantly, the proposed scheme enables the development of the very first sign-based FedAvg algorithm (z-SignFedAvg) to accelerate the convergence. Theoretically, we show that z-SignFedAvg achieves a faster convergence rate than existing sign-based methods and, under the uniformly distributed noise, can enjoy the same convergence rate as its uncompressed counterpart. Extensive experiments are conducted to demonstrate that the z-SignFedAvg can achieve competitive empirical performance on real datasets and outperforms existing schemes.
Fe b 20 12 Invariant bipartite random graphs on R d

Suppose that red and blue points occur in R according to two simple point process with finite intensities λR and λB, respectively. Furthermore, let ν and μ be two probability distributions on the strictly positive integers. Assign independently a random number of stubs (half-edges) to each red and blue point with laws ν and μ, respectively. We are interested in translation-invariant schemes to match stubs between points of different colors in order to obtain random bipartite graphs in which each point has a prescribed degree distribution with law ν or μ depending on its color. Let X and Y be random variables with law ν and μ, respectively. For a large class of point processes we show that we can obtain such translation-invariant schemes matching a.s. all stubs if and only if
Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment

Deep brain stimulation (DBS) has shown great promise toward treating motor symptoms caused by Parkinson's disease (PD), by delivering electrical pulses to the Basal Ganglia (BG) region of the brain. However, DBS devices approved by the U.S. Food and Drug Administration (FDA) can only deliver continuous DBS (cDBS) at a fixed amplitude; this energy inefficient operation reduces battery lifetime of the device, cannot adapt treatment dynamically for activity, and may cause significant side-effects (e.g., gait impairment). In this work, we introduce an offline reinforcement learning (RL) framework, allowing the use of past clinical data to train an RL policy to adjust the stimulation amplitude in real time, with the goal of reducing energy use while maintaining the same level of treatment (i.e., control) efficacy as cDBS. Moreover, clinical protocols require the safety and performance of such RL controllers to be demonstrated ahead of deployments in patients. Thus, we also introduce an offline policy evaluation (OPE) method to estimate the performance of RL policies using historical data, before deploying them on patients. We evaluated our framework on four PD patients equipped with the RC+S DBS system, employing the RL controllers during monthly clinical visits, with the overall control efficacy evaluated by severity of symptoms (i.e., bradykinesia and tremor), changes in PD biomakers (i.e., local field potentials), and patient ratings. The results from clinical experiments show that our RL-based controller maintains the same level of control efficacy as cDBS, but with significantly reduced stimulation energy. Further, the OPE method is shown effective in accurately estimating and ranking the expected returns of RL controllers.
Analysis of high-energy drop impact onto deep liquid pool

The present work is devoted to the analysis of drop impact onto a deep liquid pool. It is focused on the effects of high-energy splash regimes, caused by the impact of large raindrops at high velocities. Such cases are characterized by short time scales and complex mechanisms, and they have thus received little attention until now. The BASILISK open-source solver is used to perform three-dimensional Direct Numerical Simulations (DNS). The capabilities of the octree adaptive mesh refinement techniques enable to capture the small-scale features of the flow, while the Volume of Fluid (VOF) approach combined with a balanced force surface tension calculation is applied to advect the volume fraction of the liquids and reconstruct the interfaces. The numerical results compare well with experimental visualizations: both the evolution of crown and cavity, the emanation of ligaments, the formation of bubble canopy, and the growth of the downward spiral jet that pierces through the cavity bottom, are correctly reproduced. Reliable quantitative agreements are also obtained regarding the time evolution of rim positions, cavity dimensions and droplet distributions through an observation window. Furthermore, simulation gives access to various aspects of the internal flows (velocity, vorticity, pressure, energy budget), which allows to better explain the corresponding physical phenomena. Details of the early dynamics of bubble ring entrapment and splashing performance, the formation/collapse of bubble canopy, and the spreading of drop liquid, are discussed. The statistics of droplet size show the bimodal distribution in time, corroborating distinct primary mechanisms of droplet production at different stages.
Does higher capital maintenance drive up banks cost of equity? Evidence from Bangladesh

This paper assesses whether the higher capital maintenance drives up banks cost of equity. We investigate the hypothesis using fixed effect panel estimation with the data from a sample of 28 publicly listed commercial banks over the 2013 to 2019 periods. We find a significant negative relationship between banks capital and cost of equity. Empirically our baseline estimates entail that a 10 percent increase in capital would reduce the cost of equity by 4.39 percent.
Numerical methods for detecting symmetries and commutant algebras

For families of Hamiltonians defined by parts that are local, the most general definition of a symmetry algebra is the commutant algebra, i.e., the algebra of operators that commute with each local part. Thinking about symmetry algebras as commutant algebras allows for the treatment of conventional symmetries and unconventional symmetries (e.g., those responsible for weak ergodicity breaking phenomena) on equal algebraic footing. In this work, we discuss two methods for numerically constructing this commutant algebra starting from a family of Hamiltonians. First, we use the equivalence of this problem to that of simultaneous block-diagonalization of a given set of local operators, and discuss a probabilistic method that has been found to work with probability 1 for both Abelian and non-Abelian symmetries or commutant algebras. Second, we map this problem onto the problem of determining frustration-free ground states of certain Hamiltonians, and we use ideas from tensor network algorithms to efficiently solve this problem in one dimension. These numerical methods are useful in detecting standard and non-standard conserved quantities in families of Hamiltonians, which includes examples of regular symmetries, Hilbert space fragmentation, and quantum many-body scars, and we show many such examples. In addition, they are necessary for verifying several conjectures on the structure of the commutant algebras in these cases, which we have put forward in earlier works. Finally, we also discuss similar methods for the inverse problem of determining local operators with a given symmetry or commutant algebra, which connects to existing methods in the literature. A special case of this construction reduces to well-known ``Eigenstate to Hamiltonian"methods for constructing Hermitian local operators that have a given state as an eigenstate.
Radar Clutter Covariance Estimation: A Nonlinear Spectral Shrinkage Approach

In this article, we exploit the spiked covariance structure of the clutter plus noise covariance matrix for radar signal processing. Using state-of-the-art techniques high dimensional statistics, we propose a nonlinear shrinkage-based rotation invariant spiked covariance matrix estimator. We state the convergence of the estimated spiked eigenvalues. We use a dataset generated from the high-fidelity, site-specific physics-based radar simulation software RFView to compare the proposed algorithm against the existing rank constrained maximum likelihood (RCML)-expected likelihood (EL) covariance estimation algorithm. We demonstrate that the computation time for the estimation by the proposed algorithm is less than the RCML-EL algorithm with identical Signal to Clutter plus Noise (SCNR) performance. We show that the proposed algorithm and the RCML-EL-based algorithm share the same optimization problem in high dimensions. We use Low-Rank Adaptive Normalized Matched Filter (LR-ANMF) detector to compute the detection probabilities for different false alarm probabilities over a range of target signal-to-noise ratios (SNR). We present preliminary results which demonstrate the robustness of the detector against contaminating clutter discretes using the challenge dataset from RFView. Finally, we empirically show that the minimum variance distortionless beamformer error variance for the proposed algorithm is identical to the error variance resulting from the true covariance matrix.
Finite-Time Analysis of Crises in a Chaotically Forced Ocean Model

We consider a coupling of the Stommel Box and the Lorenz models, with the goal of investigating the so-called"crises"that are known to occur given sufficient forcing. In this context, a crisis is characterised as the bifurcation of a system that is produced by the application of forcing to a bistable system, thereby reducing it to a monostable system. We document the variety of chaotic attractors and crises possible in our model and demonstrate the possibility of a merging between the stable chaotic attractor that persists after a crisis with either a chaotic transient or a ghost attractor. An investigation of the finite time Lyapunov exponents around crisis levels of forcing reveals a strong alignment between the first Stommel and neutral Lorenz exponents, particularly at points of a trajectory most sensitive to divergence around these levels. We discuss possible predictors that may identity those chaotic attractors liable to collapse as a consequence of a crisis and show the chaotic saddle collisions that occur in a boundary crisis. Finally, we comment on the generality of our findings by coupling the Stommel Box model with other strange attractors and thereby show that many of the behaviours are quite generic and robust.
Proposing Novel Extrapolative Compounds by Nested Variational Autoencoders

Materials informatics (MI), which uses artificial intelligence and data analysis techniques to improve the efficiency of materials development, is attracting increasing interest from industry. One of its main applications is the rapid development of new high-performance compounds. Recently, several deep generative models have been proposed to suggest candidate compounds that are expected to satisfy the desired performance. However, they usually have the problem of requiring a large amount of experimental datasets for training to achieve sufficient accuracy. In actual cases, it is often possible to accumulate only about 1000 experimental data at most. Therefore, the authors proposed a deep generative model with nested two variational autoencoders (VAEs). The outer VAE learns the structural features of compounds using large-scale public data, while the inner VAE learns the relationship between the latent variables of the outer VAE and the properties from small-scale experimental data. To generate high performance compounds beyond the range of the training data, the authors also proposed a loss function that amplifies the correlation between a component of latent variables of the inner VAE and material properties. The results indicated that this loss function contributes to improve the probability of generating high-performance candidates. Furthermore, as a result of verification test with an actual customer in chemical industry, it was confirmed that the proposed method is effective in reducing the number of experiments to $1/4$ compared to a conventional method.
Cross-Modal Fusion Techniques for Utterance-Level Emotion Recognition from Text and Speech

Multimodal emotion recognition (MER) is a fundamental complex research problem due to the uncertainty of human emotional expression and the heterogeneity gap between different modalities. Audio and text modalities are particularly important for a human participant in understanding emotions. Although many successful attempts have been designed multimodal representations for MER, there still exist multiple challenges to be addressed: 1) bridging the heterogeneity gap between multimodal features and model inter- and intramodal interactions of multiple modalities; 2) effectively and efficiently modeling the contextual dynamics in the conversation sequence. In this paper, we propose Cross-Modal RoBERTa (CM-RoBERTa) model for emotion detection from spoken audio and corresponding transcripts. As the core unit of the CM-RoBERTa, parallel self- and cross- attention is designed to dynamically capture inter- and intra-modal interactions of audio and text. Specially, the mid-level fusion and residual module are employed to model longterm contextual dependencies and learn modality-specific patterns. We evaluate the approach on the MELD dataset and the experimental results show the proposed approach achieves the state-of-art performance on the dataset.