Miracle-dz/UniFAR-FTUs huggingface dataset:
2D electromagnetic simulations of RF heating via inductive coupling in the SPIDER device

SPIDER is the prototype ion source of MITICA, the full-size neutral beam heating system conceived for the ITER tokamak. It includes eight drivers to heat and sustain the inductively coupled plasma (ICP). Owing to their near cylindrical symmetry, the coupling between the radio-frequency (RF) active currents and the source plasma is studied using a 2D electromagnetic approach with simplified expressions for the plasma electrical conductivity taken from the literature. The power absorbed by the plasma and the effect of the induced plasma currents in lowering the inductance of the driver are based on data from the dedicated S16 experimental campaign (y.~2020) of SPIDER: plasma electron densities on the order of $10^{18}$ m$^{-3}$, electron temperatures $\sim 10$ eV; neutral gas pressure $\sim 0.3$ Pa and up to $50$ kW of net power per driver. It is found that the plasma conductivity cannot be explained by the friction forces associated to local collisional processes alone. The inclusion of an effective collisionality associated to non-local processes seems also insufficient to explain the experimental information. Only when the electrical conductivity is reduced where the RF magnetic field is more intense, can the heating power and driver inductance be acceptably reproduced. We present the first 2D electromagnetic ICP calculations in SPIDER for two types of plasma, without and with the addition of a static magnetic field. The power transfer efficiency to the plasma of the first drivers of SPIDER, in view of these models, is around 50%
Is a Video worth n n Images? A Highly Efficient Approach to Transformer-based Video Question Answering

Conventional Transformer-based Video Question Answering (VideoQA) approaches generally encode frames independently through one or more image encoders followed by interaction between frames and question. However, such schema would incur significant memory use and inevitably slow down the training and inference speed. In this work, we present a highly efficient approach for VideoQA based on existing vision-language pre-trained models where we concatenate video frames to a $n\times n$ matrix and then convert it to one image. By doing so, we reduce the use of the image encoder from $n^{2}$ to $1$ while maintaining the temporal structure of the original video. Experimental results on MSRVTT and TrafficQA show that our proposed approach achieves state-of-the-art performance with nearly $4\times$ faster speed and only 30% memory use. We show that by integrating our approach into VideoQA systems we can achieve comparable, even superior, performance with a significant speed up for training and inference. We believe the proposed approach can facilitate VideoQA-related research by reducing the computational requirements for those who have limited access to budgets and resources. Our code will be made publicly available for research use.
The structure of the moduli of gauged maps from a smooth curve

For a reductive group $G$, Harder-Narasimhan theory gives a structure theorem for principal $G$ bundles on a smooth projective curve $C$. A bundle is either semistable, or it admits a canonical parabolic reduction whose associated Levi bundle is semistable. We extend this structure theorem by constructing a $\Theta$-stratification of the moduli stack of gauged maps from $C$ to a projective-over-affine $G$-variety $X$. The open stratum coincides with the previously studied moduli of Mundet semistable maps, and in special cases coincides with the moduli of stable quasi-maps. As an application of the stratification, we provide a formula for K-theoretic gauged Gromov-Witten invariants when $X$ is an arbitrary linear representation of $G$. This can be viewed as a generalization of the Verlinde formula for moduli spaces of decorated principal bundles. We establish our main technical results for smooth families of curves over an arbitrary Noetherian base. Our proof develops an infinite-dimensional analog of geometric invariant theory and applies the theory of optimization on degeneration fans.
Fitting Tweedie's Compound Poisson Model to Insurance Claims Data: Dispersion Modelling

Abstract We reconsider the problem of producing fair and accurate tariffs based on aggregated insurance data giving numbers of claims and total costs for the claims. Jørgensen and de Souza (Scand Actuarial J., 1994) assumed Poisson arrival of claims and gamma distributed costs for individual claims. Jørgensen and de Souza (1994) directly modelled the risk or expected cost of claims per insured unit, μ say. They observed that the dependence of the likelihood function on μ is as for a linear exponential family, so that modelling similar to that of generalized linear models is possible. In this paper we observe that, when modelling the cost of insurance claims, it is generally necessary to model the dispersion of the costs as well as their mean. In order to model the dispersion we use the framework of double generalized linear models. Modelling the dispersion increases the precision of the estimated tariffs. The use of double generalized linear models also allows us to handle the case where only the total cost of claims and not the number of claims has been recorded.
Capturing Humans’ Mental Models of AI: An Item Response Theory Approach

Improving our understanding of how humans perceive AI teammates is an important foundation for our general understanding of human-AI teams. Extending relevant work from cognitive science, we propose a framework based on item response theory for modeling these perceptions. We apply this framework to real-world experiments, in which each participant works alongside another person or an AI agent in a question-answering setting, repeatedly assessing their teammate’s performance. Using this experimental data, we demonstrate the use of our framework for testing research questions about people’s perceptions of both AI agents and other people. We contrast mental models of AI teammates with those of human teammates as we characterize the dimensionality of these mental models, their development over time, and the influence of the participants’ own self-perception. Our results indicate that people expect AI agents’ performance to be significantly better on average than the performance of other humans, with less variation across different types of problems. We conclude with a discussion of the implications of these findings for human-AI interaction.
Estimating properties of a quantum state by importance-sampled operator shadows

Measuring properties of quantum systems is a fundamental problem in quantum mechanics. We provide a simple method for estimating the expectation value of observables with an unknown quantum state. The idea is to use a data structure to sample the terms of observables based on the Pauli decomposition proportionally to their importance. We call this technique operator shadow as a shorthand for the procedure of preparing a sketch of an operator to estimate properties. Only when the numbers of observables are small for multiple local observables, the sample complexity of this method is better than the classical shadow technique. However, if we want to estimate the expectation value of a linear combination of local observables, for example the energy of a local Hamiltonian, the sample complexity is better on all parameters. The time complexity to construct the data structure is $2^{O(k)}$ for $k$-local observables, similar to the post-processing time of classical shadows.
Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model

Medical image segmentation is a critical step in computer-aided diagnosis, and convolutional neural networks are popular segmentation networks nowadays. However, the inherent local operation characteristics make it difficult to focus on the global contextual information of lesions with different positions, shapes, and sizes. Semi-supervised learning can be used to learn from both labeled and unlabeled samples, alleviating the burden of manual labeling. However, obtaining a large number of unlabeled images in medical scenarios remains challenging. To address these issues, we propose a Multi-level Global Context Cross-consistency (MGCC) framework that uses images generated by a Latent Diffusion Model (LDM) as unlabeled images for semi-supervised learning. The framework involves of two stages. In the first stage, a LDM is used to generate synthetic medical images, which reduces the workload of data annotation and addresses privacy concerns associated with collecting medical data. In the second stage, varying levels of global context noise perturbation are added to the input of the auxiliary decoder, and output consistency is maintained between decoders to improve the representation ability. Experiments conducted on open-source breast ultrasound and private thyroid ultrasound datasets demonstrate the effectiveness of our framework in bridging the probability distribution and the semantic representation of the medical image. Our approach enables the effective transfer of probability distribution knowledge to the segmentation network, resulting in improved segmentation accuracy. The code is available at https://github.com/FengheTan9/Multi-Level-Global-Context-Cross-Consistency.
Regularity of matrix coefficients of a compact symmetric pair of Lie groups

We consider symmetric Gelfand pairs $(G,K)$ where $G$ is a compact Lie group and $K$ a subgroup of fixed point of an involutive automorphism. We study the regularity of $K$-bi-invariant matrix coefficients of $G$. The results rely on the analysis of the spherical functions of the Gelfand pair $(G,K)$. When the symmetric space $G/K$ is of rank $1$ or isomorphic to a Lie group, we find the optimal regularity of $K$-bi-invariant matrix coefficients. Furthermore, in rank $1$ we also show the optimal regularity of $K$-bi-invariant Herz-Schur multipliers of $S_p(L^2(G))$. We also give a lower bound for the optimal regularity in some families of higher rank symmetric spaces. From these results, we make a conjecture in the general case involving the root system of the symmetric space. Finally, we prove that if all $K$-bi-invariant matrix coefficients of $G$ have the same regularity, then so do all $K$-finite matrix coefficients.
Multi-domain FEM-BEM coupling for acoustic scattering

We model time-harmonic acoustic scattering by an object composed of piece-wise homogeneous parts and an arbitrarily heterogeneous part. We propose and analyze new formulations that couple, adopting a Costabel-type approach, boundary integral equations for the homogeneous subdomains with domain variational formulations for the heterogeneous subdomain. This is an extension of Costabel FEM-BEM coupling to a multi-domain configuration, with cross-points allowed, i.e. points where three or more subdomains abut. While generally just the exterior unbounded subdomain is treated with the BEM, here we wish to exploit the advantages of BEM whenever it is applicable, that is, for all the homogeneous parts of the scattering object. Our formulation is based on the multi-trace formalism, which initially was introduced for acoustic scattering by piece-wise homogeneous objects; here we allow the wavenumber to vary arbitrarily in a part of the domain. We prove that the bilinear form associated with the proposed formulation satisfies a G{\aa}rding coercivity inequality, which ensures stability of the variational problem if it is uniquely solvable. We identify conditions for injectivity and construct modified versions immune to spurious resonances.
The Effect of Droplet Size Distribution on the Determination of Cloud Droplet Effective Radius

Cloud microphysical processes can provide links between cloud radiative effect and hydrological cycle and create several feedback mechanisms linking clouds and climate. For instance, the aerosols can affect the climate through their indirect effect on clouds, which modifies cloud microphysical properties and hence cloud radiative properties, proving an increase in cloud albedo and a net radiative cooling (Twomey et al. 1984; Charlson et al. 1992). The key microphysical parameters affecting both radiation budget and hydrological cycle like cloud liquid water content (w) and droplet effective radius (re) are generally determined by the specification of cloud droplet size distribution, i.e.,
Log-concavity and log-convexity of series containing multiple Pochhammer symbols

In this paper, we study power series with coefficients equal to a product of a generic sequence and an explicitly given function of a positive parameter expressible in terms of the Pochhammer symbols. Four types of such series are treated. We show that logarithmic concavity (convexity) of the generic sequence leads to logarithmic concavity (convexity) of the sum of the series with respect to the argument of the explicitly given function. The logarithmic concavity (convexity) is derived from a stronger property, namely, positivity (negativity) of the power series coefficients of the so-called generalized Turánian. Applications to special functions such as the generalized hypergeometric function and the Fox-Wright function are also discussed.
Is infrared-collinear safe information all you need for jet classification?

Machine learning-based jet classifiers are able to achieve impressive tagging performance in a variety of applications in high energy and nuclear physics. However, it remains unclear in many cases which aspects of jets give rise to this discriminating power, and whether jet observables that are tractable in perturbative QCD such as those obeying infrared-collinear (IRC) safety serve as sufficient inputs. In this article, we introduce a new classifier, Jet Flow Networks (JFNs), in an effort to address the question of whether IRC unsafe information provides additional discriminating power in jet classification. JFNs are permutation-invariant neural networks (deep sets) that take as input the kinematic information of reconstructed subjets. The subjet radius serves as a tunable hyperparameter, enabling the sensitivity to soft emissions and nonperturbative effects to be gradually increased as the subjet radius is decreased. We demonstrate the performance of JFNs for quark vs. gluon and QCD vs. $Z$ jet tagging. For small subjet radii, the performance of JFNs is equivalent to the IRC-unsafe Particle Flow Networks (PFNs), demonstrating that infrared-collinear unsafe information is not necessary to achieve strong discrimination for both cases. As the subjet radius is increased, the performance of the JFNs remains essentially unchanged until physical thresholds that we identify are crossed. For relatively large subjet radii, we show that the JFNs may offer an increased model independence with a modest tradeoff in performance compared to classifiers that use the full particle information of the jet. These results shed new light onto how machines learn patterns in high-energy physics data.
Sparsifying Sums of Norms

Abstract-For any norms $N_{1}, \ldots, N_{m}$ on $\mathbb{R}^{n}$ and $N(x):= N_{1}(x)+\cdots+N_{m}(x)$, we show there is a sparsified norm $\tilde{N}(x)= w_{1} N_{1}(x)+\cdots+w_{m} N_{m}(x)$ such that $|N(x)-\tilde{N}(x)| \leqslant \varepsilon N(x)$ for all $x \in \mathbb{R}^{n}$, where $w_{1}, \ldots, w_{m}$ are non-negative weights, of which only $O\left(\varepsilon^{-2} n \log (n / \varepsilon)(\log n)^{2.5}\right)$ are non-zero. Additionally, we show that such weights can be found with high probability in time $O\left(m(\log n)^{O(1)}+\right. $ poly $\left. (n)\right) T$, where T is the time required to evaluate a norm $N_{i}(x)$, assuming that $N(x)$ is poly $(n)$ equivalent to the Euclidean norm. This immediately yields analogous statements for sparsifying sums of symmetric submodular functions. More generally, we show how to sparsify sums of p th powers of norms when the sum is p-uniformly smooth.1
Plasmonic sensing using Babinet’s principle

Abstract Developing methods to sense local variations in properties of nearby materials, such as their refractive index and thickness, are important in numerous fields including chemistry and biomedical applications. Localized surface plasmons (LSPs) excited in plasmonic nanostructures have been demonstrated to be useful in this context due to the spectral location of their associated resonances being sensitive to changes in the environment near the plasmonic structures. This manuscript explores Babinet’s principle by exploiting LSP resonances excited in complementary metal-dielectric cylindrical plasmonic structures (plasmonic particle-dimers and aperture-dimers in our case). Both plasmonic structures are evaluated numerically and experimentally using electron energy loss spectroscopy (EELS), providing a full physical understanding of the complementary nature of the excited LSP resonances. These plasmonic structures are then exploited for dielectric sensing under two configurations: when a thin dielectric film is positioned atop the plasmonic structures and when the analyte surrounds/fills the plasmonic particles/apertures. The complementary sensing performance of both proposed structures is also evaluated, showing the approximate validity of the Babinet principle with sensitivity values of up to ∼650 nm/RIU for thin dielectric sensing.
Kites and Quails: Monetary Policy and Communication with Strategic Financial Markets

We develop a simple game-theoretic model to determine the consequences of explicitly including financial market stability in the central bank objective function, when policymakers and the financial market are strategic players, and market stability is negatively affected by policy surprises. We find that the inclusion of financial sector stability among the policy objectives can induce an inefficiency, whereby market anticipation of policymakers' goals biases investment choices. When the central bank has private information about its policy intentions, the equilibrium communication is vague, because fully informative communication is not credible. The appointment of a ``kitish'' central banker, who puts little weight on market stability, reduces these inefficiencies. If interactions are repeated, communication transparency and overall efficiency can be improved if the central bank punishes any abuse of market power by withholding forward guidance. At the same time, repeated interaction also opens the doors to collusion between large investors, with uncertain welfare consequences.
Monitoring Multicountry Macroeconomic Risk

We propose a multicountry quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows for summarizing these two heterogeneities in a parsimonious way. We develop two algorithms for posterior inference that feature varying level of trade-off between estimation precision and computational speed. Using monthly data for the euro area, we establish the good empirical properties of the QFAVAR as a tool for assessing the effects of global shocks on country-level macroeconomic risks. In particular, QFAVAR short-run tail forecasts are more accurate compared to a FAVAR with symmetric Gaussian errors, as well as univariate quantile autoregressions that ignore comovements among quantiles of macroeconomic variables. We also illustrate how quantile impulse response functions and quantile connectedness measures, resulting from the new model, can be used to implement joint risk scenario analysis.
OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research

AI systems empowered by reinforcement learning (RL) algorithms harbor the immense potential to catalyze societal advancement, yet their deployment is often impeded by significant safety concerns. Particularly in safety-critical applications, researchers have raised concerns about unintended harms or unsafe behaviors of unaligned RL agents. The philosophy of safe reinforcement learning (SafeRL) is to align RL agents with harmless intentions and safe behavioral patterns. In SafeRL, agents learn to develop optimal policies by receiving feedback from the environment, while also fulfilling the requirement of minimizing the risk of unintended harm or unsafe behavior. However, due to the intricate nature of SafeRL algorithm implementation, combining methodologies across various domains presents a formidable challenge. This had led to an absence of a cohesive and efficacious learning framework within the contemporary SafeRL research milieu. In this work, we introduce a foundational framework designed to expedite SafeRL research endeavors. Our comprehensive framework encompasses an array of algorithms spanning different RL domains and places heavy emphasis on safety elements. Our efforts are to make the SafeRL-related research process more streamlined and efficient, therefore facilitating further research in AI safety. Our project is released at: https://github.com/PKU-Alignment/omnisafe.
Probabilistic Distance-Based Outlier Detection

The scores of distance-based outlier detection methods are difﬁcult to interpret, making it challenging to determine a cut-off threshold between normal and outlier data points without additional context. We describe a generic transformation of distance-based outlier scores into interpretable, probabilistic estimates. The transformation is ranking-stable and increases the contrast between normal and outlier data points. Determining distance relationships between data points is necessary to identify the nearest-neighbor relationships in the data, yet, most of the computed distances are typically discarded. We show that the distances to other data points can be used to model distance probability distributions and, subsequently, use the distributions to turn distance-based outlier scores into outlier probabilities. Our experiments show that the probabilistic transformation does not impact detection performance over numerous tabular and image benchmark datasets but results in interpretable outlier scores with increased contrast between normal and outlier samples. Our work generalizes to a wide range of distance-based outlier detection methods, and because existing distance computations are used, it adds no signiﬁcant computational overhead.
Random Forest Weighted Local Fr\'echet Regression with Random Objects

Statistical analysis is increasingly confronted with complex data from metric spaces. Petersen and M\"uller (2019) established a general paradigm of Fr\'echet regression with complex metric space valued responses and Euclidean predictors. However, the local approach therein involves nonparametric kernel smoothing and suffers from the curse of dimensionality. To address this issue, we in this paper propose a novel random forest weighted local Fr\'echet regression paradigm. The main mechanism of our approach relies on a locally adaptive kernel generated by random forests. Our first method uses these weights as the local average to solve the conditional Fr\'echet mean, while the second method performs local linear Fr\'echet regression, both significantly improving existing Fr\'echet regression methods. Based on the theory of infinite order U-processes and infinite order $M_{m_n}$-estimator, we establish the consistency, rate of convergence, and asymptotic normality for our local constant estimator, which covers the current large sample theory of random forests with Euclidean responses as a special case. Numerical studies show the superiority of our methods with several commonly encountered types of responses such as distribution functions, symmetric positive-definite matrices, and sphere data. The practical merits of our proposals are also demonstrated through the application to New York taxi data and human mortality data.