Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-similar measures with unusual local dimension properties

Let μ be a self-similar measure satisfying the finite type condition. It is known that the set of attainable local dimensions for such a measure is a union of disjoint intervals, where some intervals may be degenerate points. Despite this, it has not been shown if this full complexity of attainable local dimensions is achievable. In this paper we give two different constructions. The first is a measure μ where the set of all attainable local dimensions is the union of an interval union and an arbitrary number of disjoint points. The second is a measure μ where the set of all attainable local dimensions is the union of an arbitrary number of disjoint intervals. As an application to these construction, we study the multi-fractal spectrum fμ(α) and the L-spectrum τμ(q) of these measures. We given an example of a μ where fμ(α) is not concave, and where τμ(q) has two points of non-differentiability.
QCD analysis of electromagnetic Dalitz decays $J/\psi\rightarrow\eta^{(\prime)}\ell^{+}\ell^{-}$

The electromagnetic Dalitz decays J/ψ → η(′)e+e− with large recoil momentum are studied in the framework of perturbative QCD. Meanwhile, the soft contributions from the small recoil momentum region are described by the overlap of soft wave functions, and the resonance contributions are estimated by the vector meson dominance model. Based on this dynamical picture, the transition form factors fψη(′)(q 2) in full kinematic region are calculated for the first time, and we find that the transition form factors are insensitive to the shapes of η(′) distribution amplitudes. Our prediction of the normalized transition form factor Fψη(q 2) ≡ fψη(q)/fψη(0) agrees well with its experimental data. In addition, we also find that the branching ratios B(J/ψ → η(′)e+e−) are dominated by the contributions of perturbative QCD, and the resonance contributions are negligibly small as well as the soft contributions due to the suppression of the kinematic factor. With all these contributions, our results of the branching ratios B(J/ψ → η(′)e+e−) and the ratio Re J/ψ = B(J/ψ → ηe+e−)/B(J/ψ → η′e+e−) are in good agreement with their experimental data. Using the obtained Fψη(′)(q 2), we give the predictions of the branching ratios B(J/ψ → η(′)μ+μ−) and their ratio R J/ψ.
Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

Estimation of causal effects using machine learning methods has become an active research field in econometrics. In this paper, we study the finite sample performance of meta-learners for estimation of heterogeneous treatment effects under the usage of sample-splitting and cross-fitting to reduce the overfitting bias. In both synthetic and semi-synthetic simulations we find that the performance of the meta-learners in finite samples greatly depends on the estimation procedure. The results imply that sample-splitting and cross-fitting are beneficial in large samples for bias reduction and efficiency of the meta-learners, respectively, whereas full-sample estimation is preferable in small samples. Furthermore, we derive practical recommendations for application of specific meta-learners in empirical studies depending on particular data characteristics such as treatment shares and sample size.
Confirming the 3:2 Resonance Chain of K2-138

The study of orbital resonances allows for the constraint of planetary properties of compact systems. K2-138 is an early K-type star with six planets, five of which have been proposed to be in the longest chain of 3:2 mean motion resonances. To observe and potentially verify the resonant behavior of K2-138's planets, we run N-body simulations using previously measured parameters. Through our analysis, we find that 99.2% of our simulations result in a chain of 3:2 resonances, although only 11% of them show a five-planet resonance chain. We find that we are able to use resonances to constrain the orbital periods and masses of the planets. We explore the possibility of this system forming in situ and through disk migration, and we investigate the potential compositions of each planet using a planet structure code.
SPAGHETTI

Neural implicit fields are quickly emerging as an attractive representation for learning based techniques. However, adopting them for 3D shape modeling and editing is challenging. We introduce a method for Editing Implicit Shapes Through Part Aware GeneraTion, permuted in short as SPAGHETTI. Our architecture allows for manipulation of implicit shapes by means of transforming, interpolating and combining shape segments together, without requiring explicit part supervision. SPAGHETTI disentangles shape part representation into extrinsic and intrinsic geometric information. This characteristic enables a generative framework with part-level control. The modeling capabilities of SPAGHETTI are demonstrated using an interactive graphical interface, where users can directly edit neural implicit shapes. Our code, editing user interface demo and pre-trained models are available at github.com/amirhertz/spaghetti.
ItôWave: Itô Stochastic Differential Equation is all You Need for Wave Generation

In this paper, we propose a vocoder based on a pair of forward and reverse-time linear stochastic differential equations (SDE). The solutions of this SDE pair are two stochastic processes, one of which turns the distribution of wave, that we want to generate, into a simple and tractable distribution. The other is the generation procedure that turns this tractable simple signal into the target wave. The model is called ItôWave. It uses the Wiener process as a driver to gradually subtract the excess signal from the noise signal to generate realistic corresponding meaningful audio respectively, under the conditional inputs of original mel spectrogram. The results of the experiment show that the mean opinion scores (MOS) of ItôWave can exceed the current state-of-the-art (SOTA) methods, and reached 4.35±0.115. The generated audio samples are available online1.
Error scaling laws for kernel classification under source and capacity conditions

In this manuscript we consider the problem of kernel classification. While worst-case bounds on the decay rate of the prediction error with the number of samples are known for some classifiers, they often fail to accurately describe the learning curves of real data sets. In this work, we consider the important class of data sets satisfying the standard source and capacity conditions, comprising a number of real data sets as we show numerically. Under the Gaussian design, we derive the decay rates for the misclassification (prediction) error as a function of the source and capacity coefficients. We do so for two standard kernel classification settings, namely margin-maximizing support vector machines and ridge classification, and contrast the two methods. We find that our rates tightly describe the learning curves for this class of data sets, and are also observed on real data. Our results can also be seen as an explicit prediction of the exponents of a scaling law for kernel classification that is accurate on some real datasets.
Teaching modeling in introductory statistics: A comparison of formula and tidyverse syntaxes

This paper reports on a head-to-head comparison run in a pair of introductory statistics labs, one conducted fully in the formula syntax, the other in tidyverse. Analysis of pre- and post-survey data show minimal differences between the two labs, with students reporting a positive experience regardless of section. Analysis of data from YouTube and RStudio Cloud show interesting distinctions. The formula section appeared to watch a larger proportion of pre-lab YouTube videos, but spend less time computing on RStudio Cloud. Conversely, the tidyverse section watched a smaller proportion of the videos and spent more time computing. Analysis of lab materials showed that tidyverse labs tended to be slightly longer in terms of lines in the provided RMarkdown materials and minutes of the associated YouTube videos. Both labs relied on a relatively small vocabulary of consistent functions, which can provide a starting point for instructors interested in teaching introductory statistics in R. The tidyverse labs exposed students to more distinct R functions, but reused functions more frequently. This work provides additional evidence for instructors looking to choose between syntaxes for introductory statistics teaching.
Evaluating a Methodology for Increasing AI Transparency: A Case Study

In reaction to growing concerns about the potential harms of artificial intelligence (AI), societies have begun to demand more transparency about how AI models and systems are created and used. To address these concerns, several efforts have proposed documentation templates containing questions to be answered by model developers. These templates provide a useful starting point, but no single template can cover the needs of diverse documentation consumers. It is possible in principle, however, to create a repeatable methodology to generate truly useful documentation. Richards et al. [25] proposed such a methodology for identifying specific documentation needs and creating templates to address those needs. Although this is a promising proposal, it has not been evaluated. This paper presents the first evaluation of this user-centered methodology in practice, reporting on the experiences of a team in the domain of AI for healthcare that adopted it to increase transparency for several AI models. The methodology was found to be usable by developers not trained in user-centered techniques, guiding them to creating a documentation template that addressed the specific needs of their consumers while still being reusable across different models and use cases. Analysis of the benefits and costs of this methodology are reviewed and suggestions for further improvement in both the methodology and supporting tools are summarized.
Data-driven structure-preserving model reduction for stochastic Hamiltonian systems

In this work we demonstrate that SVD-based model reduction techniques known for ordinary differential equations, such as the proper orthogonal decomposition, can be extended to stochastic differential equations in order to reduce the computational cost arising from both the high dimension of the considered stochastic system and the large number of independent Monte Carlo runs. We also extend the proper symplectic decomposition method to stochastic Hamiltonian systems, both with and without external forcing, and argue that preserving the underlying symplectic or variational structures results in more accurate and stable solutions that conserve energy better than when the non-geometric approach is used. We validate our proposed techniques with numerical experiments for a semi-discretization of the stochastic nonlinear Schr\"odinger equation and the Kubo oscillator.
Investigation of the two-cut phase region in the complex cubic ensemble of random matrices

We investigate the phase diagram of the complex cubic unitary ensemble of random matrices with the potential [Formula: see text], where t is a complex parameter. As proven in our previous paper [Bleher et al., J. Stat. Phys. 166, 784–827 (2017)], the whole phase space of the model, [Formula: see text], is partitioned into two phase regions, [Formula: see text] and [Formula: see text], such that in [Formula: see text] the equilibrium measure is supported by one Jordan arc (cut) and in [Formula: see text] by two cuts. The regions [Formula: see text] and [Formula: see text] are separated by critical curves, which can be calculated in terms of critical trajectories of an auxiliary quadratic differential. In Bleher et al. [J. Stat. Phys. 166, 784–827 (2017)], the one-cut phase region was investigated in detail. In the present paper, we investigate the two-cut region. We prove that in the two-cut region, the endpoints of the cuts are analytic functions of the real and imaginary parts of the parameter t, but not of the parameter t itself (so that the Cauchy–Riemann equations are violated for the endpoints). We also obtain the semiclassical asymptotics of the orthogonal polynomials associated with the ensemble of random matrices and their recurrence coefficients. The proofs are based on the Riemann–Hilbert approach to semiclassical asymptotics of the orthogonal polynomials and the theory of S-curves and quadratic differentials.
A GPS data-based analysis of built environment influences on bicyclist route preferences

ABSTRACT This study examines the effects of built environment features, including factors of land use and road network, on bicyclists' route preferences using the data from the city of Seattle. The bicycle routes are identified using a GPS dataset collected from a smartphone application named “CycleTracks. ” The route choice set is generated using the labeling route approach, and the cost functions of route alternatives are based on principal component analyses. Then, two mixed logit models, focusing on random parameters and alternative-specific coefficients, respectively, are estimated to examine bicyclists' route choice. The major findings of this study are as follows: (1) the bicycle route choice involves the joint consideration of convenience, safety, and leisure; (2) most bicyclists prefer to cycle on shorter, flat, and well-planned bicycle facilities with slow road traffic; (3) some bicyclists prefer routes surrounded by mixed land use; (4) some bicyclists favor routes which are planted with street trees or installed with street lights; and (5) some bicyclists prefer routes along with city features. This analysis provides valuable insights into how well-planned land use and road network can facilitate efficient, safe, and enjoyable bicycling.
Optimization and implementation of a surface-electrode ion trap junction

We describe the design of a surface-electrode ion trap junction, which is a key element for large-scale ion trap arrays. A bi-objective optimization method is used for designing the electrodes, which maintains the total pseudo-potential curvature while minimizing the axial pseudo-potential gradient along the ion transport path. To facilitate the laser beam delivery for parallel operations in multiple trap zones, we implemented integrated optics on each arm of this X-junction trap. The layout of the trap chip for commercial foundry fabrication is presented. This work suggests routes to improving ion trap junction performance in scalable implementations. Together with integrated optical addressing, this contributes to modular trapped-ion quantum computing in interconnected two-dimensional arrays.
An asymptotic approach to Borwein-type sign pattern theorems

The celebrated (First) Borwein Conjecture predicts that for all positive integers n the sign pattern of the coefficients of the “Borwein polynomial” (1− q)(1− q)(1− q)(1− q) · · · (1− q3n−2)(1− q3n−1) is + − − + − − · · · . It was proved by the first author in [Adv. Math. 394 (2022), Paper No. 108028]. In the present paper, we extract the essentials from the former paper and enhance them to a conceptual approach for the proof of “Borwein-like” sign pattern statements. In particular, we provide a new proof of the original (First) Borwein Conjecture, a proof of the Second Borwein Conjecture (predicting that the sign pattern of the square of the “Borwein polynomial” is also +−−+−− · · · ), and a partial proof of a “cubic” Borwein Conjecture due to the first author (predicting the same sign pattern for the cube of the “Borwein polynomial”). Many further applications are discussed.
Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI

Deep learning methods have become the state of the art for undersampled MR reconstruction. Particularly for cases where it is infeasible or impossible for ground truth, fully sampled data to be acquired, self-supervised machine learning methods for reconstruction are becoming increasingly used. However potential issues in the validation of such methods, as well as their generalizability, remain underexplored. In this paper, we investigate important aspects of the validation of self-supervised algorithms for reconstruction of undersampled MR images: quantitative evaluation of prospective reconstructions, potential differences between prospective and retrospective reconstructions, suitability of commonly used quantitative metrics, and generalizability. Two self-supervised algorithms based on self-supervised denoising and the deep image prior were investigated. These methods are compared to a least squares fitting and a compressed sensing reconstruction using in-vivo and phantom data. Their generalizability was tested with prospectively under-sampled data from experimental conditions different to the training. We show that prospective reconstructions can exhibit significant distortion relative to retrospective reconstructions/ground truth. Furthermore, pixel-wise quantitative metrics may not capture differences in perceptual quality accurately, in contrast to a perceptual metric. In addition, all methods showed potential for generalization; however, generalizability is more affected by changes in anatomy/contrast than other changes. We further showed that no-reference image metrics correspond well with human rating of image quality for studying generalizability. Finally, we showed that a well-tuned compressed sensing reconstruction and learned denoising perform similarly on all data. The datasets acquired for this paper will be made available online; see https://www.melba-journal.org/papers/2022:022.html for details.
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks

Graph neural networks (GNNs) have been increasingly deployed in various applications that involve learning on non-Euclidean data. However, recent studies show that GNNs are vulnerable to graph adversarial attacks. Although there are several defense methods to improve GNN robustness by eliminating adversarial components, they may also impair the underlying clean graph structure that contributes to GNN training. In addition, few of those defense models can scale to large graphs due to their high computational complexity and memory usage. In this paper, we propose GARNET, a scalable spectral method to boost the adversarial robustness of GNN models. GARNET first leverages weighted spectral embedding to construct a base graph, which is not only resistant to adversarial attacks but also contains critical (clean) graph structure for GNN training. Next, GARNET further refines the base graph by pruning additional uncritical edges based on probabilistic graphical model. GARNET has been evaluated on various datasets, including a large graph with millions of nodes. Our extensive experiment results show that GARNET achieves adversarial accuracy improvement and runtime speedup over state-of-the-art GNN (defense) models by up to 13.27% and 14.7x, respectively.
Learning Collective Action under Risk Diversity

Collective risk dilemmas (CRDs) are a class of n-player games that represent societal challenges where groups need to coordinate to avoid the risk of a disastrous outcome. Multi-agent systems incurring such dilemmas face difficulties achieving cooperation and often converge to sub-optimal, risk-dominant solutions where everyone defects. In this paper we investigate the consequences of risk diversity in groups of agents learning to play CRDs. We find that risk diversity places new challenges to cooperation that are not observed in homogeneous groups. We show that increasing risk diversity significantly reduces overall cooperation and hinders collective target achievement. It leads to asymmetrical changes in agents' policies -- i.e. the increase in contributions from individuals at high risk is unable to compensate for the decrease in contributions from individuals at low risk -- which overall reduces the total contributions in a population. When comparing RL behaviors to rational individualistic and social behaviors, we find that RL populations converge to fairer contributions among agents. Our results highlight the need for aligning risk perceptions among agents or develop new learning techniques that explicitly account for risk diversity.
AutoSNN: Towards Energy-Efficient Spiking Neural Networks

Spiking neural networks (SNNs) that mimic information transmission in the brain can energy-efficiently process spatio-temporal information through discrete and sparse spikes, thereby receiving considerable attention. To improve accuracy and energy efficiency of SNNs, most previous studies have focused solely on training methods, and the effect of architecture has rarely been studied. We investigate the design choices used in the previous studies in terms of the accuracy and number of spikes and figure out that they are not best-suited for SNNs. To further improve the accuracy and reduce the spikes generated by SNNs, we propose a spike-aware neural architecture search framework called AutoSNN. We define a search space consisting of architectures without undesirable design choices. To enable the spike-aware architecture search, we introduce a fitness that considers both the accuracy and number of spikes. AutoSNN successfully searches for SNN architectures that outperform hand-crafted SNNs in accuracy and energy efficiency. We thoroughly demonstrate the effectiveness of AutoSNN on various datasets including neuromorphic datasets.
Effect of interfacial disturbances on instabilities of buoyancy – thermocapillary convection in a two-fluid two-layer system in rectangular cavities

Effect of interfacial disturbances on instabilities of buoyant/thermocapillary convective flows in rectangular cavities is studied in a series of numerical experiments. The computations are carried out for several two-liquid two-layer systems taking into account properties of liquids used in previously published experiments. Relation between the interface deformations and the Boussinesq approximation is discussed. It is shown that in some systems, including the interface disturbances in the model can alter the critical temperature difference by approximately 10%, producing either destabilizing, or stabilizing effect. The interface oscillations appear as standing or travelling waves whose wavelength can vary from short wave lengths to a single wave occupying all the available space. Rough estimations show that in some liquid-liquid systems the interface oscillations amplitude can reach several tens of microns. Patterns of the most unstable disturbances are presented and discussed. It is argued that instabilities in some two layer systems develop similarly to the Holmboe instabilities in stratified mixing layers.
A Unified Approach to Entity-Centric Context Tracking in Social Conversations

In human-human conversations, Context Tracking deals with identifying important entities and keeping track of their properties and relationships. This is a challenging problem that encompasses several subtasks such as slot tagging, coreference resolution, resolving plural mentions and entity linking. We approach this problem as an end-to-end modeling task where the conversational context is represented by an entity repository containing the entity references mentioned so far, their properties and the relationships between them. The repository is updated turn-by-turn, thus making training and inference computationally efficient even for long conversations. This paper lays the groundwork for an investigation of this framework in two ways. First, we release Contrack, a large scale human-human conversation corpus for context tracking with people and location annotations. It contains over 7000 conversations with an average of 11.8 turns, 5.8 entities and 15.2 references per conversation. Second, we open-source a neural network architecture for context tracking. Finally we compare this network to state-of-the-art approaches for the subtasks it subsumes and report results on the involved tradeoffs.