Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensemble feature selection with clustering for analysis of high-dimensional, correlated clinical data in the search for Alzheimer's disease biomarkers

Healthcare datasets often contain groups of highly correlated features, such as features from the same biological system. When feature selection is applied to these datasets to identify the most important features, the biases inherent in some multivariate feature selectors due to correlated features make it difficult for these methods to distinguish between the important and irrelevant features and the results of the feature selection process can be unstable. Feature selection ensembles, which aggregate the results of multiple individual base feature selectors, have been investigated as a means of stabilising feature selection results, but do not address the problem of correlated features. We present a novel framework to create feature selection ensembles from multivariate feature selectors while taking into account the biases produced by groups of correlated features, using agglomerative hierarchical clustering in a pre-processing step. These methods were applied to two real-world datasets from studies of Alzheimer’s disease (AD), a progressive neurodegenerative disease that has no cure and is not yet fully understood. Our results show a marked improvement in the stability of features selected over the models without clustering, and the features selected by these models are in keeping with the findings in the AD literature.
Non-asymptotic convergence bounds for modified tamed unadjusted Langevin algorithm in non-convex setting

We consider the problem of sampling from a high-dimensional target distribution $\pi_\beta$ on $\mathbb{R}^d$ with density proportional to $\theta\mapsto e^{-\beta U(\theta)}$ using explicit numerical schemes based on discretising the Langevin stochastic differential equation (SDE). In recent literature, taming has been proposed and studied as a method for ensuring stability of Langevin-based numerical schemes in the case of super-linearly growing drift coefficients for the Langevin SDE. In particular, the Tamed Unadjusted Langevin Algorithm (TULA) was proposed in [Bro+19] to sample from such target distributions with the gradient of the potential $U$ being super-linearly growing. However, theoretical guarantees in Wasserstein distances for Langevin-based algorithms have traditionally been derived assuming strong convexity of the potential $U$. In this paper, we propose a novel taming factor and derive, under a setting with possibly non-convex potential $U$ and super-linearly growing gradient of $U$, non-asymptotic theoretical bounds in Wasserstein-1 and Wasserstein-2 distances between the law of our algorithm, which we name the modified Tamed Unadjusted Langevin Algorithm (mTULA), and the target distribution $\pi_\beta$. We obtain respective rates of convergence $\mathcal{O}(\lambda)$ and $\mathcal{O}(\lambda^{1/2})$ in Wasserstein-1 and Wasserstein-2 distances for the discretisation error of mTULA in step size $\lambda$. High-dimensional numerical simulations which support our theoretical findings are presented to showcase the applicability of our algorithm.
Eﬀect of small-angle scattering on the anisotropic behavior of the commensurability oscillations in the thermoelectric power and the thermal conductivity of unidirectional lateral superlattices

We have calculated the thermoelectric conductivity tensor ε ij and the thermal conductivity tensor λ ij of a unidirectional lateral superlattice (ULSL) ( i, j = x, y , with the x -axis aligned to the principal axis of the ULSL), based on the asymptotic analytic formulas of the conductivity tensor σ ij in the literature valid at low magnetic ﬁelds where large numbers of Landau levels are occupied. With the resulting analytic expressions, we clarify the conditions for the Mott formula (Wiedemann-Franz law) to be applicable with high precision to ε ij ( λ ij ). We further present plots of the commensurability oscillations δε ij , δλ ij , and δS ij in ε ij , λ ij , and the thermopower tensor S ij , calculated using typical parameters for a ULSL fabricated from a GaAs/AlGaAs two-dimensional electron gas (2DEG). Notable features of the δS ij are (i) anisotropic behavior ( δS xx 6 = δS yy ) and (ii) the dominance of the xy component over the other components ( | δS xy | ≫ | δS yx | , | δS xx | , | δS yy | ). Both (i) and (ii) are at variance with the previous theory and are attributable to the inclusion of a damping factor due to the small-angle scattering characteristic of GaAs/AlGaAs 2DEGs, which have not been taken into consideration in δS ij thus far.
Topology, vorticity, and limit cycle in a stabilized Kuramoto–Sivashinsky equation

Significance This paper extends our earlier work where a potential whose minima yield the stationary states of an evolving out-of-equilibrium system. The dynamics are examined in detail and are shown to decompose into longitudinal and transverse parts. The former describes dissipation and is responsible for the formation of stationary states. This paper discusses the role of the transverse part and its importance in identifying some of the complex time dependence of such dynamical systems such as limit cycles. We study a particular test evolution equation that displays most of the phenomena observed in evolving real systems.
Gradient-Free Kernel Stein Discrepancy

Stein discrepancies have emerged as a powerful statistical tool, being applied to fundamental statistical problems including parameter inference, goodness-of-fit testing, and sampling. The canonical Stein discrepancies require the derivatives of a statistical model to be computed, and in return provide theoretical guarantees of convergence detection and control. However, for complex statistical models, the stable numerical computation of derivatives can require bespoke algorithmic development and render Stein discrepancies impractical. This paper focuses on posterior approximation using Stein discrepancies, and introduces a collection of non-canonical Stein discrepancies that are gradient free, meaning that derivatives of the statistical model are not required. Sufficient conditions for convergence detection and control are established, and applications to sampling and variational inference are presented.
Baseline and other effects for a sterile neutrino at DUNE

We analyze the sensitivity of the Deep Underground Neutrino Experiment (DUNE) to a sterile neutrino, combining information from both near and far detectors. We quantify often-neglected effects which may impact the event rate estimation in a 3+1 oscillation scenario. In particular, we find that taking into account the information on the neutrino production point, in contrast to assuming a pointlike neutrino source, affects DUNE's sterile exclusion reach. Visible differences remain after the inclusion of energy bin-to-bin uncorrelated systematics. Instead, implementing exact oscillation formulae for near detector events, including a two slab density profile, does not result in any visible change in the sensitivity.
Stellar Loci. VI. An Updated Catalog of the Best and Brightest Metal-poor Stars

We employ Gaia, Two Micron All Sky Survey, and ALLWISE photometry, as well as astrometric data from Gaia, to search for relatively bright very metal-poor (VMP; [Fe/H] < −2.0) giant star candidates using three different criteria: (1) the derived Gaia photometric metallicities from Xu et al., (2) the lack of stellar molecular absorption near 4.6 μm, and (3) their high tangential velocities. With different combinations of these criteria, we have identified six samples of candidates with G < 15: the Gold sample (24,304 candidates), the Silver GW sample (40,157 candidates), the Silver GK sample (120,452 candidates), the Bronze G sample (291,690 candidates), the Bronze WK sample (68,526 candidates), and the Low-b sample (4645 candidates). The Low-b sample applies to sources with low Galactic latitude, ∣b∣ < 10°, while the others are for sources with ∣b∣ > 10°. By crossmatching with results derived from medium-resolution spectroscopy (R ∼ 1800) from LAMOST DR8, we establish that the success rate for identifying VMP stars is 60.1% for the Gold sample, 39.2% for the Silver GW sample, 41.3% for the Silver GK sample, 15.4% for the Bronze G sample, 31.7% for the Bronze WK sample, and 16.6% for the Low-b sample, respectively. An additional strict cut on the quality parameter RUWE < 1.1 can further increase the success rate of the Silver GW, Silver GK, and Bronze G samples to 46.9%, 51.6%, and 29.3%, respectively. Our samples provide valuable targets for high-resolution follow-up spectroscopic observations and are made publicly available.
Cooperative Distribution Alignment via JSD Upper Bound

Unsupervised distribution alignment estimates a transformation that maps two or more source distributions to a shared aligned distribution given only samples from each distribution. This task has many applications including generative modeling, unsupervised domain adaptation, and socially aware learning. Most prior works use adversarial learning (i.e., min-max optimization), which can be challenging to optimize and evaluate. A few recent works explore non-adversarial flow-based (i.e., invertible) approaches, but they lack a unified perspective and are limited in efficiently aligning multiple distributions. Therefore, we propose to unify and generalize previous flow-based approaches under a single non-adversarial framework, which we prove is equivalent to minimizing an upper bound on the Jensen-Shannon Divergence (JSD). Importantly, our problem reduces to a min-min, i.e., cooperative, problem and can provide a natural evaluation metric for unsupervised distribution alignment. We show empirical results on both simulated and real-world datasets to demonstrate the benefits of our approach. Code is available at https://github.com/inouye-lab/alignment-upper-bound.
Ultra-High Extinction Dual-Output Thin-Film Lithium Niobate Intensity Modulator

A low voltage, wide bandwidth compact electro-optic modulator is a key building block in the realization of tomorrow’s communication and networking needs. Recent advances in the fabrication and application of thin-film lithium niobate, and its integration with photonic integrated circuits based in silicon make it an ideal platform for such a device. In this work, a high-extinction dual-output folded electro-optic Mach Zehnder modulator in the silicon nitride and thin-film lithium niobate material system is presented. This modulator has an interaction region length of 11 mm and a physical length of 7.8 mm. The device demonstrates a fiber-to-fiber loss of roughly 12 dB using on-chip fiber couplers and DC half wave voltage (<inline-formula> <tex-math notation="LaTeX">$\text{V}\pi $ </tex-math></inline-formula>) of less than 3.0 V, or a modulation efficiency (<inline-formula> <tex-math notation="LaTeX">$\text{V}\pi \cdot \text{L}$ </tex-math></inline-formula>) of 3.3 <inline-formula> <tex-math notation="LaTeX">$\text{V}\cdot $ </tex-math></inline-formula>cm. The device shows a 3 dB bandwidth of roughly 30 GHz. Notably, the device demonstrates a power extinction ratio over 45 dB at each output port without the use of cascaded directional couplers or additional control circuitry; roughly 31 times better than previously reported devices. Paired with a balanced photo-diode receiver, this modulator can be used in various photonic communication systems. Such a detecting scheme is compatible with complex modulation formats such as differential phase shift keying and differential quadrature phase shift keying, where a dual-output, ultra-high extinction device is fundamentally paramount to low-noise operation of the system.
Bouncing universe dual to the concordance model: Scalar-tensor theories in the Jordan frame

Scalar-tensor theories of gravity provide mathematically equivalent descriptions of Einstein’s gravity with a scalar ﬁeld, in a conformally connected spacetime, described in terms of the Jordan frame and the Einstein frame. In this paper, we use the Jordan frame-Einstein frame correspondence to explore dual universes with contrasting cosmological evolutions. We study the mapping between Einstein and Jordan frames where the Einstein frame universe eﬀectively describes the late-time evolution of the physical universe, driven by dark energy and non-relativistic matter. The Brans-Dicke theory of gravity is taken to be the dual scalar-tensor theory in the Jordan frame. We show that the standard Einstein frame universe, with dark energy and non-relativistic matter, always corresponds to a bouncing Jordan frame universe, if it is governed by a Brans-Dicke theory. This essentially leads to an alternative description of the late-time evolution of the physical universe, in terms of a bouncing Brans-Dicke universe in the Jordan frame. Previous studies have shown that for a bouncing Jordan frame, particularly for an early-time accelerating phase of the universe, the map between the Einstein and Jordan frames may become singular in the perturbative regime, causing the conformal correspondence to break down. In order to check whether the present bouncing model for late-time acceleration is free of such instabilities, we study the evolution of scalar metric perturbations. The Jordan frame metric perturbations are numerically solved, ﬁrst via pulling the perturbations in the Einstein frame using the conformal correspondence, and then directly in the Jordan frame. The evolutions of perturbations obtained in these two cases are in a good agreement. Thus, the duality between the Einstein frame, mimicking the physical universe,
J ul 2 02 2 Building Reputations via Summary Statistics

A patient seller interacts with a sequence of myopic consumers. Each consumer decides whether to trust the seller after she observes the number of times that the seller took each of his actions in the last K periods, but not the order with which these actions were taken. I assume that the seller’s effort and consumers’ trust are strategic complements, and that with positive probability, the seller is a commitment type who exerts the highest effort in every period. I show that the seller sustains his reputation for exerting the highest effort in all equilibria if and only if K is below some cutoff. Although a larger K allows more consumers to observe the seller’s opportunistic behavior, it weakens consumers’ incentives to punish the seller after they observe opportunistic behaviors. This effect undermines the seller’s reputational incentives and lowers consumers’ welfare. I also show that coarsening the summary statistics observed by the consumers may encourage the seller to sustain his reputation and may improve consumers’ welfare.
Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series Transformer

Astrophysical light curves are particularly challenging data objects due to the intensity and variety of noise contaminating them. Yet, despite the astronomical volumes of light curves available, the majority of algorithms used to process them are still operating on a per-sample basis. To remedy this, we propose a simple Transformer model -- called Denoising Time Series Transformer (DTST) -- and show that it excels at removing the noise and outliers in datasets of time series when trained with a masked objective, even when no clean targets are available. Moreover, the use of self-attention enables rich and illustrative queries into the learned representations. We present experiments on real stellar light curves from the Transiting Exoplanet Space Satellite (TESS), showing advantages of our approach compared to traditional denoising techniques.
A Tutorial on the Spectral Theory of Markov Chains

Abstract Markov chains are a class of probabilistic models that have achieved widespread application in the quantitative sciences. This is in part due to their versatility, but is compounded by the ease with which they can be probed analytically. This tutorial provides an in-depth introduction to Markov chains and explores their connection to graphs and random walks. We use tools from linear algebra and graph theory to describe the transition matrices of different types of Markov chains, with a particular focus on exploring properties of the eigenvalues and eigenvectors corresponding to these matrices. The results presented are relevant to a number of methods in machine learning and data mining, which we describe at various stages. Rather than being a novel academic study in its own right, this text presents a collection of known results, together with some new concepts. Moreover, the tutorial focuses on offering intuition to readers rather than formal understanding and only assumes basic exposure to concepts from linear algebra and probability theory. It is therefore accessible to students and researchers from a wide variety of disciplines.
Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning

Machine learning (ML) models are typically optimized for their accuracy on a given dataset. However, this predictive criterion rarely captures all desirable properties of a model, in particular how well it matches a domain expert's understanding of a task. Underspecification refers to the existence of multiple models that are indistinguishable in their in-domain accuracy, even though they differ in other desirable properties such as out-of-distribution (OOD) performance. Identifying these situations is critical for assessing the reliability of ML models. We formalize the concept of underspecification and propose a method to identify and partially address it. We train multiple models with an independence constraint that forces them to implement different functions. They discover predictive features that are otherwise ignored by standard empirical risk minimization (ERM), which we then distill into a global model with superior OOD performance. Importantly, we constrain the models to align with the data manifold to ensure that they discover meaningful features. We demonstrate the method on multiple datasets in computer vision (collages, WILDS-Camelyon17, GQA) and discuss general implications of underspecification. Most notably, in-domain performance cannot serve for OOD model selection without additional assumptions.
On atomic state purity operator, degree of state purity and concurrence in the JC and anti-JC models

The state of an atom in a bipartite qubit, Jaynes-Cummings (JC) or anti-Jaynes-Cummings (aJC) interaction is described by a reduced density operator. The purity of the state has been measured by taking the trace of the square of the reduced density operator. In this article, we define the square of the reduced density operator as the state purity operator, composed of a completely pure state part and a completely mixed state part. The coefficient of the completely mixed state part is the mixed state measure, formally obtained as the determinant of the reduced density operator and it is therefore directly related to tangle, the square of concurrence of the bipartite system. Expressed in various equivalent forms, the mixed state measure provides all the characteristic elements of state purity or entanglement, such as eigenvalues of the reduced density operator, nonclassicality measures and a state purity complex amplitude. The argument of the state purity complex amplitude in polar form is the phase of the state purity measure, which defines the degree of purity of the state. We find that the degree of purity and concurrence are complementary quantifiers satisfying a complementarity relation.
Longitudinal Analysis of SARS-CoV-2 Vaccine Breakthrough Infections Reveals Limited Infectious Virus Shedding and Restricted Tissue Distribution

Abstract Background The global effort to vaccinate people against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during an ongoing pandemic has raised questions about how vaccine breakthrough infections compare with infections in immunologically naive individuals and the potential for vaccinated individuals to transmit the virus. Methods We examined viral dynamics and infectious virus shedding through daily longitudinal sampling in 23 adults infected with SARS-CoV-2 at varying stages of vaccination, including 6 fully vaccinated individuals. Results The durations of both infectious virus shedding and symptoms were significantly reduced in vaccinated individuals compared with unvaccinated individuals. We also observed that breakthrough infections are associated with strong tissue compartmentalization and are only detectable in saliva in some cases. Conclusions Vaccination shortens the duration of time of high transmission potential, minimizes symptom duration, and may restrict tissue dissemination.
Characterizing and Mitigating the Difficulty in Training Physics-informed Artificial Neural Networks under Pointwise Constraints

Neural networks can be used to learn the solution of partial differential equations (PDEs) on arbitrary domains without requiring a computational mesh. Common approaches integrate differential operators in training neural networks using a structured loss function. The most common training algorithm for neural networks is backpropagation which relies on the gradient of the loss function with respect to the parameters of the network. In this work, we characterize the difficulty of training neural networks on physics by investigating the impact of differential operators in corrupting the back propagated gradients. Particularly, we show that perturbations present in the output of a neural network model during early stages of training lead to higher levels of noise in a structured loss function that is composed of high-order differential operators. These perturbations consequently corrupt the back-propagated gradients and impede convergence. We mitigate this issue by introducing auxiliary flux parameters to obtain a system of first-order differential equations. We formulate a non-linear unconstrained optimization problem using the augmented Lagrangian method that properly constrains the boundary conditions and adaptively focus on regions of higher gradients that are difficult to learn. We apply our approach to learn the solution of various benchmark PDE problems and demonstrate orders of magnitude improvement over existing approaches.