Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing Many Constraints in Possibly Irregular Models Using Incomplete U-Statistics

We consider the problem of testing a null hypothesis defined by equality and inequality constraints on a statistical parameter. Testing such hypotheses can be challenging because the number of relevant constraints may be on the same order or even larger than the number of observed samples. Moreover, standard distributional approximations may be invalid due to irregularities in the null hypothesis. We propose a general testing methodology that aims to circumvent these difficulties. The constraints are estimated by incomplete U-statistics, and we derive critical values by Gaussian multiplier bootstrap. We show that the bootstrap approximation of incomplete U-statistics is valid for kernels that we call mixed degenerate when the number of combinations used to compute the incomplete U-statistic is of the same order as the sample size. It follows that our test controls type I error even in irregular settings. Furthermore, the bootstrap approximation covers high-dimensional settings making our testing strategy applicable for problems with many constraints. The methodology is applicable, in particular, when the constraints to be tested are polynomials in U-estimable parameters. As an application, we consider goodness-of-fit tests of latent tree models for multivariate data.
Functional varying index coefficient model for dynamic gene-environment interactions

Rooted in genetics, human complex diseases are largely influenced by environmental factors. Existing literature has shown the power of integrative gene-environment interaction analysis by considering the joint effect of environmental mixtures on a disease risk. In this work, we propose a functional varying index coefficient model for longitudinal measurements of a phenotypic trait together with multiple environmental variables, and assess how the genetic effects on a longitudinal disease trait are nonlinearly modified by a mixture of environmental influences. We derive an estimation procedure for the nonparametric functional varying index coefficients under the quadratic inference function and penalized spline framework. Theoretical results such as estimation consistency and asymptotic normality of the estimates are established. In addition, we propose a hypothesis testing procedure to assess the significance of the nonparametric index coefficient function. We evaluate the performance of our estimation and testing procedure through Monte Carlo simulation studies. The proposed method is illustrated by applying to a real data set from a pain sensitivity study in which SNP effects are nonlinearly modulated by the combination of dosage levels and other environmental variables to affect patients' blood pressure and heart rate.
On the main distance-based entropies: the eccentricity- and Wiener-entropy

We deﬁne the Wiener-entropy, which is together with the eccentricity-entropy one of the most natural distance-based graph entropies. By deriving the (asymptotic) extremal behaviour, we conclude that the Wiener-entropy of graphs of a given order is more spread than is the case for the eccentricity-entropy. We solve 3 conjectures on the eccentricity-entropy and give a conjecture on the Wiener-entropy related to some surprising behaviour on the graph minimizing it.
SIR Epidemics on Evolving Erd\H{o}s-R\'enyi Graphs

In the standard SIR model, infected vertices infect their neighbors at rate $\lambda$ independently across each edge. They also recover at rate $\gamma$. In this work we consider the SIR-$\omega$ model where the graph structure itself co-evolves with the SIR dynamics. Specifically, $S-I$ connections are broken at rate $\omega$. Then, with probability $\alpha$, $S$ rewires this edge to another uniformly chosen vertex; and with probability $1-\alpha$, this edge is simply dropped. When $\alpha=1$ the SIR-$\omega$ model becomes the evoSIR model. Jiang et al. proved in \cite{DOMath} that the probability of an outbreak in the evoSIR model converges to 0 as $\lambda$ approaches the critical infection rate $\lambda_c$. On the other hand, numerical experiments in \cite{DOMath} revealed that, as $\lambda \to \lambda_c$, (conditionally on an outbreak) the fraction of infected vertices may not converge to 0, which is referred to as a discontinuous phase transition. In \cite{BB} Ball and Britton give two (non-matching) conditions for continuous and discontinuous phase transitions for the fraction of infected vertices in the SIR-$\omega$ model. In this work, we obtain a necessary and sufficient condition for the emergence of a discontinuous phase transition of the final epidemic size of the SIR-$\omega$ model on \ER\, graphs, thus closing the gap between these two conditions.
Decentralized Nonlinear Control of Redundant Upper Limb Exoskeleton with Natural Adaptation Law

The aim of this work is to utilize an adaptive decentralized control method called virtual decomposition control (VDC) to control the orientation and position of the end-effector of a 7 degrees of freedom (DoF) right-hand upper-limb exoskeleton. The prevailing adaptive VDC approach requires tuning of 13n adaptation gains along with 26n upper and lower parameter bounds, where $n$ is the number of rigid bodies. Therefore, utilizing the VDC scheme to control high DoF robots like the 7-DoF upper-limb exoskeleton can be an arduous task. In this paper, a new adaptation function, so-called natural adaptation law (NAL), is employed to eliminate these burdens from VDC, which results in reducing all 13n gains to one and removing dependency on upper and lower bounds. In doing so, VDC-based dynamic equations are restructured, and inertial parameter vectors are made compatible with NAL. Then, the NAL adaptation function is exploited to design a new adaptive VDC scheme. This novel adaptive VDC approach ensures physical consistency conditions for estimated parameters with no need for upper and lower bounds. Finally, the asymptotic stability of the algorithm is proved with the virtual stability concept and the accompanying function. The experimental results are utilized to demonstrate the excellent performance of the proposed new adaptive VDC scheme.
Supervised Dimensionality Reduction and Classification with Convolutional Autoencoders

The joint optimization of the reconstruction and classiﬁcation error is a hard non convex problem, especially when a non linear mapping is utilized. In order to overcome this obstacle, a novel optimization strategy is proposed, in which a Convolutional Autoencoder for dimensionality reduction and a classiﬁer composed by a Fully Connected Network, are combined to simultaneously produce supervised dimensionality reduction and predictions. It turned out that this methodology can also be greatly beneﬁcial in enforcing explainability of deep learning architectures. Addi-tionally, the resulting Latent Space, optimized for the classiﬁcation task, can be utilized to improve traditional, interpretable classiﬁcation algorithms. The experimental results, showed that the proposed methodology achieved competitive results against the state of the art deep learning methods, while being much more eﬃcient in terms of parameter count. Finally, it was empirically justiﬁed that the proposed methodology in-troduces advanced explainability regarding, not only the data structure through the produced latent space, but also about the classiﬁcation behaviour.
Enforcing Delayed-Impact Fairness Guarantees

Recent research has shown that seemingly fair machine learning models, when used to inform decisions that have an impact on peoples' lives or well-being (e.g., applications involving education, employment, and lending), can inadvertently increase social inequality in the long term. This is because prior fairness-aware algorithms only consider static fairness constraints, such as equal opportunity or demographic parity. However, enforcing constraints of this type may result in models that have negative long-term impact on disadvantaged individuals and communities. We introduce ELF (Enforcing Long-term Fairness), the first classification algorithm that provides high-confidence fairness guarantees in terms of long-term, or delayed, impact. We prove that the probability that ELF returns an unfair solution is less than a user-specified tolerance and that (under mild assumptions), given sufficient training data, ELF is able to find and return a fair solution if one exists. We show experimentally that our algorithm can successfully mitigate long-term unfairness.
Uniqueness of ground states to fractional nonlinear elliptic equations with harmonic potential

In this paper, we prove the uniqueness of ground states to the following fractional nonlinear elliptic equation with harmonic potential, $$ (-\Delta)^s u+ \left(\omega+|x|^2\right) u=|u|^{p-2}u \quad \mbox{in}\,\, \R^n, $$ where $n \geq 1$, $0-\lambda_{1,s}$, $20$ is the lowest eigenvalue of $(-\Delta)^s + |x|^2$. The fractional Laplacian $(-\Delta)^s$ is characterized as $\mathcal{F}((-\Delta)^{s}u)(\xi)=|\xi|^{2s} \mathcal{F}(u)(\xi)$ for $\xi \in \R^n$, where $\mathcal{F}$ denotes the Fourier transform. This solves an open question in \cite{SS} concerning the uniqueness of ground states.
A Perturbation Resistant Transformation and Classification System for Deep Neural Networks

Deep convolutional neural networks accurately classify a diverse range of natural images, but may be easily deceived when designed, imperceptible perturbations are embedded in the images. In this paper, we design a multi-pronged training, input transformation, and image ensemble system that is attack agnostic and not easily estimated. Our system incorporates two novel features. The first is a transformation layer that computes feature level polynomial kernels from class-level training data samples and iteratively updates input image copies at inference time based on their feature kernel differences to create an ensemble of transformed inputs. The second is a classification system that incorporates the prediction of the undefended network with a hard vote on the ensemble of filtered images. Our evaluations on the CIFAR10 dataset show our system improves the robustness of an undefended network against a variety of bounded and unbounded white-box attacks under different distance metrics, while sacrificing little accuracy on clean images. Against adaptive full-knowledge attackers creating end-to-end attacks, our system successfully augments the existing robustness of adversarially trained networks, for which our methods are most effectively applied.
Contrastive Audio-Language Learning for Music

As one of the most intuitive interfaces known to humans, natural language has the potential to mediate many tasks that involve human-computer interaction, especially in application-focused fields like Music Information Retrieval. In this work, we explore cross-modal learning in an attempt to bridge audio and language in the music domain. To this end, we propose MusCALL, a framework for Music Contrastive Audio-Language Learning. Our approach consists of a dual-encoder architecture that learns the alignment between pairs of music audio and descriptive sentences, producing multimodal embeddings that can be used for text-to-audio and audio-to-text retrieval out-of-the-box. Thanks to this property, MusCALL can be transferred to virtually any task that can be cast as text-based retrieval. Our experiments show that our method performs significantly better than the baselines at retrieving audio that matches a textual description and, conversely, text that matches an audio query. We also demonstrate that the multimodal alignment capability of our model can be successfully extended to the zero-shot transfer scenario for genre classification and auto-tagging on two public datasets.
Optical IFU observations of GOALS sample with KOOLS-IFU on Seimei Telescope: Initial results of nine U/LIRGs at z < 0.04

We present ionized gas properties of nine local ultra/luminous infrared galaxies (U/LIRGs) at z < 0.04 through Integral Field Unit (IFU) observations with KOOLS-IFU on the Seimei Telescope. The observed targets are drawn from the Great Observatories All-sky LIRG Survey (GOALS), covering a wide range of merger stages. We successfully detect emission lines such as Hβ, [O iii]λ5007, Hα, [N ii]λλ6549, 6583, and [S ii]λλ6717, 6731 with a spectral resolution of R = 1500–2000, which provides (i) a spatially resolved (∼200–700 pc) moment map of ionized gas and (ii) diagnostics for an active galactic nucleus (AGN) within the central ∼3–11 kpc in diameter for our sample. We find that the [O iii] outflow that is expected to be driven by an AGN tends to be stronger (i) towards the galactic center and (ii) as a sequence of the merger stage. In particular, the outflow strength in the late-stage (stage D) mergers is about 1.5 times stronger than that in the early-state (stage B) mergers, which indicates that galaxy mergers could induce AGN-driven outflow and play an important role in the co-evolution of galaxies and supermassive black holes.
Exact calculation of the mean first-passage time of continuous-time random walks by nonhomogeneous Wiener–Hopf integral equations

We study the mean first-passage time (MFPT) for asymmetric continuous-time random walks in continuous-space characterised by waiting-times with finite mean and by jump-sizes with both finite mean and finite variance. In the asymptotic limit, this well-controlled process is governed by an advection-diffusion equation and the MFPT results to be finite when the advecting velocity is in the direction of the boundary. We derive a nonhomogeneous Wiener–Hopf integral equation that allows for the exact calculation of the MFPT by avoiding asymptotic limits and it emerges to depend on the whole distribution of the jump-sizes and on the mean-value only of the waiting-times, thus it holds for general non-Markovian random walks. Through the case study of a quite general family of asymmetric distributions of the jump-sizes that is exponential towards the boundary and arbitrary in the opposite direction, we show that the MFPT is indeed independent of the jump-sizes distribution in the opposite direction to the boundary. Moreover, we show also that there exists a length-scale, which depends only on the features of the distribution of jumps in the direction of the boundary, such that for starting points near the boundary the MFPT depends on the specific whole distribution of jump-sizes, in opposition to the universality emerging for starting points far-away from the boundary.
Microsoft Word-Okajima et al.revised3.docx

Department of Biological Science, Graduate School of Science, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan, RIKEN Harima Institute| Spring-8, 1-1-1 Kouto, Mikaduki, Sayo, Hyogo 679-5148, Japan, Department of Botany, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan, Department of Physics, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Kanagawa 223-8522, Japan
On Reality and the Limits of Language Data

Recent advances in neural network language models have shown that it is possible to derive expressive meaning representations by leveraging linguistic associations in large-scale natural language data. These potentially Gestalt representations have enabled state-of-the-art performance for many practical applications. It would appear that we are on a pathway to empirically deriving a robust and expressive computable semantics. A key question that arises is how far can language data alone enable computers to understand the necessary truth about the physical world? Attention to this question is warranted because our future interactions with intelligent machines depends on how well our techniques correctly represent and process the concepts (objects, properties, and processes) that humans commonly observe to be true. After reviewing existing protocols, the objective of this work is to explore this question using a novel and tightly controlled reasoning test and to high-light what models might learn directly from pure linguistic data.
Data Augmentation for Graph Data: Recent Advancements

Graph Neural Network (GNNs) based methods have recently become a popular tool to deal with graph data because of their ability to incorporate structural information. The only hurdle in the performance of GNNs is the lack of labeled data. Data Augmentation techniques for images and text data can not be used for graph data because of the complex and non-euclidean structure of graph data. This gap has forced researchers to shift their focus towards the development of data augmentation techniques for graph data. Most of the proposed Graph Data Augmentation (GDA) techniques are task-specific. In this paper, we survey the existing GDA techniques based on different graph tasks. This survey not only provides a reference to the research community of GDA but also provides the necessary information to the researchers of other domains.
Low-temperature ordering in a substitutional alloy with injecting nonequilibrium vacancies: The FePt case

Achieving the compositionally ordered state in a substitutional alloy of two or more species can often be even critical for improving its functional properties. To produce a highly ordered alloy, a longtime high-temperature (up to T  1000 K) treatment of the alloy is typically necessary because of insufficient vacancy concentration ( c v ) and their mobility. However, such processing affects the morphology and complicates the technology of functional alloys. We show theoretically that the ordering in the practically important FePt system (Fe x Pt 1- x with x being close to 0.5) is already achievable at T  450 K for reasonable times t <10 3 s due to frozen nonequilibrium vacancies. Our simulation is based on the Dienes equation for relaxation of the long-range order parameter ( S ), with taking additionally into account that the ordering kinetics in the alloy is mediated by vacancies. Importantly, the results of such simulation are in good agreement with previous experimental data on the ordering kinetics. We also find that nanosecond laser pulses can be employed to achieve a sufficient level of c v  10 -5 for effective low-temperature ordering.
Disorder-induced spin-charge separation in the 1-D Hubbard model

Many-body localisation is believed to be generically unstable in quantum systems with continuous non-Abelian symmetries, even in the presence of strong disorder. Breaking these symmetries can stabilise the localised phase, leading to the emergence of an extensive number of quasi-locally conserved quantities known as local integrals of motion, or $l$-bits. Using a sophisticated non-perturbative technique based on continuous unitary transforms, we investigate the one-dimensional Hubbard model subject to both spin and charge disorder, compute the associated $l$-bits and demonstrate that the disorder gives rise to a novel form of spin-charge separation. We examine the role of symmetries in delocalising the spin and charge degrees of freedom, and show that while symmetries generally lead to delocalisation through multi-particle resonant processes, certain subsets of states appear stable.
TEP-GNN: Accurate Execution Time Prediction of Functional Tests using Graph Neural Networks

Predicting the performance of production code prior to actually executing or benchmarking it is known to be highly challenging. In this paper, we propose a predictive model, dubbed TEP-GNN, which demonstrates that high-accuracy performance prediction is possible for the special case of predicting unit test execution times. TEP-GNN uses FA-ASTs, or flow-augmented ASTs, as a graph-based code representation approach, and predicts test execution times using a powerful graph neural network (GNN) deep learning model. We evaluate TEP-GNN using four real-life Java open source programs, based on 922 test files mined from the projects’ public repositories. We find that our approach achieves a high Pearson correlation of 0.789, considerable outperforming a baseline deep learning model. However, we also find that more work is needed for trained models to generalize to unseen projects. Our work demonstrates that FA-ASTs and GNNs are a feasible approach for predicting absolute performance values, and serves as an important intermediary step towards being able to predict the performance of arbitrary code prior to execution.
Institutional Knowledge at Singapore Management University Financial Development and the Patterns of International Capital Flows

We develop a tractable two-country overlapping-generations model and show that cross-country diﬀerences in ﬁnancial development can explain three recent empirical patterns of international capital ﬂows: Financial capital ﬂows from relatively poor to relatively rich countries while foreign direct investment ﬂows in the opposite direction; net capital ﬂows go from poor to rich countries; despite of its negative net international investment positions, the United States receives a positive net investment income. We also explore the welfare and distributional eﬀects of international capital ﬂows and show that the patterns of capital ﬂows may reverse along the convergence process of a developing country.
The Effects of the COVID-19 Pandemic on American Utilities Usage

— The COVID-19 pandemic has affected the ways many Americans live their daily lives, including the amount of time spent at home. This study seeks to address how much the assumed extra time spent at home has affected energy, gas, and water consumption. Preliminary research suggests that due to increased time indoors, utilities consumption increased between 2019 and 2021, when the COVID-19 pandemic (henceforth simply “pandemic”) began. This study intends to look at a relatively new phenomena and show how the recent pandemic has changed the lives of average Americans. Because the pandemic is ongoing, this research could provide the baseline for future examinations of the effects of the pandemic in years to come. For this study, the University of Dayton MEE 420/RCL 569 class of Spring 2021 provided their last three years of utilities usage and graphed their 2019, 2020, and 2021 data against each other. From there, the data was analyzed in MATLAB using the energy consumption data as well as weather to determine if a significant change in usage occurred between 2019 and 2021 that was not caused by unusual temperatures. From MATLAB, the mean difference values and p-values for each energy source consumption was recorded along with the plots of pre-COVID-19 and post-COVID-19 utility consumption. With the mean difference values being mostly positive, they suggest that the energy usage during the pandemic was greater than before the pandemic. Although this is true, the p-values indicate there is not enough evidence to suggest that the energy usage was greater during the pandemic than before it. This implicates that our study does not have enough information to definitively conclude that energy consumption increased during the pandemic.