Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentation

Learning with multiple modalities is crucial for automated brain tumor segmentation from magnetic resonance imaging data. Explicitly optimizing the common information shared among all modalities (e.g., by maximizing the total correlation) has been shown to achieve better feature representations and thus enhance the segmentation performance. However, existing approaches are oblivious to partial common information shared by subsets of the modalities. In this paper, we show that identifying such partial common information can significantly boost the discriminative power of image segmentation models. In particular, we introduce a novel concept of partial common information mask (PCI-mask) to provide a fine-grained characterization of what partial common information is shared by which subsets of the modalities. By solving a masked correlation maximization and simultaneously learning an optimal PCI-mask, we identify the latent microstructure of partial common information and leverage it in a self-attention module to selectively weight different feature representations in multi-modal data. We implement our proposed framework on the standard U-Net. Our experimental results on the Multi-modal Brain Tumor Segmentation Challenge (BraTS) datasets outperform those of state-of-the-art segmentation baselines, with validation Dice similarity coefficients of 0.920, 0.897, 0.837 for the whole tumor, tumor core, and enhancing tumor on BraTS-2020.
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks

While leveraging additional training data is well established to improve adversarial robustness, it incurs the unavoidable cost of data collection and the heavy computation to train models. To mitigate the costs, we propose Guided Adversarial Training (GAT), a novel adversarial training technique that exploits auxiliary tasks under a limited set of training data. Our approach extends single-task models into multi-task models during the min-max optimization of adversarial training, and drives the loss optimization with a regularization of the gradient curvature across multiple tasks. GAT leverages two types of auxiliary tasks: self-supervised tasks, where the labels are generated automatically, and domain-knowledge tasks, where human experts provide additional labels. Experimentally, GAT increases the robust AUC of CheXpert medical imaging dataset from 50% to 83% and On CIFAR-10, GAT outperforms eight state-of-the-art adversarial training and achieves 56.21% robust accuracy with Resnet-50. Overall, we demonstrate that guided multi-task learning is an actionable and promising avenue to push further the boundaries of model robustness.
Continuously Indexed Graphical Models

Let $X = \{X_{u}\}_{u \in U}$ be a real-valued Gaussian process indexed by a set $U$. It can be thought of as an undirected graphical model with every random variable $X_{u}$ serving as a vertex. We characterize this graph in terms of the covariance of $X$ through its reproducing kernel property. Unlike other characterizations in the literature, our characterization does not restrict the index set $U$ to be finite or countable, and hence can be used to model the intrinsic dependence structure of stochastic processes in continuous time/space. Consequently, this characterization is not in terms of the zero entries of an inverse covariance. This poses novel challenges for the problem of recovery of the dependence structure from a sample of independent realizations of $X$, also known as structure estimation. We propose a methodology that circumvents these issues, by targeting the recovery of the underlying graph up to a finite resolution, which can be arbitrarily fine and is limited only by the available sample size. The recovery is shown to be consistent so long as the graph is sufficiently regular in an appropriate sense. We derive corresponding convergence rates and finite sample guarantees. Our methodology is illustrated by means of a simulation study and two data analyses.
Strong picosecond optical pulse propagation in semiconductor optical amplifiers at transparency

The propagation of strong picosecond optical pulses in semiconductor optical amplifiers (SOAs) is investigated numerically by taking into account carrier heating, spectral hole-burning, and two-photon absorption, as well as ultrafast nonlinear refraction. Very good agreement with published experimental results are observed in both the time and frequency domains. It is shown that the effects of two-photon absorption and ultrafast nonlinear refraction are very important in determining the output pulse properties for pulse energy larger than 1 pJ.
RoboCup 2022 AdultSize Winner NimbRo: Upgraded Perception, Capture Steps Gait and Phase-based In-walk Kicks

Beating the human world champions by 2050 is an ambitious goal of the Humanoid League that provides a strong incentive for RoboCup teams to further improve and develop their systems. In this paper, we present upgrades of our system which enabled our team NimbRo to win the Soccer Tournament, the Drop-in Games, and the Technical Challenges in the Humanoid AdultSize League of RoboCup 2022. Strong performance in these competitions resulted in the Best Humanoid award in the Humanoid League. The mentioned upgrades include: hardware upgrade of the vision module, balanced walking with Capture Steps, and the introduction of phase-based in-walk kicks.
Représentation des systèmes dynamiques substitutifs non unimodulaires

On montre que la condition combinatoire de fortes coïncidences est suffisante pour qu'un système substitutif de type Pisot soit isomorphe en mesure à un échange de morceaux dans un compact autosimilaire de mesure non nulle dans le produit d'un espace euclidien et d'extensions finies de corps p-adiques. En particulier, tout système substitutif de type Pisot est une extension finie de son facteur équicontinu maximal ; en règle générale, ce dernier contient une translation p-adique si et seulement si la matrice d'incidence de la substitution est nilpotente modulo p. The combinatorial condition of strong coincidence is proved to be a sufficient condition for the dynamical system associated with a non-unimodular substitution of Pisot type to be measure-theoretically isomorphic with an exchange of domain on a set called the Rauzy fractal of the substitution. The Rauzy fractal is a self-similar compact subset of the product of an Euclidean space with finite extensions of p-adic fields. As a consequence, every substitutive dynamical system of Pisot type is a finite extension of its maximal equicontinuous factor. We prove that this maximal factor contains a p-adic translation if and only if the incidence matrix of the substitution is nilpotent modulo p.
A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operations

Universality is a key hypothesis in mechanistic interpretability -- that different models learn similar features and circuits when trained on similar tasks. In this work, we study the universality hypothesis by examining how small neural networks learn to implement group composition. We present a novel algorithm by which neural networks may implement composition for any finite group via mathematical representation theory. We then show that networks consistently learn this algorithm by reverse engineering model logits and weights, and confirm our understanding using ablations. By studying networks of differing architectures trained on various groups, we find mixed evidence for universality: using our algorithm, we can completely characterize the family of circuits and features that networks learn on this task, but for a given network the precise circuits learned -- as well as the order they develop -- are arbitrary.
Spatial and Temporal Correlation among Oklahoma Mesonet and OASIS Surface-Layer Measurements

Abstract A major challenge in meteorology is determining the manner and scale at which the land surface interacts with the atmosphere. A majority of field programs, designed to address this issue, have been limited in space and time and thus have been unable to span the seasonal cycle across a regional to a statewide area. In an effort to address this problem, data for one year were collected and archived from 89 sites during 2000 from the Oklahoma Mesonet and Oklahoma Atmospheric Surface-Layer Instrumentation System (OASIS). Mean and variance estimates of radiation, air and skin temperature, relative humidity, surface fluxes, and soil moisture were investigated. Site-to-site correlation coefficients of these variables also were examined. Furthermore, Hovmoeller diagrams of atmospheric and surface variables were plotted and were discussed in relation to statewide patterns of rainfall, vegetation, and topography. The data revealed complex interactions among the more slowly varying parameters, such as soil .
Influence of Variable Viscosity on the Onset of Hydrothermal Waves in Thermocapillary Liquid Bridges

The float-zone erystal-growth process is investigated in the framework of the half-zone model. The influence of a temperature-dependent viscosity on the axisymmetric steady thermocapillary flow and its stability with respect to nonaxisymmetric perturbations is examined by means of a linear-stability analysis. The onset of oscillatory convection is studied numerically by a mixed Chebyshev-collocation finite-difference method. Detailed calculations have been carried out for Pr=4 and unit aspect ratio. It is found that the temperature-dependent viscosity decreases the critical Reynolds number for the onset of three-dimensional flow. Since the threshold shift is a function of the azimuthal wavenumber of the neutral mode, the critical mode may depend on the magnitude of the viscosity variation.
Network Autoregression for Incomplete Matrix-Valued Time Series

We study the dynamics of matrix-valued time series with observed network structures by proposing a matrix network autoregression model with row and column networks of the subjects. We incorporate covariate information and a low rank intercept matrix. We allow incomplete observations in the matrices and the missing mechanism can be covariate dependent. To estimate the model, a two-step estimation procedure is proposed. The first step aims to estimate the network autoregression coefficients, and the second step aims to estimate the regression parameters, which are matrices themselves. Theoretically, we first separately establish the asymptotic properties of the autoregression coefficients and the error bounds of the regression parameters. Subsequently, a bias reduction procedure is proposed to reduce the asymptotic bias and the theoretical property of the debiased estimator is studied. Lastly, we illustrate the usefulness of the proposed method through a number of numerical studies and an analysis of a Yelp data set.
Determinants of Economic Literacy at University Level: A Case of Pakistan

The main intention of this study is to identify the determinants of economic literacy at university level. The data used in this study was collected by researchers through field survey by randomly selecting 200 students from different departments of the universities located in the Southern part of the Punjab province in Pakistan. Ordinary least square (OLS) technique and Logit model were used for empirical analysis. It is found that spending, age, gender, father’s education and student’s education were positive and significantly related to economic literacy. While the variables; mother’s education and bank account were statistically significant but negatively related to the respondents’ level of economic literacy. However, ATM used record, internet connection; resident’s location and guardian’s occupation were statistically insignificant with economic literacy. The study contributes towards the current understanding of the state of economic literacy among the young students. It also suggests the factors, which have significantly contributed towards the current economic literacy in Pakistani students.
First steps towards quantum machine learning applied to the classification of event-related potentials

Low information transfer rate is a major bottleneck for brain-computer interfaces based on non-invasive electroencephalography (EEG) for clinical applications. This led to the development of more robust and accurate classifiers. In this study, we investigate the performance of quantum-enhanced support vector classifier (QSVC). Training (predicting) balanced accuracy of QSVC was 83.17 (50.25) %. This result shows that the classifier was able to learn from EEG data, but that more research is required to obtain higher predicting accuracy. This could be achieved by a better configuration of the classifier, such as increasing the number of shots.
Limiting distributions of conjugate algebraic integers

Let $\Sigma \subset \mathbb{C}$ be a compact subset of the complex plane, and $\mu$ be a probability distribution on $\Sigma$. We give necessary and sufficient conditions for $\mu$ to be the weak* limit of a sequence of uniform probability measures on a complete set of conjugate algebraic integers lying eventually in any open set containing $\Sigma$. Given $n\geq 0$, any probability measure $\mu$ satisfying our necessary conditions, and any open set $D$ containing $\Sigma$, we develop and implement a polynomial time algorithm in $n$ that returns an integral monic irreducible polynomial of degree $n$ such that all of its roots are inside $D$ and their root distributions converge weakly to $\mu$ as $n\to \infty$. We also prove our theorem for $\Sigma\subset \mathbb{R}$ and open sets inside $\mathbb{R}$ that recovers Smith's main theorem~\cite{Smith} as special case. Given any finite field $\mathbb{F}_q$ and any integer $n$, our algorithm returns infinitely many abelian varieties over $\mathbb{F}_q$ which are not isogenous to the Jacobian of any curve over $\mathbb{F}_{q^n}$.
Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing

Sequence-to-Sequence (S2S) models have achieved remarkable success on various text generation tasks. However, learning complex structures with S2S models remains challenging as external neural modules and additional lexicons are often supplemented to predict non-textual outputs. We present a systematic study of S2S modeling using contained decoding on four core tasks: part-of-speech tagging, named entity recognition, constituency, and dependency parsing, to develop efficient exploitation methods costing zero extra parameters. In particular, 3 lexically diverse linearization schemas and corresponding constrained decoding methods are designed and evaluated. Experiments show that although more lexicalized schemas yield longer output sequences that require heavier training, their sequences being closer to natural language makes them easier to learn. Moreover, S2S models using our constrained decoding outperform other S2S approaches using external resources. Our best models perform better than or comparably to the state-of-the-art for all 4 tasks, lighting a promise for S2S models to generate non-sequential structures.
Mobile Cell-Free Massive MIMO: Challenges, Solutions, and Future Directions

Cell-free (CF) massive multiple-input multiple-output (MIMO) systems, which exploit many geographically distributed access points to coherently serve user equipments via spatial multiplexing on the same time-frequency resource, has become a vital component of the next-generation mobile communication networks. Theoretically, CF massive MIMO systems have many advantages, such as large capacity, great coverage, and high reliability, but several obstacles must be overcome. In this article, we study the paradigm of CF massive MIMO-aided mobile communications, including the main application scenarios and associated deployment architectures. Furthermore, we thoroughly investigate the challenges of CF massive MIMO-aided mobile communications. We then exploit a novel predictor antenna, hierarchical cancellation, rate-splitting and dynamic clustering system for CF massive MIMO. Finally, several important research directions regarding CF massive MIMO for mobile communications are presented to facilitate further investigation.
Recent Developments in Warm Inflation

Warm inflation, its different particle physics model implementations, and the implications of dissipative particle production for its cosmology are reviewed. First, we briefly present the background dynamics of warm inflation and contrast it with the cold inflation picture. An exposition of the space of parameters for different well-motivated potentials, which are ruled out, or severely constrained in the cold inflation scenario, but not necessarily in warm inflation, is provided. Next, the quantum field theory aspects in realizing explicit microscopic models for warm inflation are given. This includes the derivation of dissipation coefficients relevant in warm inflation for different particle field theory models. The dynamics of cosmological perturbations in warm inflation are then described. The general expression for the curvature scalar power spectrum is shown. We then discuss in detail the relevant regimes of warm inflation, the weak and strong dissipative regimes. We also discuss the results predicted in these regimes of warm inflation and how they are confronted with the observational data. We explain how the dissipative dynamics in warm inflation can address several long-standing issues related to (post-) inflationary cosmology. This includes recent discussions concerning the so-called swampland criteria and how warm inflation can belong to the landscape of string theory.
On the efficacy of simulated maximum likelihood for estimating the parameters of stochastic differential Equations *

Abstract. A method for estimating the parameters of stochastic differential equations (SDEs) by simulated maximum likelihood is presented. This method is feasible whenever the underlying SDE is a Markov process. Estimates are compared to those generated by indirect inference, discrete and exact maximum likelihood. The technique is illustrated with reference to a one‐factor model of the term structure of interest rates using 3‐month US Treasury Bill data.
Migration Reframed? A multilingual analysis on the stance shift in Europe during the Ukrainian crisis

The war in Ukraine seems to have positively changed the attitude toward the critical societal topic of migration in Europe – at least towards refugees from Ukraine. We investigate whether this impression is substantiated by how the topic is reflected in online news and social media, thus linking the representation of the issue on the Web to its perception in society. For this purpose, we combine and adapt leading-edge automatic text processing for a novel multilingual stance detection approach. Starting from 5.5M Twitter posts published by 565 European news outlets in one year, beginning September 2021, plus replies, we perform a multilingual analysis of migration-related media coverage and associated social media interaction for Europe and selected European countries. The results of our analysis show that there is actually a reframing of the discussion illustrated by the terminology change, e.g., from “migrant” to “refugee”, often even accentuated with phrases such as “real refugees”. However, concerning a stance shift in public perception, the picture is more diverse than expected. All analyzed cases show a noticeable temporal stance shift around the start of the war in Ukraine. Still, there are apparent national differences in the size and stability of this shift.
Equal Predictive Ability Tests Based on Panel Data with Applications to OECD and IMF Forecasts

We propose two types of equal predictive ability (EPA) tests with panels to compare the predictions made by two forecasters. The first type, namely $S$-statistics, focuses on the overall EPA hypothesis which states that the EPA holds on average over all panel units and over time. The second, called $C$-statistics, focuses on the clustered EPA hypothesis where the EPA holds jointly for a fixed number of clusters of panel units. The asymptotic properties of the proposed tests are evaluated under weak and strong cross-sectional dependence. An extensive Monte Carlo simulation shows that the proposed tests have very good finite sample properties even with little information about the cross-sectional dependence in the data. The proposed framework is applied to compare the economic growth forecasts of the OECD and the IMF, and to evaluate the performance of the consumer price inflation forecasts of the IMF.
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement Learning

Behavioral cloning (BC) provides a straightforward solution to offline RL by mimicking offline trajectories via supervised learning. Recent advances (Chen et al., 2021; Janner et al., 2021; Emmons et al., 2021) have shown that by conditioning on desired future returns, BC can perform competitively to their value-based counterparts, while enjoying much more simplicity and training stability. While promising, we show that these methods can be unreliable, as their performance may degrade significantly when conditioned on high, out-of-distribution (ood) returns. This is crucial in practice, as we often expect the policy to perform better than the offline dataset by conditioning on an ood value. We show that this unreliability arises from both the suboptimality of training data and model architectures. We propose ConserWeightive Behavioral Cloning (CWBC), a simple and effective method for improving the reliability of conditional BC with two key components: trajectory weighting and conservative regularization. Trajectory weighting upweights the high-return trajectories to reduce the train-test gap for BC methods, while conservative regularizer encourages the policy to stay close to the data distribution for ood conditioning. We study CWBC in the context of RvS (Emmons et al., 2021) and Decision Transformers (Chen et al., 2021), and show that CWBC significantly boosts their performance on various benchmarks.
Asymptotic expansion of smooth functions in deterministic and iid Haar unitary matrices, and application to tensor products of matrices

Let $U^N$ be a family of $N\times N$ independent Haar unitary random matrices and their adjoints, $Z^N$ a family of deterministic matrices, $P$ a self-adjoint noncommutative polynomial, i.e. such that for any $N$, $P(U^N,Z^N)$ is self-adjoint, $f$ a smooth function. We prove that for any $k$, if $f$ is smooth enough, there exist deterministic constants $\alpha_i^P(f,Z^N)$ such that $$ \mathbb{E}\left[\frac{1}{N}\text{Tr}\left( f(P(U^N,Z^N)) \right)\right]\ =\ \sum_{i=0}^k \frac{\alpha_i^P(f,Z^N)}{N^{2i}}\ +\ \mathcal{O}(N^{-2k-2}) . $$ Besides the constants $\alpha_i^P(f,Z^N)$ are built explicitly with the help of free probability. As a corollary, we prove that given $\alpha<1/2$, for $N$ large enough, every eigenvalue of $P(U^N,Z^N)$ is $N^{-\alpha}$-close to the spectrum of $P(u,Z^N)$ where $u$ is a $d$-tuple of free Haar unitaries. We also prove the convergence of the norm of any polynomial $P(U^N\otimes I_M, I_N\otimes Y^M)$ as long as the family $Y^M$ converges strongly and that $M\ll N \ln^{-5/2}(N)$.
Electromagnetic scattering by nonplanar junctions of resistive sheets

Approximate uniform asymptotic expressions are provided to determine the field scattered by a penetrable wedge illuminated at normal incidence. The wedge is formed by two resistive sheets or two thin dielectric slabs definable as resistive sheets having identical geometric and electromagnetic characteristics. The solution is limited to wedge angles and source positions where internal reflections cannot occur. It is obtained by using a geometrical optics (GO) approximation for the field internal to the slabs and by performing a uniform asymptotic evaluation of the physical optics (PO) radiation integral in the hypothesis that a resistive sheet condition is valid. Samples of numerical results so obtained are presented and compared with other methods to demonstrate the effectiveness of the proposed technique.